The COP of the spa resort, 4.8, is also an improvement. Both systems of the rice depot and the spa resort represent the first year after opening the facility, so it is considered that the optimization of system control was insufficient. The COPs might become better as the operation of the system is improved.

For the shiitake mushroom GHs, excellent energy efficiency was verified. Even though some problems occurred, such as filter clogging and inappropriate piping design, the COP exceeded 10. The last year of data, which are the values in parentheses, show better results because of fewer problems.

<table><caption>**Table 3.** Coefficient of performance (COP) of four systems.</caption><thead><tr><th></th><th>Q [GJ]</th><th>P [GJ]</th><th>COP</th></tr></thead><tbody><tr><td>Rice depot</td><td></td><td></td><td></td></tr><tr><td>PR room</td><td>71.9</td><td>13.7</td><td>5.3</td></tr><tr><td>BR room</td><td>36.0</td><td>14.4<br>(4.9)*1</td><td>2.5<br>(7.3)*1</td></tr><tr><td>Spa resort</td><td>36.8</td><td>7.6</td><td>4.8</td></tr><tr><td>Shiitake GHs</td><td>37.1<br>(77.0)*2</td><td>3.3<br>(5.4)*2</td><td>11.2<br>(14.3)*2</td></tr><tr><td>SA lavatory</td><td>11.5</td><td>2.3</td><td>4.9</td></tr></tbody></table>

For the SA lavatory cooling considering only the electricity consumption of the FCUs and pumps, the COP was 4.9, which is almost equal to a high-ranking electric cooler. In this case, because the pump operated during the night time, the system can be improved. If the pump stopped during the night time, the COP could be improved to 8.08. However, if accounting for the energy consumption for snow-packing and transportation every morning, the energy efficiency of the snow delivery cooling system may not be acceptable.

## 5.2 Cost performance

Evaluations of the cost performance of the four snow-cooling facilities are summarized in **Table 4** by comparing the equivalent electric cooling units. In the case of the rice depot, the initial cost (IC) was estimated by the average

construction cost of a mid-scale snow storehouse of 80 x 10³ yen/t. The statutory durable years of steel-framed reinforced concrete constructions is 38 years. The running cost (RC) included the electricity cost and excluded the snow-throwing cost, because it is the ordinary snow-removal operation cost of parking lots. For an equivalent electric cooler, the IC was estimated from the price of a commercial cooler (not including installation cost). The RC is taken as 13 years from the statute. By assuming running hours and electricity cost, the RC was calculated.

At the spa resort facility, the IC of the snow-cooling system was estimated as the sum of the snow storehouse, 45,000 x 10³ yen, and the air-conditioning equipment, 5,000 x 10³ yen. The lifetime (L) and RC values were given in the same manner. The total cost, TC, which was the cost of the first year of the snow cooler, was about 4 times greater than that of the electric cooler, whereas the RC of the snow cooler was about one-fourth; therefore, the break-even point (BEP), is 10.5 years after construction. It can be said that the BEP of the rice depot of 2.7 shows an excellent cost performance. This result means facilities with lower control temperatures may achieve better cost effectiveness.

The IC of the snow-cooling system of the shiitake GH assumed the anti-scattering net, piping material, pump and FCUs have a lifespan of 2 years. The RC included snow mound construction, insulation covering work and electricity consumption. As shown in the table, the TC of the snow-cooling system is less than that of an electric cooler from the first year. The reason for such a cost advantage is that it did not require construction of a snow storehouse, which is the main factor contributing to the cost of a snow-cooling system. Another contributing factor is that the site of the snow mound was a rice paddy, where it was not required to remove rice shells as insulation material after the snow-cooling operation.

The IC of the SA lavatory snow cooler takes into account the FCUs of 150 x 10³ yen, a pump of 105 x 10³ yen and the SSCU of 1,000 x 10³ yen. The durable years of the FCUs and pump are taken as 13 and 7, respectively, and that of the SSCU is 7. The RC of the lavatory snow cooler must include the snow-packing and transportation cost. The rental fee for a backhoe and a dump truck with a crane for 34 days was estimated as 762 x 10³ yen. Because of the high packing/transportation cost, the BEP is not

<table><caption>**Table 4.** Cost estimation and break-even point of four systems.</caption><thead><tr><th></th><th></th><th>IC<br>[10<sup>3</sup> Yen]</th><th>L<br>[y]</th><th>IC/L<br>[10<sup>3</sup> Yen/y]</th><th>RC<br>[10<sup>3</sup> Yen/y]</th><th>TC<br>[10<sup>3</sup> Yen/y]</th><th>BEP<br>[y]</th></tr></thead><tbody><tr><td rowspan="2">Rice depot</td><td>Snow cooler</td><td>48,000</td><td>38</td><td>1,263</td><td>122</td><td>1385</td><td rowspan="2">2.7</td></tr><tr><td>Electric Cooler</td><td>2,400</td><td>13</td><td>184</td><td>524</td><td>708</td></tr><tr><td rowspan="2">Spa resort</td><td>Snow cooler</td><td>50,000</td><td>38</td><td>1,320</td><td>34</td><td>1,354</td><td rowspan="2">10.5</td></tr><tr><td>Electric Cooler</td><td>3,400</td><td>13</td><td>262</td><td>135</td><td>397</td></tr><tr><td rowspan="2">Shiitake GHs</td><td>Snow cooler</td><td>1,120</td><td>2</td><td>560</td><td>42</td><td>602</td><td rowspan="2">0.5</td></tr><tr><td>Electric Cooler</td><td>5,200</td><td>13</td><td>400</td><td>354</td><td>754</td></tr><tr><td rowspan="2">SA lavatory</td><td>Snow cooler</td><td>150/1104</td><td>13/7</td><td>179</td><td>122+762</td><td>1063</td><td rowspan="2">-</td></tr><tr><td>Electric Cooler</td><td>450</td><td>13</td><td>35</td><td>523</td><td>558</td></tr></tbody></table>

<footnote>*1 only for snow-cooling operation.</footnote>

<footnote>*2 experimental results in 2013 summer.</footnote>
shown. It was concluded that snow cooling with daily
delivery is not cost effective. But, if the snow is also used
for summer events, the value of the snow must not be
evaluated only in terms of energy saving. Summer snow
events are popular all over Japan, and the snow could be
used in such events.

## 6 Conclusion

In the summer of 2014, four different snow-utilizing
facilities in Niigata and Nagano Prefectures in Japan were
evaluated by their air-conditioning performance.

The systems with a snow storeroom, a rice depot and
spa resort, had advantages such as stable and flexible air
temperature control. Especially, the air circulation system
achieved high deodorizing and dedusting effects.
Moreover, the energy efficiency of both systems was
better than that of an equivalent electric heat pump unit,
and the cost of investment will be earned back in less than
10 years.

The system with the snow mound, which was the
shiitake mushroom greenhouses, showed that the annual
cost will be cheaper than the heat pump unit starting in the
first year. It was concluded that the snow-cooling of the
greenhouses was highly suitable for the agricultural
industry. For the SA lavatory system, it was concluded that
snow cooling with daily delivery is not cost effective.

## Acknowledgements

The research was supported by a grant from the
Ministry of Environment, Japan. The authors gratefully
acknowledge the owners of the facilities investigated and
the staff at the Niigata Prefecture Environmental
Protection Agency.

## References

1. Hokkaido Bureau of Economy, Trade and Industry, Cool Energy 5. (2012) (in Japanese)

2. Kamimura, S. and Ito, Y., A simple air-conditioning system with built-in snow storeroom, Proceedings of 6th Int'l Conf. on Snow Engineering in Whistler. (2008)

3. Kamimura, S., Kato, R., and Kamiura, K., Evaluation of air-conditioning performance of an ice cooling system, J. of SHASE Japan, **201** (2013) (in Japanese)

4. Yuki-daruma Foundation, Research review and prospect of quality keeping and improvement effect on food stored in Snowroom. (2015) (in Japanese)

5. Y. Nakamura, H. Sugiyama and S. Kamimura, Experimental Study on Open Air Storage of Snow with Simple Insulation Using Agricultural Wastes, , Proc. 5th Int'l Conf. on Snow Eng. in Davos. (2004)
Session 7

Transport
# Modelling the thermal conductivity of melting snow layers on heated pavements

Anne Nuijten<fnref n="1" />,ª, Knut Vilhelm Høyland<fnref n="1" />,<fnref n="2" />, Cor Kasbergen<fnref n="3" /> and Tom Scarpas<fnref n="3" />

**Abstract.** A snow layer on a heated pavement largely affects the energy balance at the pavement surface. Its physical properties are not constant, but a function of the thermal properties and snow microstructure. This study looks into the change in thermal conductivity during the melting process of snow on heated pavements. The energy balance of the heated pavement system is described and the effective thermal conductivity of the melting snow layer during the melting process of snow on a heated pavement system are calculated based on pavement properties and measured temperatures and meteorological data. The results show that the effective thermal conductivity of a melting snow layer on a heated pavement can be best described a combination of a parallel and series system where ice and air are modelled as a series system and water and the combination of the ice and air system are modelled in parallel.

## 1 Introduction

A snow layer on a heated pavement largely affects the energy balance at the pavement surface and its physical properties are not constant, but a function of the thermal properties and snow microstructure. They vary greatly during the melting process.

The thermal conductivity is one of the most fundamental properties determining the rate of snow metamorphism [1]. The thermal conductivity of the snow layer seems to be mostly affected by the density and by the snow microstructure. It can vary from 0.025 W m⁻¹K⁻<fnref n="1" /> to 0.56 W m⁻¹K⁻<fnref n="1" /> for densities varying from 10 to 550 kg m⁻<fnref n="3" /> [2].

Various methods have been developed to model the effective thermal conductivity of snow. The effective thermal conductivity is often described as a function of the density [3-6]. Most relationships are based on data retrieved from thermal conductivity measurements of various types of seasonal snow with densities up to 600 kg m⁻<fnref n="3" />. The increase of density of melting snow on heated pavements differs from the density increase of snow packs. On heated pavements the snow density increases up till a value of 999.8 kg m⁻<fnref n="3" /> (density of water), mostly due to changes in the moisture content, while snow in a snow pack increases in density over a longer time without necessarily the same increase in moisture content.

Alternatively the effective thermal conductivity can be modelled based on the volume fractions of ice, water and air. The ice, water and air layers can either be modelled in parallel, in series or in another configuration, such as the model by Schwerdtfeger [7] in which dense snow is modelled as ice containing air bubbles and air spaces in light snow are modelled as parallelepipeds or the SNOWPACK model [8] which is based on the work of Adams and Sato [9]. In later developed models the effective thermal conductivity is modelled based on the snow microstructure where x-rays of snow samples are used [6].

This study looks into the change in thermal conductivity during the melting process of dry uncompressed snow on heated pavements. A snow melting experiment was done to gain a better understanding of the snow melting process on heated pavements. The energy balance of the heated pavement system is described and the effective thermal conductivity of the melting snow layer during the melting process of snow on a heated pavement system is calculated based on the volume fractions of ice, water and air. The results show that the thermal conductivity of a melting snow layer on a heated pavement can be best described a combination of a parallel and series system where ice and air are modelled as a series system and water and the combination of the ice and air system are modelled in parallel.

## 2 Experimental setup and procedure

The snow melting experiment was executed in the cold laboratories at the NTNU in Norway. An experimental setup of a heated pavement system was built, representative for an asphalt road with a heated pavement system. On the bottom side of the 5cm thick asphalt plate heating films were connected to the slabs. On the top side the slabs were covered with a 4mm thick binder layer to make the structure water tight (Figure 1). To minimize heat losses insulation was placed to the side as well as under the slabs. The setup was equipped with sensors measuring the pavement (nr. 1 in Figure 1), snow (nr. 2) and air (nr. 3) temperatures, the humidity (nr. 3), incoming longwave radiation (nr. 4) and wind speed (nr. 5). Additionally the height was measured and the surface condition was registered (nr. 6).

A clouded winter night with temperatures around -5°C and low wind speed was simulated. The pavement was covered with approximately 4 cm of dry snow at the

<figure><img src="image_493.png" /></figure>

<footnote n="1">¹NTNU, Department of Civil and Transport Engineering, Trondheim, Norway</footnote>

<footnote n="2">²Sustainable Arctic Marine and Coastal Technology (SAMCoT), Centre for Research-based Innovation (CRI), Norwegian University of Science and Technology, Trondheim, Norway</footnote>

<footnote n="3">³TU Delft, Department of Structural Engineering, Delft, the Netherlands</footnote>

<footnote>ª Corresponding author: anne.nuijten@ntnu.no</footnote>
start of the experiment. For a detailed description of the experimental setup and method is referred to [10].

<figure><img src="image_494.png" /><figcaption>Figure 1. Overview of the experimental setup</figcaption></figure>

## 3 Thermal conductivity model

Based on the heat balance of the snow melting system the mass fluxes of ice and water and consequently the volume fractions of ice, water and air in the snow layer are calculated. The change of the snow properties is calculated based on the volume fractions.

### 3.1 Heat balance

The heat balance of the snow melting system is described as:

$$ q_m = q_h - q_s - \rho_1 c_1 \left( \frac{\partial T}{\partial t} \right)_1 z_1 - \rho_2 c_2 \left( \frac{\partial T}{\partial t} \right)_2 z_2 - \rho_3 c_3 \left( \frac{\partial T}{\partial t} \right)_3 z_3 \quad (1) $$

where $q_m$ is the energy used to melt the snow (W m⁻²), $q_h$ is the heat flux from the heating film (W m⁻²), $q_s$ is the surface heat flux, $\rho$ is the density (kg m⁻³), $c_p$ is the specific heat capacity (J kg⁻¹K⁻¹), $\partial T / \partial t$ is the rate of temperature difference (°C s⁻¹) and $z$ is the height of the layer (m). The subscripts 1, 2 and 3 are used to indicate the different layers from top to bottom; the surface layer (snow/water), the binder layer and the asphalt layer. The heat flux at the top of the surface layer $q_s$ is given as:

$$ q_s = q_{LW} + q_{conv} + q_{evap} + q_{subl} \quad (2) $$

where $q_{LW}$ is the longwave radiation, $q_{conv}$ is the convective heat flux, $q_{evap}$ is the heat flux due to evaporation and $q_{subl}$ is the heat flux due to sublimation.

### 3.1.1 Longwave radiation

The longwave radiative heat flux $q_{LW}$ (W m⁻²) can be calculated as:

$$ q_{LW} = \varepsilon_s \cdot \sigma \cdot T_s^4 - \varepsilon_s \cdot \varepsilon_{eff} \cdot \sigma \cdot T_{sky}^4 \quad (3) $$

where the first term is the amount of energy that the pavement surface radiates and the second term the amount of incoming radiation. In this formula $\varepsilon_{eff}$ and $\varepsilon_s$ are the effective and surface emissivity respectively (-), $\sigma$ is the Stephan-Boltzmann constant, which is $5.68 \cdot 10^{-8}$ W m⁻² K⁻⁴, $T_s$ is the surface temperature (K) and $T_{sky}$ is the sky temperature (K). $\varepsilon_{eff}$ can be calculated as [11, 12]:

$$ \varepsilon_{eff} = \varepsilon_{cs} (1 - n_c^2) + \varepsilon_{cl} n_c^2 \quad (4) $$

where $\varepsilon_{cs}$ is the clear sky emissivity (-), $\varepsilon_{cl}$ is the cloud sky emissivity (-) and $n_c$ is the cloud cover which can be taken as 1 for this experiment. Denby et al. [11] used a constant value for cloud sky emissivity, $\varepsilon_{cl}$, of 0.97, as suggested by Konzelmann et al. [12]. The values used for the emissivity coefficient used during this study are given in Table 1.

<table><caption>Table 1 Emissivity coefficient for different surface conditions</caption><thead><tr><th>Condition</th><th>Surface emissivity<br>εs</th></tr></thead><tbody><tr><td>Dry asphalt</td><td>0.85 – 0.90</td></tr><tr><td>Wet asphalt</td><td>0.90 – 0.96</td></tr><tr><td>Wet snow / slush</td><td>0.90</td></tr><tr><td>Dry snow</td><td>0.97</td></tr></tbody></table>

### 3.1.2 Convection

The convective heat flux $q_{conv}$ (W m⁻²) is given by (Incropera, 2013):

$$ q_{conv} = h_c \cdot (T_s - T_a) \quad (5) $$

where $h_c$ is the convection heat transfer coefficient (in W m⁻² K⁻¹) and $T_a$ is the air temperature (°C). $h_c$ is given as:

$$ h_c = \rho_a \cdot C_p / r_T \quad (6) $$

where $\rho_a$ is the atmospheric density (kg m⁻³), $C_p$ is the heat capacity of dry air (J kg⁻¹ K⁻¹) and $r_T$ is the aerodynamic resistance for temperature (s m⁻¹).

### 3.1.3 Evaporation

The heat flux for evaporation and condensation $q_{evap/cond}$ (W m⁻²) is described as:

$$ q_{evap/cond} = L_v \cdot \dot{m}_{wc} \quad (7) $$

where $L_v$ is the latent heat of vaporization (J kg⁻¹) and $\dot{m}_{wc}$ is the mass flux of condensation and evaporation (kg m⁻² s⁻¹).
### 3.1.4 Sublimation

The heat flux of sublimation $q_{depo/subl}$ (W m⁻²) can be calculated by:

$$q_{depo/subl} = L_i \cdot \dot{m}_{id} \quad (8)$$

where $L_i$ is the latent heat of sublimation (J kg⁻¹) and $\dot{m}_{id}$ is the deposition and sublimation flux (kg m⁻² s⁻¹).

## 3.2 Mass fluxes

The mass flux of water $\dot{m}_w$ (kg m⁻² s⁻¹) is determined by adding the flux of condensation and evaporation $\dot{m}_{wc}$ and the mass flux of melting $\dot{m}_m$ (kg m⁻² s⁻¹):

$$\dot{m}_w = \dot{m}_{wc} + \dot{m}_m \quad (9)$$

$\dot{m}_{wc}$ is given by [11]:

$$\dot{m}_{wc} = \rho_a \cdot (q_a - q_s) / r_q \quad (10)$$

where $\rho_a$ is the density of the air (kg m⁻³), $r_q$ is the aerodynamic resistance for water vapour (s m⁻¹) and $q_a$ and $q_s$ are the atmospheric and surface specific humidity respectively. $\dot{m}_m$ is given by:

$$\dot{m}_m = \frac{q_m}{L_f} \quad (11)$$

where $L_f$ is the latent heat of fusion.

The mass flux of ice is determined by subtracting the mass flux of melting and freezing $\dot{m}_m$ (kg m⁻² s⁻¹) from the deposition and sublimation flux $\dot{m}_{id}$ (kg m⁻² s⁻¹):

$$\dot{m}_i = \dot{m}_{id} - \dot{m}_m \quad (12)$$

where $\dot{m}_{id}$ is calculated according to equation (11).

## 3.3 Volume fractions

Based on the densities and mass fluxes of snow, water and air and the height of the surface layer the volume fractions of ice, water and air are calculated as follows:

$$1 = \theta_i + \theta_w + \theta_a \quad (13)$$

where $\theta_i$, $\theta_w$ and $\theta_a$ are the volume fractions of ice, water and air. $\theta_i$ and $\theta_w$ are given as:

$$\theta_i = \frac{M_i \cdot \rho_i}{M_s \cdot \rho_i} \quad (14)$$

$$\theta_w = \frac{M_w \cdot \rho_i}{M_s \cdot \rho_w} \quad (15)$$

where $M_i$, $M_w$ and $M_s$ are the mass of ice, water and the surface layer, $\rho_i$ is the density of the surface layer and $\rho_w$ and $\rho_a$ are the density of ice, water and air.

## 3.4 Snow properties

The specific heat capacity of the surface layer $c_1$ can be calculated as follows [13]:

$$c_1 = \frac{1}{\rho_1} (\rho_i \cdot c_i \cdot \theta_i + \rho_w \cdot c_w \cdot \theta_w + \rho_a \cdot c_a \cdot \theta_a) \quad (16)$$

where $c_i$, $c_w$ and $c_a$ are the specific heat capacities of ice, water and air.

The thermal conductivity of the surface layer is calculated based on the volume fractions and the thermal conductivity of ice, water and air in the following ways: As a parallel system:

$$\lambda_1 = \lambda_i \cdot \theta_i + \lambda_w \cdot \theta_w + \lambda_a \cdot \theta_a \quad (17)$$

As a series system:

$$\lambda_1 = \frac{1}{\left( \frac{\theta_i}{\lambda_i} + \frac{\theta_w}{\lambda_w} + \frac{\theta_a}{\lambda_a} \right)} \quad (18)$$

Additionally a combination of a parallel and series system is proposed in which ice and air are modelled as a series system and water and ice/air in parallel:

$$\lambda_1 = \frac{\theta_i + \theta_a}{\left( \frac{\theta_i}{\lambda_i} + \frac{\theta_a}{\lambda_a} \right)} + \lambda_w \cdot \theta_w \quad (19)$$

An explicit finite difference method is used to solve the equations and to calculate the heat and mass fluxes and snow properties.

# 4 Input parameters

Input parameters for the model not yet described in section 2 are the pavement properties and the height of the surface layer as was measured during the test.

## 4.1 Pavement properties

The density of the asphalt and binder layer is taken as 2100 kg m⁻³ [14] and the specific heat capacity is taken as 0.92 kJ kg⁻¹ °C⁻¹.
## 4.2 Height and density of the surface layer

Figure 2 shows the height profiles of the snow layer found during the tests and the change of the surface condition as was visible from the top. The same trend is visible for all tests; during the first 40 minutes the height did not change much. After an hour the height started to decrease linearly till it reached a height of 4-5 mm after which it stayed more of less constant. This decrease happened when the snow changed from dry snow into slush.

<figure><img src="image_495.png" /><figcaption>Figure 2. Change in height of the snow layer</figcaption></figure>

Figure 3 shows the average change in density of one of the tests (continuous thin line) and the average density including the measuring error of 1 mm (dotted line). The density is calculated by dividing the weight of the snow on the plate by the volume.

The calculated average density at the end of the test was 800 kg m⁻³. Since at the end of the test all snow was melted and only melt water was left the actual density was 999.8 kg m⁻³. Towards the end of the experiment the formula for calculating the density becomes very sensitive for small changes in the height. The height of the surface layer at the end of the experiment is 3-4 mm and the accuracy of the measuring method was around 1 mm. To increase the accuracy of the input data, the value at the end of the density curve is replaced with the value

<figure><img src="image_496.png" /><figcaption>Figure 3. Average change in density of the surface layer during one of the tests (continuous thin line), average +/- measuring error (dotted line) and an adjusted curve which ends at the density of water (continuous thick line)</figcaption></figure>

for the density of water (999.8 kg m⁻³) and the curve is fitted for the last part of the test. The adjusted curve used for the model is shown as a continuous thick line in Figure 3.

# 5 Results

## 5.1 Volume fractions

The volume fractions are the main input parameters for the effective thermal conductivity. The accuracy of the results depends on the accuracy of the input parameters, e.g. the accuracy of the meteorological data, temperatures, the pavement properties, the height and density of the snow layer and possible heat losses near the heating film.

A sensitivity study is done to look into the effect of the following parameters on the volume fractions and melting time: 1) the height of the snow layer (which also affects the density), 2) the surface heat flux (q_s), 3) the pavement heat flux (q_p), 4) the heat from the heating film (q_h).

Table 2 shows the results of the sensitivity analysis. The model originally predicts that all ice is melted after 101 minutes while as can be seen in Figure 2 the surface condition consists at that moment of wet snow. Changing the height of the snow layer with 1 mm (the measuring error) and changing the density accordingly does not seem to have an effect on the melting time. A change in the surface heat flux of 20% does not seem to affect the melting time either. An increase in the pavement flux by 20% or a decrease of 20% of the heat generated by the heating film, e.g. due to heat losses, increases the melting time with respectively 5 and 17 minutes. Figure 4 shows the range of volume fractions of ice, water and air during the melting process and the effect of the accuracy of the pavement and heating film flux on the accuracy of the results. By combining an increase in the pavement flux and a decrease in the amount of heat coming from the heating film the melting time is increased till 126 minutes which is the transition phase from wet snow into slush.

<figure><img src="image_497.png" /><figcaption>Figure 4. Change of the volume fractions of ice, water and air during the melting process, based on the values used in the sensitivity analysis. The inaccuracy of the results based on the sensitivity analysis done is marked in grey.</figcaption></figure>
<table><caption>**Table 2** Effect of the accuracy of various input parameters on the melting time</caption><thead><tr><th colspan="3">height snow layer</th><th colspan="2">surface flux</th><th colspan="2">pavement flux</th><th colspan="2">heating film</th><th rowspan="2">melting time (min)</th></tr><tr><th>-1 mm</th><th>mean</th><th>+ 1 mm</th><th>0.8 qs</th><th>qs</th><th>qp</th><th>1.2 qp</th><th>0.80 qh</th><th>qh</th></tr></thead><tbody><tr><td></td><td>x</td><td></td><td>x</td><td>x</td><td>x</td><td></td><td>x</td><td></td><td>101</td></tr><tr><td>x</td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td>x</td><td></td><td>101</td></tr><tr><td></td><td></td><td>x</td><td>x</td><td>x</td><td>x</td><td></td><td>x</td><td></td><td>101</td></tr><tr><td></td><td>x</td><td></td><td>x</td><td>x</td><td>x</td><td></td><td>x</td><td></td><td>101</td></tr><tr><td></td><td>x</td><td></td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td></td><td>106</td></tr><tr><td></td><td>x</td><td></td><td>x</td><td>x</td><td></td><td></td><td>x</td><td></td><td>118</td></tr></tbody></table>

## 5.2 Thermal conductivity

Figure 5 shows the change in effective thermal conductivity during the melting process of dry uncompressed snow on a heated pavement. The effective thermal conductivity is calculated with a parallel system, a series system and a combined parallel/series system. The effective thermal conductivity formulas which are based on the volume fractions are compared to the formulas based on the density by Yen [3], Sturm et al. [4] and Calonne et al. [6] which were developed for snow with density values up till 600 kg m⁻³.

The series and the combined parallel/series system start around the same values as the curves by Yen [3], Sturm et al. [4] and Calonne et al. [6]. Only the parallel system starts at a higher value.

At the end of the test the effective thermal conductivity of the surface layer goes towards the thermal conductivity of water for the parallel and series systems.

The formulas by Yen [3], Sturm et al. [4] and Calonne et al. [6] give much higher values towards the end of the test. Those formulas are based on measurements on seasonal snow from a snow pack with densities up to 600 kg m⁻³ where the snow density often increases due to settling and to a lesser extent due to an increase in moisture content.

Figure 6 shows the effect of the accuracy of various input parameters on the accuracy of the modelled effective thermal conductivity. Height measurements have an effect on the accuracy of the modelled effective thermal conductivity, especially towards the end of the test when the snow has changed into slush. The accuracy of the pavement flux and the accuracy of the heat from the heating film affect the accuracy of the result for the parallel system during the change from dry snow to slush. It slightly affects the accuracy of the modelled effective thermal conductivity with the combined parallel/series system.

<figure><img src="image_498.png" /><figcaption>**Figure 5.** Effective thermal conductivity during the melting process of dry uncompressed snow calculated based on the volume fractions of ice, water and ice with a parallel system, a series system and a combined parallel/series system and compared to the curves by Yen [3], Sturm et al. [4] and Calonne et al. [6]</figcaption></figure>
<figure><img src="image_499.png" /><figcaption>a)</figcaption></figure>

<figure><img src="image_500.png" /><figcaption>b)</figcaption></figure>

<figure><img src="image_501.png" /><figcaption>c)</figcaption></figure>

**Figure 6.** The effect of a) the accuracy of the height measurements, b) the accuracy of the heat flux from the heating film and c) the accuracy of the pavement heat flux on the modelled effective thermal conductivity during the melting process of dry uncompressed snow, where the mean value is presented as a continuous line and the effect of the various input parameters with dotted lines.

# 6 Discussion

During the first 40 minutes the snow has not started to melt and the surface condition consisted of dry snow. During this period the predicted thermal conductivity based on the volume fractions should give comparable results compared to the curves by Yen [3], Sturm et al. [4] and Calonne et al. [6]. The series and the combined parallel/series system correspond well with these curves.

After 90-120 minutes the snow is wet. Since in the curve of Calonne et al. [6] melt forms are included with densities around 500 kg m⁻³ it is expected that the formulas based on the volume fractions should approach this curve for wet snow values. The parallel and a combined parallel/series correspond better to this data than the series system. The series system gives a very low thermal conductivity at the end of the test while during that period the snow has transformed into slush and thermal conductivity values around that of water are expected.

The model predicts a faster snow melt than is observed during the experiment. The two main parameters affecting this prediction are the heating film heat flux and the pavement heat flux. In the model no heat losses are included, while these are expected to occur. Including these fluxes seem to have a positive effect on the results. The pavement flux depends largely on the thermal properties of the pavement which are estimated based on literature. Using measured properties would improve the accuracy of the results.

The accuracy of the height measurements and the accuracy of the surface flux barely affect the calculated melting time. The height measurements do however affect the results of the prediction of the volume fractions and effective thermal conductivity (Figure 6 a). Towards the end of the test, when the surface layer becomes very thin, the density is more difficult to estimate based on the height measurements, as can be seen in Figure 3. During this last part of the test the effect of the height measurements on the accuracy of the effective thermal conductivity prediction becomes larger.

# 7 Conclusion

The effective thermal conductivity of a melting snow layer on a heated pavement can be best described a combination of a parallel and series system, based on the volume fractions and the thermal conductivity of ice, water and air. In the proposed combined parallel and series system ice and air are modelled as a series system and water and ice/air are modelled in parallel.

# Acknowledgements

This work has been made possible by the financial support from the Norwegian Public Roads Administration. The authors are grateful for their support.
References

1. F. Riche, M. Schneebeli, Thermal conductivity of snow measured by three independent methods and anisotropy considerations, The Cryosphere, **7**, 217-227 (2013)

2. J. Cote, M. Rahimi, J.-M. Konrad. Thermal conductivity of compacted snow, *Proceedings of the 15th International Specialty Conference on Cold Regions Engineering* (2012)

3. Y.C. Yen, Review of the thermal properties of snow, ice and sea ice, *Cold Reg. Res. and Eng. Lab.* (1981)

4. M. Sturm, J. Holmgren, M. König, K. Morris, The thermal conductivity of seasonal snow, J. Glaciol., **43**, 26-41 (1997)

5. R.K. Aggarwal, P.S. Negi, P.K. Satyawali, New density-based thermal conductivity equations for snow, Defence Sci. J., **59**, 126-130 (2009)

6. N. Calonne, F. Flin, S. Morin, B. Lesaffre, S. Rolland du Roscoat, C. Geingreau, Numerical and experimental investigations of the effective thermal conductivity of snow. Geophys. Res. Lett., **38** (2011)

7. P. Schwerdtfeger, Theoretical derivation of the thermal conductivity and diffusivity of snow (1963)

8. M. Lehning, P. Bartelt, B. Brown, C. Fierz, P. Satyawali, A physical SNOWPACK model for the Swiss avalanche warning, Part II. Snow microstructure. Cold Reg. Sci. Technol., **35**, 147-167 (2002)

9. E.E. Adams, A. Sato, Model for Effective Thermal Conductivity of a Dry Snow Cover Composed of Uniform Ice Spheres. Ann. Glaciol., **18**, 300-304 (1993)

10. A.D.W. Nuijten, K.V. Høyland, Comparison of melting processes of dry uncompressed and compressed snow on heated pavements (manuscript submitted for publication)

11. B.R. Denby, I. Sundvor, C. Johansson, L. Pirjola, M. Ketzel, M. Norman, K. Kupiainen, M. Gustafsson, G. Blomqvist, M. Kauhaniemi, G. Omstedt, A coupled road dust and surface moisture model to predict non-exhaust road traffic induced particle emissions (NORTRIP). Part 2: Surface moisture and salt impact modelling, Atmos. Environ., **81**, 485-503 (2013)

12. T. Konzelmann, R.S.W. van de Wal, W. Greuell, R. Bintanja, E.A.C. Henneken, A. Abe- Ouchi, Parameterization of global and longwave incoming radiation for the Greenland ice sheet, Global Planet. Change, **9**, 143-164 (1994)

13. P. Bartelt, M. Lehning, A physical SNOWPACK model for the Swiss avalanche warning, Part I: numerical model. Cold Reg. Sci. Technol., **35**, 123-145 (2002)

14. O.B. Andersland, B. Ladanyi, *Frozen Ground Engineering*, John Wiley & Sons, Inc, New York (2004)
# Snow engineering questions related to road and rail vehicles

Jean-Paul Bouchetª, Sylvain Aguinaga, Pierre Palier and Philippe Delpech

Centre Scientifique et Technique du Bâtiment (CSTB), CAPE Department, 11 rue Henri-Picherit 44300 Nantes, France

**Abstract.** Snow falls induce many constraints on road and rail vehicles as well as helicopters. A selection of questions from snow engineering is presented in the paper. The experimental method that was undertaken to investigate these questions makes use of a large climatic wind tunnel, partly designed to address snow engineering problems at full scale: snow ingress in engine compartments, into ventilation systems of vehicles and snow or ice accretions on vehicles bodies.

## 1 Introduction

Snow may interact in different ways with transport systems and can induce many dysfunctions to vehicles and infrastructures exposed to atmospheric conditions. The multiform nature of snow, the deposition process and the mechanical and thermal properties of snow, bring potential problems which, in some cases, may have tragic consequences both for equipment and unfortunately users.

Beyond local mechanical constraints induced by snow accumulations and accretions, the accidental risk is increased if the accumulations occur on fast moving vehicles with possible detachments of these snow accretions due to temperature raise, vibrations or relative air velocity.

To reproduce and understand the interaction phenomena as they are observed in nature, it is valuable to implement an experimental approach that allows control of the main climatic parameters as air temperature, wind velocity, concentration of snow/ice particles and the liquid water ratio (LWR). These possibilities are offered by large climatic wind tunnels where it is possible to reproduce the phenomena at full scale or moderately reduced scale.

## 2 Snow simulation in the Jules Verne climatic wind tunnel (CWT)

The thermal circuit of Jules Verne climatic wind tunnel (Figure 1), with artificial snow making capabilities, can reproduce various kinds of snowstorm events. Wind velocity, ambient air temperature and air/water ratio in the installed snow guns are fully controllable to determine the snow properties (dry or wet snow). The appropriate thermodynamic conditions (air temperature and humidity) can be controlled by two heat exchangers (cold and warm) across the section of the wind tunnel.

The large dimension of the test chamber, 25 m long, 10 m wide and 7 m high, enables simulation of mechanisms of snow/vehicle interaction at full scale.

Snow guns (high pressure air and water technology) can be fitted in any location in the test section. They are used to generate a freezing water spray (Figure 2). The typical grain flake diameters produced by the snow guns in the wind tunnel range from 0.150 to 0.450mm (Figure 3).

<figure><img src="image_502.png" /><figcaption>Figure 1. The thermal circuit of the Jules Verne CWT</figcaption></figure>

<figure><img src="image_503.png" /><figcaption>Figure 2. Snow guns in operation in the thermal circuit of the Jules Verne CWT</figcaption></figure>

<figure><img src="image_504.png" /><figcaption>Figure 3. Particles diameter distribution (Malvern Spraytec)</figcaption></figure>

The air temperature, and air/water ratio in the snow guns, can be set to control the LWR of snow which determines the quality of the snow from dry to wet snow. LWR of the deposited snow is measured by using either a

<figure><img src="image_505.png" /></figure>

<footnote>ª Corresponding author: jean-paul.bouchet@cstb.fr</footnote>
calorimeter [1, 2] or by measuring the dielectric property of wet snow [3].

The measurement of the air flow snow concentration is conventionally measured by net collectors of snow on the duration of the test (Figure 4). The collected mass of snow can be converted into concentration (g/m³) knowing the wind velocity, the diameter of the snow collector and sampling time.

<figure><img src="image_506.png" /><figcaption>**Figure 4.** Measurement of the snow concentration in the airflow by net snow collectors</figcaption></figure>

Comparisons were made with an isokinetic sensor wherein the air input speed is controlled to be equal to the wind speed in order to avoid snow particles bypass effects due to aerodynamic forces.

For example in the case of vehicle rolling simulation at 80km/h with one snow gun in operation, the concentrations measured on the side of the vehicle by isokinetic sampling or by net collectors are close to 4.5g/m³ (+/- 0.5g/m³).

## 3 Snow ingress in the engine compartment and air intake of HVAC of vehicles

Snow ingress and accumulation under the bonnet of road vehicles and in particular into the air intake of engines can cause severe damages. Amongst the most critical conditions for road vehicles, let us mention the follow-up configuration where a vehicle follows one or several other vehicles (Figure 7). The snow falling is added to the snow picked-up by vehicles ahead made of fine particles. When combined to cold atmospheric conditions the ingress capacity of the fine and dry particles is high and can impact several systems as engine air intake and cabin HVAC system. The main induced problems could be engine dysfunctions (power loss) due to snow accumulation on the air filter or in its supply duct (air supply deficit) or severe damage to the HVAC fan due to the water ingress into the air circuit (melting of the snow collected on the cabin air filter, Figure 5).

<figure><img src="image_507.png" /><figcaption>**Figure 5.** Dry snow ingress in the HVAC air intake of a road vehicle</figcaption></figure>

When the weather is warmer and Liquid Water Ratio of the snow is higher (wet snow), these driving conditions (follow-up of vehicles) can generate significant accumulations of snow on the front face of the car. This is also the case when the roads are treated with snow melting additives (salt). The problems induced by these conditions are: snow accretion on vehicle lights (safety issue), blockage of the radiator fans and blockage of motorized air intake panels etc. (Figure 6).

<figure><img src="image_508.png" /><figcaption>**Figure 6.** Wet snow accretion on the front panel of a road vehicle</figcaption></figure>

Similar situations are experienced by helicopters that can be maintained in stationary flight over snow covered ground. In this case, it is important to locate and assess the quantity of snow that accumulates in the heated air intake channel of the turbine engines. The risk is to observe a local snow accumulation that could be released in large blocks and damage the turbine. The remedial strategy involves fitting of heating mats inside the air intake which efficiency needs to be assessed according standard procedures.

<figure><img src="image_509.png" /><figcaption>**Figure 7.** Operation of vehicles in snow conditions</figcaption></figure>

For driving snow tests, the wind tunnel is operated in a way, which enables the assessment of the air filter efficiency in severe conditions. Since the making of snow
is made while the wind is blowing at the appropriate test velocity, the location of the snow gun behind the test vehicle allow the freezing particles to go all along the wind tunnel circuit before reaching the model. This trajectory is long enough to freeze the water droplets producing dry snow (Figures 8 and 9).

<figure><img src="image_510.png" /><figcaption>**Figure 8** – Top view of the thermal unit test section, experimental setup for dry snow tests</figcaption></figure>

The experimental set-up enables the operation of the vehicle, engine on, fitted on a roll chassis dynamometer that simulates the resistant force (aerodynamic drag and rolling resistance).

<figure><img src="image_511.png" /><figcaption>**Figure 9.** Snow test of vehicles in the wind tunnel</figcaption></figure>

The test in snow storm conditions is carried for a significant duration or driving distance or until an observed breakdown. Sensitivity to high snow concentration in the air is assessed by weighing the snow collected in the air filters or others observations as snow accumulation in the electronic or cooling devices (Figure 10). In parallel measurements of pressure drop increase across the air filter are made.

<figure><img src="image_512.png" /><figcaption>**Figure 10.** Snow ingress under the bonnet of a road vehicle at the end of a wind tunnel test</figcaption></figure>

## 4 Braking and wiper blocking by snow

Apart of these snow accumulations under the bonnet, other types of deposition may occur on moving or stationary vehicles. One can mention:

- Snow deposition on the back of vehicle (due to turbulent drag flows) that may induce visibility problems of the rear lights,

- Accumulations on the roof of parked vehicles with sliding risk towards the windshield in case of sudden braking, which can lead to breakage of the wiper systems,

- Accumulations on A pillars or windshield base due to wipers operation under wet snow storm resulting in malfunction or blockage of these wiping mechanisms.

Similarly, the heat from engine components leads to snow melting at hot spots with possible re-icing at cold spots. This can be found on clutch components and more dangerously on the brake calipers by snow accumulation on the inside of the wheel rims. This aspect has been studied in the Climatic Wind tunnel Jules Verne and correlation test campaigns have been carried out between observations in proving ground (Kiruna site in Sweden) and in wind tunnel (Figures 11 and 12).

<figure><img src="image_513.png" /><figcaption>**Figure 11.** Snow braking test on a frozen lake in Kiruna</figcaption></figure>

<figure><img src="image_514.png" /><figcaption>**Figure 12.** Snow braking test in wind tunnel</figcaption></figure>

As achieved on real outdoor conditions on test site similar to Kiruna, tyre adherence tests can be undertaken in the wind tunnel. For this purpose, one needs to reproduce a homogeneous stratified and compacted snow layer in the entire test section, similar to the one observed on the snowy roads (Figure 13). This is made by a succession of snow making and compacting steps.
<figure><img src="image_515.png" /><figcaption>**Figure 13.** Tyre adherence tests on a stratified and compacted road snow layer in the wind tunnel</figcaption></figure>

**5 Wet snow accretions on high speed trains bodies**

To maintain the development of the high-speed train traffic, railway operators have to face extreme conditions, in particular during winters, with impacts on both infrastructure and rolling stock, and associated maintenance. Usual major consequences of snowfalls are both on traffic regulation with cruising speed reductions and on rolling stock with severe damages (underbody structure, windows) due to ice and snow accretions and shedding, leading for instance to ice-drop ballast projection.

In order to minimize the impacts of winter conditions on high-speed trains, wind tunnel experiments were undertaken as a part a research program conducted by the French railway company SNCF. The aim was to explore solutions to reduce the snow accumulation on trains. The first part of the study was dedicated to collect data and to identify the mechanism of snow accumulation by performing tests in a climatic wind tunnel.

Most of the tests were performed with one snow gun located on the central axis of the test section (Figure 14). The snow gun was set up in a counter-current configuration to enhance the spatial uniformity of particle concentration in the air flow. This configuration also ensures that particles are carried out to the test structure in equilibrium with the air flow.

<figure><img src="image_516.png" /><figcaption>**Figure 14** – Top view of the thermal unit test section, experimental setup for the snow tests</figcaption></figure>

Generic simple-shaped prisms and a railway shape geometry at 1/2 reduced scale were used. The obtained results consist in snow and ice growths and localizations, with the influence of different parameters, with particular

attention to the wind speed, air flow temperature, characteristics of snow particles (LWR and snow concentration) and their behavior on various angles of impact.

Two superimposed planes were used to support the prisms outside the boundary layer of the wind tunnel air flow. The prims were located underneath the supporting planes to include the impact of the horizontal plane on the accretion phenomena to the vertical surfaces of the prisms in the simulation. This arrangement intended to mimic the volume locations under a TGV floor (Figure 15).

<figure><img src="image_517.png" /><figcaption>**Figure 15:** Experimental set-up supporting the simple-shaped prisms in the wind tunnel test section (the wind comes from the right of the picture)</figcaption></figure>

The snow concentration was derived from the collected snow mass regarding the collection area, the local wind velocity and the test duration.

The prisms have been designed to explore various inclination angles of the walls, two sizes, from 0 to 90°, in respect with the main flow. The measurement of the mass of snow captured by each face, during a run, allowed to get a table of mass flux expressed in kg/m²-s depending on the faces area and the duration of the run.

The second part consisted in reproducing these mechanisms by numerical simulations which methodology has been validated by comparing computational results to measurements on both the prisms and the railway mock-up [3].

In order to supplement the prism measurements with results on a more realistic train geometry, a railway vehicle model at 1/2 reduced scale was designed. The model simulates two parts of a TGV body including the connection between two railroad cars (Figure 16). The overall train model was 8m long. A bluff body shape upstream the bogie was designed in order to model the aerodynamic influence of the front part of the train.

<figure><img src="image_518.png" /><figcaption>**Figure 16:** TGV model set-up in the climatic wind tunnel (the box on the side of the model shelters the belt drive system for the rotation of the wheels).</figcaption></figure>
An entire boogie was reproduced. The boogie model was simplified compared to a real one, but the main elements which may collect snow were reproduced, including the brake system (Figure 17).

<figure><img src="image_519.png" /><figcaption>**Figure 17:** TGV bogie model including anti-roll bar and the bogie-car drive system</figcaption></figure>

In order to reproduce the air flow underneath the train body, the model was suspended. An additional floor simulating the track was fitted 70cm above the wind tunnel floor. The height of the floor was intended to maintain the area of interest outside the boundary layer of the wind tunnel. The rotation of the wheels was simulated by a belt drive system and an electric engine located on the side of the model.

At the end of each test period, the measurements of accumulated snow were made and photographs were taken at predefined locations and angles of view (Figure 18). The snow accumulated on specific spots was collected and weighed.

<figure><img src="image_520.png" /><figcaption>**Figure 18:** Snow accumulation in the bogie cavity (wind tunnel model and real TGV train)</figcaption></figure>

A CFD methodology has been developed concurrently with wind tunnel experiments [3]. The measurements made on the prismatic bodies with various sizes and shapes allowed fast and precise tuning with the exploration of a large panel of parameters. The resulting simulation chain has been deployed on the more complex geometry, reproducing the TGV car details, and the workflow has been enriched with the mesh growing, in respect with the snow accretion rate obtained, and the associated volume of snow calculated.

# 6 Experimental and numerical modeling of snow accretion

There is no doubt about the usefulness of a model able to realistically assess the accretion of snow and ice on vehicle components.

The implementation of this type of modeling requires knowledge of climate constraints. In addition the simulation must be tuned to include the effect of the body shapes and the incident direction of the flow on the elementary surfaces of the structure for computing the

accretion mass. From this point of view, wind tunnel experiments with a cylindrical shape allow the analysis of accretion over a wide range of angle of impact.

The analysis must be complemented by a criterion that includes the sticking capacity of the freezing particles at time of impact to make the model truly operational.

Such a modelling is possible if one can assess the LWR of the snow at the impact time. Recent works have shown the primary effect of the LWR of snow on the snow accretion mass and volume [5]. The experimental approach was based on the assessment of wet snow accretion formation on simple geometric shapes. Cylinders were placed in the snow particle flow produced in the climatic wind tunnel. If all other parameters were kept constant, the ambient temperature setting (from -10°C to -2°C) allows to modify the thermodynamic state of the snow particle at time of impact. It is then possible to assess the effect of the liquid water ratio on the accretions and perform a parametric study.

A numerical model for two-phase flows simulation has also been developed in parallel. The study conducted, has been setup mainly to identify the most important parameters that drive the accretion phenomenon (Figure 19).

The results obtained with -10°C and -2°C experimental tests provided the limiting cases of the study as being the conditions were the accretion was either very thin and could not be weighted or too humid and could no longer be considered as actual snow.

<figure><img src="image_521.png" /><figcaption>**Figure 19.** Experimental and numerical modeling of wet snow impact and accretion cylinders of various diameters (diam. 50, 200 and 315mm)</figcaption></figure>

With the three intermediate ambient temperatures, 6°C, -5°C, -4°C, one can observe the evolution of the accretion sizes to the structures (Figure 20).
<figure><img src="image_522.png" /><figcaption>**Figure 20:** Examples of wet-snow accretion (ambient temperature -6°C top and -4°C bottom, left: from a 50mm cylinder, right: from a 315mm cylinder)</figcaption></figure>

The comprehension of the parameters which govern wet snow accretion, leads to the possibility to undertake a numerical study to characterize the different accretions by using a so called “stick-function” for wet snow particles. The study will also aim at producing an experimental database of climatic wind tunnel icing simulations for comparisons with outdoor real-scale phenomena.

The criterion implemented in the numerical modeling of accretion takes into account the angle between the particle path at the impact with the surface and the normal to the surface, i.e. if this angle $α_{imp}$ is lower than a threshold angle $α_t$ the particle sticks to the surface. The approach has simulated accretion shapes showing fair similarities with the experimental. However it fails to predict accretion on the upper and lower part of the cylinder. This might lead to a more comprehensive model to take into account the particle energy, the LWR and the angle of impact that have been identified as the crucial aspects involved in wet snow accretion process (Figure 21).

<figure><img src="image_523.png" /><figcaption>**Figure 21:** Comparison of accretion shapes obtained by numerical modelling with experimental results, analysis of the upper and lower part of a cylinder $d_{cyl} = 50$mm: (a) numerical simulation $α_t = 20°, 30°$ and $45°$ (b) experimental result,</figcaption></figure>

## 6 Perspectives

Due to the diversity of situations, i.e. snow ingress or accretion and accumulation in vehicles, there is not a unique approach to investigate these situations and assess the measures to alleviate the risks. But in all cases the use of experimental facilities that provide controlled ambiances and snow making capacities is valuable.

The climatic wind tunnel provides the controlled ambient environment which allows isolating influencing

variables and analyses the causes of phenomena. Nevertheless, a wind tunnel remains a simulating situation of natural environment. From this point of view natural field experiments are useful to validate lab simulations. Although the wide range of potentialities of climatic wind tunnels, many conditions remain absent from material and financial possibilities. In some cases, numerical modelling can supplement the experiments in wind tunnels. A parallel approach, experimental and experimental field tests, seems the most productive way since it enables to avoid the pitfalls of each method. It seems this should be the way to favour in the future.

Common to all of the examples given in this paper is that no absolute solution has been proposed. The presented results provided the basis of analysis for future developments. The main benefit of the experimental work, and that seems crucial, is the ability to predict the severity of the observed phenomena. This is an essential first step whatever are the corrective actions further considered, improvement of design or preventing measures.

An interesting protecting strategy is provided by ice-phobic coatings. If these kinds of coating do not yet fully prevent ice and snow deposition, they greatly reduce the adhesion. Hence, the mechanical movements of the iced structures enable the fall of the accretion. Similarly, during the melting phase, the protective coatings allow to reduce the icing cover more quickly.

With regard to the snow ingress in the engine compartment and air intake of HVAC of vehicles, optimization of air inlets is needed. Similarly, reducing the risk of snow accretion on vehicles may be due to a better design of heating de-icing system and shape optimization. But in any case, it is not certain that this eliminates the problem permanently.

## Acknowledgments

The authors acknowledge SNCF for the financial support of studies presented in this paper.

## References

1. Boisson-Kouznetzoff S., Palier P., Caractérisation de la neige produite en souflerie climatique, *Int. J. of Refrigeration* 24, 302-324, (2001)

2. Boyne H.S., Fisk D.J., A Laboratory comparison of field technique for measurement of liquid fraction of snow. CRREL Spec. Rep 90.3, (1990)

3. Denoth A. Snow dielectric measurements, Adv. Space Res. Vol 9, No1, pp 233-243, (1989)

4. Allain E., Paradot N., Ribourg m. Delpech P., Bouchet JP., De la Casa X., Pauline J. Experimental and numerical study of snow accumulation on high speed trains. Proceedings of the 49th International Symposium on Applied Aerodynamics, Lille, France, 2014

5. Vigano A. Experimental and Numerical modelling of wet snow accretion on structures. PhD thesis, France, 2012.
# Anti- and de-icing of walking and cycle paths – Field trials of new follow-up techniques for quantifying salt amount and resulting ice quality

Göran Blomqvistª, Bengt Lindström, Ida Järlskog, Emelie Karlsson and Anna Niska

Swedish National Road and Transport Research Institute (VTI), SE-581 95 Linköping, Sweden

**Abstract.** New follow-up methods for evaluating winter maintenance of walking and cycle paths have been developed and tested. The effects of different anti- and de-icing methods, salt qualities and doses as well as the processes behind salt loss are fairly well known after decades of studies on runways, highways and roads. Those relationships are, however, not valid on walking and cycle paths, since these lacks the action of high speed car tyres on the road surface. Therefore methods for quantifying the amount of residual salt on walking and cycle path surfaces before a weather event and the resulting quality of the frost or ice after the event would be a good help in order to enable follow-up of anti-icing actions taken. By manually dissolving the salt within a confined area of the pathway and measure the electric conductivity of the liquid, the salt dose on the pavement may be established and by inflicting a mechanical angular impact on the ice surface after the weather event, the quality of the ice may be established. Knowledge of the salt dose and the resulting ice quality then enables the road keeper to evaluate the efficiency of the winter maintenance actions taken.

## 1 Need for follow-up methods

There is a rapid development of methods for winter operation of pedestrian and bicycle paths. Many Swedish cities are committed to transferring car trips to cycling even in winter, which places great demands on achieving satisfactory friction for cyclists. Eight out of ten bicycle accidents are single vehicle accidents and among these, the most common cause is slipperiness caused by ice, snow or remaining gritting sand. Therefore most of the new methods developed are gritting free, usually combining a rotating brush with salting equipment for brine or pre-wetted salt, so-called sweep salting [1, 2].

The city road keepers and their contractors, however, often lack possibilities to effectively evaluate the efficiency of the actions taken, since:

* Existing knowledge and evaluation methods are taken from the highway and airport experiences and therefore need to be downscaled to walking and cycle paths,

* There can be a big difference between the pedestrian and bicycle sections within the same walking/cycle path,

* Standard friction measurements does not represent the forces that a cyclist or pedestrian is exposed to.

Therefore a new follow-up methodology based on measurements of the salt dose on the surface and the resulting ice quality was developed.

## 2 Salt measurements

Commercially available salt measurement devices, be it manually held salt sensors like the SOBO20 or sensors installed in the pavement, may be very accurate, but when it comes to salt that is undissolved, either because it is spread dry and there is not enough salt on the surface to dissolve it, or the liquids have dried up due to evaporation, such devices will inevitably underestimate the amount of salt on the road surface.

Therefore a portable device where a known amount of liquid is applied manually within a confined area and the

surface is brushed, also manually, until all salt within the area is dissolved has been developed (Figure 1).

<figure><img src="image_524.png" /><figcaption>**Figure 1.** Portable salt measuring and sampling device.</figcaption></figure>

In order to decide when all the salt on the investigated surface is dissolved, the salt measuring and sampling device is equipped with two electrodes connected to a 54€ conductivity circuit (Atlas Scientific EZO™) communicating with a 30€ digital microcontroller (Arduino UNO), presenting the calculated salt amount on an OLED graphic display in front of the operator.

Depending on the situation the operator may decide to leave the liquid and just use the calculated salt amount, or take the sample back to the laboratory for further analyses by sucking it up with a suction device and transfer it from the confined area to the sample cup. The relation between the electric conductivity and chloride concentration is

<figure><img src="image_525.png" /></figure>

<footnote>ª Corresponding author: goran.blomqvist@vti.se</footnote>
known from field and laboratory trials within the Nordic research project NordFoU-MORS (Modelling Residual Salt) [3].

<figure><img src="image_526.png" /><figcaption>**Figure 2.** Relation between the electric conductivity and chloride concentration.</figcaption></figure>

## 3 Ice quality measuring device

In order to inflict a repeatable angular impact on the ice surface investigated, an ice-hammer-pendulum was constructed (figure 3).

<figure><img src="image_527.png" /><figcaption>**Figure 3.** Ice quality measuring device.</figcaption></figure>

The pendulum head is equipped with a 32€ 9-DOF absolute orientation sensor (Adafruit BNO055) combining three axis accelerometer, gyroscope and magnetometer into the actual 3D space orientation with a 100Hz time

resolution communicating with a 30€ digital micro-controller (Arduino UNO).

By adjusting the height of the pendulum over the ice-layer tested, the impact is intended to be repeatable.

Analyses will include the hardness of the ice as calculated from the registered forces and the effect of the impact by visual inspection of the surface after the pendulum is dropped. A crucial issue is to establish whether the formed ice has bonded to the pavement or not.

## 4 Combined experiments

The combination of salt and ice impact measurements will be performed in a climate chamber during November–December 2015 where rain will be simulated on five different pavements (Table 1) at different freezing temperatures and with different anti-icing salt doses applied.

<table><caption>**Table 1.** Pavements used for salting/freezing tests in the climate chamber.</caption><thead><tr><td>Pavement type</td><td>Stone size (mm)</td><td>Stone mineral</td><td>Road surface texture (mm)</td></tr></thead><tbody><tr><td>SMA</td><td>16</td><td>Porphyry</td><td>2.1</td></tr><tr><td>SMA</td><td>11</td><td>Quartzite</td><td>1.6</td></tr><tr><td>AC</td><td>11</td><td>Limestone</td><td>1.4</td></tr><tr><td>SMA</td><td>8</td><td>Porphyry</td><td>2.1</td></tr><tr><td>SMA</td><td>16</td><td>Quartzite</td><td>1.3</td></tr></tbody></table>

SMA = Stone Mastic Asphalt, AC = Asphalt Concrete

During January–March, 2016, the same equipment will be used in field trials under real conditions in order to follow-up different winter maintenance techniques used in the City of Stockholm where the relation between salt dose, ice quality and efficiency of different salt sweepers will be addressed.

## Acknowledgments

This study is financed by the Swedish Transport Administration and performed in cooperation with the City of Stockholm. The authors also acknowledge the assistance from the student Elise Rydberg from the Linköping University and Stefan Svensson for the construction work in the VTI workshop.

## References:

1. A. Bergström [Niska], PhD Thesis, Royal Institute of Technology, Stockholm (2002),

2. J. Jansson & S. Kok, Master Thesis Linköping University (2015)

3. G. Blomqvist, M. Eram, M. Gustafsson & S. Thordarson, PIARC XIV International Winter Road Congress (2014)
# Shallow geothermal switch point heating system

Lars Staudacher<fnref n="1" />, Damian Schink<fnref n="2" />, Dr. Roman Zorn<fnref n="3" />, Dr. Hagen Steger<fnref n="4" />

**Abstract.** For safety reasons railroad switches are heated in winter to prevent malfunction due to snow or ice. Typically electric resistance heating or gas burners are applied. In Germany about 490 MW electric power are installed at German Railways and approximately 186 GWh of electric energy are consumed per year by these systems. Within the project "Geothermal Zero Emission Switch Point Heating" a low temperature heating system has been developed that runs solely using shallow geothermal energy. Low temperature heat is extracted from the ground by a CO₂ thermosyphon which consists of a borehole heat exchanger working as evaporator and condensers placed at the switch point. The whole system has been developed, tested in laboratory and finally 3 pilot plants have been built.

## 1 Principle

A geothermal switch point heating system working solely with shallow geothermal energy requires that the heat transfer between the ground and the switch point is working with a very low temperature difference. A thermosyphon is a heat exchanger which fulfils this requirement and furthermore does not require any additional energy for operation. The heat carrier is filled into a closed pipe system such that it is in a two phase state. In our case CO₂ has been chosen because of its environmentally uncritical characteristics. As the condensers are cooled due to environmental conditions the gaseous CO₂ starts to condense inside the condenser. The resulting pressure drop in the system leads to evaporation of the liquid phase in the pool (see Figure 1).

<figure><img src="image_528.png" /><figcaption>**Figure 1.** Principle of the switch point heating system</figcaption></figure>

Once the circuit has started to operate the back flowing liquid phase from the condenser wets the tube walls of the evaporator and leads to a heat flux from the entire borehole to the switch as long as the condenser surface is cooler than the evaporator surface. Thus no additional control system is necessary to start or shut down the heating system. The unique requirement that has to be fulfilled is that the supply temperature of the

gaseous CO₂ is always high enough to melt all the snow or ice that has to be expected at the site of installation.

## 2 Determination of load profiles

A load profile of the switch point heating system is necessary for dimensioning of the borehole heat exchanger. Hereby the essential heating power as a function of environmental circumstances has to be determined. In order to get this correlation two steps have been carried out [1]. First the heat transfer of a flat plate to environment has been calculated. Second the correlations have been validated at an outdoor test facility as the geometry of the heating system differs from that of a flat plate.

Figure 2 shows the heat transfer terms which are of interest.

<figure><img src="image_529.png" /><figcaption>**Figure 2.** Heat transfer at the surface</figcaption></figure>

As the plate is heated continuously there are convective losses at the surface when the plate is warmer than the surrounding air. There are also radiation losses especially at cloudless nights. If snow falls down to the plate it will be heated up to zero degrees (sensible heat) and then be melted (latent heat). There are also losses due to evaporation when the plates are wet because of rain or melted snow. All this terms together give a correlation which describes the heating power of the plate as a function of environmental conditions.

The problem of the heat demand of a surface heating system has already been investigated by different authors [2-6]. Aberrations of the results of this calculation in our case are mainly due to the fact that some of the models

<figure><img src="image_530.png" /></figure>

<footnote n="1">¹Bavarian Center for Applied Energy Research, 85748 Garching, Germany</footnote>

<footnote n="2">²Pintsch Aben geotherm GmbH, 46537 Dinslaken, Germany</footnote>

<footnote n="3">³European Institute f. Energy Research (EIFER), 76131 Karlsruhe, Germany</footnote>

<footnote n="4">⁴Karlsruher Institut für Technologie (KIT) Institut für Angewandte Geowissenschaften, 76131 Karlsruhe, Germany</footnote>

<footnote>ª Corresponding author: lars.staudacher@zae-bayern.de</footnote>
are specially designed for certain applications and do not account for all physical parameters like absorption and emission coefficients. Nevertheless all correlations lead to results of the same order. Aberrations of the yearly energy demand are within a range of ±15% based on the average value. The result of this calculation for standard weather data are illustrated in Figure 3 and Figure 4.

<figure><img src="image_531.png" /><figcaption>**Figure 3.** Exemplary Load Profile of the switch point heating system [2-6]</figcaption></figure>

<figure><img src="image_532.png" /><figcaption>**Figure 4.** Comparison of the yearly energy demand calculated with different models [2-6]</figcaption></figure>

The results strongly depend on the site. If the surface is heated continuously as in our case most of the heating demand are losses. Only around 5% of the yearly consumed energy is necessary to melt snow. Most of the energy is lost by convection. This is around 55 – 60% of the total heat demand. The rest are evaporation and radiation losses that are more or less of the same order. Simple control strategies taking into account air temperature can lead to energy savings around 25% compared to a not controlled system. If an ideal control strategy could be applied which detects exactly when heating is necessary due to snow fall up to 65% of the energy demand could be saved.

# 3 Development of the condensers

## 3.1 Design of the condensers

In order to fulfil the requirement to melt snow and ice at a very low temperature level the integration of the heating system into a railroad switch point has been the major challenge. First numerical simulations have been carried out in order to investigate if a simple coupling of heat exchangers to a switch is possible. A first prototype of

the heating system was built and tested (see Figure 5). This prototype was mounted above the slide plate with a condenser on each side. Heat transfer into the slide plate was solely by conduction.

Although this prototype worked quiet well the design had to be improved in terms of thermal and mechanical characteristics and especially costs.

<figure><img src="image_533.png" /><figcaption>**Figure 5.** First prototype of the switch point heating system</figcaption></figure>

The main improvement of the next prototype was to integrate the condenser into the slide plate. This leads to a much better coupling of the heating system to the slide plate and therefore to a better performance of the heating system. As a consequence of this the slide plate had to pass through all the safety tests that are necessary to get a permission for the German railways. Beneath a numerical stress analysis also experimental tests had to be carried out. Figure 6 shows some results of the stress analysis of the slide plate.

<figure><img src="image_534.png" /><figcaption>**Figure 6.** Stress analysis of the slide plate (Source: Pintsch Aben Geotherm)</figcaption></figure>

On the right side of the figure the hollow space of the condenser is visible in which the gaseous CO₂ condenses. Between the slide plates a melting device has been developed that also works as a condenser (see figure 7). It is made out of aluminium as an extrudet section. The channels are closed afterwards through cover plates that are welded at the melting plates. To protect the condenser but also the pipping a safety box made out of stainless steel was constructed. One part of this box is also illustrated in figure 7.
<figure><img src="image_535.png" /><figcaption>**Figure 7.** Melting plate mounted at the rail base
(Source: Pintsch Aben Geotherm)</figcaption></figure>

This aluminium condenser together with respectively one left and one right condenser of the slide plate is connected to one evaporator in a borehole. Figure 8 shows the mounted heating system but without the stock rail. The service pipe including its protective pipe is also visible.

<figure><img src="image_536.png" /><figcaption>**Figure 8.** Slide plate with integrated condensers and melting condenser between slide plates (Source: Pintsch Aben Geotherm)</figcaption></figure>

## 3.2 Laboratory test of the heating system

For this prototype laboratory tests have been carried out in order to identify weaknesses of the heating system and measure the minimum supply temperature as a function of peak power. Figure 9 shows a picture of the heating system when it starts to fail. Snow starts to accumulate at the middle of the slide plate. This is one of the most critical parts of a switch point in terms of snow or ice and it has therefore to be avoided.

<figure><img src="image_537.png" /><figcaption>**Figure 9.** Laboratory test of the heating system</figcaption></figure>

Beneath this stationary test also shock tests have been carried out in order to investigate the system behaviour in the case of big ice lumps falling inside the switch point. The new heating system passed all these tests nevertheless there is still potential for optimization. In order to supply snow to this relatively small area of about 0.3x1.5m a little snow machine has been developed as no commercial system was available (see figure 10).

<figure><img src="image_538.png" /><figcaption>**Figure 10.** Switch point test facility inside climate chamber</figcaption></figure>

With this machine a maximum snow load of 1000W/m² can be applied for approximately 4 hours exactly at the place where the heating system has to prove its functionality. Due to its modular composition this snow machine can be extended easily to bigger areas. The whole system is located in a climate chamber that is cooled through natural convection. This is necessary in order not to blow the snow away.
# 4 Development of the evaporator

## 4.1 Phenomenological discussion of heat transfer inside of a thermosyphon

Heat transfer inside of a geothermal CO₂ earth probe is a very complex process. Different hydrodynamic and heat transport phenomena occur inside of the evaporator thus predictability of heat transfer is highly uncertain [7]. Inside of the evaporator there is a liquid pool at the bottom of the earth probe. If a temperature difference is applied to it the liquid starts to boil. At small temperature differences there is convective boiling inside of the pool. If the temperature difference increases nucleate boiling will be encountered and the heat transfer coefficient will rise at this part of the evaporator. In parallel the liquid phase that flows back from the condenser to the pool evaporates along the way in the probe as long as the wall temperature is higher than the saturation temperature of the liquid. Depending on the wetting conditions of the inside surface the heat transfer in this part is better or worse. As the evaporator of a CO₂ earth probe is very long one could encounter different forms of boiling at the same time because of the backpressure inside of the pool. In the upper part there might be nucleate boiling, deeper there might be convective boiling and even deeper the pressure might be too high to even boil. How the different parts perform also depends on the applied load. Back pressure inside of the pool is principally negative as the saturation temperature rises inside of the pool. Anyway a certain pool should be assured to ensure start up and to realize a satisfactory heat transfer behaviour. Beneath different forms of boiling and flow regimes nucleate boiling strongly depends on surface properties. Exact predictions concerning heat transfer coefficients inside of a thermosyphon is therefore difficult.

## 4.2 Heat transfer limits

There are three different phenomena that limit the heat transfer inside of a thermosyphon:

* burn out limit

* boiling limit

* flooding and entrainment limit

At small filling amounts the burn out limit has to be taken into account. It occurs when the liquid film does not reach the bottom of the evaporator and no pool exists anymore. All the heat carrier is circulating inside the thermosyphon at this state. A further increase of the heat flux leads to a burn out of the evaporator. The boiling limit occurs, when the radial heat flux is so high that a closed blanket of vapour covers the inside surface of the evaporator. This vapour film increases the resistance between the heat carrier and the evaporator wall. An

increase of temperature difference will not lead to a rising power but an increasing vapour film. Flooding and entrainment in thermosyphons occurs at high axial heat flux. The reason for this limitation is the shear force between the liquid and vapour phase that flow counter current. If the transferred power is increased the shear force increases until the downward flowing liquid phase is stopped. This phenomenon is called “flooding limit” if the thermal fluid conditions are still steady and stable. Further increase of the heat input will lead to unstable and unsteady conditions in the liquid film. In this case liquid droplets are entrained from the liquid film and transported to the condenser. Both phenomena lead to an accumulation of heat carrier in the condenser section and can lead to a dry out of the evaporator.

## 4.3 Experimental results at ZAE Bayern

Aim of this experiment was to measure the power of a CO₂ evaporator as a function of the applied temperature difference between surface and outlet of the evaporator with a high accuracy of measurement. The test facility consists of three coupled circuits. One circuit to cool the condenser, one circuit to heat the evaporator and one circuit that contains the thermosyphon. The evaporator section of the thermosyphon was heated by a water quench circulating at high velocity around the evaporator tube. This setup was necessary to determine the surface temperature of the evaporator accurately. The power of the evaporator was determined by a mass flow meter that measured the mass flow rate coming back from the condenser. Together with the measured temperature at the outlet of the evaporator the power could be calculated. The experiment was carried out using a plain tube with a length of 9.22 m and an inside diameter of 11.2 mm. At the bottom of the evaporator was a liquid pool with a height of 3.4 m. The investigation of this evaporator tube showed a clear dependence of the operation behaviour on the way the evaporator is heated. Depending on how DT is changed one can observe significant differences of the output power at the same temperature difference. Figure 11 shows the relative power of this tube versus the applied DT for three different operation conditions. At small temperature differences one can observe a linear behaviour between the applied DT and the output power which is practically independent of the operation conditions. After that the power strongly depends on how the operation conditions have been before and how they are changing. For example if we start at a power of 0 W and increase the temperature difference stepwise through a decrease of the outlet temperature of the evaporator while the surface temperature is maintained at a constant level one will observe a behaviour as it is illustrated by the black crosses in figure 11. The same procedure just the other way around starting at maximum power and decreasing DT through a stepwise increase of the outlet temperature of the evaporator will lead to a behaviour illustrated by the black squares in figure 11. Another possible way of changing the operation conditions is to start at maximum power and decrease DT through an increase of the surface temperature of the evaporator
while keeping the outlet temperature constant. This
behaviour is illustrated by the grey triangles in figure 11.
All these different ways of changing the operation
conditions have in common that one can observe two
transition points. At this points is found an immediate
change of the transferred power at nearly the same
temperature difference. This transition points indicate
that there is a change of flowing, wetting or boiling
behaviour inside of the evaporator. The transition points
itself depend on the operation conditions. Another
interesting result is that the maximum power of the
evaporator could not be reached at small temperature
differences starting at a power of 0 W. To reach the
maximum power starting from zero a DT of about 7 K
was necessary. Temperature differences higher than 7 K
did not lead to an increase of the transferred power
anymore.

<figure><img src="image_539.png" /><figcaption>**Figure 11.** Operation behaviour of a plain tube working as a CO₂-evaporator</figcaption></figure>

As a result of these experiments the entrainment limit,
the essential temperature difference and the necessary
filling amount of the refrigerant could be determined for
this special case of evaporator tube.

**5 Pilot plants**

3 pilot plants have been built up to now using this
technique. The first one was built 2012 in Grünberg for
DB (German Railways). The second plant was built 2013
in Hamburg for the Harbour Railway. The third pilot
plant was built 2014 in Sponholz also for DB. The
heating System works reliable (see figure 5) and
measurements show that the ground temperature at the
borehole recovers completely during summer so that
from this side no limit of lifetime is expected.

One critical point that has to be improved is leak-
tightness. Some of the heating circuits loose CO₂ which
has to be refilled once in a while. From a technological
point of view this problem can be solved as much lower
leakage rates are achieved in chiller technology.
Nevertheless leak-tightness is a challenge as mounting,
linking of components and filling of the system has to be
done at installation site.

<figure><img src="image_540.png" /><figcaption>**Figure 5.** First Pilot Plant – Grünberg (Germany)</figcaption></figure>

**References**

1. L. Staudacher, M. Reuß, D. Schink, Geothermal Zero Emission Switch Heating, Proc. Effstock 2009, Stockholm

2. AMERICAN SOCIETY OF HEATING, REFRIGERATING AND AIR-CONDITIONING ENGINEERS (Hrsg.): 1999 ASHRAE Handbook – HVAC Applications. In: The ASHRAE Handbook CD, Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers Inc., 1996-1999

3. CHAPMAN, W.P.: Calculating the Heat Requirements of a Snow Melting System. In: Air Conditioning Heating and Ventilating Vol. 53 No. 9, 1956

4. KILKIS, I.B.: Desining of Embedded Snow Melting Systems: Part 1, Heat Requirements – An Overall Assessment and Recommendations. In: ASHRAE Transactions/100.1994, 1

5. RAMSEY, J.W.; HEWETT, M.J.; KUEHN, T.H.; PETERSEN, S.D.: Updated Design Guidelines for Snow Melting Systems. In: ASHRAE Transactions/105.1999, 1

6. VEREIN DEUTSCHER INGENIEURE (Hrsg.): VDI-Wärmeatlas : Berechnungsblätter für den Wärmeübergang. 6., erw. Aufl., Düsseldorf: VDI-Verlag, 1991

7. L. Staudacher, M. Reuß, CO₂-Thermosysphon Borehole Heat Exchanger, Proc. Innostock 2012, Lleida
# A Method for estimating road friction coefficients with ice film subjected to melting by de-icing agents

Akihiro Fujimoto<fnref n="1" />,ª, Shunsuke Tanaka<fnref n="1" />, Kenji Sato<fnref n="1" />, Roberto Tokunaga<fnref n="1" />, Naoto Takahashi<fnref n="1" />, Tateki Ishida<fnref n="1" /> and Kiyoshi Takeichi<fnref n="2" />

**Abstract.** In this study, to achieve the efficient use of the de-icing agents that are spread on icy-covered roads, we developed a method for estimating the road friction coefficient (μ) after de-icing agent spreading on such roads. A theory for estimating μ and the results of an experiment conducted in a constant low-temperature room to estimate μ for stone mastic asphalt pavement and porous asphalt pavement are described. Also, the estimated μ between the different pavement types are compared.

## 1 Introduction

Resources for guidance on the spreading of de-icing agents include the Snow and Ice Databook[1] and Clear Roads[2]. However, methods to determine the appropriate amount of de-icing agent application and its timing that give due consideration to weather conditions and the state of ice and snow on the road have not been established. In addition, various types of pavement have been constructed in cold, snowy regions as well as in other regions in recent years. Porous asphalt (PA) pavement and stone mastic asphalt (SMA) pavement (pavement that affords both the surface texture of PA pavement and the durability of mastic asphalt pavement) have rough surface textures. Such textures mean that less moisture remains on road surface, which makes them less prone to surface freezing than dense-graded asphalt (DGA) pavement is. However, their rough surface texture and drainage function make the effectiveness of de-icing agent spreading unclear because they cause a loss of de-icing agents along with the loss of moisture from the pavement surface.

With the continuing diversification of pavement types, the texture and drainage function of pavements should be taken into consideration in efforts to make de-icing agent spreading more optimal than ever. Also needed is an evaluation method for the effectiveness of spreading of de-icing agents using objective indices such as road friction coefficient (hereinafter: μ). In other words, a method for estimating μ after de-icing agents are spread needs to be established.

An experiment on the spreading of de-icing agents on icy roads was conducted in a constant low-temperature room with DGA pavement, SMA pavement, and PA pavement. By measuring the ice thickness on each pavement specimen before and after the spreading of de-icing agents and μ, a method for estimating μ after de-icing agents are spread was established [3-4], which will be introduced in this paper.

## 2 μ estimation method

### 2.1 Assumptions and conditions of the study

The method for estimating μ after de-icing agent spreading that is proposed in this paper makes the following assumptions.

i) No consideration is given to the possibility that de-icing agents can be dispersed while being spread, dispersed by passing vehicles after spreading, or effused from the sloped pavement.

ii) The melting of the ice film on the road surface caused by the spreading of de-icing agents occurs evenly to the same degree all over the road surface. In practice, the melting of the ice film on the road surface occurs unevenly and some areas of pavement surface retain ice film on them after the spreading of solid de-icing agents on roads covered with thin ice. To distinguish between the ice thickness on road surface in real situation and the ice thickness referred to in this study based on the assumption (ii) above, the latter will be called mean ice thickness $H_i$ (mm) hereinafter.

### 2.2 Estimation method

Our μ estimation method is described here (Also see the diagram in Figure-1). The upper part of the diagram shows the state of the pavement surface on which de-icing agents have not been spread (hereinafter: State 1). The lower part of the diagram shows the state of the pavement surface for which sufficient time has passed after de-icing agent spread for ice to melt (hereinafter: State 2). Thermodynamic equilibrium is reached in State 2. Where the input conditions are mean ice thickness ($H_i$ (mm), i.e., $H_{i-1}$), road surface temperature ($T$ (°C)), amount of de-icing agent spreading ($M_{ss-in}$ (kg/m<fnref n="2" />)), and pavement types for State 1. The μ of State 2 ($μ_2$) is determined by the following five steps.

1. Determine the $T$ and then estimate the concentration ($C$ ($C_2$)) of salt solution for State 2 by using the freezing-point curve.

<figure><img src="image_541.png" /></figure>

<footnote n="1">¹PWRI, CERI, Hiragishi 1-3-1-34, Toyohira-ku, Sapporo, 062-8602, Japan</footnote>

<footnote n="2">²Hokkai-Gakuen University, 1-1, Nishi 11, Minami 26, Chuo-ku, Sapporo, 064-0926, Japan</footnote>

<footnote>ª Corresponding author: afujimot@ceri.go.jp</footnote>
<figure><img src="image_542.png" /><figcaption>Figure 1. Schematic of the μ estimation method.</figcaption></figure>

2: Because there is a loss of de-icing agents on the pavement, to determine that loss, we define the mass per square meter of de-icing agent acting to melt ice on the pavement as the amount of effective salt melting ($M_{s-melt}$ (kg/m²)). The amount is determined by Equation (1).

$$M_{s-melt} = M_{ss-in} (1 - R_z) (1 - R_v) (1 - R_x) \quad (1)$$

Where, $R_x$ is the ratio of the mass per square meter of de-icing agents effused away from the sloped pavement to $M_{ss-in}$. $R_z$ is the ratio of the mass per square meter of de-icing agents infiltrated into and accumulated inside the pavement to $M_{ss-in}$ (hereinafter: the ratio of salt loss in the vertical direction). $R_v$ is the ratio of the mass per square meter of de-icing agents dispersed by passing vehicles to $M_{ss-in}$. Because our focus of this study is $R_z$ (described in 4.4), $R_x$ and $R_v$ are regarded as zero.

3: The amount of ice melting for State 2 ($M_{i-melt}$ (kg/m²)) is determined from the following equation.

$$M_{i-melt} = M_{s-melt} \cdot (1 - C)/C \quad (2)$$

4: The mean ice thickness of State 2 ($H_i$ ($H_{i-2}$)) is determined by subtracting the thickness of melted ice ($H_{i-melt}$ = $M_{i-melt}/\rho_i$, where $\rho_i$ is ice density (kg/m³)) from $H_{i-1}$.

5: Finally, $\mu_z$ is calculated from the $\mu-H_i$ relational equation (described in 4.3).

# 3 Indoor experiment

## 3.1 Experiment procedure and conditions

The laboratory experiment was conducted in a constant low-temperature room. Figure 2 shows pavement specimens after the formation of an ice film. As shown in the photo, three types of pavement specimens were used: PA pavement (top), SMA pavement (middle) and DGA pavement (bottom). Three specimens per type were used.

<figure><img src="image_543.png" /><figcaption>Figure 2. Specimens of indoor experiment</figcaption></figure>

Each specimen is 0.40m in width, 0.40m in length, and 0.05m in height. The maximum size of coarse aggregate is 13mm. The void ratio of the PA pavement is 17%. The circle marks on the ice film on specimens in the photo were made by the Dynamic Friction Tester used for measuring μ.

Following is the experiment procedure. (i) Pavement specimens are placed in the constant low-temperature room. They are left until their temperature falls to that of the room. (ii) Fresh water is sprayed on the specimens. (iii) They are left until an ice film forms on their surface. (iv) Sodium chloride is sprinkled on the ice film. (v) The specimens are left until the sodium chloride dissolves completely. (vi) $T$, $\mu$, $C$, and $H_i$ are measured in that order. In procedure (v), $T$ was regularly measured to determine the completion of sodium chloride dissolution. It was determined when $T$, which had dropped due to the endothermic reaction from the spreading of sodium chloride, returned to that of before the spreading.

Table 1 shows the experiment conditions.

## 3.2 Measurement items

$T$ was measured with a radiation thermometer for 5 seconds, and the average value was adopted. The measurement accuracy was ±2.0°C at $T$ = 0°C. A Dynamic Friction Tester was used to measure $\mu$. $\mu$
measured at a travel speed of 40km/h is adopted in this paper. The salt solution on the road surface was sampled using water-absorbent paper in order to measure C with a refraction salinity concentration meter. $H_i$ was measured using our ice film thickness meter, which uses weight of meltwater on pavement surface (the ice film thickness meter hereinafter). The meter was made on our own referring to ideas in Haavasoja, T et al. [5]. The following is the procedure for measuring $H_i$ by using the ice film thickness meter.

The ice film thickness meter consists of a box and water absorbing paper that is placed between the box and a road surface covered with thin ice, as shown in the upper picture in Figure 3. For this experiment a box was made with easily moldable laminate sheet. It was 220mm in width, 230mm in length and 100mm in height. $H_i$ is measured in the following procedure. (i) Remove moisture from the road surface by using absorbent paper. (ii) Place the ice film thickness meter on pavement specimens and pour hot water (about 95°C) into the box. (iii) 15 seconds later, detach the meter and measure the weight of the absorbent paper that has taken in the snowmelt. (iv) Repeat (ii) and (iii) until the amount of snowmelt falls below the lower limit of the amount of snowmelt. (v) Calculate $H_i$ from the total weight of snowmelt. The lower limit of the amount of snowmelt water taken by the absorbent paper to conclude the procedure (iii) was set based on the $\mu$ of the wet surface. The following is the background and procedure for setting the lower limit. By repeating (iii), the ice that fills voids inside pavement will also melt and be absorbed, but such ice does not affect $\mu$. To avoid overestimating $H_i$ due to the situation above, a preliminary experiment was conducted to measure the amount of snowmelt when $\mu$ was that of wet surface, and this amount was designated as the lower limit of snowmelt. Accordingly $H_i$ means the spatial average thickness of ice on the pavement that will affect $\mu$.

The lower photo in Figure 3 shows the difference between the area of pavement after $H_i$ is measured (left side of the photo) and the area of pavement for which measurement was not conducted (right side of the photo). As shown in the photo, the surface of coarse aggregate becomes dry after $H_i$ is measured.

# 4 Experiment results

## 4.1 Road surface conditions

Figure 4 shows the surface of the pavement specimens after the removal of surface moisture from the specimens in State 2 using absorbing paper. Photos in (a) in Figure 4 show pavement specimens on each of which 1.0kg/m² of water was sprinkled ($M_{w-in}$ = 1.0kg/m²) and the photos in (b) show pavement specimens on each of which 2.0kg/m² of water was sprinkled ($M_{w-in}$ = 2.0kg/m²). The left side photos in both (a) and (b) show specimens over each of which 20g/m² of de-icing agent was spread ($M_{ss-in}$ = 20g/m²) and the right side pictures of both (a) and (b) show specimens over each of which 60g/m² of de-icing

<table><caption>**Table 1.** List of experiment conditions</caption><thead><tr><th colspan="2">Environmental conditions</th></tr></thead><tbody><tr><td>Room temperature</td><td>-5°C</td></tr><tr><td colspan="2">Water sprinkling conditions</td></tr><tr><td>Amount of water sprinkled</td><td><ul><li>kg/m² (sprinkle twice, 0.5 kg/m² each time)</li><li>kg/m² (sprinkle 4 times, 0.5 kg/m² each time)</li><li>3.0 kg/m² (sprinkle 6 times, 0.5 kg/m² each time)</li></ul></td></tr><tr><td>Temperature of sprinkled water</td><td>0 - 1.0°C</td></tr><tr><td>Coloring</td><td><ul><li>White (aqueous acrylic paint)</li><li>Mass ratio (100:1 // fresh water :</li></ul></td></tr><tr><td colspan="2">Conditions of de-icing agent spreading</td></tr><tr><td>Type of de-icing agent</td><td>Sodium chloride</td></tr><tr><td>Amount of de-icing agent spreading</td><td>0, 20, 40, 60, 80 g/m²</td></tr><tr><td>Form</td><td>Powder</td></tr><tr><td colspan="2">Conditions of pavement specimens</td></tr><tr><td>Type of de-icing agent</td><td><ul><li>DGA pavement</li><li>SMA pavement</li><li>PA pavement</li></ul></td></tr><tr><td>Size</td><td>0.40m in width, 0.40m in length, 0.05m in height</td></tr><tr><td>Number of specimens</td><td>3 for each pavement type</td></tr><tr><td>Specifications</td><td><ul><li>Maximum size of coarse aggregate: 13mm</li><li>Void ratio: 17% (for PA pavement)</li></ul></td></tr><tr><td colspan="2">Measurement items</td></tr><tr><td>Items</td><td><ul><li>Road surface temperature</li><li>μ</li><li>Concentration of salt solution</li><li>Mean ice thickness</li></ul></td></tr></tbody></table>

<figure><img src="image_544.png" /><figcaption>**Figure 3.** Measurement of ice film thickness</figcaption></figure>
<figure><img src="image_545.png" /><figcaption>**Figure 4.** Surface of pavement specimens after removal of surface moisture</figcaption></figure>

agent was spread ($M_{ss-in} = 60\text{g/m}^2$). The upper photos show the road surface conditions for DGA pavement, the middle photos show those for SMA pavement and the lower photos show those for PA pavement.

First, look at the photos in (a) of $M_{w-in} = 1.0\text{kg/m}^2$. When $M_{ss-in} = 20\text{g/m}^2$, pavement surface and icy surface existed intermingled for DGA pavement since parts of the ice film that had been in contact with sodium chloride melted. As for the SMA pavement, aggregate was exposed on convex parts of the surface of the specimens due to contact with sodium chloride, and indentations on the surface of the pavement specimens that had ice in voids had holes in places made by sodium chloride. The road surface condition for the PA pavement specimen was similar to that for the SMA pavement specimen, but ice on the PA pavement seems thinner than that on the SMA pavement. For DGA pavement, the area where pavement was revealed on the surface was greater horizontally at $M_{ss-in} = 60\text{g/m}^2$ than at $M_{ss-in} = 20\text{g/m}^2$. As for SMA pavement and PA pavement, the area where the aggregate of convex parts of pavement was revealed was greater at $M_{ss-in} = 60\text{g/m}^2$ than at $M_{ss-in} = 20\text{g/m}^2$.

Photos in (b) of $M_{w-in} = 2.0\text{kg/m}^2$ are described next. When $M_{ss-in} = 20\text{g/m}^2$, the degree of ice melting by sodium chloride was just enough to make holes in the ice

film and the exposure of the pavement surface was limited for the DGA pavement. Although aggregate was partially exposed on the surface of the SMA pavement and the PA pavement, most of the pavement surface was covered with the ice film, and the rough surface texture was hardly recognized. For DGA pavement, the pavement surface had more holes in places made by sodium chloride at $M_{ss-in} = 60\text{g/m}^2$ than at $M_{ss-in} = 20\text{g/m}^2$. As for the SMA pavement and the PA pavement, ice film which did not have contact with sodium chloride remained the states before the spreading of de-icing agent, though parts of surface of the SMA pavement and the PA pavement were revealed.

## 4.2 Influence of the amount of de-icing agent spread on $\mu$ and on mean ice thickness

Figure 5 shows the influence of $M_{ss-in}$ on $\mu$ and on $H_i$. (a) and (b) in the figure show the results for $M_{w-in} = 1.0\text{kg/m}^2$ and $M_{w-in} = 2.0\text{kg/m}^2$, respectively. The upper tables show $\mu$, and the lower tables show $H_i$.

$\mu$ will be described. First, the results of $M_{w-in} = 1.0\text{kg/m}^2$ are described. When no de-icing agent was spread, (i.e., when $M_{ss-in} = 0\text{g/m}^2$), the $\mu$ for DGA pavement, SMA pavement and PA pavement was 0.03, 0.11 and 0.16, respectively. $\mu$ for DGA pavement increased
<figure><img src="image_546.png" /></figure>

<figure><img src="image_547.png" /><figcaption>Figure 5. Influence of amount of de-icing agent spreading on μ and on an ice thickness (upper table: μ, lower table: mean ice thickness)</figcaption></figure>

<figure><img src="image_548.png" /></figure>

<figure><img src="image_549.png" /></figure>

exponentially with increases in $M_{ss-in}$, and the value was 0.46 at $M_{ss-in} = 80 \text{ g/m}^2$. By contrast, μ for the SMA pavement and the PA pavement increased logarithmically with increases in $M_{ss-in}$, and at $M_{ss-in} = 80 \text{ g/m}^2$, the values were 0.43 and 0.41, respectively. μ for PA pavement changed at slightly higher values than the values for SMA pavement. Next, when $M_{ss-in} = 2.0 \text{ kg/m}^2$, the values of μ for all pavement types increased exponentially with increases in $M_{ss-in}$. The rate of increase was the greatest for the specimens of PA pavement, followed by those for SMA pavement and DGA pavement. Reasons for the difference in distribution of μ for SMA pavement and PA pavement by the amount of $M_{w-in}$ are assumed to have come from differences in the pavement exposure seen in Figure 4. That is, when $M_{w-in} = 1.0 \text{ kg/m}^2$, portions of the exposed surface of coarse texture increased with increases in $M_{ss-in}$, but when $M_{w-in} = 2.0 \text{ kg/m}^2$, the slight increase in portions with exposed coarse texture was observed along with increases in $M_{ss-in}$ due to relatively thick $H_i$.

$H_i$ will be described now. The values of $H_i$ for DGA pavement, SMA pavement and PA pavement without the spreading of de-icing agent were 0.92mm, 0.27mm, and 0.15mm, respectively, at $M_{w-in} = 1.0 \text{ kg/m}^2$. The values of $H_i$ for the respective pavements were 2.02mm, 0.85mm, and 0.43mm at $M_{w-in} = 2.0 \text{ kg/m}^2$. $H_i$ for DGA pavement was the greatest, and $H_i$ for PA pavement was the least. Regardless of $M_{w-in}$ or the pavement types, the values of $H_i$ decreased linearly with increase in $M_{ss-in}$. The rate of decrease was the greatest for specimens of DGA pavement, followed by those for SMA pavement and PA pavement.

## 4.3 Relationship between μ and mean ice thickness

Figure 6 shows the relation between μ and $H_i$. μ of all the specimens decreased exponentially with increase in $H_i$. The rate of decrease was the sharpest for the specimens of PA pavement, followed by those of SMA pavement and DGA pavement. The range of the data in the figure is $H_i < 0.85 \text{ mm}$ for SMA pavement and $H_i < 0.43 \text{ mm}$ for PA pavement. It is assumed that $H_i$ values greater than these would cause the pavement surface texture to exert less influence and that the values of μ for both SMA pavement and PA pavement would gradually get closer to 0.05 (which is the μ for the ice surface). Note that the value of μ when $H_i = 0$ was measured under the conditions of $T = 20^\circ\text{C}$ and $M_{w-in} = 1.0 \text{ kg/m}^2$.

The relational equation of $μ-H_i$ above mentioned is as follows.

$$ \mu = a \exp(bH_i) + c \quad (3) $$

Figure 6 shows coefficients a, b, and c corresponding to μ for each pavement specimen.
<figure><img src="image_550.png" /><figcaption>Figure 6. Relation between μ and the mean ice thickness</figcaption></figure>

<figure><img src="image_551.png" /><figcaption>(a) Amount of water sprinkled (b) Amount of water sprinkled = 2.0 kg/m²
Figure 7. Ratio of salt loss in vertical direction</figcaption></figure>

## 4.4 Ratio of salt loss in the vertical direction

In this section, $R_z$ will be calculated from the results of the experiment. This paper defines $R_z$ as the ratio of the amount of salt which did not act in melting ice film on the road surface and was lost from the road surface ($M_{ss-in}$ - $M_{s-melt}$) to $M_{ss-in}$.

$$R_z = 1 - \frac{M_{s-melt}}{M_{ss-in}} \quad (4)$$

$M_{s-melt}$ was calculated as follows: $H_{i-melt}$ was divided by $\rho_i$ to obtain $M_{i-melt}$. Then $M_{i-melt}$ and $C_2$ were assigned to Equation (2) to get $M_{s-melt}$. $H_{i-melt}$ means the ice thickness that was melted by the spreading of de-icing agent, that is, the difference between $H_i$ obtained from the experiment without de-icing agent spreading ($H_{i-1}$) and $H_i$ obtained from the experiment with de-icing agent spreading ($H_{i-2}$).

Figure 7 shows the relation between $R_z$ and $M_{ss-in}$. Table (a) shows when $M_{w-in} = 1.0$ kg/m² and table (b) shows when $M_{w-in} = 2.0$ kg/m². The figure reveals that $R_z$ is independent of $M_{ss-in}$. It can be concluded that, for DGA pavement, moisture or de-icing agents were not effused away from the pavement surface during the experiment and almost all of the de-icing agents spread worked to melt the ice film on the pavement specimens. As for SMA pavement and PA pavement, it is assumed that large portions of the de-icing agents spread on the pavement specimens moved into holes in the pavement surface and inside the pavement as solids or as highly concentrated salt solution. $R_z$ for DGA pavement was independent of $M_{w-in}$; the values were mostly around 0 and hardly changed. $R_z$ for SMA pavement and PA pavement decreased with increase in $M_{w-in}$.

Figure 8 shows the relation between $R_z$ and $H_i$ [3]. Each $R_z$ of SMA pavement and PA pavement declined linearly with increase in $H_i$. The relation is expressed in the following equation.

<figure><img src="image_552.png" /><figcaption>Figure 8. Relation between the ratio of salt loss in the vertical direction and the mean ice thickness</figcaption></figure>

$$R_z = \begin{cases} -0.33H_i + 0.80 \\ -0.29H_i + 0.96 \end{cases} \quad (5)$$

## 5 Discussion on μ after spreading

First, this chapter will show examples of estimating μ for DGA pavement, SMA pavement and PA pavement using the estimation method described in 2.2. The calculation conditions were $M_{ss-in} = 30$ g/m², $M_{w-in} = 1.0$ kg/m², and $T = -3$°C. Next, changes in μ were examined for each pavement specimen when only $T$ was varied from 0°C to -8°C. $H_i$ values used in the estimation were the values obtained from the results of experiments: 0.92 mm for DGA pavement, 0.27 mm for SMA pavement, and 0.15 mm for PA pavement.

Table 2 shows the estimation results for μ. When $T = -3$°C, $C_2 = 4.7\%$ is obtained from the freezing-point curve for sodium chloride (Step 1). Multiplying $M_{ss-in} = 30$ g/m² by $1-R_z$ produces an $M_{s-melt}$ of 30.0g/m² for DGA pavement, 7.8g/m² for SMA pavement and 2.4g/m² for PA pavement (Step 2). Calculating $M_{i-melt}$ using equation (2) (Step 3) produces a $H_{i-melt}$ of 0.66 mm for DGA pavement, 0.17mm for SMA pavement, and 0.05mm for PA pavement. Consequently $H_{i-2}$ is 0.26mm for DGA pavement.
<table><caption>Table 2. Results of μ estimation</caption><thead><tr><th></th><th>Items</th><th>Symbols</th><th>DGA pavement</th><th>SMA pavement</th><th>PA pavement</th></tr></thead><tbody><tr><td rowspan="3">Input conditions</td><td>Mean ice thickness in State 1 (before the spreading of de-icing agents)</td><td>H<sub>i-1</sub></td><td>0.92</td><td>0.27</td><td>0.15</td></tr><tr><td>Road surface temperature</td><td>T</td><td></td><td>-3°C</td><td></td></tr><tr><td>Amount of de-icing agents spreading</td><td>M<sub>ss-in</sub></td><td></td><td>30 g/m<sup>2</sup></td><td></td></tr><tr><td>Step 1</td><td>Concentration of salt solution</td><td>C<sub>2</sub></td><td></td><td>4.7%</td><td></td></tr><tr><td rowspan="2">Step 2</td><td>Ratio of salt loss in the vertical direction</td><td>R<sub>z</sub></td><td>0</td><td>0.74</td><td>0.92</td></tr><tr><td>Effective salt melting rate</td><td>M<sub>s-melt</sub></td><td>30.0 g/m<sup>2</sup></td><td>7.8 g/m<sup>2</sup></td><td>2.4 g/m<sup>2</sup></td></tr><tr><td>Step 3</td><td>Thickness of ice melted</td><td>H<sub>i-melt</sub></td><td>0.66 mm</td><td>0.17 mm</td><td>0.05 mm</td></tr><tr><td>Step 4</td><td>Mean ice thickness in State 2 (after the spreading of de-icing agents)</td><td>H<sub>i-2</sub></td><td>0.26 mm</td><td>0.10 mm</td><td>0.10 mm</td></tr><tr><td>Step 5</td><td>Tire-road friction coefficient</td><td>μ</td><td>0.24</td><td>0.37</td><td>0.36</td></tr></tbody></table>

pavement, 0.10mm for SMA pavement, and 0.10 mm for PA pavement (Step 4). Lastly, assigning $H_{i-2}$ to equation (3) will produce μ of each type of pavement in State 2; 0.24 for DGA pavement, 0.37 for SMA pavement, and 0.36 for PA pavement.

Figure 9 shows the relation between μ₂ and T under the conditions of $M_{ss-in} = 30 g/m^2$ and $M_{w-in} = 1.0 kg/m^2$. All μ values for the wet specimens decreased exponentially with decrease in T. As shown in the figure, it is suggested that μ₂ possibly varied by pavement type, even though $M_{ss-in}$, $M_{w-in}$, and T are the same. In other words, the $M_{ss-in}$ that is required to achieve a certain μ₂ may vary by pavement type. Because the authors excluded $R_x$ and $R_v$, the experimental results cannot necessary be applied to actual roads.

## 6 Conclusions

In this study, an experiment on the spreading of de-icing agents on ice-filmed pavement specimens was conducted in a constant low-temperature room. The pavements were DGA pavement, SMA pavement and PA pavement. The ice thickness and the μ after the spreading of anti-icing agents were examined for each pavement specimen. Based on the results, an estimation method for μ after the spreading of de-icing agent was developed, and the values of μ for each pavement type after the spreading of de-icing agents at a given road surface temperature were compared.

The following are the findings.

(1) Values of μ increased exponentially with decrease in mean ice thickness. The relationship between μ for each pavement type and mean ice thickness was formulated.

(2) For DGA pavement, the ratio of salt loss in the vertical direction was zero, independent of the amount of sprinkled water. For SMA pavement and PA pavement, the ratio decreased with increase in the amount of sprinkled water. SMA pavement had a greater rate of decrease than that of PA pavement. These relationships could be formulated.

(3) Although the study cases were limited to thin ice-covered roads without the passing of vehicles and without slopes in the longitudinal or lateral directions, from (1) and (2), it was found to be

possible to estimate μ after de-icing agent spreading

<figure><img src="image_553.png" /><figcaption>Figure 9. Relation between μ and road surface temperature for State 2</figcaption></figure>

for DGA pavement, SMA pavement and PA pavement.

(4) It was shown that μ after de-icing agent spreading could differ depending on the pavement type, even for a same given temperature, same amount of sprinkled water, and same amount of de-icing agent.

Challenges left behind regarding the method of estimating road surface friction explained in this paper are described below. The method of estimating road friction developed in this study has not taken into consideration aging-related changes in road surface texture and in drainage function of pavement, or the dispersion of de-icing agents by passing vehicles. To take into account changes in pavement texture and in drainage function, additional experiments need to be conducted to identify various parameters. Regarding $R_v$, please refer to Fujimoto et al., 2015 [6].

When this study further develops and the μ estimation method proposed in this study becomes applicable to real roads, the method is expected to contribute to the optimization of de-icing agent spreading (amount, timing and location), improvements in winter road safety and reductions in winter road management costs.
References

1. SNOW AND ICE DATABOOK – 2010 Edition, PIARC Technical Committee B5 Winter Service, 70, (2014).

2. L. Fay, M. Akin, S. Wang, X. Shi and D. Williams, Correlating Lab Testing and Field Performance for Deicing and Anti-icing Chemicals (Phase 1), Final Report, Western Transportation Institute, College of Engineering, Montana State University (2010).

3. A. Fujimoto, Y. Yamamoto, S. Tanaka, Y. Kawabata, and K. Takeichi, *Journal of Japan Society of Civil Engineers*, E1, **70**, 3, I_173-I_180 (2014).

4. A. Fujimoto, S. Yamada, S. Tanaka, N. Takahashi and K. Takeichi, *Journal of Japan Society of Civil Engineers*, E1, **71**, 2, 81-96 (2015).

5. T. Haavasoja, J. Nylander, and P. Nylander: Relation of road surface Friction and salt concentration, SIRWEC 2012, **16**, (2012).

6. A. Fujimoto, S. Yamada, S. Tanaka, N. Takahashi and K. Takeichi, *Journal of Japan Society of Civil Engineers*, E1, **71**, 3, (2015).
# Airplane braking friction on dry snow, wet snow or slush contaminated runways

Alex Klein-Paste<sup>a,1</sup>

¹NTNU, dept. of Civil and Transport Engineering, Winter Maintenance Research Group, Høgskoleringen 7A, N-7491 Trondheim, Norway

**Abstract.** Airplanes need tire-pavement friction during taxiing, take-off and landing. The presence of snow reduces the friction and therefore there is need to understand how much friction can be expected on the different types of snow. This study analyses the braking performance of Boeing 737 airplanes on snow or slush contaminated runways. Airplane braking performance on runways contaminated with dry snow, wet snow and slush as analysed. The main finding is that airplanes experienced wet snow covered runways more often as very slippery, compared to slush covered runways. The fraction of the landings experiencing the conditions as “poor” or “less than poor” was significantly higher on wet snow (21%), compared to landings on slush (11%). This can be caused due to higher precipitation intensity during wet snow precipitation, or possibly because wet snow, in contrast to slush, is a compressible material that gets compacted and fills the underlying pavement texture.

## 1 Introduction

Airplanes need a certain amount of tire-pavement friction for retardation and directional control during taxiing, take-off and landing. The magnitude of the required friction depend mainly on the aircraft type, gross weight, runway length, cross wind, reversed engine thrust usage and pilot skills. The presence of snow or ice on runways reduces the available friction and therefore it is important that pilots get correct information on the prevailing runway conditions.

In aviation, snow and ice is considered as a type of runway contamination (such as oil, sand and rubber deposits) and are therefore referred to as "winter-contaminants. The available braking friction for airplanes is called the "braking action". Historically, the term originated from the subjective feeling of the pilots how well the aircraft responds (decelerates) when pressing the brake pedals. The braking action was expressed in a scale from 1 to 5, ranging from “poor” to “good” [1]. More recently the scale have been extended from 0 to 6 to indicate “less than poor” and “dry runway” respectively [2]. Nowadays it is possible to measure the braking action during the parts of the landings where the frictional conditions limit the total stopping distance [3].

Before operating on winter-contaminated runways, pilots calculate the required stopping distance based on the landing weight, wind and runway conditions. These calculations are known as performance calculations. To make the performance calculations pilots need accurate information on how much braking action can be expected. Historically, airports have estimated the braking action by conducting measurements with ground friction measurement devices (GFMD's) [4]. Unfortunately, it has proven difficult to establish a reliable correlation between actual aircraft braking performance and the readings of GFMD's on winter contaminated runways [5]. An alternative approach is to estimate the braking action based on descriptive data,

such as the type, depth and spatial coverage of the contamination. This data is collected by means of a visual inspection, conducted by the ground personnel of the airport. The Talpa-Arc matrix [2] uses type, depth and temperature to estimate the braking action. Another model, known as the IRIS runway model also includes other parameters such as coverage, use of sand and chemicals, runway temperature and dew point [6]. To support further development of these models and provide a correct situation awareness, there is still a need for better understanding how aircraft perform under real-life operational winter conditions.

The Norwegian aerodrome operator Avinor performed a five-year R&D project, called IRIS (Intelligent Runway Information System) where it collected landing data and coupled this with reported runway condition information and weather data. The present paper presents a study using this dataset to investigate the differences in braking performance on dry snow, wet snow or slush.

## 2 Method

Data was collected during the winters 2008/2009 to 2012/2013 at different airports in Norway. The number of airports increased from two in 2008/2009 to 15 in 2012/2013. Data from the Quick Access Recorder (QAR) was obtained from all landings of Boeing 737-600, 700, 800, 900 models that were operated by Scandinavian Air Services (SAS) and Norwegian Air Shuttle AS.

The airplane braking coefficient $\mu_B$ was determined by a methodology described earlier [3]. In short, the total retardation force during landing is measured and the contribution of wheel braking is determined. This provides an estimate of the used friction during the landing. Only when the airplane's anti-skid system becomes activated it is certain that the airplane utilized all of the available friction and $\mu_B$ can be determined. In these cases the landing distance is limited by the

<figure><img src="image_554.png" /></figure>

<footnote>ª Corresponding author: [alex.klein-paste@ntnu.no](mailto:alex.klein-paste@ntnu.no)</footnote>
frictional conditions of the runway, hence denoted a friction limited landing (FL-landing). The obtained $\mu_B$ of the FL landings were interpreted into the common scale braking action scale, according to Table 1.

<table><caption>**Table 1.** Conversion of measured airplane braking coefficient into the common braking action scale [3].</caption><thead><tr><th>airplane braking coefficient</th><th>Braking action</th></tr></thead><tbody><tr><td>$\mu_B > 0.2$</td><td>5 – good</td></tr><tr><td>$0.15 < \mu_B \le 0.2$</td><td>4 – medium / good</td></tr><tr><td>$0.10 < \mu_B \le 0.15$</td><td>3 – medium</td></tr><tr><td>$0.075 < \mu_B \le 0.10$</td><td>2 – medium / poor</td></tr><tr><td>$0.05 < \mu_B \le 0.075$</td><td>1 – poor</td></tr><tr><td>$\mu_B \le 0.05$</td><td>0 – less than poor</td></tr></tbody></table>

During operation under winter conditions, runway inspectors regularly enter the runway and report the surface conditions for each third of the runway length (called a RWY section). The frequency of these inspections varies with the conditions, but typically range in Norway between 30 minutes and eight hours. The type of the contamination (dry, wet, rime, dry snow, wet snow, slush, compacted snow, or ice), the depth (in mm), the spatial coverage (in %) and other relevant information such as the use of sand or anti-/de-icing chemicals is registered. Weather stations placed near the runway (within 500 meters) measured air temperature, runway temperature, dew point temperature and wind at one-minute intervals.

The GPS data from the QAR was used to identify on which runway section the FL state occurred and the braking friction coefficient $\mu_B$ was coupled with the observed runway condition of that section. The current size of the database is shown in Table 2. Only data from runways contaminated with dry snow, wet snow or slush with 100 % spatial coverage were used for the present analysis.

<table><caption>**Table 2.** Size of the landing database.</caption><tbody><tr><td>Number of winter seasons</td><td>5</td></tr><tr><td>airports</td><td>15</td></tr><tr><td>landings</td><td>117849</td></tr><tr><td>FL-landings</td><td>5097</td></tr><tr><td>RWY sections*</td><td>353547</td></tr><tr><td>RWY sections with FL-landing</td><td>6418</td></tr><tr><td>Number of FL-landings on</td><td></td></tr><tr><td>dry snow, 100% coverage</td><td>135</td></tr><tr><td>wet snow, 100% coverage</td><td>382</td></tr><tr><td>slush, 100% coverage</td><td>221</td></tr></tbody></table>

## 3 Results

The measured aircraft braking coefficient on dry snow, wet snow and slush contaminated runways is plotted against runway temperature in Figure 1.

<figure><img src="image_555.png" /><figcaption>**Figure 1.** Airplane braking friction coefficient of FL landings on (a) dry snow, (b) wet snow, and (c) slush contaminated runways.</figcaption></figure>

Figure 1 shows that there is a large scatter present for each type of runway contamination. The aircraft braking friction can range from below 0.05 (less than poor) to above 0.2 (good) in all three types of runway contamination. The average braking friction coefficient is very similar (ranging only between 0.1 and 0.12), but due to the large scatter, an average number has little value/meaning.

There is no clear temperature dependency visible for any of the contamination types. Again, the scatter in the data dominates the picture. Naturally, wet snow and slush mostly occur around 0°C. However, also at lower
temperatures wet snow and slush can be present due to
the usage of anti-/de-icing chemicals on runways.

Figure 2 presents the distribution of the μB, converted
into the braking action scale (0-5) and the percentage of
landings experiencing the runway as 0 (less than poor) or
1 (poor) is highlighted by the dashed rectangles.

<figure><img src="image_556.png" /><figcaption>**Figure 2.** Distribution of braking action for landings on (a) dry Snow, (b) wet snow, (c) slush. The red rectangles highlight the percentage of landings experiencing the runway as “less than poor” or “poor”.</figcaption></figure>

On wet snow, 21 % of the landings experienced the runway as 0 or 1 (less than poor or poor). This percentage is significantly higher than on dry snow (7%) or slush (11%). Hence, the runways contaminated with wet snow were more often experienced as very slippery, compared to slush.

During operation under snowfall, airports have to
close the runway regularly to perform snow clearance
operation. In Norway, the runway inspector determines
the need for a snow clearance operation. After the

snow/slush is removed the runway is inspected before it
re-opens for air traffic. The time difference between
runway inspection and landing is called the report age
and expresses how long snow can have accumulated
before the landing took place. The report age distribution
of landings on dry snow, wet snow and slush are shown
in Figure 3.

<figure><img src="image_557.png" /><figcaption>**Figure 3.** Distribution of the report ages (in hours) for the landings on (a) dry snow, (b) wet snow, and (c) slush.</figcaption></figure>

Figure 3 shows that the distributions of the report ages
are very similar. The average report time on slush is
slightly higher (1.18 h), compared to dry snow (0.98 h)
and wet snow (1.0 h). This means that there are no large
differences in how long the airports maintain a runway
open during precipitation of dry snow, wet snow or slush.

To calculate the amount of precipitation (in mm snow
height) that had fallen between the last snow clearance
and landing one needs high-resolution precipitation
intensity data and integrate the intensity over the time between snow clearance and landing. One-minute precipitation intensity data (Optic Eye precipitation sensor) was only available for 207 landings on snow/slush. For these landings the accumulated precipitation (in geometrical snow/slush depth) was calculated and presented in Figure 4. One needs to bear in mind that there are uncertainties connected to measuring precipitation rates in geometrical snow depths, particularly on such short time scales of 0-4 hours. Hence, Figure 4 cannot give a definite answer, but should be viewed as an indication based on the available information.

<figure><img src="image_558.png" /><figcaption>**Figure 4.** Distribution of the precipitation accumulated after the last runway condition report (in mm snow/slush depth) for the landings on (a) dry snow, (b) wet snow and (c) slush.</figcaption></figure>

Figure 4 suggests that more dry snow was allowed to accumulate (on average about 6 mm), compared to wet snow (on average about 3 mm) and slush (on average about 2 mm).

# 4 Discussion

The main question that arises from this study is “why the airplanes experienced wet snow more often as “poor” or “less than poor”, compared to slush?”

One explanation can be that more wet snow was allowed to accumulate, compared to slush, as suggested by Figure 4. If this is true, it is due to higher precipitation intensities, not because the airports kept the runways longer open when wet snow was present, as shown in Figure 3.

Another explanation can be that given amount wet snow on a pavement is in fact experienced more slippery, compared to the same amount of slush. This may be counter-intuitive within the aviation community, because slush has historically received more attention than wet snow. Slush mainly consists of water and ice particles and behaves like an incompressible fluid. The braking action on slush is mainly reduced because of hydroplaning. When a tire rolls on a pavement covered with an incompressible fluid, it is squeezing the fluid from under the footprint. This squeezing process generates pressures on the surface of the tire footprint [7], delaying or preventing it to get into contact with the underlying pavement. When full hydroplaning occurs it provides almost no braking action as the tire slides over the fluid film without reaching the pavement texture at all. Hydroplaning is largely speed dependent since the speed determines how much time is available for the squeeze out. However, it is also largely dependent on the fluid thickness. As long as the thickness is limited, the tire will be able to get at least partly in contact with the pavement texture and reasonably high amounts of braking action will be experienced. Since runways are regularly cleared and the accumulation of slush is limited, the danger of hydroplaning is effectively reduced.

But for wet snow, on the other hand, the way friction is lost differs from the case of slush. Wet snow is a compressible material, consisting of ice particles, water and considerable amounts of air. Being compressible, wet snow is much more difficult to squeeze out under the footprint. It rather compacts and fills the pavement texture when an external pressure is applied. An example of a thin layer of wet snow that was compacted by an aircraft tire is shown in Figure 5. The picture was made after the runway was cleared using a runway sweeper. All uncompressed wet snow was removed but the snow compacted by the tire was firmly attached to the pavement. Hence on wet snow, runways get slippery not because of hydroplaning, but because friction is created between the compacted wet snow and the tire.
<figure><img src="image_559.png" /><figcaption>**Figure 5.** Example of a thin layer of wet snow compressed by a single aircraft tire passage. The runway sweeper was able to remove all loose wet snow, but not the snow compressed by the tire. The longitudinal grooves present in the aircraft tire can be recognized in the track.</figcaption></figure>

Historically, the aviation industry have had more focus on slush-contaminated runways, compared to runways contaminated with wet snow. This is partly due to the issue of increased rolling resistance and impingement drag [8], hampering take-off. But slush seems also to be considered more slippery as wet snow. This is for example reflected in the Talpa Arc matrix [2] where slush greater than 1/8 inch (approximately 3 mm) is considered as “medium-poor” whereas wet snow of the same depth is considered as “medium”. This study suggest that that view may need reconsideration.

Dedicated studies under controlled conditions, comparing the reduction of braking action on similar amounts of wet snow and slush would be very helpful to understand further how and how severely braking action can be reduced due to the presence of wet snow and slush.

## 5 Conclusion

Airplane braking performance on runways contaminated with dry snow, wet snow and slush as analysed. The main finding is that airplanes experienced wet snow covered runways more often as very slippery, compared to slush covered runways. The fraction of the landings

experiencing the conditions as “poor” or “less than poor” was significantly higher on wet snow (21%), compared to landings on slush (11%).

One reason for this difference can be that more wet snow had accumulated since the last snow clearance operation, as compared to slush. The airports did not keep runways contaminated with slush longer open, but the precipitation rates may have been higher during wet snowfall.

Another explanation for the difference can be that a limited amount of wet snow can be experienced more slippery by airplanes, compared to the same amount of slush because the compressible wet snow gets easily compacted in the pavement texture.

## References

1. ICAO, International standards and recommended practice, Annex 15 Aeronautical Information Services Chapter 5 NOTAM. Eleventh edition ed. 2003: International Civil Aviation Organization.

2. Subbotin, N. and S. Gardner, Takeoff and Landing Performance Assessment Validation Effort of the Runway Condition Assessment Matrix. 2013.

3. Klein-Paste, A., et al., Braking performance of commercial airplanes during operation on winter contaminated runways. Cold Regions Science and Technology, 2012. 79-80: p. 29-37.

4. Andresen, A. and J.C. Wambold, Friction Fundamentals, Concepts and Methodology. 1999.

5. Boccanfuso, A. History of the Joint Winter Runway Friction Measurement Program. 2004. Transportation Development Centre, Transport Canada.

6. Klein-Paste, A., H.-J. Bugge, and A.B. Huseby, A decision support model to assess the braking performance on snow and ice contaminated runways. Cold Regions Science and Technology, 2015. 117: p. 43-51.

7. Van Es, G.W.H., Hydroplaning of modern aircraft tires. 2001, National Aerospace Laboratory NLR.

8. Giesberts, M.K.H., Test and evaluation of precipitation drag on an aircraft caused by snow and standing water on a runway. 2001.
# Engineered pavements of snow and ice

Adrian McCallum¹ and Greg White¹

¹University of the Sunshine Coast, Queensland, Australia

**Abstract.** The complex mechanical behaviour of snow and its propensity to vary temporally and spatially makes the engineering of pavements of snow and ice challenging. Current technologies and design methods for snow and ice runways remain largely reliant on work performed in the 1950s and 1960s. This paper examines the evolution of techniques for the engineering of runways constructed using snow and ice. It is recommended that modern engineering approaches based on conventional rigid and flexible pavement design principles be considered for ice and snow runway design. The adoption of such methods requires the development of an analytical model for the prediction of snow strength, based on snow age, temperature history and density. The use of such a model would allow contemporary engineered pavements of snow and ice to be efficiently sited and constructed. This would increase the viability and reliability of engineered snow and ice pavements across the polar regions of the world.

## 1 Introduction

Polar operations depend heavily on air transportation and support for personnel and equipment [1]. However, polar terrain, climate and material availability do not generally allow for conventional runway construction [2]. Therefore, most polar runways are constructed of ice or snow, or a combination of the two. The current technologies and design methods for snow and ice runways remain largely reliant on the work performed by the Cold Regions Research and Engineering Laboratory (CRREL) of the US Army Corps of Engineers, undertaken primarily in the 1950s and 1960s. Since that time, polar exploration, research, tourism and general interest has expanded.

Current challenges for air transportation in Antarctica include the short period for wheeled aircraft operations [3], the complex mechanical behaviour of snow and ice materials [4], the risk of short annual periods of above-freezing temperatures causing localised melting [1], glacial flow that occurs in many areas of Antarctica [5], the inability to bind compacted snow at very cold temperatures [6] and the high cost of fuel in polar areas. The limited imperative to understand snow and ice mechanics for governmental or private activities limits the motivation to address these challenges [7].

This paper examines the evolution of techniques for the engineering of runways constructed using snow and ice. Snow and ice mechanics are discussed then material characterisation and site investigation is briefly considered. Finally, historical snow and ice runway design methods are contrasted with contemporary conventional pavement design. Future work including the development of an analytical model for the prediction of snow strength, based on snow age, temperature history and density is then considered.

## 2 Historical perspectives

Aviation has existed in Antarctica since the early 1900s. However, only recently has it been considered as

a possible replacement for traditional ship-borne logistics. Permanent Antarctic stations require significant volumes of large cargo that are necessarily supplied by sea. However, large numbers of personnel and lighter equipment can more efficiently be transported by air. The development of air transport systems and supporting infrastructure, especially runways, is therefore assuming greater importance [8, 9].

Both blue ice and rock runway sites have been identified in Antarctica. However, the majority of the Antarctic continent is a snow accumulation area [2] and methods to allow aircraft access to these areas is desirable. Ski-equipped aircraft for use on areas of snow have operated on the continent since the 1930s [2] and continue to be used by most Antarctic operators today. However, the advent of retractable aircraft landing gear, and the added engineering complications and expense means that equipping a modern aircraft with skis is an expensive modification; retractable skis can be used, but the use of wheeled aircraft on suitable snow runways is preferable. Unfortunately, the majority of natural snow covered areas do not provide a suitable surface for the operation of wheeled aircraft and the snow needs to be processed in order to obtain sufficient bearing capacity for wheeled aircraft.

Contemporary polar runway options include wheeled aircraft on thick (permanent) bay ice, wheeled aircraft on annual sea ice, wheeled aircraft on glacial ice, wheel aircraft on prepared snow and skiways on semi-prepared snow for use by aircraft fitted with skis [10]. Russian Ilyushin 76T aircraft have been successfully used by commercial operators at the Russian developed Novolazarevskaya and more recently at Union Glacier (originally Patriot Hills) by Antarctic Logistics and Expeditions. Australia operates a commercial A320 annually into Wilkins ice runway near Casey Station [11] and has recently completed five proof of concept flight to the Wilkins ice runway using a Royal Australian Air Force (RAAF) C-17A Globemaster. The C17A already operates into the USA controlled McMurdo Ice runway

<figure><img src="image_560.png" /></figure>

<footnote>ª Corresponding author: amccallu@usc.edu.au</footnote>
and provides a significant increase in cargo/freight capacity compared to the A320.

The US Antarctic Program desires the construction of a wheeled C17A capable runway at the South Pole (US Antarctic Program 2012). However, techniques to develop such a runway at this location have not yet been developed.

## 3 Snow mechanics

Snow is a three-phase geomaterial consisting of ice (solid), pore water (liquid) and void air (gas). Polar snow is generally a two-phase material consisting of ice and air.

Fresh snow density is around $100 \text{ kg m}^{-3}$. As surface snow is buried, it gradually compacts, eventually becoming ice. Throughout this process the snow is increasing in both density and strength. As soon as snowflakes hit the surface, the free energy of the system tends towards a minimum, such that dendritic and irregularly shaped crystals with a large surface area are gradually transformed to rounded particles [12]. This reduction in system surface energy is the primary driving force for the process of sintering: ``a thermal treatment for bonding particles into a coherent, predominantly solid structure via mass transport events that often occur on the atomic scale'' [13]. Because the radius of an ice particle is larger than the concave radius of the bond joining it to an adjacent ice particle, there is a driving force to move mass to this concave neck area. So two previously spherical particles start tending towards a ``dumb-bell'' shaped particle joined by a neck, as shown in Figure 1.

<figure><img src="image_561.png" /><figcaption>**Figure 1.** A low temperature SEM image of two ice grains, made by spraying water into liquid nitrogen, sintered at -25°C for 216 h. The grain boundary can be seen at the neck between the particles; the particles are single crystals [13].</figcaption></figure>

The bonding leads to improved strength and a lower energy system [14]. Mass redistribution occurs throughout the densification process and when occurring under additional pressure such as when snow is buried owing to accumulation, it is termed pressure sintering, and the sintering rate is increased. Although numerous processes occur throughout the pressure sintering process, not all contribute to densification [15]. Three

main phases have been identified in the densification process [16]:

Stage 1. Settling and packing. Particles of ice are displaced relative to their neighbours. Bonds may initially be broken but eventually stable bonds begin to form. Alley [15] determined grain boundary sliding to be the primary mechanism during this process. The average number of bonds between ice grains (coordination number = N) has been observed to become constant (N = 6) at a relative density of approximately 0.6 [15]. This corresponds to a density of approximately $550 \text{ kg m}^{-3}$. After this, the size and strength of the bonds increase, without additional bonds being formed. Neck growth occurs rapidly initially (explaining why snow develops some strength quickly) and smaller grains sinter at a larger rate than larger grains [17]. The strongest bonds and thus strength ultimately occur from small rounded grains, which grow at a slow rate.

Stage 2. The second stage of the process occurs from a density of $550 \text{ kg m}^{-3}$ through to about $840 \text{ kg m}^{-3}$ and involves a decrease in pore space within the snow, such that interconnected pore spaces become closed. This occurs through a process of plastic deformation via power law creep combined with recrystallization [13]. Necks between grains continue to grow. However, the rate of growth decreases, and no new bonds are formed.

Stage 3. During the third stage, pores within the snow are closed and the snow is generally considered as an isothermal system; it may be described as bubbly [13] or closed-cellular [18] ice. The density during this phase increases from about $840 \text{ kg m}^{-3}$ through to the density of pore free ice at -25 °C, 919 kg m$^{-3}$. Densification proceeds primarily by the same mechanisms as stage 2, but at a reduced rate. As the bubbles are compressed, this opposing pressure causes the process to slow.

The densification process progresses at a decreased rate in each stage, and occurs at different times throughout the year, commensurate with times of increased accumulation [19]. During densification, the microstructure or the nature of the ice grains and the physical bonding between them evolves. This evolution is influenced by the initial thermal regime within the snow, and two distinct processes have historically been used to describe this effect on grain size down to a depth of approximately 2.5 m [15].

Temperature gradient metamorphism (more recently *kinetic growth*) [20] occurs when a strong temperature gradient exists and results in rapid non-linear grain growth and a lack of cohesion. Over time this will result in coarse-grained layers with reduced density [21]. This type of snow is often seen at depth as a seasonal layer of buried surface-hoar, which has formed during a previous surface summer season when temperatures were high. When a strong temperature gradient is absent, equitemperature metamorphism (more recently *equilibrium growth*) [20] occurs. This results in surface layers experiencing slower, more linear grain growth, resulting in a finer grained layer of increased density.

Abele [22] in examining snow runway construction identified that although temperature gradient metamorphism results in an increased rate of densification and an increase in short-term strength, it is
equitemperature metamorphism that will ultimately produce a stronger pavement even though the rate of strength increase will be less. Thus although the initial strength of the snow may be less than rapidly sintered snow (nearer to 0°C), the ultimate bond strength over time will be greater. At temperatures typical at the South Pole (-25°C to -50°C) significant sintering can take months or years [22].

The primary mechanical observation is that the strength of snow is controlled by the size of the bonds between grains [17]. This is the *microstructure* of the snow, defined as the configuration of the ice and air spaces within the snow incorporating, density, porosity, specific surface area, curvature, tortuosity and coordination number [20]. The evolution of microstructure over time can be seen in Figure 2.

<figure><img src="image_562.png" /><figcaption>**Figure 2.** Evolution of the 3D structure of the ice matrix during isothermal metamorphism at -1.6°C over a 30-week period. Adapted from Kaempfer [23].</figcaption></figure>

It is the strength provided by this bonded microstructural matrix and not solely the density that will account for snow's mechanical behaviour. Therefore, any attempt to understand the mechanical behaviour of snow must consider both bulk density and microstructure.

Snow properties complicate snow runway design. At the South Pole, snow density rarely exceeds 360 kg m⁻³ regardless of treatment or age [3]. This reflects the cold and dry nature of the snow particles at typical South Pole temperatures [6].

Snow is one of the most brittle materials known [24]. The fracture toughness of snow is two to three orders of magnitude lower than that of ice and the tensile strength of snow is also much lower than that of ice [18].

In summary, sintered snow is an aggregated mass of bonded particles of crystallized pure-water ice. The properties of snow are dependent upon the strain rate, temperature, particle size distribution and density. In turn, the density of snow is influenced by the age and temperature history throughout the sintering process. This complex mechanical behavior complicates the development of micromechanical models for snow. For practical purposes, snow remains characterised as an

aggregated mixture with interdependent age, temperature and stress dependent properties.

## 4 Ice mechanics

The nature of ice reflects the mode of formation [25]. Blue ice is formed by top-down freezing of lake water and white ice results from the flooding of snow [26]. Solid sea ice is porous due to trapped air [1] and contains brine of salt water.

Pure air-free ice has a density of 920 kg m⁻³ [27] and despite its freezing point, a thin layer of water-like material can be identified on the surface of ice down to -40°C [28]. Understanding this water-like film is important for explaining many other ice properties.

Like concrete, ice is strong in compression and weak in tension [29] (Figure 3). Pure ice has a tensile strength generally between 0.7 and 3.1 MPa, with an average of 1.43 MPa between -10°C and -20°C. In contrast, compressive strength is between 5 and 25 MPa over a similar temperature range [18]. Ice modulus also increases with decreasing temperature [1].

<figure><img src="image_563.png" /><figcaption>**Figure 3.** Tensile and compressive strengths of equiaxed and randomly oriented fresh-water ice of about 1 mm grain size vs. strain rate. At the top of the figure, I, II, and III denote low-, intermediate-, and high-strain rates [29].</figcaption></figure>

The strength of ice is dependent upon density, temperature, grain size, brine content and strain rate. As the temperature of ice increases from -40°C to near the freezing point, the compressive strength of ice decreases by 75%. The influence of temperature on tensile strength is less, with a decrease of 25% over the same temperature increase [18]. Between -5°C and the melting point, ice is ductile and becomes prone to rapid creep during sustained strain [26]. At lower temperatures, ice has high resistance to creep [30]. However, ice is brittle. Its fracture toughness is low, at around 10% of that of glass [18].

Trapped air reduces the density of ice. At lower density, ice strength decreases, as does the modulus [1]. Increasing brine content also reduces ice modulus [1]. Brine volume is the portion of the ice that contains liquid
brine. Ice grain size also affects ice properties with a smaller grain size providing a significant increase in compressive strength [31]. In contrast, Petrovic [18] reported a decrease in tensile strength with smaller grain size.

Strain rate affects ice mechanics. Like many engineering materials, quickly loaded ice fractures while slowly loaded ice creeps [32]. The compressive strength of ice is dependent upon the strain rate ([18], [29], [33]) in contrast to the tensile strength, which is independent of strain rate ([18], [29]).

Ice is similar in many respects to other engineering materials. Like concrete, it is much stronger in compression than in tension. Similar to asphalt, ice creeps when loaded slowly, but when loaded fast, it fractures. Mechanical characterisation of ice is complicated by the proximity of polar temperature fluctuations to its melting point and the broad range of properties determined by the way in which it was formed.

# 5 Pavement design

Conventional (non-polar) runways are designed by one of two general approaches. Flexible pavements are designed based on protection of the subgrade or foundation. A combination of cover material stiffness and thickness is determined to reduce the vertical strain at the subgrade to a level that is (empirically) not expected to result in excessive permanent vertical deformation (ie. rutting).

Rigid pavements are designed similar to a floating beam or slab, using Westergaard's equations to limit the flexural stress within the slab. Protection of the subgrade is of less importance, although the subgrade provides support to the concrete base.

Snow is similar to a bonded particulate. Ice is not unlike concrete, a solid material of high compressive strength and relatively low tensile strength. It follows that snow and ice runways are designed using similar principles to conventional flexible and rigid pavements. However, unlike conventional runways, the ability to 'construct' a pavement in polar environments is limited. Importation of material is not feasible and the cost of energy is high, making any energy-intense construction method logistically difficult and cost prohibitive [10]. Further, seasonal changes and prevailing weather conditions limit constructability. For example, the thickness of some Antarctic sea ice runways is determined by the medium-term prevailing conditions. Increasing the structural capacity of such runways is impractical. Similarly, short annual periods of above-freezing temperature result in snowmelt that affects the structural capacity of coastal snow runways in Antarctica. Significant temperature differences between the Antarctic interior (generally -25°C to -50°C) and coastal areas (generally -10°C to -30°C) necessitates substantially different construction processes.

# 5.1 Historical snow pavement design

Numerous authors have shown that because of its high compressibility, the typical shear failure mechanism for snow is punching shear ([34], [35], [36]). However, traditionally, snow pavement design is founded upon the distribution and spreading of applied stresses, similar to conventional flexible pavements. Boussinesq's equation is used to estimate stress dissipation with depth [1]. Failure due to generated shear stresses must be defined; Abele et al. [38] suggesting that a 5 cm rut depth indicates a failed pavement. Although aircraft can continue to operate on snow pavements with greater rut depths, 5 cm was selected as it discriminated between stable pavement performance and rapidly accelerating failure [38].

Like flexible pavements, the contact stress at the pavement surface is critical to pavement performance. In recognition of the rapid dissipation of shear stress with depth, materials of reducing shear strength are acceptable as the depth from the pavement surface increases [38]. For conventional flexible pavements this results in lower quality materials being selected. For snow runways, the same snow is accepted at a reduced density. However, deeper snow is generally older and of higher density so without mechanical improvement, desired shear strengths are inverted with depth. This is not conducive to efficient snow runway construction.

Using empirical data derived from full scale testing, Abele and Frankenstein [1] concluded that necessary snow strength must extend to a depth equal to the radius of the tyre contact area. Repeated loading trials determined a 50% increase in snow strength was required for two aircraft repetitions within an hour. Ten repetitions required a 100% increase in snow strength [38]. Design charts were developed for common aircraft and snow strengths (Figure 4). This is similar to the US Army Corps of Engineers approach to the early design of conventional runway pavements.

<figure><img src="image_564.png" /><figcaption>**Figure 4.** Example of snow hardness with depth required for various aircraft [22].</figcaption></figure>

# 5.2 Contemporary Snow Pavement Design

Traditional snow runway design uses flexible pavement design methods developed for conventional roads and runways. Alternative stress estimation methods have
been considered for the design of polar infrastructure ([37], [38], [39] etc.). However, snow runway design still relies on estimating stresses under an aircraft tyre using Boussinesq's equation.

In contrast, conventional flexible pavement design has progressed to using layered elastic and finite element stress calculation tools. These modern layered elastic pavement design tools are yet to be applied to the design of contemporary snow runways. Such application would require an analytical method for the characterisation of snow incorporating temporal changes in snow density and microstructure. WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland has progressed large amounts of research into the temporal evolution of snow microstructure ([23] etc.) However, this work has yet to be incorporated within the contemporary design of polar snow runways.

## 5.3 Ice pavement design

Ice pavement design varies depending on the type of ice. Ice-on-ground pavement is designed to dissipate loads within the ice, thereby protecting the underlying subgrade [26]. This is similar to snow pavement design except the ice is working in flexure rather than as an aggregate mass.

However, runways on (floating) sea ice, are designed to resist tensile stresses at the bottom of the ice layer [26]. This is analogous to conventional rigid (concrete) pavement design methods. Either way, runway strength increases as ice flexural strength or thickness increases. As a 'rule of thumb' Arctic sea ice should be greater than ~70 cm for a ski-equipped Twin Otter to safely land and taxi. However, thinner ice can be viable if thicker ice can be taxied onto for parking/loading etc.

Because of the large number of variables incorporated within formal ice runway design and the contrastingly low number of ice runways in use, nomographs for ice runway design are generally runway specific. An example Landing and Take-off Nomograph for the United States' McMurdo Sound Sea Ice Runway is shown [30] (Figure 5).

<figure><img src="image_565.png" /><figcaption>**Figure 5.** Landing and Take-off Nomograph for the McMurdo Sound Sea Ice Runway [30].</figcaption></figure>

The design of McMurdo Station's annual sea ice runway is based on wheel load and contact pressure [30]. Estimated stresses are compared to the flexural strength determined from laboratory testing of extracted samples. Proof rolling is then performed to confirm pavement structural capacity. At temperatures greater than -5°C, re-testing of ice samples occurs to confirm adequacy of pavement strength.

For ice runways constructed over shallow water, the increase in pressure induced by the moving aircraft load must be considered. The shallow water pressure wave increases the stress in the ice layer by up to 50% [26]. Thicker or stronger ice is required for the portion of the runway located over the shallower water.

Ice runways are also constructed on glaciers or ice caps. Union Glacier runway in East Antarctica moves approximately 20 m per year and Wilkins glacial ice runway near Australia's Casey Station moves around 12 m per year as the ice flows downhill [40]. Differential movement at this runway is shown (Figure 6).

<figure><img src="image_566.png" /><figcaption>**Figure 6.** Down slope velocity vectors indicating areas of surface tension and compression within the Wilkins glacial ice runway [40].</figcaption></figure>

The flow of the variable depth ice over the bedrock also distorts the runway surface, requiring periodic correction by ice milling [11]. The ice milling also reinstates frictional properties when aircraft land directly on the ice surface. Proof rolling and testing of removed samples also generally occurs on glacial ice runways.

It is also general practice to surface ice runways with a layer of snow [5]. The snow insulates the ice from solar radiation. Rubber deposits, engine soot and other small dark particles deposited on the surface cause pitting. The regular removal of this low albedo material is necessary to reduce potential for time-consuming surface repairs.

# 6 Site investigation and material characterisation

Traditionally snow has been characterised by its bearing or shearing strength. Recent attempts to develop mechanical models for snow have achieved limited success. The specific properties of snow make
micromechanical model development challenging [41]. Like bitumen and asphalt, snow behaves in a viscous or viscoplastic manner during short duration tests at natural density [4]. However, for high stress conditions of compacted and aged snow, linear elastic models are more appropriate [7]. For most polar applications, snow transitions from one state to the other and back [7]. A unified constitutive model of snow would likely be so complex that it would also be applicable to many other engineering materials [19]. As a result, the bulk density of snow is commonly used for characterisation similar to fine crushed rock for conventional pavement design [41]. Mohr-Coulomb shear-based failure models are employed by designers and engineers of snow structures ([19], [39]).

There are three primary measurement techniques for assessing the strength of snow: surface loading, sample strength testing, or probing [22]. The applicability of the test and the usefulness of the result is inversely proportional to the convenience of the test method. For example, the Australian-developed surface loading device known as the Clegg Hammer is easy and fast to operate, but its effective measurement depth is very limited. Whereas the in situ California Bearing Ratio (CBR) test is applied internationally for assessing granular material bearing capacity, but it is impractical to perform regularly in the field.

The Swiss Rammsonde has been used to estimate the strength of snow for many years [42]. More recently, numerous mini-penetrometers have been used, primarily for avalanche research and the detection of weak layers ([43], [44], [45], [46] and [47]; Floyer [48] provides a review. McCallum [49] described the use of a modified, friction-sleeve-equipped, commercial cone penetrometer to efficiently assess the strength and physical characteristics of polar snow to depths of 10 m or more and McCallum [39] examined the direct derivation of snow density and microstructure using this device. Since its development in the 1970s the Clegg Hammer, a 4.5 kg flat-faced hammer and electronic decelerometer [27] has been a valuable addition for near-surface snow bearing capacity assessment [50]. It is easy to operate in the field and has been used extensively in Antarctica ([22], [51]). The Clegg Impact Value (CIV) is calculated by the rate of deceleration of the hammer.

Correlation between Ram hardness and UCS was presented by Abele [52] and Lee at el. [6] developed relationships between Ram hardness, CBR and CIV. More recently, McCallum [39] proposed a model for direct derivation of snow compressive strength from CPT data. For significant works, a site-specific conversion between the various strength measuring devices is desirable. For example, CBR and Ram hardness may be used for design purposes, with CIV from the Clegg Hammer used for construction quality control.

Spatial extrapolation of identified snow physical properties is also possible using geophysical methods such as Ground Penetrating Radar (GPR). McCallum [53] examined the correlation between CPT tip resistance data and GPR amplitude information and Fitter et al. [54] investigated the development of UAV-mounted GPR for the rapid spatial extrapolation of point CPT resistance

data. Additionally, McCallum [55] presented a method for the direct assessment of surface snow dielectric and density values using the surface reflection method [56].

# 7 Material processing and construction

Similar to conventional pavement construction, processing the naturally occurring material is an efficient method to improve material properties and pavement performance. Such processing can be problematic because for ice runways little gain can generally be achieved and for snow pavements, acceleration or modification of the material density or microstructure can be resource intensive. However, processing techniques are proven in the particular optimisation of snow pavements.

## 7.1 Snow processing

Techniques for building runways from processed snow have been practised for more than 50 years. When snow is disturbed by agitation or disaggregation, it begins to sinter and densify at a greater rate than undisturbed snow [6] and it is this behaviour that is exploited in the construction of compressed-snow roads and runways.

Mechanical disaggregation of snow increases the density to between 250 kg m⁻³ and 500 kg m⁻³ [58]. Optimum snow strength is achieved when the snow reaches around 700 kg m⁻³ [31]. The disaggregation process improves the distribution of the snow particle size and allows sintering to occur rapidly. Snow bearing strength can be further improved by mechanical compaction during the sintering process. Timely compaction is equivalent to six days of sintering [57]. The most efficient and effective approach to snow processing is to disaggregate and compact around -2°C, followed by sintering below -10°C. However, the inability to control the snow temperature complicates snow runway construction.

To reduce reliance on favorable weather conditions there have been attempts to temporarily and artificially warm snow during the disaggregation and compaction process. A heated snow processing capability was developed but was restricted to 45 cm processing depth and fuel requirements were logistically prohibitive [57]. Localised snow warming by watering at a rate of 3 cm per 25 cm of snow thickness was found to be more efficient than direct heating of snow, but remained logistically problematic.

The first equipment developed for snow disaggregation was a modified mechanical pulveriser, capable of processing to a depth of 45 cm. The Peter Plow was introduced in the 1960s and has the capability to process snow up to 130 cm thick [1]. The Peter Plow remains the primary equipment for snow processing during runway construction [11].

Snow has also been reinforced with sawdust to improve the physical properties. Around 3 cm of sawdust was added to 15 cm of snow with some success [57]. Dark sawdust resulted in localised snow melting and white sawdust was preferred [6]. More recently snow has
been compacted into ice bricks. A pressure of 6.9 MPa was applied to a 15 cm diameter round mould filled with snow. The resulting ice bricks were strong and durable with a density of 800-900 kg m⁻³ [31]. Despite these processes being viable, practical limitations have seen almost all snow runways constructed by disaggregating and compaction of naturally occurring snow [22].

## 7.2 Ice processing

There is little that can be done to improve or process ice. Fissures or cracks are filled with a slurry of ice, snow and water, that rapidly freezes. Such repairs are usually denser and stronger than the surrounding naturally occurring ice [11]. Ice has also been made by the flooding of snow with water. Although effective, it is prohibitively costly and logistically challenging to melt water in polar regions.

## 7.3 Runway construction

Ice runway construction generally includes the levelling of the existing ice and correction of cracks and fissures. Ice mills are used to provide a smooth and flat surface [11]. Across broader areas a bulldozer with a laser guided and chisel-tooled blade is common [5]. Runways with ice surfaces are maintained and corrected for changes in the ice surface level. Regular treatment with an ice mill, to provide surface texture for friction as well as the continuous removal of rubber deposited during aircraft landing, is also common [11]. During temperatures above -5°C, an insulating layer of snow is used to protect the ice against melting [30]. In areas of ample annual snow fall, the insulating layer of snow is maintained as a wearing course, negating the need to create texture and friction in the underlying ice.

Snow runway construction remains more common. This reflects the majority of Antarctica being covered with significant thickness of snow. Despite this, some areas of Antarctica are snow 'shadows' and blue ice runways are constructed, such as Australia's Wilkins Runway near Casey Station. Snow runway construction is a multiple step process including disaggregation, compaction, leveling and then sintering time. The essential construction technique is summarised by Russell-Head and Budd [27]:

1. Process the snow to depth. This disaggregation reduces the size of the ice particles within the snow to aid in more rapid densification;

2. Compaction. This process further assists in the densification and strength-gaining process; and

3. Levelling. The pavement is levelled for vehicle use.

A combination of the above construction processes may be performed at different time intervals depending on the runway location and specific site characteristics including surface temperatures, accumulation, and snow pack initial conditions. Lang et al. [58] in examining the characteristics of processed snow concluded that the generation of a robust snow pavement is best expedited by producing a well-graded, small-grained and firmly compacted section of snow.

Snow disaggregation is most commonly performed by Peter Plow for the increased processing depth capability. The Peter Plow has a closed drum with rotating cutting blades over 1.2 m in diameter and 2.7 m wide. It rotates between 225 and 305 rpm and can cut up to 1.5 m deep [57].

Planing and compacting must be performed immediately behind the processing [57]. A laser guided fine grader is used with a large volume blade. For compaction, a D8-sized bulldozer with crawler tracks has long been found to be effective [59]. Australian engineers developed a 38 tonne towed proof roller for deep snow compaction [3]. During the construction of Wilkins Runway, a larger, 60 tonne, proof roller was used [11].

The key to snow runway construction is timing [3]. Processing and compaction must be performed during periods of warm weather and then sintering permitted to occur during the colder weather. The strength of the snow must be monitored throughout the sintering process [22] and whenever the temperature or conditions change significantly.

## 8 Future needs and opportunities

The introduction of larger and more demanding aircraft creates a challenge for the operation of runways constructed from snow and ice. The empirical basis for design and performance of Antarctic runways was developed for aircraft significantly smaller than the A320 and C17A that will form the basis of future Australian aircraft operations in Antarctica [11]. Reliance on the work performed in the 1960s and 1970s requires supplementation to reflect the equipment and aircraft technology advances that have occurred since that time.

The ultimate goal for Antarctic aviation is the development of a wheeled aircraft snow runway at the South Pole [10]. This would revolutionise polar logistic capability [3]. An all-year wheeled A320 (or equivalent) capable runway anywhere in Antarctica would also be of great benefit [3]. However, the low summer temperatures (below -25°C) inhibit snow sintering. At temperatures below -18°C snow processing and compaction alone cannot produce snow capable of resisting heavy aircraft operations [6].

Any technology that allows construction of a C17A capable snow runway to be built at the South Pole would likely also provide significant capability to build and maintain coastal Antarctic airfields. Alternate methods to induce sintering at temperatures below -25°C appear to be the most viable opportunity. Laser treatment, heated roller tyres and chemical additives have been trialed, all with success. However, overcoming the logistical burden of operating in such a remote location has resulted in technically viable options remaining impractical.
# 9 Summary and recommendations

Snow and ice runway design is similar in principle to that of conventional runway pavement design: snow runways are like flexible pavements; ice runways are like rigid pavements. Challenges for polar runway design include the complicated nature of snow and ice mechanics, particularly near the melting point, as well as the inability to control or predict the temperature or thickness of the snow and ice comprising the runway pavement.

It is recommended that an analytical model for snow strength be developed from existing data. This would be age, temperature-history and density dependent. The model would form a key element in the development of a modern layered elastic design tool for snow runways. A similar ice runway design tool, based on conventional rigid pavement design, is also recommended. Finally, it is recommended that the feasibility of constructing a snow runway at the South Pole be revisited, in light of contemporary snow sintering methods. Such a runway would represent a revolutionary advance in the logistical support of Antarctic research efforts.

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# Performance of remote road surface sensor on different pavement types

Naoto Takahashi<sup>a</sup>, Kenji Sato and Roberto Tokunaga

Civil Engineering Research Institute for Cold Region, Traffic Engineering Research Team, 1-3 Hiragishi, Toyohira-ku, Sapporo, Japan

**Abstract.** It is important to correctly understand the road surface conditions, so that tactical decisions on the delivery of winter services can be made. Recent advances in sensing technologies have put non-invasive surface state sensors into practical use. These non-invasive sensors are able to remotely measure road surface conditions from the side of the road or from winter service vehicles. Although the sensors' abilities have been tested under various conditions, no studies are known to have addressed the reliability of the sensors on different pavement types. This study aims to test the performance of such sensors on different pavement types. The test was conducted at a test track that is paved with dense-graded asphalt, porous asphalt and stone mastic asphalt. As the non-invasive sensor, we used the Vaisala remote surface state sensor (DSC-111), which optically measures the layer thickness of water/snow/ice on the road and gives an estimation of grip level. In this study, wet and thin-ice surfaces were artificially created on the test track, and the grip levels obtained by the DSC-111 were compared with the friction values obtained by a continuous friction tester. The layer thickness values obtained by the DSC-111 were compared with those measured with a NASA water-film depth gauge.

## 1 Introduction

It is important to correctly understand the road surface conditions, so that tactical decisions on the delivery of winter services can be made and appropriate winter maintenance can be implemented. Among the most widely used tools for monitoring winter road surface conditions are road weather information systems (RWIS) [1], which are networks of weather monitoring sensors along roads. At these stations, valuable road weather data such as road surface temperature, air temperature, wind velocity and snow accumulation are collected and provided via Internet to maintenance staff in order to support decision-making [2]. However, the amount of information provided by the RWIS is still limited. Technically, data are collected only at specific locations where sensors are installed [3]. Besides, in most cases, we need to install sensors in the road.

Another approach to determining the state of the road surface involves measuring the road surface friction. This method has been used in the field by a number of road agencies, and it is widely adopted by airport authorities [4]. Various friction-measuring devices have been developed and tested by road agencies and researchers [5]. Friction has mainly been measured directly, by determining the frictional force produced from contact between the tire and the road surface. Since this method requires contact between a tire and the road surface, the measurement results are influenced by the type of vehicle and tire; what is more, the direct method cannot measure friction when the vehicle is stationary.

Recent advances in sensing technologies have put non-invasive surface state sensors into practical use. These non-invasive sensors are able to remotely measure road surface conditions from the side of the road or from winter service vehicles, and to provide cost and operational savings by eliminating the need to install anything in the road or to add any measuring mechanisms to the host vehicle.

Although the sensors' ability to provide reliable data has been tested and reported under various conditions

(e.g., [6 - 14]), no studies are known to have addressed the reliability of the sensors on different pavement types. This study aims to test the performance of such sensors on different pavement types.

## 2 Study method

### 2.1 The devices compared in the study

In this study, a continuous friction tester (CFT) and the Vaisala remote surface state sensor (DSC-111) were tested. The CFT calculates friction by measuring the axial force created by a measuring wheel installed at a 1- to 2-degree skew from the direction of travel. A friction value computed in this way is referred to as a Halliday friction number (HFN). The HFN scale was established by the device's designer, and it usually ranges from 0 to 100. The smaller the HFN value is, the more slippery the road surface is.

The DSC-111 optically measures the thickness of water/snow/ice on the road surface and gives an estimated grip level on a scale of 0.0 to 1.0. A test vehicle equipped with the CFT and the DSC-111 was used to compare the measurement results (Figure 1).

<figure><img src="image_567.png" /><figcaption>**Figure 1.** Test vehicle equipped with the CFT and the DSC-111</figcaption></figure>

<figure><img src="image_568.png" /></figure>

<footnote><sup>a</sup> Corresponding author: takahashi-n24k@ceri.go.jp</footnote>
## 2.2 Experiment site

The test was conducted at a test track of our institute. The test track is 2,700 m in circumference, and its 1,200-meter straightaway, which is paved with dense-graded asphalt (DGA), porous asphalt (PA) and stone mastic asphalt (SMA), was used.

## 2.3 Test conditions

On the straightway of the test track, wet and thin-ice surfaces were artificially created, in addition to the dry surface (Figure 2). The length of each road surface condition was set to be 100 meters (Figure 3).

The wet surfaces were created by applying water to the dry surface with a road sprinkler. The thickness of the water layer was set to be from 0.5 to 1.0 mm on the DGA.

The water thickness was measured with a NASA water-film depth gauge (Figure 4). The thin-ice surface was created by sprinkling water when the air temperature was below zero (thickness: 0.5 to 1.0 mm on the DGA).

Table 1 shows the test conditions. The test was conducted for 3 days. On the first day of the experiment, calibration of the DSC-111 (so-called “dry signals”) was conducted on the DGA. On the second day, dry signals were conducted on the SMA, and then, dry signals were conducted on the PA on the final day of the test. Unfortunately, on the first day of the test, there was a passing shower after calibration. Therefore, we were unable to obtain data for dry surfaces in the case where dry signals were conducted on the DGA.

<figure><img src="image_569.png" /><figcaption>Figure 2. Road surface conditions used for the test.</figcaption></figure>

<figure><img src="image_570.png" /><figcaption>Figure 3. Test track layout.</figcaption></figure>

<figure><img src="image_571.png" /><figcaption>Figure 4. Creation of the wet surfaces (left) and measurement of water thickness with the NASA water-film depth gauge (right).</figcaption></figure>

<table><caption>Table 1. Test conditions.</caption><thead><tr><th></th><th>21-Jan-15</th><th>26-Jan-15</th><th>28-Jan-15</th></tr></thead><tbody><tr><td>Weather</td><td>Clear</td><td>Clear</td><td>Clear</td></tr><tr><td>Time</td><td>14:50 ~ 18:51</td><td>14:01 ~ 20:02</td><td>14:01 ~ 18:03</td></tr><tr><td>Air Temp. (°C)</td><td>-4.4 ~ 4.1</td><td>1.3 ~ 9.3</td><td>-6.3 ~ 1.9</td></tr><tr><td>Surface Temp. (°C)</td><td>-4.2 ~ 5.9</td><td>-1.3 ~ 9.0</td><td>-6.4 ~ 7.0</td></tr><tr><td>Dry signals</td><td>DGA</td><td>SMA</td><td>PA</td></tr><tr><td>Surface conditions</td><td>Wet / Icy</td><td>Dry / Wet / Icy</td><td>Dry / Wet / Icy</td></tr><tr><td>Driving speed (km/h)</td><td>20</td><td>20</td><td>20</td></tr></tbody></table>
A test vehicle equipped with both devices ran on these surfaces at 20 km/h to compare the HFN values obtained from the CFT with the grip levels determined by the DSC-111. Takahashi et al. reported that the data obtained by the DSC-111 are influenced by the driving speed of the host vehicle, especially on the wet and compacted-snow surfaces [14].

It is assumed that the water and snow kicked up by the test vehicle's tires might have influenced the evaluation of water and snow layer thickness. Therefore, the driving speed of the host vehicle was set to be 20 km/h to minimize the influence of driving speed.

locally detected the ice layer on the section paved with DGA and the section paved with PA, and gave low grip levels on the wet surface, both the HFN and the grip level showed high values on the wet surfaces.

As shown in Figure 5, lags were found in the decrease in the grip level and the detection of the water, snow and ice layer relative to HFN values determined by the CFT. Takahashi et al. [14] conducted a time lag analysis. The study found that the time lag between measurement and output ranges from 4 to 10 seconds and that the time lag is unaffected by the driving speed of the host vehicle.

## 3 Test Results

Figure 5 shows an example of measurement results on the wet and icy surfaces. In Figure 5, dry signals were carried out on the section paved with DGA. Although DSC-111

### 3.1 Measurement results for dry surfaces

Figure 6 (a) shows HFN values and the grip levels for the dry surface in the case where calibration of the DSC111 was conducted on the SMA, and Figure 6 (b) shows those in the case where dry signals were conducted on the PA.

<figure><img src="image_572.png" /><figcaption>Figure 5. Example of measurement results. (a) wet, (b) icy</figcaption></figure>

<figure><img src="image_573.png" /><figcaption>Figure 6. Measurement results for dry surfaces.</figcaption></figure>
Since the time lag between the measurements and outputs had a maximum value of 10 sec., the figures were made by excluding the data of the first 10 sec. to eliminate the influence of the time lags.

As shown in the figure, the HFN showed high values on the dry surface, and the HFN for the DGA showed higher values than those for SMA and PA. It is considered that the rough surface of the SMA and the PA reduces friction-generating contact between the pavement and the tire. The grip levels also showed high values. Measurement results for the dry surface were not influenced by the pavement type on which dry signals were conducted. In contrast to the HFN values, the grip levels are consistent regardless of pavement type.

On the dry surfaces, the DSC-111 detected no water, snow or ice.

## 3.2 Measurement results for wet surfaces

Figure 7 shows the measurement results for the wet surface in the case where dry signals were conducted on the DGA. Figure 7 (a) represents the HFN and the grip level, and Figure 7 (b) represents the thickness of water, snow and ice layers detected by the DSC-111.

The HFN values for the wet surface were high, and they showed the same tendency as those for the dry surface.

The grip levels for the SMA were higher than those for the DGA, and the grip levels for the PA represented even higher values than those for the SMA. The results can be attributed to the thickness of water obtained by the DSC-111.

As shown in Figure 7 (b), the water layer on SMA is thinner than that on DGA. It is considered that the rough surface of the SMA traps some of the water. The water layer on the PA showed an even smaller value than that on the SMA. It is considered that the rough and drainable surface of the PA reduces the amount of water on the pavement surface.

Figure 8 shows the measurement results for the wet surface in the case where dry signals were conducted on the SMA. Although the DSC-111 locally detected snow on the DGA and gave a low grip level, the grip levels and the thickness of water, snow and ice in the case where dry signals were conducted on the SMA were generally high and showed the same tendency as those when dry signals were conducted on the DGA.

Figure 9 shows the measurement results for the wet surface in the case where dry signals were conducted on the PA. The grip levels and the thickness of water, snow and ice in the case where dry signals were conducted on the PA showed the same tendency as those when dry signals were conducted on the DGA and on the SMA.

<figure><img src="image_574.png" /><figcaption>Figure 7. Measurement results for wet surfaces (dry signals: DGA).</figcaption></figure>

<figure><img src="image_575.png" /></figure>

<figure><img src="image_576.png" /><figcaption>Figure 8. Measurement results for wet surfaces (dry signals: SMA).</figcaption></figure>

<figure><img src="image_577.png" /></figure>
<figure><img src="image_578.png" /><figcaption>**Figure 9.** Measurement results for wet surfaces (dry signals: PA).</figcaption></figure>

**3.3 Measurement results for icy surfaces**

Figure 10 shows the measurement results for the icy surface in the case where dry signals were conducted on the DGA.

As shown in the Figure 10 (a), both the HFN and the grip level showed low values on the icy surface. The grip levels for the PA showed higher values than those for the DGA and the SMA. The result was attributed to the ice layer thickness on the PA, which was thinner than that on SMA and DGA. The well-draining surface of the PA was thought to reduce the amount of water on the pavement surface.

On the other hand, the thickness of the ice layer on the SMA obtained by the DSC-111 was greater than the actual value. It is considered that the DSC-111 might have detected the ice within the voids in the SMA.

Figure 11 shows the measurement results for the icy surface in the case where dry signals were conducted on the SMA. Although the HFN values showed the same tendency as those when dry signals were conducted on the DGA, the grip levels showed high values relative to those when dry signals were conducted on the DGA (Figure 11 (a)).

<figure><img src="image_579.png" /><figcaption>**Figure 10.** Measurement results for icy surfaces (dry signals: DGA).</figcaption></figure>

<figure><img src="image_580.png" /><figcaption>**Figure 11.** Measurement results for icy surfaces (dry signals: SMA).</figcaption></figure>
As shown in Figure 11 (b), the DSC-111 detected not only ice but also water. It is considered that the water did not totally freeze under the condition of the lowest surface temperature of -1.3 degrees Celsius. Therefore, the DSC-111 might have detected both water and ice on the DGA and the SMA, and might have detected small amounts of water and ice on the PA because of its well-draining surface. And the water on the pavement surface might influence the estimation of the grip level.

Figure 12 shows the measurement results for the icy surface in the case where dry signals were conducted on the PA. Both the grip levels and the thickness of water, snow, and ice in the case where dry signals were conducted on the PA showed the same tendency as those when dry signals were conducted on the DGA.

## 3.4 Influence of pavement types on which dry signals were conducted

Figure 13 summarizes the results of measurement for each surface. In these figures, the vertical axis represents average grip levels for each pavement type. For both dry and wet surfaces, the grip levels are consistent. The dry signals were found to have little influence on the measurement results, regardless of the pavement type on which dry signals were conducted.

For the icy surfaces, the grip levels for each pavement type were high when dry signals were conducted on the SMA, relative to those when dry signals were conducted on DGA and on PA.

The cause is unclear, but relatively high surface temperature is considered to be the cause. It is considered that the water did not totally freeze under the condition of the lowest surface temperature of -1.3 degrees Celsius and that the water on the pavement surface influenced the estimation of the grip levels.

<figure><img src="image_581.png" /><figcaption>Figure 12. Measurement results for icy surfaces (dry signals: PA).</figcaption></figure>

<figure><img src="image_582.png" /><figcaption>Figure 13. Average grip levels for each pavement type.</figcaption></figure>
## 4 Conclusions

The study results are summarized here.

(1) Grip levels are generally consistent for each surface and correspond to the friction values obtained by the CFT. The precision is good enough to discriminate between the different surfaces.

(2) The thickness of the ice layer on the SMA obtained by the DSC-111 was occasionally greater than the actual value. The cause of this is unclear, but it seems that the DSC-111 might have detected the ice within the voids in the SMA.

(3) The pavement type on which calibration of the DSC-111 was conducted has little influence on the measurement results, except on the icy surface when dry signals were conducted on the SMA. It is assumed that the water did not totally freeze under the condition of the lowest surface temperature of -1.3 degrees Celsius and that the amount of water and ice influenced the estimation of the grip level.

## Acknowledgements

The authors wish to express their sincere appreciation to Vaisala Japan for technical support and informative suggestions.

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9. P. Bridge, Noninvasive road weather sensors, Transportation Research Board, E-C 126, pp.407-415 (2008)

10. P. Jonsson, Road status sensors: a comparison of active and passive sensors, 16th ITS World Congress (2009)

11. L. Ewan, A. Al-Kaisy and D. Veneziano, Remote sensing of weather and road surface conditions: Is Technology Mature for Reliable Intelligent Transport System Applications?, Transportation Research Record, No. 2329, pp. 8-16 (2013)

12. D. Jensen, B. Koeberlein, E. Bala and P. Bridge, Ensuring and quantifying ITS return on investment through the development of winter maintenance performance measures, 20th ITS World Congress (2013)

13. T. Vaa, Remote sensing of road surface conditions and ITS applications, 20th ITS World Congress (2013)

14. N. Takahashi, M. Kiriishi and R. Tokunaga, Comparative study of friction measurement devices, 14th International Road Weather Conference (2014)
Poster session