$C_s^*(x_r, t_r) = \int_{-T} \int_{\Omega} a(x_s, t_s, x, t)p(x, t) dv dt$, with $C_s^*(x_r, t_r)$ the concentration at $x_s$ and $t_s$ from a source shot at $x_r$ and $t_r$, solution of the adjoint equation:

$$ \frac{\partial C_s^*(x,t)}{\partial(-t)} - \mathbf{u}(x,t) \cdot \nabla_x C_s^*(x,t) = \nabla_x \cdot (D \Delta C_s^*(x,t)) + p(x,t) \quad (2) $$

where $p(x, t)$ is the forcing term of the adjoint equation, such that $p(x, t) = C_s(x, t) - d(x, t)$. It means that, as shown in Figure 1-a), the concentration at $x_r$ and $t_r$ (purple point) coming from a source $s$ at $x_s$ and $t_s$ (red point) equals, in Figure 1-b), the concentration at $x_s$ and $t_s$ (red point) coming from the same source $s$ but at $x_r$ and $t_r$ (purple point) transported in both flow and time reversed.

<figure><img src="image_55.png" /><figcaption>Figure 1: In log. scale: a) Direct concentration field with wind streamlines; b) Inverse concentration field, with wind streamlines</figcaption></figure>

This reciprocity principle remains valid for the RANS advection-diffusion equation and is equivalent to the well-mixed condition criteria (Thomson, 1987). In this study, a Lagrangian Stochastic (LS) model is used instead, as it accurately models turbulent dispersion in a complex environment while still providing reasonable computational cost, but the principle remains the same and is still valid.

The reciprocity principle is of great interest for inverse modelling. Indeed, in a steady case with fixed sensors, as the Green functions can be calculated by reverse dispersion from the sensor positions with equation (2), computing a new adjoint field at each iteration of the optimization algorithm is not necessary anymore, only requiring calculating a unique adjoint field by sensor and, at each iteration, picking the field value at the newly computed source position. Hence, if the number of receivers is smaller than the iteration number of the optimization algorithm, using a backward model is more efficient than using a forward one. Also, if there are more sources than receivers, the concentration computation is quicker using a backward model. Besides, the convolution of the forward and backward fields could increase the statistical power (for the LS model), improving the reliability of the modelled concentrations and hence of the inverse modelling. Finally, new measurement techniques appeared recently, such as the Particle Tracking Velocimetry (PTV), could experimentally verify the reciprocity.

In this study, the reciprocity principle is presented and applied to the inverse modelling workflow by using the adjoint method to a LS backward model. The approach benefits and implications will be highlighted. Finally, some validations with experimental results and applications to both academic and industrial cases are presented, on diverse datasets configurations with increasing complexity.

## References

* Pudykiewicz, J. A. (1998): Application of adjoint tracer transport equations for evaluating source parameters. *Atmospheric Environment*, 32. 3039-3050.

* Thomson, D. J. (1987): Criteria for the selection of stochastic models of particle trajectories in turbulent flows. *Journal of Fluid Mechanics*, 180. 529-556.
Atmospheric boundary layer flow
# Characterisation of the boundary layer wind tunnel facility at the University of Bristol

Nada Taouil¹, H.D. Lim¹, B. Zang¹, Mahdi Azarpeyvand¹

¹School of Civil, Aerospace and Design Engineering, University of Bristol, UK

## Abstract

The Boundary Layer Wind Tunnel (BLWT) at the University of Bristol is a new national wind tunnel facility that was recently commissioned. With an 18m long, 2m wide and 1m high test section, it is envisaged to be a pivotal enabler for environmental fluid dynamics research in the UK. To fully harness its capabilities for urban flow research, the use of appropriate flow conditioners, as shown in Fig. 1(a), is needed to accurately simulate an atmospheric boundary layer flow.

As a new wind tunnel facility, thorough measurements of the empty wind tunnel are essential to validate the flow uniformity and quality. Hot wire anemometry was used to characterise the vertical profiles of the smooth-wall turbulent boundary layer flow at 14 meters from the entrance of the test section. Key parameters were established, including the boundary layer thickness ($\delta$=200 mm) which is within 10% of theoretical estimates, the free-stream turbulence intensity (0.13%) and the friction velocity ($u^*=0.64$ m/s), which was estimated using the law of the wall based on wall similarity:

$$ \frac{u}{u^*} = \frac{1}{k} \ln \left( \frac{y.u^*}{\nu} \right) + C. \qquad (1) $$

where $k=0.42$ is the von Karman constant and $C=5$ is the smooth-wall intercept. Figure 1(b) shows that the mean velocity profiles are reasonably homogeneous at three spanwise locations, and although there are slight deviations, these are within acceptable range (<3%) (Wittwer and Möller, 2000).

Precise simulation of an atmospheric boundary layer flow is necessary to enable air pollution research. This is achieved by adopting the Counihan's methodology (Counihan, 1969) in the present study, which utilises 900 mm tall quarter-elliptic wedge spires as vortex generators, a castellated barrier wall to introduce momentum deficit at the near-wall region, and a fetch of roughness elements strategically arranged for an upstream fetch of 10 meters, as illustrated in Figure 1(a). By adjusting the design of these flow conditioners, different types of terrain flow, i.e. rural or urban can be simulated.

Figure 1(c) shows the velocity profile of one of the rough wall configurations with the roughness elements and the barrier installed. As expected, in contrast to the smooth-wall data, the boundary layer thickness of the rough-wall data is significantly larger. This, along with the roughness elements
creating additional flow turbulence and disruption, may explain the differences in the outer wake region.

Figure 1(d) shows the power law exponent (α) of the velocity profile, given by

$$ \frac{u}{u_1} = (\frac{y}{y_1})^{\alpha} \qquad (2) $$

to understand the impact of roughness elements and the barrier wall. From the analysis of these plots, it appears that adjustments to the configuration of the roughness elements is necessary to reach a power index of α=0.29, which is typical for urban flows. Additionally, further investigation into the effects of the quarter-elliptic wedge spires will be conducted to understand their impact on boundary layer characteristics. The thickness of the boundary layer generated depends on the height of the spires, which is estimated to be around 90% of the height of the spires.

In the conference presentation, further results and discussions will be provided, including mean velocity distributions, turbulence intensity, Reynolds shear stresses and power spectrum density and their comparison for both the smooth and rough configurations, with measurements taken closer to the wall to get lower values of y⁺. Moreover, comparisons with established datasets such as Engineering Sciences Data Unit (ESDU) will be conducted to validate the accuracy of the BLWT's simulations and confirm its suitability for atmospheric flow studies.

<figure><img src="image_56.png" /><figcaption>Figure 1: (a) BLWT with roughness elements (Lego blocks) and barrier installed. (b) Spanwise homogeneity analysis at three positions. (c) Log-law fit for smooth wall and rough wall data. (d) Mean velocity with power law exponent fit for two different rough wall configurations.</figcaption></figure>

## References

Counihan, J., 1969. An improved method of simulating an atmospheric boundary layer in a wind tunnel. *Atmospheric Environment*, 3(2), pp.197-214.

Wittwer, A. R., & Möller, S. V. (2000). Characteristics of the low-speed wind tunnel of the UNNE. *Journal of Wind Engineering and Industrial Aerodynamics*, 84(3), 307–320.
# Wake characteristics of a model wind turbine immersed in a boundary layer

V. Babin<fnref n="1" />, Y. Bohbot-Raviv<fnref n="1" />, L. Shmuel Sabban<fnref n="2" />, and J. Alisse<fnref n="2" />

## Abstract

Wind energy, alongside other renewable sources, has experienced significant growth in recent years, with the expectation of a sharp rise in the number of wind farms in the coming decades. This investigation is part of a broader project initiated by Noga – Israel Independent System Operator, Ltd. – aimed at determining the optimal distance needed between wind farms and high-voltage transmission lines.

A turbulent wake generated by a scaled-down wind turbine has been modeled and analyzed at the Environmental Wind Tunnel Laboratory (EWTL) of the Israel Institute for Biological Research (IIBR). The turbine was immersed in a scaled-down boundary layer that was slightly rough and neutrally stable. Modeling and characterizing the wake under idealized wind tunnel conditions serve as important tools for enhancing field measurements and numerical evaluations of field-scale wakes. The forthcoming presentation will primarily focus on the experimental details and the mean and instantaneous flow properties, while the spectral properties of the wake will be discussed separately by Bohbot-Raviv et al. in another presentation.

Our wake model is generated based on an incoming boundary layer with a scaling-length factor of approximately 400. The in-house scaled-down miniature wind turbine, utilizing a rotor design by Bastankhah and Porté-Agel (2017a,b), stands at 195 mm tall with a 3-blade rotor diameter of 150 mm (refer to Figure 1, left). The wake, observed up to 10 rotor diameters downstream, was generated under 10% streamwise turbulence intensity of the incoming flow, maintaining a mean tip-speed ratio of 5. The boundary layer's thickness at the turbine's location is estimated to be approximately 600 mm, roughly three times the height of the rotor's top tip. We conducted quantitative and qualitative spatial flow visualization employing double-pulsed plane two-component Particle Image Velocimetry (PIV). Additionally, velocity field mapping along the wake was performed through point-measurements using hot-wire anemometry (HWA) for two velocity components.

The mean velocity deficit, turbulence intensity, vertical Reynolds shear stress, and their evolution along the vertical axial mid-plane are analyzed. At least three distinct regions of the wake were identified based on the flow properties: the very-near wake (0<x<2D), near wake (2D<x<10D), and far wake (x>10D), where similarity is expected to hold. The wake deficit persists as far as 11D downstream from the turbine. These findings align well with previous experimental works (Barlas et al., 2016 and others) and Large Eddy Simulation (LES) (Wu and Porté-Agel, 2012).

<footnote n="1">¹Israel Institute for Biological Research, Environmental Sciences, Department of Applied Mathematics, P.O.B 19, 7410001 Ness-Ziona, Israel</footnote>

<footnote n="2">²Noga - Israel Independent System Operator Ltd., 8 Moshe Fliman St., Haifa, Israel</footnote>
Analyses of the PIV data in the vertical axial mid-plane along the wake (x,0,z) will also be presented. Signatures of the root and hub vortices are identified at the center of the very-near wake, creating layers of positive and negative vorticity that quickly diffuse along a 1D distance from the turbine. A vortex strip with high negative vorticity values at the top-tip height is clearly observed. Top-tip vortices and their persistence are discernible in the instantaneous vorticity field (blue circles in Figure 1, right), originating from the 3D helicoidal vortices produced by each blade of the rotor during rotation.

A comparison of wake characteristics with previous studies involving similar physical models from single wind turbines characterized by low inlet turbulence is presented (Barlas et al., 2016, among others). Future stages of the project plan will be discussed to conclude the presentation.

<figure><img src="image_57.png" /><figcaption>Figure 1: (left) depicts the scaled-down wind turbine, HWA and illuminated strip from the PIV laser sheet in the vertical axial mid-plane along the wake (x,0,z) from the experiment setup in the wind tunnel; (right) The instantaneous vorticity component of the wake in the very-near wake (0<x<2.5D), as measured by Particle Image Velocimetry (PIV) in the vertical axial mid-plane.</figcaption></figure>

References

Bastankhah, M., Port´e-Agel F. (2017). A new miniature wind turbine for wind tunnel experiments. Part i: Design and performance. *Energies*, *10*(7) 908.

Bastankhah, M., Porté-Agel F. (2017). A new miniature wind turbine for wind tunnel experiments. Part ii: wake structure and flow dynamics. *Energies* 10(7) 923.

Barlas, E., Buckingham, S., van Beeck, J. (2016). Roughness effects on wind-turbine wake dynamics in a boundary-layer wind tunnel. *Boundary-Layer Meteorology* 158(1):27–42.

Wu, Y.T., Porté-Agel, F. (2012). Atmospheric turbulence effects on wind-turbine wakes: An LES study. *Energies* 5(12):5340–5362.
# Turbulent scales in the wake of a model wind turbine immersed in a boundary layer

Y. Bohbot-Raviv<fnref n="1" />, V. Babin<fnref n="1" />, L. Shmuel Saban<fnref n="2" /> and J. Alisse<fnref n="2" />

## Abstract

The wake generated by a scaled-down horizontal axis wind turbine immersed in a slightly rough boundary layer (at a scale ratio of 1:400) is examined at the Environmental Wind Tunnel Laboratory at IIBR. Spectral analyses of the modeled wake provide a scale-by-scale energetic description of the flow within the turbine wake. This examination is part of a broader project initiated by Noga – Israel Independent System Operator, Ltd. – aimed at investigating the optimal distance needed between wind farms and high-voltage transmission lines. Identifying dominant frequencies and understanding their signatures under idealized wind tunnel conditions are important tools for enhancing field-scale measurements and numerical investigations of field-scale wakes.

The forthcoming presentation will primarily focus on the spectral properties of the modelled wake, while the experimental details and mean flow properties will be presented separately by Babin et al. in another presentation.

<figure><img src="image_58.png" /><figcaption>Figure 1: Instantaneous velocity field (u,w) and mean streamwise velocity (color) by particle image velocimetry in the axial mid-plane along the very-near wake.</figcaption></figure>

<footnote n="1">¹Israel Institute for Biological Research, Environmental Sciences, Department of Applied Mathematics, P.O.B 19, 7410001 Ness Ziona, Israel</footnote>

<footnote n="2">² NOGA - Israel Independent System Operator LtD, 8 Moshe Fliman St., Haifa Israel</footnote>
In the relatively high-frequency range, the turbine induces strong turbulent energy into the wake. The dominant frequencies of the flow and their evolution in the vertical axial mid-plane along the wake are identified and examined. Our spectral analysis confirms small scales (high frequencies) induced by the rotating turbine in the top and bottom tip heights and additional large scale (low frequency) of the flow in the wake. The evolution of the energy deficit (i.e., relative to the incoming energy) in the large-scale turbulent kinetic energy partially confirms the concept proposed by Chamorro et al. (2012) viewing the turbine acting as a ‘low-pass filter’. Several terms in the turbulent kinetic energy budget are also explored to identify dominant physical processes in the near wake.

In an attempt to associate the dominant large-scale frequencies with flow structure, particle image velocimetry qualitatively infers on a meandering motion of the wake as a whole at approximately 0.2f<sub>T</sub>, where f<sub>T</sub> is the mean rotation frequency. Such low frequencies correspond to a few hundredths of Hertz in full scale (i.e., hundreds of meters). Similar meandering frequencies have been observed in similar studies in the laboratory (Barlas et al., 2016 and Chamorro et al., 2012) and in the field (Heisel et al., 2018). Future stages of the project will conclude the presentation.

## References

Chamorro, L., Guala, M., Arndt, R., & Sotiropoulos, F. (2012). On the evolution of turbulent scales in the wake of a wind turbine model. *Journal of Turbulence*, 13:27.

Barlas, E., Buckingham, S., & van Beeck, J. (2016). Roughness effects on wind-turbine wake dynamics in a boundary-layer wind tunnel. *Boundary-Layer Meteorology*, 158(1):27–42.

Heisel, M., Hong, J., & Guala, M. (2018). The spectral signature of wind turbine wake meandering: A wind tunnel and field-scale study. *Wind Energy*, 21(9):715–731.
# Wake Characteristics of Multi-scale Buildings in a Turbulent Boundary Layer

C. Southgate-Ash<fnref n="1" />, A. Mishra<fnref n="1" />, S. Grimmond<fnref n="2" />, A. Robins<fnref n="1" />, M. Placidi<fnref n="1" />

## Introduction

Urban forms characterised by multi-scale roughness can drastically modify the wind structure within cities affecting both pedestrian comfort and air quality at street level. Despite the multiscale nature of modern cities, for simplicity, most urban flow studies focus on cuboid buildings with a single length scale (Cheng and Castro, 2002; Coceal et al., 2006; Castro, 2007; Leonardi and Castro, 2010; Castro et al., 2017). Although this is effective for certain simplified morphologies, it is often an oversimplification compared with real urban data (Grimmond and Oke, 1999; Barlow and Coceal, 2009; Makedonas et al., 2021). To study how significant additional length scales are in urban flow modelling, six multiscale forms are considered: reference cuboids for two aspect ratio (mean building height to width) cases (standard, 1; tall, 3) plus two fractal iterations of each. Although the six models have the same mean building width, height ($H_B$), and frontal area, their length scales characteristics differ, isolating the effect of progressively smaller length scales. These are used in wind tunnel experiments within a deep turbulent boundary layer.

## Experimental set up

The experiments were carried out in the University of Surrey Environmental Flow Research Centre (EnFlo) 'A' wind tunnel. This tunnel's working section covers 5000 mm (length) x 900 mm (width) x 600 mm (height). It is a blowdown, open-circuit wind tunnel with a maximum velocity of 25 ms⁻<fnref n="1" /> and a free-stream turbulence intensity below 0.1%. We consider a range of velocities, resulting in a Reynolds number range $Re_L$ = 6.7 × 10³ – 8.2 × 10⁴, based on the building height. Three-dimensional Laser Doppler Anemometry (LDA) measurements were taken to measure the vertical and lateral mean velocity profiles in the building model wakes at several downstream locations (Figure 1). Irwin spires, along with floor roughness elements were employed to produce a thick velocity profile, similar to that of an urban boundary layer with a relative boundary layer depth at the building location of $δ/H_B$ ≈ 0.24 and 0.41 for the standard and tall models respectively and $u_t$ = 0.45 ms⁻<fnref n="1" />.

## Key Findings

The vertical and lateral wake spreads behind each building model were examined via 3D LDA measurements. For both sets of models (standard and tall), a linear lateral spreading with the downstream location was found following the extent of a region characterised by strong recirculation. For the standard models, the base iteration model showed the most amount of lateral spreading and

<footnote n="1">¹University of Surrey, Faculty of Engineering and Physical Sciences, Centre for Aerodynamics and Environmental Flow, Guildford, GU2 7XH, United Kingdom</footnote>

<footnote n="2">²University of Reading, Department of Meteorology, Brian Hoskins Building, University of Reading, Whiteknights campus, RG6 7BE, United Kingdom</footnote>
the opposite was true for the tall. Vertically, any significant wake spreading was hindered by the roof-level shear layer growth. The vertical spread seemed to scale with the maximum building height in the nearwake ($X < H_B$), but with the average building height for $X > H_B$. For the base iteration models, all three velocity fluctuation components were observed to be higher in the near-wake but to decay faster than in the fractal models. Finally, the introduction of progressively smaller scales was found not to modify the shedding frequency of the models, but to affect its strength with the intensity of the shedding decreasing with each fractal iteration (i.e. length scale).

<figure><img src="image_59.png" /><figcaption>Figure 1: Wind tunnel and LDA set up showing [1] Irwin spires, [2] floor roughness, [3] building model, [4] streamwise location of vertical and lateral profiles, [5] Example of vertical measurement profile with the blue dots showing the height at which lateral profiles were taken, [6] LDA probes. Not to scale.</figcaption></figure>

## References

Barlow, J. F. and Coceal, O. (2009). "A review of urban roughness sublayer turbulence". In: Reading: Met Office 10, pp. 216–240.

Castro, I. et al. (2017). "Measurements and Computations of Flow in an Urban Street System". In: Boundary-Layer Meteorology 162, pp. 207–230.

Cheng, H. and Castro, I. P. (2002). "Near Wall Flow over Urban-like Roughness". In: Boundary-Layer Meteorology 104.2, pp. 229–259.

Castro, I. P. (2007). "Rough-wall boundary layers: mean flow universality". In: Journal of Fluid Mechanics 585, pp. 469–485.

Coceal, O. et al. (2006). "Mean Flow and Turbulence Statistics Over Groups of Urban-like Cubical Obstacles". In: Boundary-Layer Meteorology 121.3, pp. 491–519.

Leonardi, S. and Castro, I. P. (2010). "Channel flow over large cube roughness: a direct numerical simulation study". In: Journal of Fluid Mechanics 651, pp. 519–539.

Grimmond, C. S. B. and Oke, T. R. (1999). "Aerodynamic Properties of Urban Areas Derived from Analysis of Surface Form". In: Journal of Applied Meteorology 38.9, pp. 1262–1292.

Makedonas, A., Carpentieri, M. and Placidi, M. (2021). "Urban Boundary Layers Over Dense and Tall Canopies". In: Boundary-Layer Meteorology 181.1, pp. 73–93.
# Roughness Sublayer Flows over Cubes with Uniform and Non-uniform Height: A Wind Tunnel Study

Ziwei Mo<fnref n="1" />, Chun-Ho Liu <fnref n="2" />

## Abstract

Atmospheric surface layer (ASL) consists of inertial sublayer (ISL) and roughness sublayer (RSL). In view of the presence of roughness elements, RSL extends and becomes increasingly important in the mass and energy exchange between the atmospheric boundary layer and land surface. This study investigates the RSL flows over different configurations of arrays constructed by LEGO® bricks using wind tunnel measurements. Constant-temperature (CT) hot-wire anemometry (HWA) is used to measure the flows in the turbulent boundary layer (TBL). The effects of uniform and non-uniform roughness element height are examined to demystify the aerodynamic properties and turbulence structure in the RSLs. The results show that roughness element height increases the RSL thickness. Drag coefficient $C_d$ is elevated over higher, non-uniform-height elements. We propose that the RSL-to-turbulent-boundary-layer-height ratio ($Z_{RSL}/Z_{TBL}$) could be parameterized by $C_d$, which would benefit the RSL parameterization in meso-scale meteorological models. Compared with that in the ISL, the vertical gradient of mean wind speed is smaller in the RSL that is well predicted by our modified analytical model. The mixing length $l_m$ is also calculated to contrast the RSL and ISL turbulent length scale. It is found that the mixing length $l_m$ is proportional to the vertical height $z$ (i.e., $l_m = \kappa z$; where $\kappa$ is the von Kármán constant). However, $\kappa$ is equal to 0.2 in this study that is different from other studies over staggered cube arrays. As the canopy roof is difficult to be determined over arrays of non-uniform roughness elements, air exchange rate (ACH) induced by RSL turbulence, $ACH_{RSL}$, is introduced to assess the RSL ventilation capacity analogous to the roof-level ACH. A tight correlation is found between the square root of $C_d$ and $ACH_{RSL}$, suggesting a potential parameterization for evaluating the RSL ventilation. These findings demonstrate the importance of RSL flow parametrization and the implication to ventilation assessment over urban areas, facilitating sustainable urban environment development.

<footnote n="1">¹School of Atmospheric Sciences, Sun Yat-sen University, & Southern Marine Science and Engineering
Guangdong Laboratory (Zhuhai), Zhuhai, China</footnote>

<footnote n="2">²Department of Mechanical Engineering, The University of Hong Kong, Hong Kong</footnote>
<figure><img src="image_60.png" /><figcaption>Figure 1: The photos of the configurations of the cube arrays</figcaption></figure>
# Assessing the Dispersion Characteristics of Ship Exhausts in Neutral Boundary Layers: Wind Tunnel Testing

A. Sankaran, R. Hain, C. Kähler

Department of Aerospace Engineering, Institute of Fluid Mechanics and Aerodynamics, University of the Bundeswehr Munich, 85577 Neubiberg, Germany

## Abstract

The objective of this study is to model a neutral atmospheric boundary layer over sea/seashore conditions in wind tunnel and utilize it for dispersion study of ship exhaust. Counihan type spires in conjunction with a barrier are utilized to mimic the atmospheric boundary layer in the sea and shore conditions with neutral conditions. Stereoscopic Particle Image Velocimetry (sPIV) measurements were conducted to clarify if there is span-wise variation due to the presence of the spire system with spire height $H = 900$ mm.

The experimental condition with the barrier and spires to simulate the atmospheric boundary layer in the wind tunnel (Eiffel type atmospheric wind tunnel located at the University of the Bundeswehr Munich). The stereoscopic PIV setup for corresponding measurements is shown in Fig. 1. The modelled boundary layer characteristics like the mean wind profile, turbulent intensities, roughness length, and integral length scale are quantified and compared with the expected conditions at marine conditions available (Hsu et al., 1994; VDI, 2000). The average velocity profile in the main flow direction (U along x), fitted using power law (equation 1) and log-law (equation 2) formulations can be expressed as follows:

$$U = a(z - b)^{\alpha} \qquad (1)$$

$$U = U_0 \log \left( \frac{z - b}{z_0} \right) \qquad (2)$$

Here, z is the direction perpendicular to the surface, a, b, α, U₀, and z₀ are fitting parameters. b is referred to as displacement height, and z₀ represents the roughness length. For a free stream velocity of 10 ms⁻¹, the obtained fitting parameters are depicted in Table 1.

<table><caption>Table 1. Velocity profile fitting parameters</caption><thead><tr><th>a (ms<sup>-1</sup>)</th><th>b (mm)</th><th>α</th><th>U<sub>0</sub> (ms<sup>-1</sup>)</th><th>z<sub>0</sub> (mm)</th></tr></thead><tbody><tr><td>4.66</td><td>8.43</td><td>0.088</td><td>0.566</td><td>0.0126</td></tr></tbody></table>

A generic cruise ship model (using a scale 1:500) is positioned 9H downstream of the spire setup. The flow characteristics and Reynolds number independence of the flow characteristics are confirmed for the velocities studied by the sPIV measurements.
Subsequently, experiments for concentration measurements with the exhaust flow were conducted. The exhaust flow is seeded with the flow tracers of DEHS particles with average diameter of 0.4 µm. Here, we apply an optical-based particle counting method in conjunction with particle counter calibration to determine spatially resolved particle concentration measurements to evaluate the exhaust dispersion characteristics (Sankaran et al., 2024).

The inset in Fig. 1 illustrates the average concentration profile for a free stream velocity of 10 ms⁻¹ and exhaust velocity of 13 ms⁻¹ at 90° yaw angle of the ship.

The obtained results are intended for validation and comparison with numerical models with controlled incoming wind conditions.

<figure><img src="image_61.png" /><figcaption>Figure 1: Top view of the experimental setup to measure the velocity field and concentration field with the exhaust flow. The inset shows the average concentration field with 10 ms⁻¹ far-field velocity and 13 ms⁻¹ exhaust velocity at 90° ship yaw angle. Note that the concentration contour is in log scale with C in particles per cm³.</figcaption></figure>

## Acknowledgement

This research is funded by dtec.bw – Digitalization and Technology Research Center of the Bundeswehr. dtec.bw is funded by the European Union – NextGenerationEU.

## References

Hsu, S. A., Meindl, E. A., & Gilhousen, D. B. (1994). Determining the power-law wind-profile exponent under near-neutral stability conditions at sea. *Journal of Applied Meteorology and Climatology*, 33(6), 757-765.

Verein Deutscher Ingenieure (2000). Environmental Meteorology, Physical Modelling of Flow and Dispersion Processes in the Atmospheric Boundary Layer, Application of Wind Tunnels (VDI 3783-12).

Sankaran, A., Hain, R., & Kähler, C. J. (2024). Particle image based simultaneous velocity and particle concentration measurement. *Measurement Science and Technology*, 35(6), 065206.
# Intermittency Analysis of the Turbulence over Idealized Urban Areas
## Based on Empirical Mode Decomposition

Ruiqi Wang<fnref n="1" />, Chun-Ho Liu<fnref n="1" />

## Abstract

Turbulence intermittency is a unique characteristic of dynamical motions, which spans in time and space, that has attracted a lot of attention. Numerous studies have demonstrated the positive influence of turbulence intermittency in vertical transport (Wei et al., 2018) as well as near-ground air quality (Ren et al., 2019). Intermittency is observed in the probability distribution of energy dissipation, momentum fluxes, and entrophy, suggesting the implicit technical limitation of the Kolmogorov theory. Our previous works showed that sweep Q4 ($u'' > 0$ and $w'' < 0$) occurs intermittently but contributes more, especially in the roughness sublayer (RSL) that dominates the near-ground transport (Mo and Liu, 2023). This study is therefore conceived to examine the interactions among attention surface roughness, intermittency, and transport processes. It is the first one to reveal the intermittent properties of coherent structures and energy transfer processes over idealized urban areas. In the experiments, the rough surfaces in the wind tunnel are fabricated by plastic LEGO® blocks to represent idealized urban areas. Vertical profiles of velocity and turbulence statistics are sampled by a hot-wire anemometer (HWA) to sample the flows over urban canopies of different surface roughness. In order to identify intermittent structures of all sizes and obtain their phase information, two conventional methods, empirical mode decomposition (EMD) and wavelet analysis of velocity fluctuations, are used for complementary solution. The former decomposes the turbulent wind fields into a series of intrinsic mode functions (IMFs). The latter, on the other hand, utilizes the local intermittency measure (LIM) to detect the intermittent structures and energy transfer in the flows. The joint probability density functions (JPDFs) of fluctuating velocities are expressed in terms of instantaneous frequency and instantaneous amplitude that demonstrate the most frequent dynamical events (Figure 1a). To detect intermittency, LIM and the flatness factor (FF) are calculated by wavelet coefficients. Apparently, intermittency is more significant among the small-scale motions (IMF2 and IMF3) than the large-scale ones (IMF7 and IMF8; Figure 1b). In addition, intermittency diminishes with increasing wall-normal distance. Furthermore, simultaneous LIM peaks with phase synchronization can only be found between two adjacent IMFs (i.e. similar time scales). It is in turn indicated that energy transfer processes are intermittent that tend to occur in-between adjacent time scales. The finding contributes to an in-depth understanding of the nature of intermittent wind fluctuations and turbulence kinetic energy (TKE) cascade mechanisms.

<footnote n="1">¹ The Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China</footnote>
<figure><img src="image_62.png" /><figcaption>Figure 1: (a) JPDF of (1) streamwise $u''$ and (2) vertical $w''$ velocity fluctuations in the amplitude-frequency space in log scale (C4; C4D; C4T; and C4Q are arrays of roughness elements of different densities). (b) Time traces of (1) LIM; (2) phase difference; and (3) absolute phase difference of IMF pairs (IMF2 & IMF3; IMF7 & IMF8; and IMF2 & IMF5).</figcaption></figure>

References

Mo, Z., Liu, C.-H., 2023. Inertial and roughness sublayer flows over real urban morphology: A comparison of wind tunnel experiment and large-eddy simulation. Urban Climate 49, 101530. https://doi.org/10.1016/j.uclim.2023.101530

Ren, Y., Zhang, H., Wei, W., Wu, B., Liu, J., Cai, X., Song, Y., 2019. Comparison of the turbulence structure during light and heavy haze pollution episodes. Atmospheric Research 230, 104645. https://doi.org/10.1016/j.atmosres.2019.104645

Wei, W., Zhang, H., Wu, B., Huang, Y., Cai, X., Song, Y., Li, J., 2018. Intermittent turbulence contributes to vertical dispersion of PM₂.₅ in the North China Plain: cases from Tianjin. Atmospheric Chemistry and Physics 18, 12953–12967. https://doi.org/10.5194/acp-18-12953-2018
# Committees

## Organising committee

- Marilina Barulli (Ecole Centrale Lyon - LMFA)

- Perrine Charvolin-Volta (Centrale Innovation - LMFA)

- Ariane Emmanuelli (Ecole Centrale Lyon - LMFA)

- Sofia Fellini (Politecnico di Torino)

- Louis Gostiaux (CNRS - LMFA)

- Livia Grandoni (Ecole Centrale Lyon - LMFA)

- Stefano Lanzini (Ecole Centrale Lyon - LMFA)

- Marie-Noëlle Pailhès (CNRS - LMFA)

- Marie Poulain-Zarcos (Ecole Centrale Lyon - LMFA)

- Emelyne Rabany (CNRS - LMFA)

- Pietro Salizzoni (Ecole Centrale Lyon - LMFA)

- Jean Salles-Loustau (Ecole Centrale Lyon - LMFA)

- Claudia Schiavini (Ecole Centrale Lyon & Politecnico di Torino - LMFA)

## Scientific committee

- Ariane Emmanuelli (Ecole Centrale Lyon - LMFA)

- Sofia Fellini (Politecnico di Torino)

- Livia Grandoni (Ecole Centrale Lyon - LMFA)

- Christof Gromke (Karlshrue Insitut für Technology)

- Klára Jurčáková (Institute of Thermomechanics of the Czech Academy of Sciences)

- Bernd Leitl (University of Hamburg - Meteorological Institute)

- Massimo Marro (CNRS - LMFA)

- Paolo Monti (Roma La Sapienza)

- Marie Poulain Zarcos (Ecole Centrale Lyon - LMFA)

- Pietro Salizzoni (Ecole Centrale Lyon - LMFA)

- Eric Savory (University of Western Ontario) Lionel Soulhac (INSA - LMFA)
List of participants

• Ahmad Khurshid

• Alexandrou Giorgos

• Babin Valery

• Babuková Zuzana

• Badeke Ronny

• Balczo Marton

• Barulli Marilina

• Bi Dianfang

• Birch David

• Bohbot-Raviv Yardena

• Bouris Demetri

• Carpentieri Matteo

• Chaloupecka Hana

• Charvolin Perrine

• Combette Robin

• Deebank Charles

• Del Ponte Annika Vittoria

• Di Renzo Teresa

• Du Haoran

• Emmanuelli Ariane

• Esposito Antonio

• Fellini Sofia

• Gillmeier Stefanie

• Gostiaux Louis

• Grandoni Livia
• Gromke Christof

• Guoliang Chen

• Harms Frank

• Hayden Paul

• Huret Thomas

• Jakubcová Michala

• Janke David

• Jurcakova Klara

• Kellnerova Radka

• Lanzini Stefano

• Lawton Tom

• Leitl Bernd

• Li Fei

• Lim Desmond

• Liu Chun-Ho

• Márton Koren

• Marro Massimo

• Michard Marc

• Monti Paolo

• Mouzourides Petros

• Nguyen Chi Vuong

• Nosek Štěpán

• Papp Balint

• Pappa Vasiliki

• Perret Laurent

• Pini Agnese

• Poulain-Zarcos Marie

• Rabany Emelyne

• Rico Orero Joan

• Robins Alan

• Salizzoni Pietro

• Salles Loustau Jean
* Sankaran Abhilash

* Savory Eric

* Schiavini Claudia

* Schmeer Joy

* Sedlak Pavel

* Soulhac Lionel

* Southgate-Ash Cameron

* Spicer Tom

* Takumi Tachibana

* Taouil Nada

* Van Den Akker Stephan

* Vuillermoz Samuel

* Wang Ruiqi

* Wu Xuefei

* Xue Yunpeng

* Yoshie Ryuichiro
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Author Index

Adam Thomas, 97, 98

Alexandrou Giorgos, 35, 36

Alisse Jean, 109–112

Amon Thomas, 45, 46

Andrewartha Tristan, 21, 22

Azarpeyvand Mahdi, 107, 108

Babin Valery, 109–112

Babuková Zuzana, 63, 64, 75, 76

Badeke Ronny, 97, 98

Barulli Marilina, 95, 96

Beleza Vaz Guilherme, 21, 22

Bi Dianfang, 81, 82

Birch David, 61, 62, 67, 68

Bohbot-Raviv Yardena, 109–112

Bouris Demetri, 37, 38

Buccolieri Riccardo, 53, 54

Carmeliet Jan, 25, 26, 35, 36

Carpentieri Matteo, 13, 14, 19, 20, 81, 82

Cassiani Massimo, 95, 96

Chaloupecka Hana, 49

Chen Guoliang, 51, 52

Combette Robin, 73, 74

Conan Boris, 73, 74

Creyssels Mathieu, 15, 16

Deebank Charles, 13, 14

Del Ponte Annika Vittoria, 29–32

Di Bernardino Annalisa, 11, 12

Di Renzo Teresa, 43, 44

Du Haoran, 79, 80

Duclaux Olivier, 103, 104

Emmanuelli Ariane, 95, 96, 103, 104

Esposito Antonio, 53, 54

Fellini Sofia, 29–32

Fuka Vladimir, 93, 94

Gallas Quentin, 65, 66

Gillmeier Stefanie, 21, 22

Grandoni Livia, 33, 34

Grimmond Sue, 113, 114

Gromke Christof, 37, 38

Grosjean Nathalie, 33, 34

Hain Rainer, 117, 118

Harms Frank, 71, 72, 85, 86

Hayden Paul, 19, 20, 61, 62

Hempel Sabrina, 45, 46

Huret Thomas, 65, 66

Ikegaya Naoki, 87, 88

Jacquin Laurent, 65, 66

Jakubcová Michala, 63, 64

Janke David, 45, 46

Jay Jaques, 15, 16

Jaňour Zbyněk, 63, 64

Jeong Seongho, 97, 98

Jurcakova Klara, 101, 102

Jurčáková Klára, 93, 94

Kaehler Christian, 97, 98

Kellnerova Radka, 49, 63, 64, 93, 94, 101, 102

Kähler Christian, 117, 118

Lanzini Stefano, 15, 16

Leitl Bernd, 71, 72, 85, 86

Leuzzi Giovanni, 11, 12, 41, 42

Li Fei, 87, 88

Li Haiwei, 35, 36

Lim Desmond, 107, 108

Liu Chun-Ho, 51, 52, 87, 88, 119, 120

Maffeis Giuseppe, 53, 54

Mamula Milan, 49
Marro Massimo, 15, 16, 19, 20, 29–32, 43, 44,
95, 96, 99, 100

Matthias Volker, 97, 98

Mei Shou-Jun, 25, 26

Michard Marc, 33, 34

Mishra Abhishek, 81, 82, 113, 114

Mo Ziwei, 51, 52, 115, 116

Monti Paolo, 11, 12, 41, 42

Mouzourides Petros, 35, 36

Muralha Joao, 21, 22

Nammour Rami, 103, 104

Neophytou Marina, 35, 36

Nguyen Chi Vuong, 103, 104

Nosek Štěpán, 63, 64, 93, 94

Panepinto Deborah, 99, 100

Pappa Vasiliki, 37, 38

Pappaccogli Gianluca, 53, 54

Pelliccioni Armando, 41, 42

Perret Laurent, 73, 74, 79, 80

Pini Agnese, 41, 42

Placidi Marco, 13, 14, 67, 68, 81, 82, 113, 114

Querzoli Giorgio, 11, 12, 41, 42

Ravina Marco, 99, 100

Ravizza Garibaldi Giulia, 11, 12

Ridolfi Luca, 29–32, 43, 44

Robins Alan, 59, 60, 81, 82, 113, 114

Roosenboom Eric W.m., 21, 22

Ryuichiro Yoshie, 17, 18

Salizzoni Pietro, 15, 16, 19, 20, 29–34, 43, 44,
53, 54, 95, 96, 99, 100

Salles Loustau Jean, 103, 104

Sankaran Abhilash, 97, 98, 117, 118

Savory Eric, 79, 80

Schade Julian, 97, 98

Schiavini Claudia, 99, 100

Schmeer Joy, 67, 68

Shmuel Sabban Lilach, 109–112

Smith Chad, 23, 24

Soulhac Lionel, 29–32, 99, 100, 103, 104

Southgate-Ash Cameron, 113, 114

Spicer Tom, 23, 24

Suchánek Jan, 49

Sánchez Castro Luis Felipe, 21, 22

Takumi Tachibana, 17, 18

Tanguy Geoffrey, 65, 66

Taouil Nada, 107, 108

V.d.f. Lopes Paulo, 23, 24

Van Den Akker Stephan, 83, 84

Vassilicos Christos, 65, 66

Vaux Samuel, 15, 16

Vesipa Riccardo, 43, 44

Wang Ruiqi, 119, 120

Wild Jan, 49

Wu Xuefei, 45, 46

Xue Yunpeng, 25, 26

Yi Qianying, 45, 46

Yingli Xuan, 17, 18

Yoshie Ryuichiro, 77, 78

Yuan Chao, 25, 26

Zanetti Mariachiara, 99, 100

Zang Nick, 107, 108

Zelinger Zdeněk, 49

Zhao Yongling, 25, 26, 35, 36
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