# Exploring computational fluid dynamics to assess the role of vegetated planters in urban canyon microclimate regulation

Martina Garcia de Cezar, Séverine Tomas, Bruno Cheviron, Laurent Aprin

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Martina Garcia de Cezar, Séverine Tomas, Bruno Cheviron, Laurent Aprin. Exploring computational fluid dynamics to assess the role of vegetated planters in urban canyon microclimate regulation. City and Environment Interactions, 2025, 28, pp.100246. 10.1016/j.cacint.2025.100246. hal-05265057

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# Exploring computational fluid dynamics to assess the role of vegetated planters in urban canyon microclimate regulation

Martina Garcia de Cezar<sup>a,b,*</sup>, Séverine Tomas<sup>c</sup>, Bruno Chevron<sup>c</sup>, Laurent Aprin<sup>b</sup>

## ARTICLE INFO

### Keywords:

Computational fluid dynamics

Urban canyon

Planters

Vegetation

Microclimate

## ABSTRACT

Accurately modelling urban microclimates is essential for developing effective mitigation strategies against urban overheating. This study assesses the potential of ANSYS Fluent to simulate an experimental urban canyon with vegetated planters, using three different simulation methods. The analysis focuses on accuracy, operational suitability, and an improved understanding of the physical mechanisms operating at the scale of an urban canyon. Numerical results related to radiative, thermal, and aerodynamic fluxes, are evaluated based on (i) experimental data obtained from a dense network of sensors and (ii) the physical consistency obtained in the spatial distribution of the variables analysed. Despite some discrepancies in spatial and temporal variations, the model demonstrated strong agreement with experimental data, with absolute errors in air temperature and relative humidity below 3 % on average (maximum 11 %). Radiation, as the most sensitive factor for daytime thermal comfort variation in the study area, highlights the importance of improving radiative exchange in the proposed models. While certain software limitations require user-defined functions, such as representation of average radiant temperature, thermal comfort indices and multiple vegetation heat source terms, the study underscores the tool's capacity to generate detailed and high-resolution microclimate data. This rich numerical database improves our understanding of urban heat dynamics, paving the way for more efficient urban climate solutions.

## 1. Introduction

Urban overheating is an extreme phenomenon that directly affects the quality of life of living beings [1]. It originates from two main dynamics: (a) global climate change, which is projected to increase global temperatures by at least 1.5 to 2 °C by the end of the century—an effect amplified at the continental scale, where France is expected to experience a rise of up to 4 °C [2]; (b) the urban microclimate, which, due to the morphology and activities of cities, intensifies the effects of warming [3]. Urban overheating is a multidimensional challenge that extends beyond environmental concerns [3,4]. It is also a public health issue, it exacerbates social inequalities, disproportionately impacting vulnerable communities, and it questions the long-term sustainability of cities, whose current infrastructure is not adapted to future climate challenges [1]. In this context, concrete actions are needed, with urban vegetation being a key strategy for modifying urban environments and enhancing ecosystem services [5,6]. The current research problems, possibly

addressed by physics-based approaches and Computational Fluid Dynamics (CFD) modelling, lies in the optimised integration of urban vegetation, grounded in the assessment of its thermal impacts on the urban microclimate through both modelling and experimentation, while ensuring that the resulting methodology remains operational and accessible.

The integration of urban vegetation is a priority in most environmental programs led by international institutions addressing climate change [7]. There are existing numerical solutions with known limitations [8,9] and recently-identified directions of improvement [10]. Given that each urban project is unique, it is essential to identify the most suitable plant species, placement, irrigation regime, and environmental contributions that different types of urban vegetation can provide [11]. To achieve the goal of “the right plant in the right place with the right management,” a wide range of plant types, compositions, and ecosystem benefits must be considered [7]. While significant progress has been made to support decision-making, further research is needed to

<footnote><sup>a</sup> AgroParisTech, G-EAU, Univ Montpellier: AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France</footnote>

<footnote><sup>b</sup> Laboratory for the Sciences of Risks (LSR), IMT Mines Ales 30100 Ales, France</footnote>

<footnote><sup>c</sup> INRAE, G-EAU, Univ Montpellier: AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France</footnote>

<footnote>* Corresponding author at: INRAE UMR G-EAU, 361 rue Jean-François Breton, 34090, Montpellier, France.</footnote>

<footnote>E-mail address: martina.garcia-de-cezar@inrae.fr (M. Garcia de Cezar).</footnote>
establish a robust theoretical and data-driven foundation for developing increasingly adapted and operational numerical models [12,13]. These models will enhance decision-making in the design of more sustainable urban environments. Focusing on urban canyons, this study examines the thermal impact of planters with shrubs and climbing plants on pedestrians. Widely implemented in Mediterranean cities for their low cost and operational simplicity, these systems offer valuable but under-assessed contributions to local climate regulation.

Despite the wide range of numerical tools available for simulating urban canyon microclimates and pedestrian comfort indices, there remains a strong need for the validation of existing approaches and the establishment of standardised evaluation methods [14,15]. This is crucial due to the high sensitivity of microscale atmospheric flows to turbulence and both anthropogenic and natural heat releases within urban thermal environments. This gap requires complex multi-physics modelling and detailed experimental quantification to be filled [12]. Microscale atmospheric phenomena, defined by length scales below 2 km and timescales under 1 h [16], present distinct challenges that demand accurate representation and analysis. At this scale, urban overheating can be characterised using different metrics, such as air, surface, or mean radiant temperatures, or through comprehensive indices that quantify heat stress on the human body, including thermal comfort and heat stress indices [17]. When assessing population vulnerability to daytime urban heating, comprehensive indices are more suitable, as they integrate multiple microclimatic parameters—air temperature, relative humidity, wind speed, and mean radiant temperature—along with human thermal perception [1,3,18].

Advanced CFD models hold particular promise in assessing mitigation strategies for urban overheating at these scales, particularly within the urban canyon. However, a significant lack of experimental data and standardised validation processes— especially concerning thermal comfort indices—adds uncertainty to predictions, hindering model comparisons and practical applications [19–21]. Developing robust and exportable models for heatwave mitigation and urban water management is thus an urgent societal and scientific priority. Bridging the gap between academic research and practical urban applications is essential for ensuring that scientific findings are effectively implemented to promote resilient urban planning [10].

This study explores the use of high-resolution simulations as essential tools for accurately modelling the microclimatic effects of vegetation at street level in urban environments. The primary objective is to assess the accuracy of CFD models in reproducing the microclimate of an experimental street canyon equipped with planters containing climbing plants and shrubs, under varied modelling configurations. Three simulation architectures are tested to examine not only the model outputs but also the operational complexity involved, including the types and quantities of input data required. This modular and transparent approach facilitates a more accessible use of CFD by narrowing the gap between novice and expert users. The study evaluates the modelling efficiency and user accessibility of ANSYS Fluent—one of the most widely adopted CFD platforms in this context—by comparing simulated data on air temperature, relative humidity, wind speed, and solar radiation with experimental measurements obtained from a dense sensor network installed along an urban street [22,23]. Given the complexity of simulating interacting thermal and dynamic processes, the research also discusses the computational costs and data integration efforts required for such simulations. This study offers critical insights into the performance and practical demands of CFD for urban microclimate modelling and tools aimed at enhancing climate resilience through green infrastructure.

# 2. Methodology

## 2.1. Experimental site and instrumentation

The experimental street canyon, located at the INRAE UMR G-Eau

laboratory (43°64′N, 3°87′E) in Montpellier, southern France, under a Mediterranean climate, was utilised as a case study [22,23]. This experiment enables the evaluation of the impact of different planting and irrigation strategies on evapotranspiration, and consequently, the potential of irrigated vegetation to influence the microclimate and comfort indices in the vicinity of the greenery. A network of 85 air sensors was installed to monitor the temporal and spatial variations of key microclimatic variables, as shown in Fig. 1. The measurements included air temperature using thermocouples (±0.5 °C) and thermo-hygrometers (±0.3 °C), relative humidity using thermo-hygrometers (±1.8 %), grey globe temperature (±3.5 °C [24]), solar radiation using pyranometers (uncertainty < 3 %, maximum 5 %), and wind speed (±0.3 m s⁻¹) and direction (±3°) using 2D anemometer.

The experimental dataset captures microclimatic variable fluctuations with a 30-second time step. Fig. 2 illustrates the configuration and dimensions of the experiment. The data employed in this study corresponds to 20st August 2023, an overheating day in Montpellier, at 1 pm.

## 2.2. Description of CFD simulation

### 2.2.1. Computational model domain

The computational domain was defined following the best practice guidelines provided by the Architectural Institute of Japan (AIJ) [25] and COST (European Cooperation in the field of Scientific and Technical Research) [26]. Fig. 3a illustrates the computational model and domain dimensions. Upstream and downstream extents are of approximately 20H (where H represents the wall height) for a domain height of approximately 5H. To enhance the discretisation of the key area within the experimental street, particularly around the planters and to ensure the proper development of the wake zone behind the street canyon, a body of influence (BOI) was established. This BOI, measuring L = 56.0 m, W = 11.0 m, and H = 8.0 m, was used to apply finer mesh refinement in this region compared to the rest of the computational domain (Fig. 3b).

### 2.2.2. Boundary conditions

For airflow boundary conditions, prior numerical studies commonly employ velocity profiles as boundary conditions to replicate flow patterns in urban areas of varying densities [27–29]. However, this study aims to replicate the experimental pilot setup, representative of a low-density area, where establishing a representative power-law profile is challenging [30]. Since the experimental setup includes an anemometer located at the centre of the street canyon at 1.3 m above ground, along with temperature and relative humidity data, uniform free-stream air velocity, air temperature, and relative humidity were applied at the inlet of the computational domain.

A zero static pressure condition was imposed at the outlet, in line with best practice guidelines [31]. Symmetry boundary conditions were applied along all lateral boundaries, assuming zero normal velocity components and gradients at these boundaries.

### 2.2.3. Physical models

#### 2.2.3.1. Turbulence.
Species transport was also included in the domain, consisting of a mixture of liquid water and air. Regarding turbulence modelling, although Reynolds-Averaged Navier-Stokes (RANS) k-epsilon models are widely adopted in similar studies [32–34], the SST k-omega model was selected for its superior performance in capturing atmospheric boundary layer behaviour. This model leverages the k-omega formulation in the near-wall region while benefiting from the freestream independence of the k-epsilon model in the outer boundary layer, which provides enhanced accuracy in capturing atmospheric boundary layer dynamics [35]. According to the ANSYS Fluent Manual [36], depending on the discretisation of the study domain, SST k-omega allows an explicit representation of wall boundary zones. It can be
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Sensors network distribution at a height of 1.3 m from the ground in an experimental street under a Mediterranean climate. Next to each sensor stands its identification and the colours help to identify the type of instrument and the variable measured by each sensor. The 48 thermocouple sensors are identified in orange, the 15 thermo-hygrometers in yellow, the 15 grey globes in grey, the 4 pyranometers diagonally across the device in blue and the weather station with the 2D anemometer is identified in the centre of the device. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Experimental street canyon photo with details of the dimensions and plant species, for 20 August 2023 at 1 pm in Montpellier, France.</figcaption></figure>

achieved through low Reynolds number modelling (LRNM), where the $y^+$ value, at the wall-adjacent cell centre point P ($y^+ \parallel p$) should ideally be below 1. For regions where $30 < y^+ < 500$, wall functions derived from semi-empirical formulations [37] are applied, calculating flow quantities within the boundary layer. In the present study, computed $y^+$ values

ranged from 3 to 30, indicating a transitional regime between LRNM and standard wall function zones. To handle this intermediate range, “$y^+$-insensitive near-wall treatment” was employed [36], as fully resolving the near-wall mesh ($y^+ < 1$) would have resulted in an unfeasibly large mesh size given the computational resources available.
<figure><img src="image_4.png" /><figcaption>Fig. 3. (a) Computational domain representation with details of the geographical directions and sun movement, identification of the inlet and outlet, where the input fluid is considered to be uniform. (b) Body of influence used representation, with its dimensions.</figcaption></figure>

The characterization of the turbulence regime in the experiment is accessed through the Reynolds number (Re, Eq (1)), which calculates the ratio of inertial to viscous forces in the fluid, identifying whether the flow is laminar or turbulent [38]. In the Re equation, $U_{ref}$ and L represent a characteristic velocity (in m s⁻¹) and length scale (in m), while $ν$ denotes the kinematic viscosity (m² s⁻¹) of the fluid. For the urban canyon with flow oriented along the canyon, the hydraulic diameter ($L_{hd}$, Eq (2)) serves as an appropriate length scale, as it considers the canyon's characteristic height (H) and width (W) [39,40].

$$Re = \frac{U_{ref}L}{\nu} \qquad (1)$$

$$L_{hd} = \frac{HW}{(W + 2H)} \qquad (2)$$

Under experimental conditions at 1 pm on August 20th 2023, the velocity at the device's center at 1.3 m high was 0.65 m s⁻¹. With a temperature of 36 °C, the kinematic viscosity was estimated as 1.65 × 10⁻⁵ m² s⁻¹. For canyon wall height of 2.4 m and width of 5 m, the length scale is 1.22 m. This results in a Reynolds number of 48.23 × 10³, indicating turbulent flow, which supports the choice of turbulence model for the CFD simulation.

### 2.2.3.2. Radiation and surface temperature.
ANSYS Fluent's Solar Calculator was utilized to determine the distribution of radiative heat fluxes on the experimental surfaces. This Solar Calculator can calculate, in theory, the precise location of the sun, and its Ray Tracing model can compute the incident radiation on exposed surfaces. The total reflected solar irradiation is distributed among all surfaces weighted by area depending on the scattering fraction (set to 1 in this study), and angular

discretization theta and phi divisions (set to 4 in this study), a good compromise between calculation time and accuracy of results [36]. The Ray Tracing model has the limitation of simulating only one daily cycle of at most 24 h representing the day.

Additionally, the interactions among convection, conduction, and radiation were accounted for by simultaneously solving the energy equation alongside the Discrete Ordinate (DO) radiation model, which calculates radiant heat fluxes between surfaces. The physical, thermal, and radiative properties of the materials used in the simulation are provided in Table 1, with values based on literature sources. For the vegetation, reference properties specific to the species *Myristica fragrans* were employed [41].

Given the exploratory nature of this study in evaluating ANSYS Fluent as an operational and adaptable decision-making tool for designing more sustainable cities, various thermal model configurations were tested. To accurately model surface temperature within the study domain, based on the radiation configuration and surface properties of

<table><caption>**Table 1**
Specifications of the materials used in this study. References (1) [42] and (2) [41].</caption><thead><tr><th>Material</th><th>Density (kg m<sup>-3</sup>)</th><th>Specific Heat (J kg<sup>-1</sup> K<sup>-1</sup>)</th><th>Thermal Conductivity (W m<sup>-1</sup> K<sup>-1</sup>)</th><th>Emissivity</th><th>Absorbance</th></tr></thead><tbody><tr><td>Wood</td><td>610<sup>(1)</sup></td><td>1600<sup>(1)</sup></td><td>0.13<sup>(1)</sup></td><td>0.85<sup>(1)</sup></td><td>0.75<sup>(1)</sup></td></tr><tr><td>Concrete wall</td><td>2000<sup>(1)</sup></td><td>960<sup>(1)</sup></td><td>2.00<sup>(1)</sup></td><td>0.95<sup>(1)</sup></td><td>0.66<sup>(1)</sup></td></tr><tr><td>Concrete ground</td><td>2100<sup>(1)</sup></td><td>1000<sup>(1)</sup></td><td>0.70<sup>(1)</sup></td><td>0.80<sup>(1)</sup></td><td>0.80<sup>(1)</sup></td></tr><tr><td>Leaf</td><td>866<sup>(2)</sup></td><td>2000<sup>(2)</sup></td><td>0.49<sup>(2)</sup></td><td>0.98<sup>(2)</sup></td><td>0.70<sup>(2)</sup></td></tr></tbody></table>
the materials, several key aspects must be considered when using the
"coupled thermal model" [36]:

(a) At the start of the simulation, all surfaces automatically assume thermal equilibrium with the air temperature. Therefore, the simulation should be initialised before sunrise to allow solar energy storage and ensure realistic surface temperatures at the desired time of the day, considering the spatial and temporal scales of the thermal phenomenon.

(b) The surface mesh resolution affects the efficiency of energy storage and the rate of surface temperature variation. To ensure physically consistent results, all surface elements should have equal areas, refined as needed based on the obtained results and sensitivity studies.

(c) Longer simulation times (a) and finer mesh resolution (b) increase computational costs, requiring a balance between available computational resources and simulation accuracy.

Besides the “coupled thermal model” for the configuration of the
domain walls, and these three key aspects listed, another relevant
approach for this type of study is the “temperature thermal model”,
where the user imposes a fixed surface temperature for each wall in the
domain, which remains constant throughout the simulation.

Regarding the simulation time regime, different considerations can
be established. In steady-state simulations, at each iteration that ac-
counts for radiation, the domain accumulates radiative heat, leading to
continuous warming over successive iterations. In transient simulations,
the radiative model is updated at each time step, accounting for the
sun's trajectory and variations in radiation intensity within the domain.

Based on these modelling approaches, this study tested different
simulation scenarios, detailed in Section 2.3, where the selection criteria
for the thermal models and mesh configurations are further discussed.
For the specific case study on 20th August 2023, the only available
surface temperature reference came from thermocouples installed on the
substrate surface within the planters.

2.2.3.3. Description of planters with climbing plants and shrubs. The impact of planters containing climbing plants and shrubs on the experimental microclimate arises from a combination of aerodynamic effects, solar radiation shading, longwave heat exchange with the surrounding environment, and latent and sensible heat fluxes within the foliage [43]. In CFD modelling, various methods can be employed to capture the aerodynamic effects of vegetation, from the configuration of the volume type (solid or porous) and geometry. The more sophisticated approach models vegetation as a porous medium, introducing a momentum source term to the fluid flow equations, with additional source terms in the turbulent kinetic energy (k) and turbulent dissipation rate (ε) transport equations to simulate turbulence interactions within the plant canopy [34,44–47]. An alternative approach involves assigning a pressure loss coefficient to cells occupied by the canopy. Both methods are dependent on vegetation-specific parameters such as the mechanical drag coefficient (C_d) and leaf area density (LAD). Aerodynamic effects can also be simplified by modelling the canopy volume as a solid zone. Furthermore, the geometry of vegetation in CFD can also be represented through different ways, including simplified geometry, grid model, vertically layered model, realistic model, and planar stretching model, as outlined by Y. Yang et al. [15] and Zhu et al. [43].

Thermal effects in CFD models are typically addressed by solving the
leaf energy balance to incorporate latent and sensible heat fluxes, as well
as radiation absorbed by the leaves. Sensible heat flux, or convective
heat flux, is calculated based on the temperature gradient between the
leaf surface and the ambient air, along with boundary layer resistance.
The latent heat flux can be determined using several methods: (a)
Volumetric cooling power per unit vegetation volume, either as a
function of LAD or empirically based on species characteristics [32]; (b)

leaf latent heat flux through stomata, which depends on vapour pressure
at the leaf, the air above the leaf boundary layer, and stomatal resistance
[48]; or (c) empirical methods for species-specific evapotranspiration
estimation [44].

In this study, we investigate various configurations for the numerical
representation of vegetation, with emphasis on convergence behaviour,
accuracy relative to experimental results, and the exploration of
simplified, computationally less demanding approaches to enhance the
replicability of the methodology. Vegetation is represented using
simplified geometries, specifically cubic volumes that approximate the
actual dimensions of the plant types under consideration. The di-
mensions adopted for shrubs were 0.90 × 2.00 × 0.50 m and for
climbing plants 0.40 × 2.00 × 1.25 m, corresponding to width, length
and height, respectively.

To capture the aerodynamic effects of vegetation in the absence of
specific validation data, two approaches were assessed: (i) modelling
vegetation as a solid volume without evapotranspiration but offering the
possibility of thermal and radiation modelling, and (ii) modelling it as a
porous medium with evapotranspiration but considered as a semi-
transparent media for radiation (more detail in Section 2.3). In the
porous medium configuration, using the cubic dimensions, a momentum
source term of 0.01 Pa, derived from sensitivity studies, was applied in
the flow direction (x-axis) to assess its influence on the surrounding
environment. Additionally, evapotranspiration was represented through
a source term corresponding to the specific evapotranspiration rate was
introduced, based on empirical data. The assumed evapotranspiration
rates were 1.7 L day⁻¹ (1.56 × 10⁻⁵ kg m⁻³ s⁻¹) for shrubs and 5.3 L
day⁻¹ (4.85 × 10⁻⁵ kg m⁻³ s⁻¹) for climbing plants, enabling the
assessment of their impact on local microclimatic conditions. These
values were derived from experimental data on the soil–plant-atmo-
sphere water balance, as detailed in Garcia de Cezar et al. [22]. While
these simplifications enhance the feasibility of implementation, they
reduce the physical fidelity of evapotranspiration and porosity esti-
mates, thus representing a limitation of the adopted methodology.

2.2.4. Solver setting

All simulations were conducted using the commercial software ANSYS Fluent v23 (research package, limited to a maximum of 15 cores) on a workstation equipped with 64 Go of RAM and 20 physical cores. The SIMPLE algorithm was employed for pressure-velocity coupling, with second-order discretisation schemes applied to both convection and viscous terms within the governing equations, while PRESTO! was specified for pressure discretisation. A double-precision solver was also selected for CFD calculations. Convergence criteria were defined as minimum residuals of 10⁻⁶ for velocity components, 10⁻⁵ for the turbulent kinetic energy (k), and 10⁻⁵ for the continuity equation.

2.2.5. Grid and mesh independence study

Poly-hexcore cells and smooth-transition boundary layers in the envelope of the domain with five layers (transition ratio 0.272 and growth rate 1.2) generated the grids. Additionally, ensuring the quality of the mesh is essential for achieving high modelling accuracy, with mesh cells as equilateral or equiangular as possible, minimising deformation. To assess mesh quality, ANSYS Fluent [36] employs two key metrics: skewness and orthogonal quality. A good mesh should exhibit a skewness value between 0.5 and 0, where lower values indicate a mesh closer to the ideal, more equiangular form. Similarly, orthogonal quality should range from 0.75 to 1, where higher values denote a more orthogonal and better-quality mesh.

In the literature, various approaches have been employed to study
grid sensitivity. Xiong & Chen [49] generated three hexahedral grid
resolutions and calculated the correlation coefficient (R) between the
results obtained for fine, medium, and coarse grids, focusing on the
prediction of air velocities at sensor height in the middle of the canyon.
Another example is provided by Sen et al. [50], who evaluated the dif-
ferences in average wind speed and air temperature over roads at a
<figure><img src="image_5.png" /><figcaption>Fig. 4. Representation of the vertical (v1, vc, v2) and horizontal (hL) lines used in the study domain to conduct the sensitivity study.</figcaption></figure>

height of 2 m, also calculating the Grid Convergence Index (GCI) for various grids [51].

In the present study, grid independence was assessed by comparing vertical and horizontal profiles of average wind speed, temperature and relative humidity at four specific locations within the experimental street (Fig. 4).

Quantitative analysis of grid independence, was performed using the GCI method proposed by Roache [51] from Eq. (3). For this study, the difference in grid refinement between fine, medium, and coarse meshes was set at 10 % across all domain dimensions (including the body of influence, surface, and volume).

$$GCI = \frac{F_s |\epsilon|}{(r^p - 1)} \qquad (3)$$

The grid refinement ratio (r) was 1.1 for a 10 % mesh difference, while the order of the numerical method (p) was set at 2, corresponding to second-order discretisation. The functional form (Fs) was 3, as recommended for comparisons between two grids, and the relative error (ε) was calculated with the different results. Meshes were considered to have achieved convergence when GCI values were below 5 %, with no significant deviations observed between the results [34].

Finally, the time step in the study zone was established according to the Courant-Friedrichs-Lewy (CFL) number, which should not exceed 1 for stability purposes in the explicit density-based formulation [31].

The sensitivity study was conducted with the meshes and

characteristics shown in Table 2. This included a simulation with 380 time steps and constant velocity (0.6 m s⁻¹), air temperature (36 °C) and relative humidity (30 %) as input data. In terms of the physical model configuration, a transient simulation was carried out, with the surface temperature calculated from the coupled thermal model, considering the vegetation as a solid medium and without evapotranspiration, the SST k-omega turbulence model, the DO radiative model and Solar Ray Tracing.

The meshes analysed showed a convergence of results where the GCI found was less than 5 %, with the averages of each profile shown in Table 3. To optimise simulation time, the medium mesh was selected (Fig. 5), with a BOI target mesh size of 0.48 m, a surface mesh maximum size of 0.9 m and minimum size of 0.006 m, and a volume mesh cell length ranging from a minimum of 6 × 10⁻⁴ m to a maximum of 6.1 × 10⁻¹ m.

## 2.3. Description of modelling scenarios

The capacity of ANSYS Fluent to replicate an east-west oriented urban canyon with a slight inclination of 346°N, featuring planters with shrubs and climbing plants, was assessed through multiple scenarios. This approach aimed to determine the level of modelling complexity necessary to accurately represent the study area, and the precision achieved depending on the configuration employed.

For each simulated scenario, the required simulation time was recorded, along with point measurements of spatial variations in air temperature and relative humidity, based on the sensor positions within the experimental setup (Fig. 1). Additionally, a horizontal line (hL) at 1.3 m above ground level, oriented in the XY direction, and horizontal planes at 0.3 m (Ph1) and 1.3 m (Ph2) above ground level were analysed. The hL extends from -0.2 m, representing the northernmost point towards the climbing plants, to 4.8 m, marking the southernmost point towards the shrubs. Ph2 and hL were aligned with the sensor height to evaluate the dynamics of radiative, thermal, and aerodynamic flows (Fig. 6).

Three different cases were simulated, each with distinct configurations, representing conditions on 20th August 2023 at 1 pm, as detailed in Table 4. Given the high computational and time demands required to simulate the surface temperature of each object in the study area, alternative approaches were explored to efficiently and realistically represent the analysis domain. The representation of the selected time of day is achieved from a simulation of 255 s, in transient regime, where all three study cases obtained numerical equilibrium with quasi-steady behaviour reached. It is an arbitrary limitation of time simulation, as it represents the conditions of only one moment of the day, to focus on August 20th at 1 pm.

In Case 01, the objective was to assess the relevance of the material properties used as reference, as well as the impact of shadow projection

### Table 2

<table><caption>Characteristics of the meshes used in the sensitivity study, with the characteristics of the simulation and the real simulation time required to obtain the result.</caption><thead><tr><th>Mesh</th><th>Cells</th><th>Cell quality Average Orthogonal</th><th>Surface quality Average Skewness</th><th>Number of Time steps</th><th>Time step size</th><th>Max Interactions per time steps</th><th>Real time</th></tr></thead><tbody><tr><td>Course</td><td>347,604</td><td>0.952</td><td>0.017</td><td>380</td><td>1 s</td><td>50</td><td>2 h40</td></tr><tr><td>Medium</td><td>490,185</td><td>0.962</td><td>0.016</td><td>380</td><td>1 s</td><td>50</td><td>3 h00</td></tr><tr><td>Fine</td><td>794,972</td><td>0.972</td><td>0.013</td><td>380</td><td>1 s</td><td>50</td><td>10 h00</td></tr></tbody></table>

### Table 3

<table><caption>Main of the GCI calculation results of wind speed (Va), air temperature (Tair) and relative humidity (RH) for the different profiles and the comparison of the medium mesh with the course (GCImc) and the fine mesh with the medium (GCIfm).</caption><thead><tr><th rowspan="2">GCI (%)</th><th colspan="3">Horizontal line</th><th colspan="3">Vertical line 1</th><th colspan="3">Vertical line 2</th><th colspan="3">Vertical line c</th></tr><tr><th>Va</th><th>Tair</th><th>RH</th><th>Va</th><th>Tair</th><th>RH</th><th>Va</th><th>Tair</th><th>RH</th><th>Va</th><th>Tair</th><th>RH</th></tr></thead><tbody><tr><td>GCImc</td><td>1.021</td><td>0.001</td><td>0.011</td><td>0.186</td><td>0.001</td><td>0.009</td><td>0.211</td><td>0</td><td>0.005</td><td>0.266</td><td>0.001</td><td>0.010</td></tr><tr><td>GCIfm</td><td>0.831</td><td>0.001</td><td>0.015</td><td>0.132</td><td>0.001</td><td>0.011</td><td>0.209</td><td>0</td><td>0.004</td><td>0.224</td><td>0.001</td><td>0.017</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>Fig. 5. Computational mesh configuration used for the simulations, highlighting refinement regions: (a) top view of the surface mesh across the entire modelled domain, (b) horizontal cross-section of the mesh at 1.3 m above ground level, and (c) side view illustrating the volumetric mesh distribution within the modelled domain.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6. (a) Horizontal line at 1.3 m above ground (XY orientation), from -0.2 m (north, climbing plants) to 4.8 m (south, shrubs); (b) horizontal planes $P_{h1}$ (0.3 m) and $P_{h2}$ (1.3 m) above ground, used for scenario comparisons.</figcaption></figure>

and surface temperature variations within the domain due to interactions with solar radiation. To this end, the "coupled" wall thermal model was employed, and the base mesh described in Section 2.2.5 was refined using the FaceSize function, achieving a minimum cell size of 0.1 m in key surface regions. The resulting mesh configuration, with targeted refinement around buildings, vegetation, and street surfaces, is shown in Fig. 7. A steady-state simulation was initially performed to heat the domain walls until at least one surface reached 50 °C, aligning with the maximum substrate temperature recorded at the study site. Once this temperature was reached, the simulation was switched to a transient regime, modelling 255 s of real conditions. The vegetation was represented as a solid volume, without evapotranspiration.

In Case 02, the average surface temperature obtained in Case 01 was applied as a constant surface temperature for all objects using the surface "temperature" thermal model. This approach made it possible to assess the effects of the absence of heterogeneous surface temperatures on each object in the domain, since conduction and radiation dynamics are not considered. The vegetation was modelled identically to Case 01 as a solid volume, and the simulation remained transient, modelling 255 s of real conditions.

In Case 03, the same thermal surface model and transient simulation settings as Case 02 were applied. However, the vegetation volume was represented as a porous medium with a momentum source term of 0.01 Pa in the flow direction. In that case, the vegetation is considered as a fluid and radiation is not modelled within it. Additionally, a constant evapotranspiration rate was introduced: $1.56 \times 10^{-5} \text{ kg m}^{-3} \text{ s}^{-1}$ for

shrubs and $4.85 \times 10^{-5} \text{ kg m}^{-3} \text{ s}^{-1}$ for climbing plants, allowing an assessment of its impact on the simulated domain.

## 2.4. Specified analysis

### 2.4.1. Comparison with experiment

By assessing the Mean Squared Error (MSE, Eq. (4)), Root Mean Squared Error (RMSE, Eq. (5)), and Pearson correlation coefficient (R, Eq. (6)) between simulated and experimental data, insight into the precision and degree of similarity of the results was obtained.

$$MSE = \frac{\sum_{i=1}^{N} (experimental_i - modelled_i)^2}{N} \qquad (4)$$

$$RMSE = \sqrt{MSE} \qquad (5)$$

$$R = \frac{\sum (experimental - \overline{experimental})(modelled - \overline{modelled})}{\sqrt{\sum (experimental - \overline{experimental})^2 \sum (modelled - \overline{modelled})^2}} \qquad (6)$$

### 2.4.2. Swirl and mixing structures

To analyse the velocity profile in the study domain, the Q-Criterion was used. It is a method based on the difference between the rotational and strain rate terms of the velocity gradient tensor. It is defined as Eq. (7), where $\Omega$ represents the rate of rotation and S the rate of strain [52]. Regions where $Q > 0$ indicate vortex-dominated areas, which enhance
<table><caption>**Table 4**

Characteristics of the case studies evaluated.</caption><thead><tr><th>Case</th><th>01</th><th>02</th><th>03</th></tr></thead><tbody><tr><td>Mesh density</td><td>6,210,827 cells</td><td>490,185 cells</td><td>Same as Case 02</td></tr><tr><td>Time</td><td>Steady – 70 int.<br>Transient – 255 int.<br>time steps of 1 s</td><td>Transient – 255 int.<br>time steps of 1 s</td><td>Same as Case 02</td></tr><tr><td>Inlet conditions</td><td>Va = 0.65 m s<sup>-1</sup><br>Tair = 36 °C<br>RH = 33 % ~ 0.0122 kg m<sup>-3</sup> s<sup>-1</sup></td><td>Same as Case 01</td><td>Same as Case 01</td></tr><tr><td>Turbulence model</td><td>SST k-omega</td><td>Same as Case 01</td><td>Same as Case 01</td></tr><tr><td>Vegetation representation</td><td>Solid volume<br>No evapotranspiration</td><td>Same as Case 01</td><td>Porous zone<br>With evapotranspiration</td></tr><tr><td>Thermal model</td><td>Coupled<br>Surface temperature modelled</td><td>Temperature<br>Surface temperature based on the result of case 01</td><td>Same as Case 02</td></tr><tr><td>Radiation model</td><td>DO radiation model<br>Solar Calculator<br>Ray Tracing model<br>Input conditions – 1st January at 10 am (+1 GMT)</td><td>Same as Case 01</td><td>Same as Case 01</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Computational mesh for Case 01, showing refinement zones: (a) top view of the surface mesh across the entire modelled domain, (b) horizontal cross-section at 1.3 m above ground level, and (c) side view illustrating the volumetric mesh distribution within the modelled domain.</figcaption></figure>

mixing and heat transport by convection, while zones with $Q < 0$ are characterized by strong shear, where the flow is more stratified and heat transfer may be more limited [53,54]. Applying this criterion allows the identification of vertical structures within the studied urban canyon, providing a better understanding of ventilation dynamics and thermal exchanges between solid surfaces and the atmospheric flow [55].

$$Q = \frac{1}{2} (\|\Omega\|^2 - \|S\|^2) \qquad (7)$$

## 3. Results

### 3.1. Impact of modelling scenarios on microclimate

Figs. 8 and 9 compare wind speed, air temperature, relative humidity, and radiation temperature across Cases 01, 02, and 03 at two horizontal planes: Ph1 (0.3 m) and Ph2 (1.3 m) above ground level. Fig. 10 illustrates the variation of these variables along the horizontal line hL at 1.3 m height, aligned with the XY direction. This line extends from -0.2 m, corresponding to the northernmost point on the wall with climbing plants, to 4.8 m, marking the southernmost point on the wall with shrubs. The following sections provide a detailed analysis of these results.

#### 3.1.1. Radiation: distribution and model performance

##### 3.1.1.1. Solar radiation: simulation accuracy and deviations.
Fig. 2 illustrates the shading conditions observed in the experiment on 20th August at 1 pm, when the recorded solar radiation within the domain was 865 W m⁻². Using the pilot site coordinates (longitude 43.64591°, latitude 3.873166°, time zone + 2 GMT) for 20th August at 13:00, inconsistencies were identified in the DO and Solar Ray Tracing models in ANSYS Fluent. Specifically, both the solar trajectory and the solar radiation intensity did not align with experimental conditions.

Further analysis of the solar calculator's performance revealed fluctuations in solar radiation intensity over a 12-hour period (6 am to 6 pm), with the peak occurring at 11 am. However, this does not reflect actual conditions in Montpellier, where sunlight extends until 9 pm, and the maximum solar intensity occurs at 2 pm.

As an alternative, a simulation was performed using the global positioning of the experiment but with input conditions set to 1st January at 10 am (+1 GMT), where the radiation intensities indicated by the model are surprisingly more intense than those in August. This configuration produced a solar projection and intensity (832.1 W m⁻²) closer to the experimental observations, making it a more representative approach for this study (Fig. 11).

The results highlight the impact of different modelling configurations on the distribution of solar radiation within the domain. In Case
<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Results for each study case are presented vertically, with fields corresponding to $P_{h1}$ at 0.3 m above ground. Each line represents a different analysed variable with corresponding contours: radiation temperature (Tradiation, 36 to 50 °C), wind speed (v, 0 to 0.90 m s⁻¹), air temperature (Tair, 36 to 50 °C) and relative humidity (RH, 34 to 19 %).</figcaption></figure>

01, which features a finer surface mesh, solar radiation is distributed more precisely across surfaces, capturing detailed variations. In contrast, Cases 02 and 03, which employ a coarser surface mesh, exhibit a more diffuse distribution of solar radiation. Additionally, among the simulated cases, Case 03, where vegetation is treated as a fluid, did not interact with the radiative model, resulting in 0 W m⁻² on its surfaces and intensifying the intensity of solar radiation on walls, planters and the ground. This limitation arises from the ANSYS Fluent DO model, which treats porous media as transparent. However, this approach was intentionally tested to evaluate whether prioritising the dynamics of porous media or its thermal impact provides a more accurate representation of the impact of urban vegetation on its surroundings.

### 3.1.1.2. Radiation temperature: spatial patterns in the domain.
To assess the contribution of radiative fluxes within the study domain, Figs. 8 and 9 presents the results for horizontal planes of Radiation Temperature, a local variable that does not integrate view factors or perception and is automatically available in the post-processing of ANSYS Fluent. These results highlight the combined effect of direct and diffuse radiation within the domain, revealing for the first time in this study the impact of shading. This is particularly evident in Case 01, where radiation temperature is lower at both 0.3 m and 1.3 m above the ground.

Furthermore, in Case 03, where vegetation is modelled as a semi-transparent medium allowing radiation to pass through, the radiation temperature within and around the vegetation volumes is higher. Specifically, there is an increase of approximately 4 °C on the side with shrubs (XY 3.8 m) and 0.7 °C on the side with climbing plants (XY 0.2 m), as shown in Fig. 10. The interpolation of line graphs for each case highlights the behaviour of radiation temperature within the vegetated volumes and at the extremities of the hL line, specifically on the

northern and southern walls of the study area. Comparing Cases 01 and 02, Case 01 exhibits a dynamic heat conduction process within the vegetated volume due to its coupled model configuration, which allows for internal heat transfer. In contrast, Case 02, using the temperature model with a fixed internal temperature, shows significantly less variation. Regarding the radiation temperature on the wall surfaces, Cases 02 and 03 display similar trends, as both assume a uniform temperature within the wall volume. However, slight variations occur near the wall surfaces due to the contribution of diffuse and solar radiation. The most significant differences appear on the northern wall, where direct solar radiation is strongest: 37 °C in Case 01, 39 °C in Case 02, and 41.4 °C in Case 03. These differences stem from the way each model accounts for vegetation and shading effects. Case 01 incorporates vegetation shadows, modifying both surface and wall temperatures, thereby reducing the overall radiation temperature. Case 02 considers the thermal mass of the wall, attenuating direct solar radiation on its surface. Case 03, however, does not account for temperature variations due to shading or vegetation volume, leading to the highest radiation temperature values.

### 3.1.2. Wind flow: speed, direction, and flow structures

Regarding the wind speed and direction results, as shown in Figs. 8–10, the velocity profile at 0.3 m and 1.3 m above the ground indicates a maximum velocity of 0.9 m s⁻¹ within the domain. An increase in velocity is observed in the central area of the experiment, where the reduction in the flow passage contributes to the establishment of a *Venturi effect*. For all cases studied, the computational mesh is sufficiently fine to capture the detachment of the boundary layer near walls exposed to frontal (upstream) flow.

Regarding the representation of vegetation, in Cases 01 and 02,
<figure><img src="image_10.png" /><figcaption>**Fig. 9.** Results for each study case are presented vertically, with fields corresponding to $P_{h2}$ at 1.3 m above ground. Each line represents a different analysed variable with corresponding contours: radiation temperature (Radiation, 36 to 50 °C), wind speed (v, 0 to 0.90 m s⁻¹), air temperature (Air, 36 to 50 °C) and relative humidity (RH, 34 to 19 %).</figcaption></figure>

where the plants are modelled as solid zones, the flow bypasses these volumes. In Case 03, where vegetation is represented as a porous zone, the flow passes through the volume, resulting in airflow within the plants.

In the downstream region, particularly around the planters, larger zones of flow deceleration and recirculation are observed. These regions are characterised by zones where $Q < 0$ (strong shear) and $Q > 0$ (vortices), as shown in Fig. 12 for Case 01, which represents the same phenomenon observed in the other cases. We therefore confirm that mixing, and consequently heat transfer, is enhanced in these regions. This effect is further supported by the analysis of temperature and relative humidity profiles.

### 3.1.3. Temperature: surface and air distributions

The simulations revealed that, as excepted, surface temperature distribution was influenced by the simulation timescale, surface mesh resolution, and thermal model selection (Fig. 13). In Case 01, which combined steady-state and transient simulations with a fine surface mesh and a coupled thermal model, surface temperatures reflected the interaction of conduction, convection, and radiation. This configuration resulted in tridimensional spatial temperature variations on the same object and particularly highlighted the effect of shading. The ground surface temperature varied from approximately 50 °C in areas exposed to direct sunlight to 42 °C in shaded zones.

In Case 02, surface temperatures remained constant according to the predefined values, which were based on the average temperature of each object in Case 01. In Case 03, the forced surface temperatures followed the prescribed conditions. However, vegetation volumes, modelled as porous media, reached thermal equilibrium with the air, with surface

temperatures of 37.8 °C for climbing plants and 36.7 °C for shrubs. These values were lower than in Cases 01 and 02, as shown in Fig. 10, where climbing plants had surface temperatures of 38.7 °C and 38.5 °C in Cases 01 and 02, respectively, and shrubs reached 37.9 °C and 37.5 °C.

The air temperature distribution (Figs. 8–10 and 13) shows a homogeneous thermal field within the study area. At 0.3 m above ground, the mean air temperature is slightly higher (~38 °C) compared to 1.3 m above ground, where it is ~ 36.8 °C. This temperature difference is likely due to convective effects associated with the ground surface, particularly concrete, which absorbs and retains more heat. However, the effect of the shade in Case 01 did not significantly alter the air temperature.

Air temperature is higher on the north side, where surface temperatures are elevated, and lower on the south side, reflecting the variations in surface temperature, as shown in Fig. 10. In Case 03, temperature gradients are less pronounced due to the semi-transparent representation of vegetation, which alters radiative and thermal exchanges differently compared to solid surfaces.

Surface temperature appears to govern air temperature, which is subsequently modulated by local flow structures, such as vortices and mixing layers. However, this effect is primarily confined to the first 0.4 m near the domain walls. Additionally, the superposition of results along the hL line further highlights the consistency between the physical processes established in the domain and the evolution of temperature within the vegetated volume and on the wall surfaces.

### 3.1.4. Relative humidity: spatial variation and vegetation effects

The relative humidity variation profiles showed a correlation
<figure><img src="image_11.png" /><figcaption>**Fig. 10.** Variation of different analysed variable for the three studied cases along the horizontal line hL, at a height of 1.3 m above ground level and oriented in the XY direction within the domain, where -0.2 m represents the northernmost point on the side with climbing plants, and 4.8 m marks the southernmost point on the side with shrubs.</figcaption></figure>

between temperature, wind intensity, and humidity fluctuations within the environment (Figs. 8–10). In areas where temperatures varied, relative humidity changes were more pronounced, especially in regions with lower wind speeds. The observed trend indicated that cooler areas had higher relative humidity, while warmer areas exhibited lower humidity levels.

In all three cases studied, the relative humidity profile remained constant throughout the domain, similar to air temperature. In Case 01,

where surface temperature varied according to shading, no significant impact on relative humidity was observed. In Case 03, however, the inclusion of a source term for evapotranspiration, based on experimental measurements, led to slightly higher relative humidity (Fig. 10). This effect was amplified by lower temperatures within the vegetation volume compared to Cases 01 and 02. As a result, relative humidity was approximately 1 % higher near the climbing plants, 0.5 % higher near the shrubs, and around 0.2 % higher at the centre of the domain.
<figure><img src="image_12.png" /><figcaption>**Fig. 11.** Results for each study case are presented horizontally. The two columns of results represent the study domain with its walls. The first column highlights the Solar Heat Flux contours on the walls, with contours ranging from 832.1 W m⁻² to 0.0 W m⁻², as indicated at the top of the figure. The second column shows the Solar Heat Flux from a different angle.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 12.** Results for Case 01. The profiles in the first column correspond to $P_{h1}$ at a height of 0.3 m from the ground. The contours represent the Q Criterion Raw values, with the left graph ranging from 0 s⁻² to -10 s⁻² and the right graph ranging from 10 s⁻² to 0 s⁻².</figcaption></figure>

Additionally, within the vegetation volume, relative humidity remained at 0 % in Cases 01 and 02, where no evapotranspiration source terms were included.

## 3.2. Comparison of modelling results with experimental data

The three simulated cases were compared with experimental results, showing mean spatial differences for temperature and relative humidity related to thermo-hygrometer and thermocouple air temperature readings of -2.2 %, 1.4 %, and -1.5 % for Case 01; -2.1 %, 1.2 %, and -1.6 % for Case 02; and 1.9 %, 0.8 %, and -1.2 % for Case 03, respectively. These accuracy values are within the expected range for CFD-type modelling [56,57]. The smallest difference between simulated and

experimental results was observed in Case 03. Additionally, MSE, RMSE, and R values were calculated to assess the agreement between simulations and measurements (Table 5). Results showed higher MSE and RMSE for air temperature at the thermocouple positions, while relative humidity at the thermo-hygrometer positions exhibited smaller discrepancies, indicating greater accuracy across all cases. Correlation was lowest in Cases 01 and 02 for air temperature at the thermo-hygrometer positions, whereas the strongest correlation was found for air temperature at the thermo-hygrometers in Case 03.

A more detailed analysis of the results, identifying where the simulation failed to accurately capture temperature and relative humidity fluctuations, is presented in Figs. 14 and 15.

One key observation from the comparison is the greater fluctuation
<figure><img src="image_14.png" /><figcaption>**Fig. 13.** Results for each study case are presented horizontally. The two columns of results represent the study domain with its walls. The first column highlights the Surface Temperature contours on the walls, with contours ranging from 50 °C to 36 °C, as indicated at the top of the figure. The second column shows the Temperature from a different angle with a vertical plane in the middle of the study domain to emphasise the relationship between surface temperature and air temperature.</figcaption></figure>

**Table 5**

Metrics for assessing the results obtained from the various numerical cases in comparison with experimental data. "Real time" refers to the computational duration required to generate the results. Metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Pearson correlation coefficient (R). The abbreviations TH Tair and TH RH denote the results for air temperature and relative humidity from thermo-hygrometers, while TA Tair refers to the air temperature measurements from thermocouples.

<table><thead><tr><th rowspan="2">CASE</th><th rowspan="2">Real time</th><th colspan="3">MSE</th><th colspan="3">RMSE</th><th colspan="3">R</th></tr><tr><th>TH Tair</th><th>TH RH</th><th>TA Tair</th><th>TH Tair</th><th>TH RH</th><th>TA Tair</th><th>TH Tair</th><th>TH RH</th><th>TA Tair</th></tr></thead><tbody><tr><td>01</td><td>52 h30</td><td>0.8</td><td>0.6</td><td>1.1</td><td>0.9</td><td>0.8</td><td>1.0</td><td>0.1</td><td>-0.3</td><td>-0.2</td></tr><tr><td>02</td><td>23 h</td><td>0.7</td><td>0.6</td><td>1.3</td><td>0.9</td><td>0.8</td><td>1.1</td><td>-0.1</td><td>0.5</td><td>-0.3</td></tr><tr><td>03</td><td>8 h</td><td>0.6</td><td>0.6</td><td>0.8</td><td>0.8</td><td>0.7</td><td>0.9</td><td>0.5</td><td>-0.3</td><td>0.2</td></tr></tbody></table>

in experimental data compared to modelled results, both for air temperature and relative humidity. This difference reflects the nature of the measurements: while the modelled values represent the virtual temperature and relative humidity at a given point, experimental values are influenced by various sources of error and the inclusion of numerous sensors that interact with the surrounding environment. These are primarily radiative effects, affected by factors such as wind, radiation exposure, surface coatings, and the underlying ground material [58].

Measurements from the thermo-hygrometers show that air temperature is higher near the climbing plants, where solar radiation is more intense, and decreases near the shrub vegetation, where shading is more pronounced. However, this trend is not reflected in the simulation results. Case 03, which does not account for vegetation volumes, presents

the most uniform temperatures across the study area. In contrast, Case 01, and to a lesser extent Case 02, show an opposite trend, with air temperature increasing towards the shrub. This suggests an indirect effect, likely due to radiation from the plant's solid volume and its role as a flow obstacle. These results suggest that the vegetation representation should be optimised, as physically consistent behaviour would imply lower air temperatures near dense, shaded foliage. Furthermore, air temperature measurements obtained with the thermo-hygrometers consistently indicated lower values at all analysed points compared to the simulations, with absolute differences ranging from 0.2 to 1.3 °C (±0.3 °C). Regarding relative humidity, all simulated values fall within the expected range when considering experimental error bars. This makes it difficult to determine whether the observed differences are due
<figure><img src="image_15.png" /><figcaption>**Fig. 14.** Comparison of experimental results for (a) air temperature (Tair) and (b) relative humidity (RH) for 20th August 2023 at 1 pm obtained with thermo-hygrometer sensors and simulated cases with ANSYS Fluent, in the positions indicated for each sensor. Error bars are identified in the experimental data for air temperature (±0.3 °C) and relative humidity (±1.8 %).</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>**Fig. 15.** Comparison of the experimental air temperature results obtained with thermocouple sensors for 20th August 2023 at 1 pm and the cases simulated with ANSYS Fluent, in the positions indicated for each sensor. Error bars are identified in the experimental data for air temperature (±0.5 °C).</figcaption></figure>

to measurement errors or inaccuracies in humidity modelling, despite the small discrepancy between experimental and simulated results.

The comparison between experimental air temperature measurements from thermocouples and numerical simulation results reveals a similar pattern to the thermo-hygrometer data, with shading and radiation intensity influencing air temperature differences between the north (warmer) and south (cooler) sides of the setup. However, the simulated temperature remains more uniform across most of the domain.

Significant discrepancies between experimental and numerical data are observed at positions TA6, TA24, TA30, and TA48, all located near shrub plants, with absolute differences ranging from 1.3 to 4.3 °C (±0.5 °C). In these areas, the simulation predicts them as some of the warmest zones, whereas experimental measurements indicate lower temperatures. This highlights the need for a more accurate representation of vegetation in the model, as the physical behaviour should be the opposite—areas with dense vegetation and reduced solar radiation exposure should exhibit lower air temperatures.

# 4. Discussions and perspectives

## 4.1. Evaluation of the three modelling scenarios

The three modelling approaches examined in this study, while involving a complex analysis of urban microclimate multiphysics using CFD software, were evaluated through a transparent and detailed methodology that prioritises simplicity. This allowed not only an assessment of the model outputs but also an evaluation of the operational complexity, including the nature and volume of input data required. Simulations of only five minutes of real conditions, with a modelling time step of one second, yielded results that were broadly consistent across models, yet enabled the identification of each model's strengths and weaknesses. These are summarised in Table 6, highlighting the three aspects that exhibited the greatest differences between the methods: radiative, thermal, and vegetation modelling.

In the context of urban microclimate studies, different strategies can be adopted for the application of numerical models and the representation of vegetation. As noted by Garcia de Cezar et al. [10], the
suitability of each approach depends on several factors, including the stage of the urban project, the objectives of the study, and the availability of resources for both computational modelling and characterisation of field conditions. The proposed model, while requiring further refinement—particularly in terms of vegetation modelling and thermal comfort indices— constitutes a structured and systematic framework for simulations using ANSYS Fluent. It offers a foundation for gradually extending capabilities and identifying limitations of the model, supporting incremental improvements that can benefit the wider research community.

## 4.2. Limitation verified in the radiative model

Considering radiation within the study domain, it was evident that this variable plays a crucial role in assessing pedestrian thermal vulnerability. While air temperature and relative humidity exhibited relatively uniform distributions in the model, radiation temperature effectively captured the domain's spatial variability, reflecting surface temperature changes and shading effects. However, it was not possible to immediately assess, in numerical terms, the accuracy of the simulated average radiant temperature, which is used in thermal comfort indices such as the Universal Thermal Climate Index (UTCI). This variable represents the overall radiative heat exchange perceived by a human body [59], whereas the radiation temperature automatically calculated

in ANSYS Fluent is a local variable based on domain radiative fluxes, without integrating form factors or perception effects [36]. Therefore, to accurately assess this variable, an User-Defined Function (UDF) must be implemented to incorporate the necessary factors, as demonstrated in previous studies [28,44,60].

Another key aspect of radiation modelling in ANSYS Fluent was the need for adjustments in the configuration to correctly capture solar projection and intensity. In the Solar Ray Tracing settings, the projected intensity for the simulated date (20th August at 1 pm) was inconsistent. A sensitivity analysis revealed discrepancies in the simulated solar hours and intensity. For the present study, the desired conditions were achieved by setting the date to 1st January at 10 am. This highlights the need for a more robust solar radiation configuration, potentially using a UDF, although other studies have applied this functionality without modifications [28,29,61].

## 4.3. Accuracy and suitability of ANSYS Fluent for reproducing experimental conditions and guiding urban mitigation strategies

To address this question, three different case studies were conducted using simplified configurations to compare and evaluate the performance of the tool. The experimental setup represents a street with an extensive network of sensors distributed within the study area, collecting data on air temperature, relative humidity, wind speed, solar

<table><caption>**Table 6**
Summary of the strengths and weaknesses of the three proposed simulation scenarios.</caption><thead><tr><th>Description</th><th>Case</th><th>Strengths</th><th>Weaknesses</th></tr></thead><tbody><tr><td rowspan="3">Radiative model</td><td>01</td><td>→ The distribution of radiation in the study area is homogeneous and physically consistent due to the refined surface mesh with equal cell size across all surfaces;<br/>→ Enables the modelling of the radiation impact over a 24-hour cycle.</td><td>→ Requires a finer mesh, which considerably increases the simulation time and iterations per time step;<br/>→ Demands prior knowledge of the specific surface properties of the study area.</td></tr><tr><td>02</td><td>→ Provides an adequate representation of solar radiation projection and intensity within the study area;<br/>→ Requires a less detailed mesh, allowing for faster simulations.</td><td>→ Radiation effects cannot be reliably accounted for in surface temperature estimation, as they lead to non-physical results.</td></tr><tr><td>03</td><td>→ Same as case 02.</td><td>→ Same as case 02;<br/>→ The methodology adopted to represent vegetation is limited by its treatment as a semi-transparent medium in the radiation model, which does not accurately reflect the physics of vegetation-atmosphere interactions.</td></tr><tr><td rowspan="3">Thermal model</td><td>01</td><td>→ Surface temperature is modelled based on radiative, convective, and conductive exchanges, allowing spatial variations in surface temperature of the same object according to local physics;<br/>→ The effect of shading on surface temperature is captured;<br/>→ Enables the modelling of surface temperature variations over a full 24-hour cycle.</td><td>→ Requires a finer mesh, significantly increasing simulation time and iterations per time step;<br/>→ Demands precise knowledge of site-specific surface properties;<br/>→ In transient simulations, the run must start before sunrise to account for the surface heat storage effect;<br/>→ To reduce computational cost, a steady-state initialisation may be used to approximate surface temperature conditions, but this approach requires prior reference values for calibration.</td></tr><tr><td>02</td><td>→ Faster simulation, as fewer numerical models are involved and convergence is easier to achieve;<br/>→ Direct control over the domain boundary conditions;<br/>→ No need to specify the exact material properties of the study area, since their influence on the results is limited.</td><td>→ Requires the specification of surface temperature values for objects, which are applied as an average over the entire surface;<br/>→ Shadow projection within the study area has no effect on surface temperature in the domain.</td></tr><tr><td>03</td><td>→ Same as case 02.</td><td>→ Same as case 02;<br/>→ The methodology used to represent vegetation is limited by treating it as a semi-transparent medium in the thermal model, which does not reflect the actual physics of vegetation-atmosphere interactions.</td></tr><tr><td rowspan="3">Vegetation representation</td><td>01</td><td>→ Requires only limited initial information, namely estimated surface properties and dimensions;<br/>→ Low modelling complexity, which reduces simulation time;<br/>→ Radiative effects on vegetation surfaces are accounted for through shadow projection and surface temperature variation.</td><td>→ The vegetation volume does not account for energy loss by evapotranspiration, leading to excessively high surface temperatures and radiative impact;<br/>→ The solid volume generates non-physical air circulation, resulting in temperature gradients inconsistent with expected behaviour;<br/>→ Low physical representativeness of vegetation.</td></tr><tr><td>02</td><td>→ Requires only limited initial information, namely estimated surface temperature and dimensions;<br/>→ Low modelling complexity, which reduces simulation time.</td><td>→ Same as case 01.</td></tr><tr><td>03</td><td>→ Produces more physically realistic results compared to Cases 01 and 02, by modelling vegetation as a porous medium and accounting for increased relative humidity in the vicinity of vegetation;<br/>→ Requires minimal initial input data for implementation of the porous effect, given the applied methodology (sensitivity study).</td><td>→ Considers the impact of porosity only in the flow direction, rather than in all directions;<br/>→ Relies on experimental data to empirically estimate the evapotranspiration of each plant type;<br/>→ For greater accuracy, the pressure loss associated with the porous zone should be better characterized experimentally;<br/>→ Radiative and thermal effects are neglected, as the volume is treated as semi-transparent.</td></tr></tbody></table>
radiation, and globe temperature. However, limited information was available regarding the surface properties of different materials in the domain and the boundary conditions outside the street environment, which both impacts the results.

The accuracy of ANSYS Fluent in representing the physics of the studied case was evaluated based on its ability to: (a) reproduce variations in air temperature and relative humidity throughout the domain, assessed by comparing experimental and simulated data, and (b) maintain physical consistency in the spatial variations of the analysed variables.

Regarding the numerical results for air temperature and relative humidity, the mean error was found to be below 3% for all analysed points, with a maximum error of less than 11%, which falls within the acceptable range for CFD models [56,57]. In terms of capturing the physical dynamics of the study area, air temperature and relative humidity variations were consistent with wind speed and surface temperature dynamics. However, specific inconsistencies were identified in certain areas of the domain, suggesting that vegetation modelling properties require further investigation.

Concerning the adaptability of ANSYS Fluent for operational decision-making in urban environments, four main aspects were considered: (a) complexity of case study setup, (b) required level of detail in domain characterisation, (c) computational time and resource requirements, and (d) quality of output data. Despite ANSYS Fluent providing a user-friendly interface, the lack of documentation on adapting model functionalities for urban microclimate simulations makes configuration challenging. While the study case included a dense sensor network to monitor microclimatic variations, key missing data included surface temperature variations, boundary conditions upstream of the street, and accurate surface properties. Despite these limitations, results were satisfactory, suggesting that the need for detailed domain information depends on the desired level of precision.

Regarding computational performance, even with a relatively simple model configuration and a simulated period of less than five minutes, the computational time was considerable (Table 5): 52 h 30 min, 23 h, and 8 h for Cases 01, 02, and 03, respectively. This represents a major limitation for its operational use, although the numerical configuration can still be optimised. However, the ability of ANSYS Fluent to generate highly detailed simulations enables precise identification of critical areas and tailored mitigation strategies, making it a valuable tool for designing more sustainable urban spaces.

### 4.4. Challenges in validating urban microclimate dynamics

According to best practice guidelines for CFD simulations, results can only be considered reliable after critical evaluation and comparison with experimental data [56]. However, in urban microclimate modelling, particularly during daytime, the high variability of atmospheric variables and the influence of the measurement instrumentation itself pose significant challenges in obtaining accurate experimental data.

In this study, a clear difference was observed between simulated and experimental data. The simulated values represent idealised microclimatic conditions at specific points, unaffected by environmental interferences. In contrast, experimental data—especially air temperature measurements from naturally ventilated (passive) shelters—were influenced by radiative errors, leading to spatial variations. Similar challenges have been highlighted in previous studies [62,63], which assessed the performance of different sensors and shelters for validating experimental data. These studies report, for example, temperature differences of up to 2 °C during peak solar radiation hours between passive and actively ventilated shelters. Other research suggests that even ventilated shelters, which reduce radiative errors in air temperature measurements, may not always be the most appropriate, depending on microclimatic conditions [58]. This highlights the need for both improved numerical modelling techniques and more precise, less intrusive measurement instruments to enhance accuracy in

microclimate analysis.

Furthermore, the need for standardised validation protocols for urban microclimate models has been widely recognised [19,20,64] as suggested in the benchmarking approach for urban microclimate models by Gresse et al. [65,66], and this study reinforces that necessity. Establishing guidelines on the most suitable sensors and the minimum number of measurement points per zone—depending on its complexity and resolution—would streamline research efforts and enhance comparability between studies.

To contribute to the development of validation protocols, this study suggests that during periods of stable radiation and low wind speed, air temperature and relative humidity profiles remain predominantly constant. In such cases, fewer sensors are required to capture trends while minimising disturbance to the experimental domain, allowing for investment in higher-precision instruments. Conversely, for radiation measurements, significant spatial gradients exist, making it advisable to place sensors in both high-exposure and shaded areas. This approach would enable greater investment in surface temperature monitoring and material property assessments, which are crucial for understanding urban thermal dynamics.

## 5. Conclusions

This study evaluated the performance of ANSYS Fluent in reproducing microclimate conditions within an experimental street canyon with vegetated planters. Three modelling configurations were examined: dynamic surface temperatures, prescribed uniform temperatures, and a porous vegetation model with evapotranspiration. The key findings can be summarised as follows:

- Accuracy: Average errors in air temperature and relative humidity remained below 3%, with a maximum deviation of 11%, results that are consistent with typical CFD performance.

- Vegetation representation: Current approaches fail to adequately capture the spatial and temporal variability in vegetated zones, highlighting the need for more accurate modelling.

- Radiative processes: Radiation is the dominant driver of thermal vulnerability. However, ANSYS Fluent requires the use of an User-Defined Function (UDF) to estimate mean radiant temperature.

- Software limitations: Vegetation, represented as a porous medium, is treated by the model as semi-transparent, thereby limiting the simultaneous representation of porosity and radiative effects. In addition, inconsistencies observed in the Solar Calculator and Solar Ray Tracing models highlight the need for further investigation and refinement to ensure reliable boundary conditions.

- Operational feasibility: Computational cost and data acquisition remain critical challenges, reinforcing the importance of robust validation protocols and streamlined modelling workflows.

While the proposed model requires further refinement—particularly in vegetation representation and the integration of thermal comfort indices—it provides a structured and operational framework for CFD-based microclimate simulations. It offers a foundation for gradually extending model capabilities while clarifying current limitations, thereby supporting incremental methodological improvements in the research community. Ultimately, the findings highlight the importance of radiative control and strategic vegetation design as practical levers for enhancing thermal comfort and strengthening resilience to urban overheating.

### CRediT authorship contribution statement

**Martina Garcia de Cezar:** Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization.

**Séverine Tomas:** Writing – review & editing, Visualization, Supervision, Project administration, Methodology, Data curation. Bruno
**Chevron:** Writing – review & editing, Visualization, Supervision, Project administration, Methodology, Data curation. **Laurent Aprin:** Writing – review & editing, Visualization, Supervision, Project administration, Methodology, Data curation.

## Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We acknowledge the supporting of the PRESTI INRAE UMR G-Eau technical team for their support in building and maintaining the experiment and the team of developers for their support in assembling the experiment dataset.

## Data availability

Data will be made available on request.

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