Urban street canyon thermal environment optimization method based on numerical simulation

Abstract

The invention discloses a numerical simulation-based urban street canyon thermal environment optimization method, which comprises the following steps of: firstly, acquiring meteorological data, soil data, material data and plant data of an urban street canyon and satellite data of the urban street canyon, establishing an initial model of the urban street canyon according to the acquired data, and setting working conditions; secondly, the initial model of the urban street canyon is applied and expanded to simulation of cooling effects of different thermal mitigation strategies, key parameters related to the thermal mitigation strategies in the model are adjusted, a comparison model is constructed, and the actual cooling efficiency of each thermal mitigation strategy is deeply analyzed; according to the comparison model, building microenvironments of the urban street canyons under different thermal mitigation strategies are simulated, and general thermal climate indexes of the urban street canyons under different thermal mitigation strategies are obtained; and finally, selecting the thermal mitigation strategy corresponding to the lowest general thermal climate index as the optimal thermal mitigation strategy of the urban street canyon.

Description

Technical Field

[0001] The present invention relates to the field of building technology, and in particular to an optimization method for the thermal environment of urban street canyons based on numerical simulation. Background Art

[0002] In the urban environment, the continuous increase in population has led to more and more buildings replacing the natural environment, and the resulting urban heat island poses a serious threat to human health. Currently, 54% of the world's population lives in urban areas. It is expected that this figure will increase sharply by 2050, reaching 66% globally, which may lead to more serious heat stress problems in future cities. The heat and mass transfer process in the urban environment is very complex, involving various mechanisms such as solar radiation, convective heat transfer, conduction, and evaporative cooling. CFD can accurately simulate these complex physical processes through numerical methods, providing high-resolution spatial and temporal data, enabling researchers to deeply understand the details of the urban thermal environment. By simulating the specific effects of different heat mitigation strategies (such as increasing green spaces, using highly reflective materials, introducing water bodies, etc.) in the urban environment through CFD, they can be quantified and visualized. This helps urban planners and decision-makers choose the most effective strategies to reduce the urban heat island effect and improve the urban living environment.

[0003] To mitigate the hazards caused by the urban heat island, urban climate scientists have proposed many methods to alleviate high temperatures, including using vegetation to cover building surfaces for evaporative cooling, using water bodies and highly reflective materials to change the urban form, and using trees or artificial structures for shading. Since vegetation can cool down through shading and evaporative cooling, it is considered one of the most attractive and cost-effective options. However, the cooling effect of vegetation depends on the local climate, vegetation species, and vegetation quantity. Therefore, it is of great significance to use numerical simulation methods to evaluate its impact and thus formulate effective heat mitigation strategies. CFD can simulate the momentum, heat, and mass exchange between vegetation and air and provide high-resolution results within the region. In the research so far, except for a few using one-dimensional conjugate heat transfer models, there is usually a lack of analysis of the impact of vegetation on heat transfer and humidity in the urban environment. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the technical problem to be solved by the present invention is to provide an optimization method for the thermal environment of urban street canyons based on numerical simulation.

[0005] The technical solution for the present invention to solve the above technical problem is to provide an optimization method for the thermal environment of urban street canyons based on numerical simulation, characterized in that the method comprises the following steps:

[0006] Step 1: Obtain the meteorological data, soil data, material data, plant data of the urban street canyon, as well as the satellite data of the urban street canyon, and then establish an initial model of the urban street canyon based on the above obtained data and set the working conditions;

[0007] Step 2: Apply and expand the initial model of the urban street canyon obtained in Step 1 to the simulation of the cooling effects of different heat mitigation strategies. By adjusting the key parameters related to the heat mitigation strategies in the model, construct a comparison model to deeply analyze the actual cooling efficiency of each heat mitigation strategy;

[0008] Step 3: According to the comparison model in Step 2, simulate the building microenvironment of the urban street canyon in Step 1 under different heat mitigation strategies, and obtain the Universal Thermal Climate Index of the urban street canyon under different heat mitigation strategies;

[0009] Step 4: According to the Universal Thermal Climate Index under different heat mitigation strategies obtained in Step 3, select the heat mitigation strategy corresponding to the lowest Universal Thermal Climate Index as the optimal heat mitigation strategy for the urban street canyon.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0011] (1) The present invention creates an urban simulation model that integrates the urban street canyon environment and the shading, ventilation, transpiration, and evaporation effects of plants, relatively realistically reproducing the existing urban street canyon thermal environment. By simulating the vegetation transpiration effect, the model can accurately evaluate the impact of different greening strategies on the urban thermal environment.

[0012] (2) The transpiration of the vegetation in the present invention can regulate the microclimate by absorbing heat and increasing air humidity. The model can quantify this regulation effect, providing data support for optimizing urban planning and design, thereby improving the quality of life and comfort of urban residents.

[0013] (3) The model of the present invention can be used to simulate and evaluate the effectiveness of different coping strategies, such as temporarily increasing greening or changing the albedo of building materials during heatwaves, to help cities better cope with extreme climate events. Description of the Drawings

[0014] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0015] Figure 2 It is a schematic diagram of the initial model and grid division of the urban street canyon in Embodiment 1 of the present invention;

[0016] Figure 3 It is a schematic diagram of the cool pavement strategy of the present invention;

[0017] Figure 4 It is a schematic diagram of the cool roof strategy of the present invention;

[0018] Figure 5 Schematic diagram of the green roof strategy of the present invention;

[0019] Figure 6 CFD simulation result diagrams under different heat mitigation strategies in Example 1 of the present invention;

[0020] Figure 7 Comparison diagram of 2m temperature simulation results under different heat mitigation strategies in Example 1 of the present invention;

[0021] Figure 8 UTCI comparison diagram under different heat mitigation strategies in Example 1 of the present invention. Detailed implementation manners

[0022] Specific embodiments of the present invention are given below. The specific embodiments are only used for further detailed description of the present invention and do not limit the protection scope of the present invention.

[0023] The present invention provides an optimization method for the thermal environment of urban street canyons based on numerical simulation (hereinafter referred to as the method), which is characterized in that the method includes the following steps:

[0024] Step 1: Obtain the meteorological data, soil data, material data, plant data of the urban street canyon and the satellite data of the urban street canyon, and then establish an initial model of the urban street canyon that can simulate the transpiration, shading and ventilation effects of vegetation and set the working conditions; The initial model of the urban street canyon has a powerful simulation ability and can accurately depict the complex interaction mechanism between vegetation and the environment;

[0025] Preferably, in Step 1, the meteorological data includes the average wind speed, relative humidity, air temperature and wind direction at the reference height in the hottest month of summer; the soil data includes the density, specific heat capacity, thermal conductivity, temperature and water content of the soil; the material data includes the density, specific heat capacity, thermal conductivity and temperature of the building materials used; the plant data includes the tree height, crown width, leaf area density, leaf area index, characteristic leaf size data and vegetation albedo; the satellite data of the urban street canyon includes the height, floor area of the buildings, the relative positions between buildings and the relative positions between plants and buildings.

[0026] Preferably, in Step 1, the initial model of the urban street canyon is established in CFD software and the working conditions are set.

[0027] Preferably, in Step 1, the establishment of the initial model of the urban street canyon includes the following process:

[0028] S11: For the location area where the urban street canyon is located, establish a computational simulation area;

[0029] Preferably, in step S11, the distance between the inlet boundary of the simulation area and the urban street canyon is calculated to be 5 to 15 times the maximum building height in the urban street canyon, the distance between the outlet boundary of the simulation area and the urban street canyon is calculated to be 5 to 15 times the maximum building height in the urban street canyon, the distance between the two side boundaries of the simulation area and the urban street canyon is calculated to be 5 to 15 times the maximum building height in the urban street canyon, and the distance between the top boundary of the simulation area and the urban street canyon is calculated to be 3 to 5 times the maximum building height in the urban street canyon.

[0030] S12. Use polyhedral unstructured grids to divide the computational simulation area; during the grid division process, locally refine the grids in the areas where the buildings and vegetation are located in the urban street canyon;

[0031] S13. After the grid division, set the boundary conditions for the computational simulation area according to the meteorological data of the urban street canyon;

[0032] Preferably, in step S13, for the boundary conditions at the inlet of the computational simulation area, assuming neutral stratification, the inlet profiles of the horizontal wind speed U, turbulent kinetic energy k, and turbulent dissipation rate ε are as shown in equations (1) - (3):

[0033]

[0034] In equation (1), U(Z) represents the horizontal wind speed at height Z; represents the friction velocity of the atmospheric boundary layer; κ is the von Kármán constant; z0 is the aerodynamic roughness length, and z0 is generally taken as 2 for the city center;

[0035]

[0036] In equation (2), C μ is a model constant, and in this embodiment, it is taken as 0.09;

[0037]

[0038] For the boundary conditions at the outlet of the computational simulation area, use the pressure outlet boundary conditions; for the boundary conditions at the top and two sides of the computational simulation area, use the slip boundary conditions.

[0039] S14. Create a porous vegetation area representing the vegetation in the area where the vegetation is located in the urban street canyon, load user-defined functions (UDF, User-defined functions) that implement the functions of vegetation evaporation, shading, and ventilation in the porous vegetation area, and then input the plant data into the user-defined functions to obtain the initial model of the urban street canyon.

[0040] Preferably, in step S14, the specific content of the user-defined function is as follows: The user-defined function consists of two parts: a leaf energy balance model and a source term equation; the leaf energy balance model obtains a calculation result based on the input plant data, and the calculation result is the mass flux of water vapor g entering the air from the leaf v,leaf and the sensible heat flux q of the leaf sen,leaf ; then the source term equation provides the source terms of heat, mass, and momentum based on the calculation result of the leaf energy balance model, and finally adds the calculation result of the source term equation to the Reynolds-averaged Navier-Stokes (RANS) equation and turbulence model in the CFD software to realize the transpiration, shading, and ventilation effects of the simulated vegetation in the initial model of the urban street canyon.

[0041] Preferably, in step S14, the specific leaf energy balance model is as follows:

[0042] The leaf energy balance model takes into account the shading and aerodynamic effects of the trees, as well as the long-wave heat exchange with the environment and the generation of latent heat flux and sensible heat flux within the tree leaves. It is assumed that there is always enough water in the soil for the roots to absorb, and the stomata do not close due to water limitation; the tree leaves are discretized into finite volumes, each volume is characterized by air temperature and vapor pressure, and the leaf energy balance model is solved within each volume;

[0043] The heat exchange and mass exchange between the vegetation and the air are simulated through the leaf energy balance model. It is assumed that the leaf energy balance model is stable and the dynamic storage of heat in the leaf is ignored. The leaf energy balance model is:

[0044] q rad,leaf -q lat,leaf -q sen,leaf = 0 (4)

[0045] In formula (4), q rad,leaf is the net radiation flux on the leaf surface, W / m 2 ; q lat,leaf is the latent heat flux of the leaf, W / m 2 ; q sen,leaf is the sensible heat flux of the leaf, W / m 2 ;

[0046] The sensible heat flux q sen,leaf and the latent heat flux q lat,leaf of the leaf are for the heat to transfer from the leaf to the air;

[0047] Among them, the sensible heat flux q sen,leaf of the leaf caused by convective heat transfer from the leaf surface to the air is:

[0048]

[0049] In Equation (5), h c,h is the convective heat transfer coefficient, W / (m 2 ·K); T leaf is the blade surface temperature, K; T is the air temperature, K; ρ is the air density; c p is the specific heat capacity of air, J / (kg·K); r a is the aerodynamic resistance of the air in the boundary layer around the blade, s / m; when the sensible heat flux of the blade appears on both sides of the blade, there is a factor of 2 in Equation (5); the aerodynamic resistance r a of the air in the boundary layer around the blade is calculated by the formula:

[0050]

[0051] In Equation (6), C is a proportionality factor, which is taken as 130 s 0.5 / m in this embodiment; l is the characteristic leaf size, with the unit of m, ranging from 0.02 m for conifers to 0.5 m for tropical plants; is the average velocity vector;

[0052] Among them, the latent heat flux q lat,leaf from the blade to the air caused by evapotranspiration is:

[0053] q lat,leaf = L v g v,leaf (7)

[0054] In Equation (7), L v is the latent heat of vaporization, which is taken as 2.5×10 6 J / Kg in this embodiment; the mass flux g v,leaf (kg / (m 2 ·s)) of water vapor entering the air from the blade is:

[0055]

[0056] In Equation (8), h c,m is the convective mass transfer coefficient, s / m; p v,leaf is the vapor pressure at the blade, Pa; p v is the vapor pressure above the blade boundary layer, Pa; R a is the gas constant of dry air, which is taken as 287.042 J / (kg·K) in this embodiment; p is the ambient pressure, which is set to the standard atmospheric pressure in this embodiment; R v is the gas constant of water vapor, which is taken as 461.524 J( / kg·K) in this embodiment; r s is the stomatal resistance, s / m;

[0057] The model assumes that there is no condensation or rain on the leaf surface, and evaporation occurs only through transpiration through leaf stomata; therefore, the vapor pressure at the leaf is the vapor pressure inside the leaf stomata, and this vapor pressure is close to the saturation vapor pressure p vsat,stom (T leaf ). Therefore, it can be assumed that p v,leaf = p vsat,stom (T leaf ); r s is modeled as a function of the short-wave radiation flux in the air and the saturation vapor pressure difference D (i.e., the difference between the saturation vapor pressure and the air vapor pressure) in the leaf energy balance model. However, in the leaf energy balance model, only the net radiation flux on the leaf surface and the short-wave radiation flux in the air at a solar altitude of 90°, i.e., noon, can be calculated, and the change in the mass and heat exchange between the plant and the surrounding environment over time cannot be simulated. Therefore, the present invention uses Equation (9) to calculate the stomatal resistance, making up for the defect of only being able to simulate the transpiration effect of the plant at noon:

[0058] r s = r s,min f1f2 (9)

[0059] In Equation (9), r s,min is the minimum stomatal resistance on the leaf surface; f1 and f2 are multiplicative functions that respectively describe the changes in stomatal resistance caused by the incident solar radiation and the saturation vapor pressure difference D;

[0060]

[0061] f2 = 0.4372(1 + D) 0.204 (11)

[0062] In Equations (10) and (11), q rad,inc is the incident solar radiation, W / m 2 ; D is the saturation vapor pressure difference, Pa;

[0063] Among them, the net radiation flux q rad,leaf on the leaf surface is:

[0064] q rad,leaf = (1 - τ l - ρ l )q rad,inc (12)

[0065] In Equation (12), ρ l is the reflectivity of the leaf to solar radiation; τ l is the transmittance of the leaf to solar radiation, τ l = e -γLAI ; γ is the solar radiation extinction coefficient, which is taken as 0.5 in this embodiment; LAI is the leaf area index.

[0066] Preferably, in step S14, the source term equations include:

[0067] The air quality source term s ρ (kg / (m 3 ·s)) caused by vegetation is:

[0068] s ρ = a·g v,leaf (13)

[0069] In Equation (14), a is the leaf area density, defined as the one-sided leaf surface area within a given volume, m 2 / m 3 ; g v,leaf is the mass flux of water vapor entering the air from the leaves, kg / (m 2 ·s);

[0070] The momentum source term caused by vegetation is s u (N / m 3 ):

[0071]

[0072] In Equation (14), c d is the leaf drag coefficient, which is taken as 0.2 in this embodiment; ρ is the air density; is the average velocity vector; for turbulent flow, it is assumed that the viscous drag can be neglected compared to the form drag; in addition, compared with the vegetation drag, the momentum of transpiration can also be neglected; therefore, the momentum transport equation can be solved with a divergence-free constraint ;

[0073] The temperature source term caused by vegetation is s T (K / s):

[0074]

[0075] In Equation (15), c p is the specific heat capacity of air, J / kg·K;

[0076] The humidity source term caused by vegetation is s w (kg / (kg·s)):

[0077]

[0078] The turbulent kinetic energy source term s k (W / m) caused by vegetation is:

[0079]

[0080] In Equation (17), k is the turbulent kinetic energy of air, W / m3 ; the constant β p is the fraction of the average kinetic energy converted into turbulent kinetic energy, and the value taken in the embodiment is 1; the constant β d describes the reduction of turbulent kinetic energy and turbulent dissipation rate by vegetation, and the value taken in this embodiment is 5.1;

[0081] The source term s of the turbulent dissipation rate caused by vegetation ε is:

[0082]

[0083] In formula (18), ε is the turbulent dissipation rate of air, W / m 3 ·s; C 4ε and C 5ε are both model constants, and the values taken in this embodiment are both 0.9.

[0084] Step 2: Apply and expand the initial model of the urban street canyon obtained in Step 1 to simulate the cooling effects of different heat mitigation strategies. By adjusting the key parameters related to the heat mitigation strategies in the model, construct a comparison model to deeply analyze the actual cooling efficiency of each heat mitigation strategy;

[0085] Preferably, in Step 2, the heat mitigation strategies are respectively the cool pavement strategy, the cool roof strategy and the green roof strategy; as Figure 3-5 shown, the cool pavement strategy means replacing the dark concrete pavement with a light - colored white cement concrete, which has a higher surface albedo and a lower heat capacity; the cool roof strategy means replacing the dark concrete roof with a highly reflective roof; the green roof strategy means adding a layer of plants to the building roof, usually covered with shrubs and small trees.

[0086] Step 3: According to the comparison model in Step 2, simulate the building micro - environment of the urban street canyon in Step 1 under different heat mitigation strategies, and obtain the Universal Thermal Climate Index (UTCI) of the urban street canyon under different heat mitigation strategies;

[0087] Preferably, in Step 3, in Step 3, output the air temperature (T a ) at the pedestrian height in the area of interest, output the mean radiant temperature (T mrt ), wind speed (v a ) and relative humidity (RH) of the air in the operation results of the comparison model; then calculate the Universal Thermal Climate Index according to the air temperature (T a ), mean radiant temperature (T mrt ), wind speed (v a ) and relative humidity (RH).

[0088] Step 4: According to the general thermal climate index under different thermal mitigation strategies obtained in Step 3, select the thermal mitigation strategy corresponding to the lowest general thermal climate index as the optimal thermal mitigation strategy for the urban street canyon.

[0089] Example 1:

[0090] Step 1: In this example, meteorological data can be obtained from the meteorological information of the location area of the urban street canyon, and the values used in the specific implementation should be determined according to the common wind speed and wind direction in local summer.

[0091] The establishment of the initial model in this example includes the following process: Establish a computational simulation area in the location area of the urban street canyon. Specifically, when setting the simulation area of the urban street canyon, as Figure 2 shown, the street canyon in the simulation area consists of seven dormitory buildings with a height of 36m. In this example, the distance between the inlet boundary of the computational simulation area and the urban street canyon is 5 times the maximum building height within the urban street canyon, the distance between the outlet boundary of the computational simulation area and the urban street canyon is 15 times the maximum building height within the urban street canyon, the distance between the two side boundaries of the computational simulation area and the urban street canyon is 5 times the maximum building height within the street canyon, and the distance between the top boundary of the computational simulation area and the urban street canyon is 5 times the maximum building height within the street canyon. A polyhedral mesh composed of hexahedrons, prisms, and tetrahedrons is used for mesh division. The mesh refines the areas where buildings and vegetation are located, and the minimum cell size is about 0.01m. Near the building surface, it can not only capture the surface shape of the building but also capture the physical properties of wall flows well. Set the boundary conditions according to the meteorological data. The calculation of the parameters required for the wind speed equation at the inlet boundary can be determined according to the wind speed at the corresponding height in the meteorological data. If there is corresponding data, the aerodynamic roughness z0 can be set according to the actual situation.

[0092] Step 2: In this example, comparison models are constructed by adopting different thermal mitigation strategies. Specifically, it includes 3 different strategies, namely the cool pavement strategy, the cool roof strategy, and the green roof strategy. Among them, the albedo of the dark concrete material used for the roof and pavement in the initial model of the urban street canyon is 0.2, and the emissivity is 0.85; the albedo of the white cement concrete material used for the cool pavement strategy and the cool roof strategy is 0.8, and the emissivity is 0.9.

[0093] In Step 3, output the air temperature (T a ) at a height of 2m in the Fluent operation result, as Figure 6As shown, different heat mitigation strategies produce different cooling effects. From the perspective of the air temperature at a height of 2m, the cold pavement produces the largest cooling effect, with a cooling amplitude reaching 0.32°C. Output the average radiant temperature (T mrt ) of the air, wind speed (v a ), and relative humidity (RH) in the operation results of the comparison model, and calculate the UTCI index based on the above parameters. The purpose of this implementation case is to mitigate the heat island effect. Therefore, the smaller the corresponding UTCI index, the more significant the heat mitigation strategy.

[0094] Step 4: To mitigate the heat island effect, the smaller the corresponding UTCI index, the more significant the heat mitigation strategy. Therefore, select the heat mitigation strategy corresponding to the lowest Universal Thermal Climate Index as the best heat mitigation strategy for urban street canyons.

[0095] Figure 7 shows the air cooling effect at a height of 2m for different heat mitigation strategies. The largest cooling effect occurs between the cold pavement strategy and the base scenario, reaching 0.32°C. And according to Figure 8 it shows that from the perspective of the UTCI index, the value of the green roof strategy is the lowest, which is the best heat mitigation strategy.

[0096] Matters not described in this invention are applicable to the prior art.

Claims

1. An optimization method for the thermal environment of urban street canyons based on numerical simulation, characterized in that, The method includes the following steps: Step 1: Obtain the meteorological data, soil data, material data, plant data of the urban street canyon, and the satellite data of the urban street canyon, and then establish an initial model of the urban street canyon according to the obtained above data and set the working conditions; Step 2: Apply and expand the initial model of the urban street canyon obtained in Step 1 to the simulation of the cooling effects of different heat mitigation strategies. By adjusting the key parameters related to the heat mitigation strategies in the model, construct a comparison model to deeply analyze the actual cooling efficiency of each heat mitigation strategy; Step 3: According to the comparison model in Step 2, simulate the building microenvironment of the urban street canyon in Step 1 under different heat mitigation strategies, and obtain the universal thermal climate index of the urban street canyon under different heat mitigation strategies; Step 4: According to the universal thermal climate index under different heat mitigation strategies obtained in Step 3, select the heat mitigation strategy corresponding to the lowest universal thermal climate index as the best heat mitigation strategy for the urban street canyon.

2. The optimization method for the thermal environment of urban street canyons based on numerical simulation according to claim 1, wherein In Step 1, the meteorological data includes the average wind speed, relative humidity, air temperature, and wind direction at the reference height in the hottest month of summer; the soil data includes the density, specific heat capacity, thermal conductivity, temperature, and moisture content of the soil; the material data includes the density, specific heat capacity, thermal conductivity, and temperature of the building materials used; the plant data includes the tree height, crown width, leaf area density, leaf area index, characteristic leaf size data, and vegetation albedo; the satellite data of the urban street canyon includes the height, floor area of the buildings, the relative positions between buildings and between plants and buildings.

3. The optimization method for the thermal environment of urban street canyons based on numerical simulation according to claim 1, characterized in that In Step 1, establish the initial model of the urban street canyon and set the working conditions in the CFD software.

4. The optimization method for the thermal environment of urban street canyons based on numerical simulation according to claim 1 or 3, characterized in that, In Step 1, the establishment of the initial model of the urban street canyon includes the following process: S11: For the location area where the urban street canyon is located, establish a computational simulation area; S12: Use polyhedral unstructured grids to mesh the computational simulation area; during the meshing process, locally refine the grids in the areas where the buildings and vegetation are located in the urban street canyon; S13: After meshing, set the boundary conditions for the computational simulation area according to the meteorological data of the urban street canyon; S14: Create a porous vegetation area representing vegetation in the vegetation area of the urban street canyon, load user-defined functions that realize the transpiration, shading, and ventilation effects of the vegetation in the porous vegetation area, and then input the plant data into the user-defined functions to obtain the initial model of the urban street canyon.

5. The optimization method for the thermal environment of urban street canyons based on numerical simulation according to claim 4, characterized in that In Step S11, the distance between the inlet boundary of the computational simulation area and the urban street canyon is 5 - 15 times the maximum building height in the urban street canyon, the distance between the outlet boundary of the computational simulation area and the urban street canyon is 5 - 15 times the maximum building height in the urban street canyon, the distance between the two side boundaries of the computational simulation area and the urban street canyon is 5 - 15 times the maximum building height in the urban street canyon, and the distance between the top boundary of the computational simulation area and the urban street canyon is 3 - 5 times the maximum building height in the urban street canyon.

6. The optimization method for the thermal environment of urban street canyons based on numerical simulation according to claim 4, characterized in that In step S13, for the boundary conditions at the inlet of the computational simulation region, assuming neutral stratification, the inlet profiles of the horizontal wind speed U, turbulent kinetic energy k, and turbulent dissipation rate ε are shown in Equations (1)-(3): In Equation (1), U(Z) represents the horizontal wind speed at height Z; represents the atmospheric boundary layer friction velocity; κ is the von Kármán constant; z0 is the aerodynamic roughness length; In formula (2), C μ is a model constant; For the boundary conditions at the outlet of the computational simulation region, a pressure outlet boundary condition is adopted; for the boundary conditions at the top and both sides of the computational simulation region, a slip boundary condition is adopted.

7. The optimization method for the thermal environment of urban street canyons based on numerical simulation according to claim 4, wherein In step S14, the specific content of the user-defined function is as follows: The user-defined function consists of two parts, namely, the leaf energy balance model and the source term equation; the leaf energy balance model obtains the calculation result based on the input plant data, and the calculation result is the mass flux of water vapor g v,leaf entering the air from the leaf and the sensible heat flux q sen,leaf of the leaf; then the source term equation provides the source terms of heat, mass, and momentum according to the calculation result of the leaf energy balance model, and finally adds the calculation result of the source term equation to the Reynolds-averaged Navier-Stokes equation and the turbulence model in the CFD software to realize the transpiration, shading, and ventilation effects of the simulated vegetation in the initial model of the urban street canyon.

8. The optimization method for the thermal environment of urban street canyons based on numerical simulation according to claim 7, characterized in that, In step S14, the specific content of the blade energy balance model is as follows: The heat exchange and mass exchange between vegetation and air are simulated through the blade energy balance model. It is assumed that the blade energy balance model is stable and the dynamic storage of heat in the blade is ignored. The blade energy balance model is as follows: q rad,leaf -q lat,leaf -q sen,leaf = 0 (4) In Equation (4), q rad,leaf is the net radiation flux on the leaf surface; q lat,leaf is the latent heat flux of the leaf; q sen,leaf is the sensible heat flux of the leaf; Leaf sensible heat flux q sen,leaf and leaf latent heat flux q lat,leaf is the heat transfer from the leaf to the air; Among them, the sensible heat flux q of the blade caused by convective heat transfer between the blade surface and the air sen,leaf is as follows: In Equation (5), h c,h is the convective heat transfer coefficient; T leaf is the blade surface temperature; T is the air temperature; ρ is the air density; c p is the specific heat capacity of air; r a is the aerodynamic resistance of the air in the boundary layer around the blade; the calculation formula for the aerodynamic resistance r a of the air in the boundary layer around the blade is: In formula (6), C is the proportionality factor; l is the characteristic leaf size; is the average velocity vector; where the latent heat flux q from the leaf to the air caused by evapotranspiration lat,leaf is given by: q lat,leaf = L v g v,leaf (7) In Equation (7), L v is the latent heat of evaporation; the mass flux of water vapor g v,leaf entering the air from the leaf is as follows: In Equation (8), h c,m is the convective mass transfer coefficient; p v,leaf is the vapor pressure at the leaf; p v is the vapor pressure above the leaf boundary layer; R a is the gas constant of dry air; p is the ambient pressure; R v is the gas constant of water vapor; r s is the stomatal resistance; The vapor pressure at the leaf is the vapor pressure inside the leaf stomata, and this vapor pressure is close to the saturation vapor pressure p vsat,stom (T leaf ), so it can be assumed that p v,leaf = p vsat,stom (T leaf ); r s In the leaf energy balance model, it is a function of the short-wave radiation flux in the air and the saturation vapor pressure difference D, and the stomatal resistance is calculated using Equation (9): r s = r s,min f1f2 (9) In Equation (9), r s,min is the minimum stomatal resistance on the leaf surface; f1 and f2 are multiplicative functions that describe the changes in stomatal resistance caused by the incident solar radiation and the saturation vapor pressure deficit D, respectively; f2 = 0.4372(1 + D) 0.204 (11) In equations (10) and (11), q rad,inc is the incident solar radiation; D is the saturation vapor pressure deficit; Among them, the net radiation flux q on the blade surface rad,leaf is as follows: q rad,leaf = (1 - τ l - ρ l )q rad,inc (12) In Equation (12), ρ l is the reflectivity of the leaf to solar radiation; τ l is the transmittance of the leaf to solar radiation, and τ l = e -γLAI ; γ is the solar radiation extinction coefficient; LAI is the leaf area index.

9. The optimization method for the thermal environment of urban street canyons based on numerical simulation according to claim 7, characterized in that In step S14, the source term equation includes: The air quality source term s caused by vegetation ρ is as follows: s ρ = a·g v,leaf (13) In Equation (14), a is the leaf area density; g v,leaf is the mass flux of water vapor entering the air from the leaves; The momentum source term caused by vegetation is s u : In Equation (14), c d is the blade drag coefficient; ρ is the air density; is the average velocity vector; The temperature source term caused by vegetation is s T : In formula (15), c p is the specific heat capacity of air; The humidity source term caused by vegetation is s w : The turbulent kinetic energy source term s caused by vegetation k is as follows: In Equation (17), k is the turbulent kinetic energy of air; the constant β p is the fraction of the mean kinetic energy converted into turbulent kinetic energy; the constant β d describes the reduction of the turbulent kinetic energy and the turbulent dissipation rate by vegetation; Source term \(s\) of turbulent dissipation rate caused by vegetation ε is as follows: In Equation (18), ε is the turbulent dissipation rate of air; C 4ε and C 5ε are both model constants.

10. The optimization method for the thermal environment of urban street canyons based on numerical simulation according to claim 1, characterized in that In step 2, the heat mitigation strategies are the cool pavement strategy, the cool roof strategy, and the green roof strategy; the cool pavement strategy means replacing the dark concrete pavement with a light white cement concrete pavement; the cool roof strategy means replacing the dark concrete roof with a highly reflective roof; the green roof strategy means adding a layer of plants to the building roof. In step 3, the air temperature at the pedestrian height in the region of interest is output, and the mean radiant temperature, wind speed, and relative humidity of the air in the comparison model operation results are output; then the universal thermal climate index is calculated based on the air temperature, mean radiant temperature, wind speed, and relative humidity.

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