Street type vegetable market ceiling optimization design method based on CFD simulation

Abstract

A CFD (computational fluid dynamics) simulation-based street type vegetable market ceiling optimization design method relates to the field of urban environmental engineering and comprises the following steps: 1) performing field investigation and surveying and mapping on a selected street type vegetable market; 2) modeling a street type vegetable market according to a field investigation and surveying result; 3) utilizing a CFD simulation technology to simulate wind fields of the street type vegetable market and surrounding residential areas; 4) designing different ceiling covering conditions; 5) gaseous pollutant identification and diffusion simulation; and 6) according to different gaseous pollutant types, setting corresponding concentration control standards, according to pollutant diffusion simulation results, analyzing distribution of gaseous pollutant concentrations in the internal space of the vegetable market covered by the ceiling and on the surface of a residential building above the ceiling, determining key parameters in ceiling design, and obtaining an optimal scheme of ceiling design. By optimizing the ceiling design of the vegetable market, the influence of air pollution on resident health is reduced, and coordinated development of the environment and human health is promoted.

Description

Technical Field

[0001] The present invention relates to the field of urban environmental engineering, and particularly to an optimized design method for the ceiling of a street - type wet market based on CFD simulation. Background Art

[0002] With the acceleration of the urbanization process, the renewal and transformation of old urban areas have become important issues in urban planning and development. Especially in old urban areas, street - type wet markets, as an important part of residents' daily lives, their environmental quality directly affects the living quality and health of residents. Street - type wet markets are usually located in the streets of residential areas. Due to historical and spatial limitations, these markets often have problems such as dirty and messy internal space environments and poor ventilation effects. Especially in the seafood and meat sales areas, the diffusion of odors not only affects the environmental hygiene of the market but may also have a negative impact on the living environment of surrounding residents.

[0003] The street - type wet markets in old urban areas are insufficient in natural ventilation, resulting in poor internal air quality. Especially during the high - temperature period in summer, the problems of odors and humidity are particularly prominent. The traditional design of the ceiling of street - type wet markets often relies on the experience and intuition of designers, lacking systematic scientific analysis and optimization. Existing research mainly focuses on the wind environment characteristics such as wind speed and wind direction in wet markets, while there is relatively little research on the diffusion characteristics of pollutants, especially the impact of gaseous pollutants such as odors on the internal environment of wet markets and the surrounding environment. In addition, the design of the wet - market ceiling plays an important role in improving ventilation conditions and controlling pollutant diffusion, but there is currently a lack of a systematic method for optimizing the design of wet - market ceilings. Summary of the Invention

[0004] The purpose of the present invention is to solve the above problems in the prior art, and provide an optimized design method for the ceiling of a street - type wet market based on CFD simulation. By using computational fluid dynamics (CFD) simulation to guide the ceiling design, and through simulation analysis of the influence of different ceiling design parameters on the internal wind environment and pollutant diffusion in the wet market, a scientific optimization plan for the wet - market ceiling design is provided, in order to improve the wind environment of the market, control pollutant diffusion, reduce the impact on the surrounding residential areas, and enhance the overall environmental quality of old urban areas.

[0005] To achieve the above - mentioned purpose, the present invention adopts the following technical solutions:

[0006] An optimized design method for the ceiling of a street - type wet market based on CFD simulation, comprising the following steps:

[0007] 1) Conduct on - site investigation and surveying of the selected street - type wet market, including street scale, building height, road width, and booth layout information;

[0008] 2) Based on the results of on-site investigation and surveying, model the street-corner wet market, including buildings, roads, stalls, and canopy details;

[0009] 3) Use CFD simulation technology to simulate the wind field of the street-corner wet market and the surrounding residential areas;

[0010] 4) Design different canopy covering conditions, including a combination of various canopy heights and canopy opening sizes;

[0011] 5) Identification and diffusion simulation of gaseous pollutants: Measure the types of gaseous pollutants in the street-corner wet market on-site, and simulate the transmission and transformation processes of the corresponding pollutants in the fluid through user-defined functions (UDF), and simulate the influence of different canopy design parameters on the pollutant concentrations around different residential floors of the street-corner wet market and its surroundings;

[0012] 6) Obtain the optimal canopy design plan: According to different types of gaseous pollutants, set corresponding concentration control standards, analyze the distribution of gaseous pollutant concentrations in the internal space of the wet market under the canopy coverage and on the surface of the residential buildings above the canopy based on the pollutant diffusion simulation results, determine the key parameters in the canopy design, and obtain the optimal canopy design plan.

[0013] In step 3), the CFD simulation includes computational domain setting, mesh generation, fluid model setting, boundary condition setting, solution algorithm setting, and wind field calculation setting.

[0014] For the computational domain setting, based on the height H of the tallest building, the building is 5H away from the inflow boundary, 10H away from the outflow boundary, 5H on both sides in the width direction, and 5H in the height direction of the wind field.

[0015] For the mesh generation, a hybrid mesh is used, and the mesh is refined at the bottom and in the building group area, with the minimum mesh resolution being 0.5 - 5m; the maximum surface mesh of the overall area model is 10m, and the minimum is 10mm; the maximum surface mesh of the detail model is 1m, and the minimum is 10mm. Finally, all computational domains are discretized using hexahedral meshes.

[0016] For the fluid model setting, the Realizable k-ε model in RANS is used to predict the average air flow velocity and pollutant concentration, and ANSYS Fluent 2020R1 is used for simulation, and the finite volume method is selected to discretize the governing equations.

[0017] The boundary conditions are set as follows: the inlet is a velocity inlet, the outlet is a pressure outlet, the ground and buildings are set as non-slip wall surfaces, the two sides and the top of the domain are symmetric boundaries, and the top and sides of the computational domains of the street-corner wet market model and the canopy model are both in-wind conditions.

[0018] The specific parameters for the boundary condition setting include the vertical wind speed at the air inlet , the friction velocity of the atmospheric boundary layer , the turbulent kinetic energy and the energy dissipation rate ;

[0019] The vertical wind speed profile at the air inlet follows the exponential law, and the specific calculation formula is:

[0020]

[0021] where is the average wind speed at the standard reference height, z is any height, is the standard reference wind speed height, is the ground roughness index;

[0022] The specific calculation formulas for the friction velocity of the atmospheric boundary layer, the turbulent kinetic energy and the energy dissipation rate are as follows:

[0023]

[0024]

[0025]

[0026] where is an empirical constant, k is the von Kármán constant, is the ground roughness length.

[0027] For the solution algorithm setting, the SIMPLEC algorithm is used to solve the coupled pressure-velocity equation and the second-order upwind scheme is used for discretization; for the wind field calculation setting, all field variables converge with a residual of 10 -5 , the pressure converges with a residual of 10 -4 , and for the scalar transport simulation, the residual converges to 10 -6 , and the calculation iteration runs for more than 1000 times.

[0028] In step 4), a typical three-piece sloping ceiling is selected, where H1 is the vertical distance from the eaves of the two sloping ceilings to the ground, and H2 is the vertical height difference between the eaves of the two ceilings and the eaves of the middle ceiling. The heights H1 and H2 are controlled with single variables respectively to form a variety of combinations of ceiling heights and ceiling opening sizes. Among them, the maximum ceiling height does not exceed 4.5 m to 5 m, and the minimum ceiling height is 2.5 to 3 m.

[0029] In step 5), the pollutant control equation is as follows:

[0030]

[0031] Among them, is the pollutant concentration, is the velocity component of the fluid in the direction, is the diffusion coefficient of the pollutant, indicating the transport of the pollutant in space due to molecular diffusion, represents the source term, indicating the generation or consumption rate of the pollutant within the considered control volume, and t is the time, used to describe the change of the pollutant concentration over time.

[0032] Compared with the prior art, the beneficial effects achieved by the technical solution of the present invention are:

[0033] 1) Environmental quality improvement: By simulating the influence of different ceiling design parameters on the pollutant concentration, the present invention can propose effective ceiling optimization schemes, reduce the pollutant concentration inside and around the vegetable market, and thus directly improve the living environmental quality of residents;

[0034] 2) Provision of scientific basis: The evaluation method of the present invention provides a scientific basis for urban air quality management, helps to guide the actual air quality improvement work, and thus realizes more effective air quality control and management;

[0035] 3) Provision of targeted improvement measures: Based on the evaluation results of the present invention, more targeted specific improvement measures can be proposed, such as adjusting the ceiling design, optimizing the market layout, etc. These measures can more effectively solve specific environmental problems and improve the implementation effect of the improvement measures;

[0036] 4) Promotion of the coordinated development of the environment and health: By optimizing the ceiling design of the vegetable market, the present invention helps to reduce the impact of air pollution on the health of residents and promotes the coordinated development of the environment and human health. Description of the Drawings

[0037] Figure 1 is a flow schematic diagram of the present invention.

[0038] Figure 2 is a schematic diagram of the ceiling section in a specific embodiment of the present invention.

[0039] Figure 3 is a distribution diagram of the average concentration of the current situation Case1 in the Y value and Z value in a specific embodiment of the present invention; among them, a is Y = 975m; b is Y = 960m; c is Y = 950m; d is Z = 1m; e is Z = 1.5m; f is Z = 5.5m; g is Z = 9.5m.

[0040] Figure 4This is the pollutant concentration profile distribution diagram at Y = 960m with the change of H1 height in the specific embodiment of the present invention; where a is Case2 (H1 = 3m); b is Case3 (H1 = 3.5m); c is Case4 (H1 = 4m).

[0041] Figure 5 This is the pollutant concentration profile distribution diagram at Y = 960m with the change of H2 height in the specific embodiment of the present invention; where a is Case5 (H2 = 0.6m); b is Case9 (H2 = 0.7m); c is Case13 (H2 = 0.8m); d is Case16 (H2 = 0.9m); e is Case19 (H2 = 1m). Specific Embodiment

[0042] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer and more understandable, the following further elaborates on the present invention in combination with the accompanying drawings and embodiments.

[0043] See Figure 1 , a method for optimizing the design of the ceiling of a street - type wet market based on CFD simulation according to the present invention includes the following steps:

[0044] 1) On - site investigation and surveying: Conduct on - site investigation and surveying on the selected street - type wet market to determine specific street scales, building heights, road widths, stall layouts and other information;

[0045] 2) Establishing a geometric model: According to the results of on - site investigation and surveying, model the street - type wet market, including details such as buildings, roads, stalls and ceilings;

[0046] 3) Using CFD simulation technology to simulate the wind field of the street - type wet market and its surrounding residential areas;

[0047] 4) Designing different ceiling covering conditions, including a combination of various ceiling heights and ceiling opening sizes;

[0048] 5) Gaseous pollutant identification and diffusion simulation: Determine the types of gaseous pollutants in the street - type wet market on - site, and simulate the transmission and conversion processes of the corresponding pollutants in the fluid through user - defined functions (UDF), and simulate the influence of different ceiling design parameters on the pollutant concentrations around different residential floors of the street - type wet market and its surroundings;

[0049] 6) Obtain the optimal ceiling design plan: Set corresponding concentration control standards according to different types of gaseous pollutants. Based on the pollutant diffusion simulation results, analyze the distribution of gaseous pollutant concentrations in the internal space of the wet market under the ceiling coverage and on the surface of the residential building above the ceiling, identify the high-risk areas of pollutant concentration, as well as the trends and patterns of pollutant concentration changes. Further, by comparing the simulation results under different working conditions, determine the optimal solutions for the key parameters in the ceiling design, and then obtain the optimal ceiling design plan to ensure that while meeting the comfort and health needs of pedestrians, the pollutant diffusion to the surrounding residential areas is minimized to the greatest extent.

[0050] In step 3), the CFD simulation includes computational domain setting, mesh generation, fluid model setting, boundary condition setting, solution algorithm setting, and wind field calculation setting.

[0051] For the computational domain setting, it is set according to the AIJ guidelines of the Architectural Institute of Japan. Based on the height H of the tallest building, the building is 5H away from the inflow boundary, 10H away from the outflow boundary, 5H in the width direction on both sides, and 5H in the height direction of the wind field.

[0052] For the mesh generation, to ensure that the results are not affected by the mesh layout, a hybrid mesh is set, and its resolution is determined through mesh independence tests. Further, the bottom and the building complex area of the model computational domain are meshed more densely, with the minimum mesh resolution being 0.5 - 5m; the maximum surface mesh of the overall area model is 10m, and the minimum is 10mm; the maximum surface mesh of the detailed model is 1m, and the minimum is 10mm. Finally, all computational domains are discretized by hexahedral meshes to obtain the simulation meshes of the overall area model.

[0053] For the fluid model setting, the Realizable k-ε model in RANS is used to predict the average air velocity and pollutant concentration, and ANSYS Fluent 2020R1 is used for simulation, and the finite volume method is selected to discretize the governing equations.

[0054] The boundary conditions are set as follows: the inlet is a velocity inlet, the outlet is a pressure outlet, the ground and the buildings are set as non-slip wall surfaces, the two sides and the top of the domain are symmetric boundaries, and the top and sides of the computational domains of the street-type wet market model and the ceiling model are both in-wind conditions.

[0055] The specific parameters of the boundary condition setting include the vertical wind speed at the air inlet, the friction velocity of the atmospheric boundary layer, the turbulent kinetic energy and the energy dissipation rate ;

[0056] The vertical wind speed profile at the air inlet follows the exponential law, and the specific calculation formula is:

[0057]

[0058] Among them, is the average wind speed at the standard reference height (10 m). In the present invention, is set to 3.4 m / s, which is the average summer wind speed recorded in Xiamen (Calculation Standard for Green Performance of Civil Buildings JGJ / T 449-2018). z is any height. is the standard reference wind speed height, with a value of 10 m. is the ground roughness index, which is taken as 0.22 according to the urban characteristics and building density.

[0059] The friction velocity of the atmospheric boundary layer, the turbulent kinetic energy and the energy dissipation rate

[0060]

[0061]

[0062]

[0063] Among them, is an empirical constant, k = 0.41 is the von Kármán constant. is the ground roughness length, with a value of 0.1 m, representing the situation of ground crops or occasional large obstacles.

[0064] For the setting of the solution algorithm, the SIMPLEC algorithm is used to solve the coupled pressure-velocity equation, and the second-order upwind scheme is used for discretization; for the setting of the wind field calculation, all field variables converge with a residual of 10 -5 , the pressure converges with a residual of 10 -4 . For the scalar transport simulation (pollutant dispersion), the residual convergence reaches 10 -6 , and the calculation iteration runs more than 1000 times.

[0065] See Figure 2 . In step 4), a typical three-piece sloping roof is selected. Among them, H1 is the vertical distance from the eaves of the two sloping roofs to the ground, and H2 is the vertical height difference between the eaves of the two roofs and the eaves of the middle roof. The heights H1 and H2 are respectively controlled with a single variable to form a variety of combinations of roof heights and roof opening sizes; among them, limited by the building height, the maximum roof height does not exceed 4.5 m to 5 m, and at the same time, ventilation and pedestrian movement should be ensured. The minimum roof height is 2.5 to 3 m. Therefore, H1 + H2 ≤ 4.5 m and H1 ≥ 2.5 m.

[0066] Referring to Table 1, it is designed for different ceiling covering conditions. In this embodiment, the current condition Case1 is used as the reference scenario, with the height H1 being 2.5 m and the height H2 being 0.5 m. Further, the heights H1 and H2 are controlled with single variables respectively. H2 is fixed at 0.5 m, and the variable of H1 is controlled to change at a height of 0.5 m; H1 is fixed at 2.5 m, and the variable of H2 is controlled to change at a height of 0.1 m. The specific solutions are as follows: Among them, when controlling the change of H1 height, it corresponds to Case2, Case3, and Case4; among them, when controlling the change of H2 height, it corresponds to Case5, Case9, Case13, Case16, and Case19.

[0067] Table 1

[0068]

[0069] In step 5), the pollutant control equation is as follows:

[0070]

[0071] This equation combines the effects of convection, diffusion, and source terms. Among them, is the pollutant concentration, is the velocity component of the fluid in the direction; is the diffusion coefficient of the pollutant, indicating the transport of the pollutant in space due to molecular diffusion; represents the source term, indicating the generation or consumption rate of the pollutant within the considered control volume, which can be the change in pollutant concentration caused by chemical reactions, boundary conditions, or other sources; t is time, used to describe the change of pollutant concentration over time.

[0072] Figure 3 In a~c, they are the average concentration distribution diagrams of the current condition Case1 in the present invention's specific embodiment at Y = 950 m, Y = 960 m, and Y = 975 m. Comparing the three groups of wind profiles, the position at Y = 975 m is at the air outlet, and the position at Y = 950 m is at the intersection of the longitudinal and transverse streets. The results of both are greatly affected by other factors. Therefore, in the specific embodiment of the present invention, the one at Y = 960 m will be selected for cloud map comparison and analysis.

[0073] Figure 3Figures d - g show the average pollutant concentration distribution diagrams at different heights for the current operating condition Case1 (Y = 960m). Among them, in figure d, Z = 1m; in figure e, Z = 1.5m; in figure f, Z = 5.5m; in figure g, Z = 9.5m; the 1m height represents the breathing zone when the vendor is sitting, the 1.5m height represents the breathing zone of the ground - floor pedestrians, the 5.5m height represents the breathing zone of the second - floor residents, and the 9.5m height represents the breathing zone of the third - floor residents. Considering that the vendors and pedestrians mainly move at a height of 1.5m, and the impact of gaseous pollutants on the three - story residents is relatively small. Therefore, in order to more clearly analyze the relationship between the impact of gaseous pollutants on human health and living quality, in this specific embodiment, the impacts of heights H1 and H2 on the wind speed (V1) and concentration value (C1) at 1.5m inside the ceiling and the wind speed (V2) and concentration value (C2) at 5.5m above the ceiling are mainly discussed.

[0074] In this embodiment, the simulation results show that the changes in the ceiling height H1 and the opening size H2 have a significant impact on the airflow distribution and pollutant diffusion inside the street - type wet market and on the surface of the residential building above the ceiling. As Figure 4 shown, based on the ceiling design parameters of Case1, as the ceiling height H1 increases, both the concentration C1 inside the ceiling and the concentration C2 above the ceiling show a downward trend. Among them, the changes in the concentration C2 above the ceiling for Case2, Case3, and Case4 are not significant; as Figure 5 shown, as the opening size H2 of the ceiling increases, the concentration C1 inside the ceiling shows a trend of first increasing and then decreasing, while the concentration C2 above the ceiling continues to increase. It can be seen that increasing the ceiling height H1 is beneficial to increasing the wind speed V1 inside the ceiling and reducing the concentration C1 inside the ceiling. When the opening size H2 of the ceiling increases, the airflow distribution and concentration inside and outside the ceiling are negatively correlated.

[0075] For the airflow distribution in this embodiment, the change in the opening size H2 of the ceiling is more significant than the change in the ceiling height H1, and their combined change can increase the wind speed V1 inside the ceiling.

[0076] For the distribution of gaseous pollutant concentration in this embodiment, as the ceiling height H1 increases, both the concentration C1 inside the ceiling and the concentration C2 above it decrease. As the opening size H2 increases, the concentration C1 inside the ceiling decreases, but the concentration C2 above the ceiling shows the opposite trend. When the ceiling height H1 and the opening size H2 act synergistically, the influence of the opening size is more significant than the change in height.

[0077] Therefore, for this embodiment, when adding a local ceiling, the ceiling height can be determined first, and then the opening size of the ceiling can be determined to meet the health needs of residents and achieve the goal of improving the comfortable living environment.

[0078] In summary, the present invention provides an optimized design method for the ceiling of a street-type wet market based on CFD simulation. By simulating the effects of different ceiling design parameters on the wind environment and pollutant diffusion, it scientifically evaluates and proposes optimization solutions, aiming to improve the air quality inside the wet market, reduce the pollutant diffusion to the surrounding residential areas, enhance the quality of the living environment of residents, and at the same time provide a scientific basis and technical support for urban air quality management and the improvement of the street-type market environment.

[0079] As described above, it is only an embodiment of the present invention, and does not impose any limitation on the technical scope of the present invention. Therefore, any minor modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A street-type vegetable market ceiling optimization design method based on CFD simulation, characterized in that: The following steps are involved: 1) Conduct on-site surveys and mapping of selected street-type vegetable markets, including street scale, building height, road width, and stall layout information; 2) Based on the results of on-site investigation and mapping, model the street-type vegetable market, including details of buildings, roads, stalls and roofs; 3) Use CFD simulation technology to simulate the wind field of street-type vegetable markets and surrounding residential areas; 4) Design different ceiling coverage conditions, including various combinations of ceiling heights and ceiling opening sizes; 5) Identification and diffusion simulation of gaseous pollutants: The types of gaseous pollutants in the street-type vegetable market were measured on site, and the transmission and transformation process of the corresponding pollutants in the fluid was simulated through user-defined functions (UDFs). The effects of different ceiling design parameters on the concentration of pollutants around the street-type vegetable market and different residential floors were simulated; 6) Determine the optimal solution for ceiling design: According to the different types of gaseous pollutants, set the corresponding concentration control standards. According to the pollutant diffusion simulation results, analyze the distribution of gaseous pollutant concentrations in the interior space of the vegetable market covered by the ceiling and on the surface of the residential building above the ceiling, determine the parameters in the ceiling design, and obtain the optimal solution for the ceiling design.

2. The method for optimizing the ceiling design of a street-type vegetable market based on CFD simulation as claimed in claim 1, characterized in that: In step 3), the CFD simulation includes calculation domain setting, grid division, fluid model setting, boundary condition setting, solution algorithm setting and wind field calculation setting.

3. The method for optimizing the ceiling design of a street-type vegetable market based on CFD simulation as claimed in claim 2, characterized in that: The calculation domain is set up with the height H of the highest building as the benchmark, the building is 5H away from the inflow boundary, 10H away from the outflow boundary, 5H in the width direction on both sides, and 5H in the wind field height direction.

4. The method for optimizing the ceiling design of a street-type vegetable market based on CFD simulation as claimed in claim 2, characterized in that: The grid division adopts a hybrid grid, and the grids in the bottom and building complex areas are encrypted, with a minimum grid resolution of 0.5~5m; the maximum value of the surface grid of the overall area model is 10m, and the minimum value is 10mm; the maximum value of the surface grid of the detail model is 1m, and the minimum value is 10mm. Finally, all calculation domains are discretized through hexahedral grids.

5. The method for optimizing the ceiling design of a street-type vegetable market based on CFD simulation as claimed in claim 2, characterized in that: The fluid model is set up using the Realizable k-ε model in RANS to predict the average airflow velocity and pollutant concentration, ANSYS Fluent 2020R1 is used for simulation, and the finite volume method is used to discretize the control equations.

6. The method for optimizing the ceiling design of a street-type vegetable market based on CFD simulation as claimed in claim 2, characterized in that: The boundary conditions are set as follows: the inlet is a velocity inlet, the outlet is a pressure outlet, the ground and the building are set as no-slip walls, the two sides and the top of the domain are symmetrical boundaries, and the top and sides of the calculation domain of the street-type vegetable market model and the ceiling model are all wind inlet conditions.

7. The method for optimizing the ceiling design of a street-type vegetable market based on CFD simulation as claimed in claim 2, characterized in that: The specific parameters of the boundary condition setting include the vertical wind speed at the air inlet , friction velocity of the atmospheric boundary layer , turbulent kinetic energy and energy dissipation rate ; The vertical wind speed at the air inlet The profile follows the exponential law, and the specific calculation formula is: in, is the average wind speed at the standard reference height, z is an arbitrary height, is the standard reference wind speed height, is the surface roughness index; The friction velocity of the atmospheric boundary layer , turbulent kinetic energy and energy dissipation rate The specific calculation formula is as follows: in, is an empirical constant, k is the von Karman constant, The length of the rough ground.

8. The method for optimizing the ceiling design of a street-type vegetable market based on CFD simulation as claimed in claim 2, characterized in that: The solution algorithm is set to use the SIMPLEC algorithm to solve the coupled pressure-velocity equation and discretize it using the second-order upwind format; the wind field calculation is set to use 10 for all field variables. -5 The residual converges, and the pressure is 10 -4 The residual convergence of 10 -6 , running the calculation iterations for more than 1000 times.

9. The method for optimizing the ceiling design of a street-type vegetable market based on CFD simulation as claimed in claim 1, characterized in that: In step 4), a typical three-piece sloping ceiling is selected, where H1 is the vertical distance from the eaves of the sloping ceilings on both sides to the ground, and H2 is the vertical height difference between the eaves of the ceilings on both sides and the eaves of the middle ceiling. The heights H1 and H2 are controlled as single variables to form a combination of various ceiling heights and ceiling opening sizes, where the highest ceiling height does not exceed 4.5m~5m, and the lowest ceiling height is 2.5~3m.

10. The method for optimizing the ceiling design of a street-type vegetable market based on CFD simulation as claimed in claim 1, characterized in that: In step 5), the pollutant control equation is as follows: in, is the pollutant concentration, The fluid is The velocity component in the direction, is the diffusion coefficient of the pollutant, which indicates the transport of the pollutant in space due to molecular diffusion. represents the source term, which indicates the generation or consumption rate of pollutants in the control volume under consideration, and t is the time, which is used to describe the change of pollutant concentration over time.

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