A clean room air conditioning air outlet optimization design method based on fluid dynamics

Abstract

The application relates to the technical field of clean room air conditioner air outlet design, in particular to a clean room air conditioner air outlet optimization design method based on fluid dynamics; through construction of a multi-physical field coupling simulation model based on a fluid dynamics model, accurate prediction of a temperature field, a flow velocity field and a pollutant concentration field in the clean room is realized; the method takes Reynolds average NS equations and a discrete phase model as cores, combines a Standard k-epsilon turbulent flow model and a wall surface function method, effectively captures complex air flow organization and particle diffusion behavior in the clean room, provides a high-precision data basis for subsequent multi-target optimization, minimizes energy consumption under the premise of guaranteeing air quality, and has important engineering application value; and the technical problem that existing clean room air conditioner air outlet design methods mainly depend on experience and industry standards or focus on optimization of a single parameter and fail to fully consider the action of multiple factors is solved.

Description

Technical Field

[0001] This invention relates to the field of cleanroom air conditioning vent design technology, and in particular to a cleanroom air conditioning vent optimization design method based on fluid dynamics. Background Technology

[0002] Cleanrooms, as highly controlled environments, are crucial in the design of their air conditioning vents and are widely used in numerous fields such as integrated circuit manufacturing, pharmaceutical production, and medical surgery. In these industries, the presence of airborne particles, microorganisms, and chemical contaminants can severely impact product quality, patient health, and the stability of production processes. Therefore, the design and optimization of cleanrooms are essential to ensuring a high-quality production environment.

[0003] Traditional cleanroom design methods rely primarily on experience and industry standards. While these methods can meet basic cleanliness requirements, they often fall short when faced with complex airflow patterns, the interaction of multiple physical quantities (such as temperature, air velocity, and contaminant concentration), and energy efficiency demands. This is especially true for large, complex cleanroom structures, where traditional design methods struggle to accurately predict airflow and contaminant distribution, hindering the precise design of air conditioning vent parameters. This can lead to issues such as airflow short-circuiting and excessive contaminant concentrations, impacting production efficiency and product quality. With the development of fluid dynamics (CFD) technology, simulating and optimizing airflow has become possible. However, existing CFD applications mainly focus on optimizing single parameters, such as airflow velocity or temperature distribution, without comprehensively considering the combined effects of multiple key factors, including temperature, air velocity, and contaminant concentration. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a fluid dynamics-based method for optimizing the design of cleanroom air conditioning vents. This method solves the technical problem that existing cleanroom air conditioning vent design methods mainly rely on experience and industry standards, or focus on optimizing a single parameter, failing to fully consider the effects of multiple factors.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for optimizing the design of cleanroom air conditioning vents based on fluid dynamics, the method specifically including the following steps:

[0006] Create a 3D model of the cleanroom containing all the objects;

[0007] A fluid dynamics model based on the Reynolds-averaged Navier-Stokes equations and the discrete phase model was constructed within the 3D model, and the Standard k-epsilon turbulence model and the wall function method were selected to calculate the flow field of the cleanroom.

[0008] Establish the governing equations, initial conditions, and boundary conditions for fluid dynamics model simulation to obtain simulation results that include the temperature field, flow velocity field, and contaminant concentration field within the cleanroom.

[0009] Based on the simulation results, multi-objective optimization was carried out with constraints such as thermal comfort, velocity non-uniformity, and pollutant concentration in the clean room, and with the optimization objectives of minimizing pollutant concentration and minimizing energy consumption, to obtain the optimized air conditioning vent parameters.

[0010] Preferably, the step of selecting the Standard k-epsilon turbulence model and the wall function method for flow field calculation in the cleanroom specifically includes:

[0011] The 3D model of the cleanroom is divided into a turbulent core area and a region near the wall.

[0012] The flow field of the cleanroom was calculated using the Standard k-epsilon turbulence model and the wall function method for the turbulent core region and the region near the wall, respectively.

[0013] Preferably, the governing equations include the mass conservation equation, momentum conservation equation, energy conservation equation, turbulent kinetic energy equation, turbulent dissipation rate equation, turbulent viscosity formula, and component conservation equation.

[0014] Preferably, the steps for setting up the discrete phase model are as follows:

[0015] The particle motion physics model in the discrete phase model is designed to consider Samarf lift and virtual mass force.

[0016] The jet source type is set to a wall-mounted particle jet source, the particle type ejected by the particle jet source is inert particles, the particle size and total particle flow rate are preset values, and the particle size is uniform.

[0017] Particle property parameters that correspond to the actual cleanroom environment are set in the material region of the discrete phase model.

[0018] Preferably, the boundary conditions are:

[0019] Set the cleanroom roof and walls in the 3D model as insulated walls;

[0020] Personnel and lighting are set as surface heat sources with a constant heat flux density, equipment is set as volume heat sources with a fixed internal heat source intensity, and corresponding heat source intensities are set for each heat source.

[0021] Calculate the supply air velocity based on the rated air volume and air outlet size, and set it as the velocity inlet boundary condition;

[0022] Set the air outlet location as a pressure outlet boundary condition.

[0023] Preferably, the specific steps for obtaining simulation results including the temperature field, flow velocity field, and contaminant concentration field within the cleanroom are as follows:

[0024] Mesh the computational region in the 3D model and give each mesh point a unique control volume around it;

[0025] A set of discrete equations is obtained by integrating the control equations to be solved over each control volume.

[0026] The discrete equations are solved using the SIMPLE algorithm to obtain simulation results that include the temperature field, flow velocity field, and pollutant concentration field within the cleanroom.

[0027] Preferably, the specific steps of the multi-objective optimization are as follows:

[0028] Sampling is performed based on simulation results from a fluid dynamics model;

[0029] Based on the sampling results, a mathematical model of the agent is constructed using the response surface methodology;

[0030] A mathematical model based on an agent is used to perform multi-objective optimization based on the optimization objective and constraints to obtain the Pareto front.

[0031] The optimized air conditioning vent parameters are obtained by selecting a set of solutions that satisfy the optimization objective from the Pareto front.

[0032] Preferably, the specific steps for constructing the mathematical model of the agent are as follows:

[0033] Based on the sampling results, a mathematical model of the agent is constructed using the response surface methodology;

[0034] Set up verification points that include air conditioning vent parameters, substitute the data from the verification points into the agent's mathematical model, and calculate the fitting accuracy between the simulation results of the verification points on the fluid dynamics model and the prediction results output by the agent's mathematical model.

[0035] If the fitting accuracy meets the preset requirements, the mathematical model of the agent will be output.

[0036] Otherwise, output the default prompt.

[0037] By employing the above technical solution, the present invention provides a method for optimizing the design of cleanroom air conditioning vents based on fluid dynamics, which has at least the following beneficial effects:

[0038] 1. This invention achieves accurate prediction of temperature, velocity, and pollutant concentration fields in cleanrooms by constructing a multiphysics coupled simulation model based on a fluid dynamics model. It breaks through the limitations of traditional experience-based design and significantly improves the scientificity and reliability of air outlet design. This method takes the Reynolds-averaged Navier-Stokes equations and discrete phase models as its core, combined with the Standard k-epsilon turbulence model and wall function method, to effectively capture the complex airflow organization and particulate matter diffusion behavior in cleanrooms. This provides a high-precision data foundation for subsequent multi-objective optimization, thereby minimizing energy consumption while ensuring air quality, and has significant engineering application value.

[0039] 2. This invention employs a multi-objective optimization strategy, using thermal comfort, velocity non-uniformity, and pollutant concentration as constraints, and minimizing pollutant concentration and energy consumption as dual objectives. By constructing a surrogate model using response surface methodology and combining it with Pareto front analysis, the system optimization of parameters such as air supply velocity, temperature, and air outlet location and size is achieved. This method can significantly reduce energy consumption and improve the overall energy efficiency of the system while meeting the ISO 7 cleanroom standard, while avoiding the local optimum problem that may be caused by single parameter optimization. It has strong practicality and promotion value.

[0040] 3. This invention introduces a discrete phase model (DPM) and sets particulate matter physical properties that match the actual environment, enabling accurate simulation of the movement trajectory and concentration distribution of solid particles in cleanrooms. It is particularly suitable for clean environments with strict requirements on the concentration of particles ≥0.5μm. This method considers key physical mechanisms such as Samarf lift, virtual mass force, and wall interaction, significantly improving the accuracy and reliability of pollutant diffusion prediction and providing strong technical support for cleanroom air quality assessment and vent design.

[0041] 4. This invention utilizes the response surface methodology to construct a high-precision surrogate model, replacing time-consuming direct CFD simulation, which significantly improves the computational efficiency of multi-objective optimization. By verifying the fitting accuracy at validation points, the reliability of the surrogate model is ensured. Thus, while guaranteeing the accuracy of optimization results, the design cycle is significantly shortened and the computational resource requirements are reduced. This method is suitable for rapid iterative design and parameter optimization in complex cleanroom environments, and has good engineering applicability and economic benefits. Attached Figure Description

[0042] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0043] Figure 1 This is a flowchart of the cleanroom air conditioning vent optimization design method based on fluid dynamics, as described in this invention. Detailed Implementation

[0044] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0045] To address the problem that existing cleanroom air conditioning vent design methods primarily rely on experience and industry standards, or focus on optimizing a single parameter without fully considering the effects of multiple factors, this invention provides a fluid dynamics-based cleanroom air conditioning vent optimization design method. This method comprehensively considers temperature, flow rate, and contaminant concentration to improve air quality while reducing energy consumption. The method specifically includes the following steps:

[0046] First, a 3D model of the cleanroom containing various objects needs to be created. These objects mainly include various equipment, obstacles, walls, and other objects within the cleanroom.

[0047] First, the selection of a turbulence model is necessary. Theoretically, the Navier-Stokes (NS) equations are considered a closed system of equations, containing one mass conservation equation and three momentum conservation equations to solve for four physical quantities: three velocity components and pressure. However, due to the limitations of Direct Numerical Simulation (DNS) methods, which consume a large amount of computational resources, this method is difficult to widely apply in industry. Instead, the Reynolds-averaged (RANS) method is used to perform time averaging on the turbulence fluctuation terms. That is, a flow field model based on the Reynolds-averaged NS equations is constructed within the 3D model. This method simplifies turbulence calculation by eliminating the time scale of instantaneous fluctuations through statistical averaging, reducing the computational load to a level that can be handled by conventional computers without affecting the accuracy of the calculation results. However, it introduces additional nonlinear terms, making the NS equations no longer closed. Therefore, various turbulence models have emerged, each suitable for solving the flow field states that they are good at.

[0048] The fluid dynamics (CFD) model includes a total of 11 turbulence models, such as the Inviscid inviscid model, the Laminar laminar model, the Spalart-Allmaras (SA) single-equation model, the k-epsilon two-equation model, the k-omega two-equation model, the Transition k-kl-omega three-equation model, the Transition SST four-equation model, the Reynolds Stress model, the Scale-Adaptive Simulation (SAS) turbulence model, the Detached Eddy Simulation (DES) separated eddy model, and the Large Eddy Simulation (LES) large eddy simulation model. Among them, the inviscid model is used for inviscid calculations and is typically applied to flows where viscous forces are negligible relative to inertial forces. The laminar model is only used when the flow state within the computational domain is laminar. The SA model is very effective for calculating geometrically simple external flow fields, especially in flow regions with adverse pressure gradients, and is therefore often used for aerospace external flow field calculations. The k-omega model considers the effects of low Reynolds number, fluid compressibility, and shear flow diffusion, and is therefore generally suitable for calculations such as wake flow, jet flow, and free shear flow. The three-equation transition model and the four-equation transition model are typically used for the transition process from laminar to turbulent flow. The Noorst stress model does not use the isotropic assumption of other RANS models, making it more suitable for strong swirling flow scenarios. The SAS turbulence model is mainly used for transient turbulent flow problems. The DES separated eddy model is a hybrid model that uses RANS in the near-wall region and switches to LSE in the separation region far from the wall. It is suitable for separated flows in turbine blade passages and high Reynolds number wall flows in pipe abrupt expansion flows, as well as external flow field problems with significant flow separation such as aircraft wing stall, car wake, and missile separation. The LSE large eddy simulation model directly analyzes large-scale turbulent structures and is suitable for combustion instability analysis of turbulent mixing and interaction of combustion chamber flames.

[0049] The focus of cleanroom simulation should be on the uniformity of airflow organization and the results of pollutant control, rather than transient vortex structures. It should accurately reflect the airflow organization and pollutant concentration distribution within the cleanroom. This provides a basis for the selection of turbulence models. Considering its actual geometric dimensions, such as a large-span cleanroom with a length of 51.8m, a width of 44.35m, and a height of 3.3m, the airflow within the cleanroom will exhibit relatively stronger two-dimensional diffusion characteristics, while the vertical gradient change will not be particularly significant. In this case, using a RANS-type model is entirely reasonable. Furthermore, due to the large span of the building space, its turbulence scale is large, with the main vortex size reaching the meter level. There are no complex geometric abrupt changes or high curvature regions within the cleanroom, and there are no strong separation flows or recirculation zones. The overall mainstream flow field exhibits low-speed steady-state flow, which fully conforms to the isotropic turbulence assumption of the k-epsilon model. In addition, the airflow characteristics of this cleanroom belong to weak pressure gradient flow driven mainly by static pressure difference with no obvious flow separation. The turbulence characteristics considered by the k-epsilon model are highly consistent with this.

[0050] Next, a further selection of a turbulence model is needed. In CFD, the k-epsilon turbulence model is further subdivided into three categories: Standard model, RNG model, and Realizable model. The Standard k-epsilon model is the most widely used turbulence model in industrial flow calculations due to its good stability and high computational accuracy. The RNG k-epsilon model, in addition to considering the rotational effect, is more suitable for calculations of strongly rotating flows. The Realizable k-epsilon model can more accurately simulate the diffusion velocity of a circular jet, and also provides more realistic calculation results in boundary layer calculations with directional pressure gradients and separated flow calculations. Considering that the airflow characteristics of this cleanroom are weak pressure gradient flows without flow separation, and there are no strong rotating flow or separated flow calculation scenarios, the Standard k-epsilon turbulence model can be directly used for flow field simulation calculations.

[0051] Furthermore, considering that the Standard k-epsilon turbulence model is characterized by good simulation results for fully developed turbulence stages, making it highly suitable for simulating the actual airflow organization in non-unidirectional cleanrooms, this characteristic also becomes a drawback. For flows near the wall, the Reynolds number is low, turbulence development is insufficient, and the pulsation effect of turbulence is less significant than the effect of intermolecular viscous forces. Therefore, the Standard k-epsilon turbulence model cannot be used for calculations near the wall. Here, we adopt the wall function method, which is based on the idea of ​​using it in conjunction with the Standard k-epsilon model. For the flow in the turbulent core region, the Standard k-epsilon turbulence model is still used for solution. However, in the region near the wall, no solution is performed; instead, semi-empirical formulas are used to directly relate the physical quantities on the wall to the solution variables in the turbulent core region, directly obtaining the nodal variable values ​​of the control volume adjacent to the wall. Although this method is an approximation, it is widely used in industrial flow problem calculations. The wall function method in CFD can be further divided into the Standard wall function (Standard) method. Wall functions, scalable wall functions, and non-equilibrium wall functions are all available. For cleanroom wall shear flow problems, the first standard wall function method can be used to solve the problem well, enabling flow field calculations in the region near the wall. When there is a certain form of separation in the flow or when dealing with jet flow problems, the non-equilibrium wall function method should be considered for flow field calculations. According to fluid mechanics theory, the turbulent core region and the region near the wall are generally divided by the dimensionless distance y+.

[0052] Next, the discrete phase model is selected. The core pollutants in cleanrooms are solid particulate matter in the air, such as dust and microbial carriers. ISO Class 7 cleanrooms pay particular attention to the concentration of suspended particulate matter with a particle size ≥ 0.5 μm. These particles are discrete entities and can be regarded as a series of individual entities existing in the continuous phase of air. Since the particles have a considerable particle size range, their mass cannot be ignored. Their flow state should conform to the basic inertial effect, that is, the flow of particles, especially in the high turbulence intensity area where the airflow direction changes rapidly, does not completely follow the airflow. Inertia causes their trajectory to deviate from the streamline. For larger particles, they will also be subject to significant gravity, leading to particle settling. In addition, the interaction between particles and the cleanroom surface should be fully considered. During the movement, pollutant particles will collide, bounce, adhere, or be captured with the walls, floors, and equipment surfaces, and escape outside the room at the air outlet.

[0053] Based on the above characteristics of particulate matter motion and the actual situation where the volume fraction of the dispersed phase is far less than 10%, a multiphase flow model is set up in CFD. Among them, the Discrete Phase Model (DPM) can explicitly consider the physical properties of particles such as diameter, density, initial velocity, mass, and temperature to directly simulate the characteristics of particulate matter. It also includes a gravity term to simulate particle settling and capture inertial effects to accurately calculate particle trajectories. This model can also define behaviors such as rebound, adhesion, and capture when particles collide with walls, fully reflecting the interaction between particulate matter and solid surfaces and other key physical behaviors. Its principle is based on the Laplace framework, which treats pollutant particles as discrete particles. It calculates the motion trajectories of a sufficiently large number of statistically representative particles in a continuous gas phase flow field and statistically calculates the number of particles and residence time in a specific control volume to finally obtain the spatial concentration distribution of particulate matter. Based on this principle, DPM is particularly suitable for the transport of low volume fraction discrete particles in a continuous gas phase, which is a typical scenario for the diffusion of particulate pollutants in cleanrooms. It can be considered that DPM is designed specifically to simulate the motion of such discrete particles in continuous fluids. Therefore, the DPM model is selected in CFD to simulate the diffusion of solid particulate pollutants in cleanrooms.

[0054] DPM Model Setup: First, set up the discrete phase model. In CFD, select and open the discrete phase model, choosing the interaction model with the continuous phase. In the particle motion physics model setup, consider and select Samarf lift and virtual mass force. Then, set up the particle jet source: Create a jet source, setting the source type to wall, the particle type to inert particles, and the particle size to a preset value and uniformity. Considering that ISO 7 cleanrooms are particularly concerned with the concentration of suspended particles ≥0.5μm, the particle size can be set to 0.5μm. The total particle flow rate is set to a preset value, typically determined based on the actual contaminant particle production rate in the cleanroom. After creation, the density and specific heat capacity of the discrete particles can be modified in the material region according to the actual situation to ensure the simulation matches the actual scenario.

[0055] Then, CFD simulation can be performed in the 3D model. At this stage, it is necessary to calculate the temperature field, flow rate field, and pollutant concentration field in the clean room, and analyze the impact of each parameter on air quality.

[0056] The calculation process mainly involves first establishing the governing equations and defining the initial and boundary conditions, then dividing the computational grid to discretize the governing equations, initial and boundary conditions, and finally using the flow field calculation method described above to solve the discretized equations and obtain the solutions.

[0057] Step 1: Establish the governing equations: When solving flow and heat transfer problems using the Standard k-epsilon turbulence model, the governing equations include the mass conservation equation, momentum conservation equation, energy conservation equation, turbulent kinetic energy equation, turbulent dissipation rate equation, and turbulent viscosity formula. Considering mass transfer, the component conservation equation should also be added. These equations can all be expressed in the following general form:

[0058]

[0059] In the above formula: Indicates time, , and The x, y, and z coordinates of the spatial coordinates are respectively. Indicates density, Represents the general dependent variable. , and These represent the velocity components in the x-axis, y-axis, and z-axis directions, respectively. Indicates the diffusion coefficient. This indicates a custom source item.

[0060] Any flow problem involving heat exchange must satisfy the mass conservation equation, momentum conservation equation, and energy conservation equation. The expression for the mass conservation equation is:

[0061]

[0062] The equation for the conservation of momentum is expressed as follows:

[0063]

[0064] Energy conservation equation:

[0065]

[0066] In the above formula, Indicates static pressure. and Let i and j represent the components of the spatial coordinates in the i-direction and j-direction, respectively. and These represent the velocity components in the i-direction and the j-direction, respectively. Represents the stress tensor. This represents the gravitational volume force in direction i. This represents the external volume force in direction i. Indicates temperature. Indicates specific heat capacity. Represents the gradient operator, Indicates the fluid heat transfer coefficient. Represents the viscous dissipation term. This represents the divergence operator.

[0067] Discrete Phase Model: This model is actually a model of the interaction between continuous and discrete phase materials. Generally, the continuous phase flow field is calculated first, including the temperature field, velocity field, and pressure field. The calculation is performed by solving the corresponding governing equations. However, due to the complexity of the equations, it is difficult to solve. Therefore, models adapted to different situations have been developed. In CFD, this involves solving the turbulence model and the energy model. The governing equations are discretized using the finite volume method. The pressure correction algorithm (SIMPLE) is used to couple the velocity and pressure fields. The turbulence model and wall functions are combined to handle complex flows. Finally, the complete distribution of the continuous phase flow field is obtained through iterative solution. The calculation is essentially solving a set of nonlinear partial differential equations on a discrete grid. The calculation process of the SIMLIE algorithm is given in the last step of the solution process according to the CFD settings.

[0068] After the flow field calculation converges, the forces acting on discrete phase particles are calculated using the discrete phase model through the flow field variables, thereby determining their motion state and trajectory. At this point, the flow involves the mixing and interaction of different components, and the system must also obey the component conservation equation. The expression for the component conservation equation is as follows:

[0069]

[0070] Since the airflow in the cleanroom is turbulent, the system must also follow the turbulent transport equations in the additional Standard k-epsilon model. The solutions for k and ε are obtained by solving the turbulent kinetic energy equation and the turbulent dissipation rate equation. Then, the values ​​of k and ε are substituted into the turbulent viscosity formula to further calculate the turbulent viscosity. Finally, the solution for the Reynolds stress is obtained through the Boussinesq assumption. The expression for the turbulent kinetic energy equation is as follows:

[0071]

[0072] The expression for the turbulent dissipation rate equation is:

[0073]

[0074] The formula for turbulent viscosity is:

[0075]

[0076] In the above formula, Represents the convection term coefficient. Represents the mass diffusion coefficient. Indicates the quality source item. Represents turbulent kinetic energy. Indicates dynamic viscosity. Indicates turbulent viscosity. This represents the turbulent kinetic energy increment generated by the average velocity gradient. This represents the increase in turbulent kinetic energy generated by buoyancy. This indicates pulsating expansion in compressible turbulent flow. Denotes the first empirical constant. Denotes the second empirical constant. Represents the third empirical constant. Indicates the turbulent viscosity coefficient. The Prandtl number, representing the turbulent kinetic energy, The Prandtl number, representing the turbulent dissipation rate, Indicates the turbulent dissipation rate. This indicates pulsating expansion in compressible turbulent flow. and These represent the custom source terms for turbulent kinetic energy and turbulent dissipation rate, respectively.

[0077] The second step is to establish boundary conditions: Determining the initial and boundary conditions is a prerequisite for solving the equations. Initial conditions generally include basic parameters within the 3D model, such as the initial velocity field, initial pressure field, initial temperature field, initial turbulence parameters, and initial contaminant concentration, to initiate the simulation and serve as the starting point for calculations. The combination of the governing equations and corresponding boundary conditions constitutes a complete mathematical description of a physical process. Therefore, after establishing the complete governing equations, appropriate boundary conditions need to be given based on the actual physical scenario. In this design, the cleanroom area is surrounded by air conditioning systems in the surrounding corridors and adjacent rooms, so there is no heat transfer between different rooms. The cleanroom roof, walls, and floor are set... The walls are insulated. The simulated scenario is a dynamic cleanroom. Considering that personnel, lighting, and equipment are heat sources, personnel and lighting are set as surface heat sources with a constant heat flux density. The surface heat source intensity of the human body can be set to 111W / ㎡, and the surface heat source intensity of the lighting can be set to 204W / ㎡. The equipment is set as a volume heat source with a fixed internal heat source intensity, which can be set to 100W / m³. The air supply velocity obtained from the rated air volume and the air outlet size is set as the velocity inlet boundary condition. For example, the rated air volume is 2000m³ / h, the effective size of the FFU is fixed at 1175mm×1175mm, and the air velocity is the ratio of the two, which is 0.4m / s.

[0078] The air outlet position is directly set as the pressure outlet boundary condition, and the pressure value can be the standard atmospheric pressure of 101325 Pa, so the gauge pressure can be set to 0 Pa.

[0079] Step 3: Mesh Generation: After determining the governing equations and corresponding boundary conditions used to solve the problem, they need to be solved. Theoretically, analytical solutions exist, but due to the complexity of the equations themselves, it is difficult to obtain exact solutions. Therefore, when solving the governing equations, we try to discretize them in a spatial domain and then solve the resulting discrete equation system. To achieve the discretization of the governing equations in the spatial domain, a mesh must be used. First, the computational domain is discretized, that is, the continuous computational domain in space is divided into many sub-regions, and the nodes in each region are determined to generate a mesh. Then, the governing equations in the form of partial differential equations are transformed into a system of algebraic equations at each node, thus achieving the discretization of the governing equations on the mesh. In CFD, the finite volume method is often used for discretization. The basic idea is to divide the computational domain into a grid and make each grid point have a unique control volume around it. The control equations to be solved are integrated over each control volume to obtain a set of discrete equations. The specific mesh generation process needs to be performed in dedicated mesh generation software. The previously created 3D model is imported into the mesh generation software, the geometry is selected and local dimensions are added, and then the global maximum and minimum mesh sizes are restricted to generate a surface mesh. The next step is to describe the geometry as a void-free fluid region and share the topology. Finally, the boundary conditions are updated and the boundary condition type is set to fill the volume mesh to complete the mesh generation.

[0080] Step 4: Solution Process: The generated discrete equations need to be solved using specific flow field calculation methods. For solving the incompressible flow field in the cleanroom, the Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm is used, which is a semi-implicit method for pressure-coupled equations. First, the initial pressure field is assumed, and the momentum conservation equation is solved to obtain the velocity field. The mass conservation equation is then solved using the velocity field to obtain the pressure field correction value. The velocity field and pressure field are updated based on the obtained pressure correction value. Then, convergence is checked. If convergence is not achieved, the obtained pressure field is used as the new assumed pressure field, and the process is repeated. Finally, as the iteration proceeds, the pressure and velocity values ​​obtained gradually approach the true values, thus solving the equations and finally obtaining the simulation results of the temperature field, flow velocity field, and contaminant concentration field in the cleanroom.

[0081] Finally, a comprehensive assessment of air quality is required based on the simulation results, including the temperature field, flow velocity field, and pollutant concentration field within the cleanroom. The process is as follows:

[0082] The most important indicator for cleanrooms is the concentration of pollutants. Appendix B of GB / T 33556.1-2017 stipulates that the maximum concentration limit for particles ≥0.5µm in ISO Class 7 cleanrooms is 352,000 particles / m³. The concentration of pollutants is related to the location of the pollution source, the intensity of pollutant emission, and the supply and return air configuration. Under the premise of a fixed supply and return air configuration with top supply and side return and a certain intensity of pollutant emission, the concentration of pollutants in the cleanroom varies with the location of the pollution source. The closer the pollution source is to the return air vent, the easier it is for the emitted pollutants to be drawn into and discharged outdoors. The lower the pollutant concentration, the closer the pollution source is to the vortex zone. The longer the indoor air stay time in this area, the easier it is for pollutants to accumulate, which is not conducive to the discharge of pollutants and leads to a localized high concentration of pollutants.

[0083] Secondly, the supply air velocity affects the air quality of cleanrooms. This effect can be divided into two parts. First, the supply air velocity directly determines the number of air changes. The air change rate of an ISO 7 cleanroom should be no less than 25 times per hour. The lower the supply air velocity, the fewer the air changes. This indicates that the indoor air renewal frequency is low, which is not conducive to the replacement and discharge of pollutants and can easily lead to the accumulation of pollutants. Second, the supply air velocity affects the comfort of personnel in the work area. Too high a velocity can easily cause a drafty feeling, while too low a velocity cannot deliver cool air into the work area, resulting in a lower temperature at the top of the room and a higher temperature at the bottom, creating a significant temperature gradient in the vertical direction.

[0084] Supply air temperature also has a certain impact on air quality. For process air conditioners with room temperature fluctuation range of >±1℃, the supply air temperature difference should be ≤15℃. If the supply air temperature difference is too small, the supply air temperature will be too high to reduce the indoor air temperature and will not meet the indoor air conditioning design temperature requirements. If the supply air temperature difference is too large, the indoor temperature will be much lower than the air conditioning design temperature, which is also not conducive to personnel and equipment production.

[0085] Based on the above principles, multi-objective optimization is carried out: based on the simulation results, with the thermal comfort, velocity non-uniformity and pollutant concentration of the cleanroom space as constraints, minimizing pollutant concentration and minimizing energy consumption as optimization objectives, the air supply velocity, air supply temperature, air outlet location, size and other air conditioning outlet parameters are optimized.

[0086] The core steps of multi-objective optimization are: sampling based on CFD simulation results; constructing a proxy mathematical model using response surface methodology based on the sampling results; establishing optimization objectives and constraints for multi-objective optimization; analyzing the Pareto front; and making decisions. Its advantage lies in transforming physical field simulation into rapid mathematical function evaluation through efficient experimental design and response surface modeling, thereby supporting feasible multi-objective optimization searches to obtain optimization results. First, CFD simulations are used to calculate the pollutant concentration field, temperature field, velocity field, PMV results, and intermediate variables used to calculate energy consumption, such as volumetric flow rate and supply air enthalpy difference, under different combinations of design variables. Then, the pollutant concentration from the CFD calculation results... Using PMV, nonuniformity, and energy consumption as responses, a quadratic polynomial regression model is used to fit each response value. After obtaining the equivalent mathematical model, it cannot be used directly; the goodness of fit needs to be evaluated. To ensure the accuracy of the model's predictions, CFD simulations are performed on a small number of additional validation points to compare the predicted values ​​of the surrogate mathematical model with the actual CFD results. If the fitting effect is poor, a preset prompt is output, indicating that more sample points need to be added or a more complex model needs to be selected for refitting. If the fitting effect is good, i.e., the fitting accuracy meets the preset requirements, then it is considered that the mathematical function has approximately represented the relationship between the calculated variables and the optimization objective, and multi-objective optimization can be performed based on this surrogate model.

[0087] The process essentially involves finding a Pareto optimal solution that simultaneously optimizes multiple objectives, under the constraints of cleanroom contaminant concentration ≤352,000 particles / m³ (≥0.5µm); PMV value within the range of -0.5 to +0.5; and airflow non-uniformity in the work area <0.3. A common algorithm used is the multi-objective evolutionary algorithm. In each iteration of the algorithm, instead of using time-consuming CFD, a pre-constructed high-precision response surface mathematical model is used to evaluate the objective function value and constraint violation. This significantly improves optimization efficiency. The algorithm continuously refines a set of candidate solutions, eventually converging to the Pareto front. The resulting Pareto front is a set of optimal solutions, where each point represents a specific combination of design variables, corresponding to a set of optimization objectives: contaminant concentration and energy consumption. It demonstrates the trade-off between contaminant concentration and energy consumption resulting from different design choices. Points closer to low energy consumption may correspond to lower supply air velocity but slightly higher contaminant concentration (still satisfying basic constraints); points closer to low contaminant concentration may correspond to higher supply air velocity but significantly increased energy consumption. The algorithm provides all good options, but ultimately selects the solution set that best meets the overall project objectives from the Pareto front as the final design scheme. Determining the corresponding parameters such as supply air temperature and supply air velocity requires manually determining the weights. For example, in this project, the weight we consider is to minimize energy consumption while meeting the minimum requirements for pollutant concentration in ISO 7 cleanrooms. Therefore, on the obtained Pareto front curve, we should select the point with the highest pollutant concentration (meeting the constraints) and the lowest energy consumption, which should correspond to the endpoint of the curve. The air conditioning vent parameters such as supply air velocity, supply air temperature, vent location, and size corresponding to this result are the parameters we are looking for.

[0088] Taking a Class 1000 (or Class 10,000) cleanroom as an example, after establishing a 3D model, initial conditions are set, a standard k-ε model is selected, and CFD simulation is completed. After analyzing the flow rate and pollutant distribution, air conditioning vent parameters such as vent location and size are adjusted, and simulation is repeated for verification. Finally, after optimization, the pollutant concentration is reduced by a certain percentage, and energy consumption is reduced by a certain percentage. Optimizing vent design through CFD simulation effectively reduces pollutant concentration, improves air quality, and reduces energy consumption and design costs, demonstrating significant economic benefits and application value.

[0089] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Since the above embodiments are substantially similar to the method embodiments, their descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0091] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimizing the design of cleanroom air conditioning vents based on fluid dynamics, characterized in that, The method specifically includes the following steps: Create a 3D model of the cleanroom containing all the objects; A fluid dynamics model based on the Reynolds-averaged Navier-Stokes equations and a discrete phase model is constructed within the 3D model. The Standard k-epsilon turbulence model and the wall function method are selected to calculate the flow field of the cleanroom. The steps for setting up the discrete phase model are as follows: the particle motion physics model in the discrete phase model is set to consider Samarf lift and virtual mass force. The jet source type is set as a wall-mounted particle jet source, the particle type jetted by the particle jet source is inert particles, the particle size and total particle flow rate are preset values, and the particle size is uniform; in the material region of the discrete phase model, particle physical property parameters that match the actual cleanroom environment are set. The governing equations, initial conditions, and boundary conditions for fluid dynamics model simulation are established to obtain simulation results including the temperature field, velocity field, and contaminant concentration field within the cleanroom. The boundary conditions are as follows: the cleanroom roof, walls, and floor in the 3D model are set as insulated walls; personnel and lighting are set as surface heat sources with a constant heat flux density, and equipment is set as volumetric heat sources with a fixed internal heat source intensity, with corresponding heat source intensities set for each heat source; the supply air velocity is calculated based on the rated air volume and vent size and set as a velocity inlet boundary condition; the outlet location is set as a pressure outlet boundary condition. Based on the simulation results, multi-objective optimization is performed with constraints on thermal comfort, velocity non-uniformity, and pollutant concentration in the cleanroom, and minimizing pollutant concentration and energy consumption as optimization objectives, to obtain the optimized air conditioning vent parameters. The specific steps of the multi-objective optimization are as follows: sampling based on the simulation results of the fluid dynamics model; constructing a surrogate mathematical model based on the sampling results and using the response surface methodology; performing multi-objective optimization based on the surrogate mathematical model and according to the optimization objectives and constraints to obtain the Pareto front; and selecting the solution set that satisfies the optimization objectives from the Pareto front to obtain the optimized air conditioning vent parameters. The specific steps for constructing the mathematical model of the agent are as follows: construct the mathematical model of the agent based on the sampling results and using the response surface methodology; set verification points containing air conditioning vent parameters, substitute the data of the verification points into the mathematical model of the agent, and calculate the fitting accuracy between the simulation results of the verification points on the fluid dynamics model and the prediction results output by the mathematical model of the agent. If the fitting accuracy meets the preset requirements, the mathematical model of the agent will be output; otherwise, a preset prompt will be output.

2. The method for optimizing the design of cleanroom air conditioning vents according to claim 1, characterized in that, The selection of the Standard k-epsilon turbulence model and the wall function method for flow field calculation in the cleanroom specifically includes: The 3D model of the cleanroom is divided into a turbulent core area and a region near the wall. The flow field of the cleanroom was calculated using the Standard k-epsilon turbulence model and the wall function method for the turbulent core region and the region near the wall, respectively.

3. The method for optimizing the design of cleanroom air conditioning vents according to claim 1, characterized in that, The governing equations include the mass conservation equation, momentum conservation equation, energy conservation equation, turbulent kinetic energy equation, turbulent dissipation rate equation, turbulent viscosity formula, and component conservation equation.

4. The method for optimizing the design of cleanroom air conditioning vents according to claim 1, characterized in that, The specific steps for obtaining simulation results including the temperature field, flow velocity field, and contaminant concentration field within the cleanroom are as follows: Mesh the computational region in the 3D model and give each mesh point a unique control volume around it; A set of discrete equations is obtained by integrating the control equations to be solved over each control volume. The discrete equations are solved using the SIMPLE algorithm to obtain simulation results that include the temperature field, flow velocity field, and pollutant concentration field within the cleanroom.

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