A method, system, and storage medium for constant temperature clean room design

Abstract

The application discloses a constant-temperature clean room design method and system and a storage medium, and the system comprises a scheme data acquisition module, a simulation model building and calculation module, a simulation result evaluation module, a scheme optimization adjustment module and a scheme comparison and output module. The simulation model of the clean room is established, the simulation result analysis and the temperature design effect judgment of the scheme are determined, the temperature distribution degree of the clean room under different schemes can be accurately quantitatively analyzed, the influence of different scheme measures on the temperature distribution effect can be reflected in time, and adjustment is made according to the simulation result. Compared with the traditional method, the dynamic design of the high-precision constant-temperature laboratory can be better realized.

Description

Technical Field

[0001] This invention relates to the field of cleanroom technology, and in particular to a constant temperature cleanroom design method, system, and storage medium. Background Technology

[0002] Cleanrooms in high-end manufacturing industries such as flat panel displays and semiconductors have extremely stringent requirements for the production environment. Because their production processes are complex and involve numerous precision steps, the temperature distribution within the cleanroom directly affects the product yield and performance stability. As related processes develop towards greater precision and integration, the high-precision temperature control design of cleanrooms has become an increasingly challenging problem.

[0003] Currently, traditional cleanroom temperature design methods primarily rely on the experience of designers. During factory design, general design specifications are referenced, and the heating and cooling loads and air conditioning systems within the cleanroom are checked to determine if the temperature design requirements can be met. Therefore, cleanroom temperature design is not accurate enough. Traditional cleanroom temperature design depends on standard calculations and designer experience. However, the internal environment of a cleanroom is complex, involving multiple physical processes such as fluid flow, heat conduction / convection / radiation, and numerous factors influencing temperature distribution. Standard calculations and experience alone cannot accurately predict and optimize the complex temperature distribution within a cleanroom. Even when designers use CFD tools to assist in temperature design and predict indoor temperature distribution, there is a lack of objective and detailed analytical standards for judging the indoor temperature distribution, making it difficult to assess the effectiveness of the current solution and thus hindering accurate temperature design. Furthermore, cleanroom temperature design suffers from low efficiency. The influence of various factors on temperature within a cleanroom varies, and when a temperature design fails to meet requirements, designers struggle to accurately and quickly determine the temperature distribution of the current design and how to adjust the cleanroom structure accordingly. Often, repeated trials are necessary to find a solution that meets the design requirements. Meanwhile, the modification of the plan relies too much on the subjective judgment of a single designer, and the modification and adjustment process may be quite lengthy, making it difficult to provide flexible and diverse modification solutions conveniently. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a design method, system and storage medium for a constant temperature cleanroom, which effectively solves the problems of inaccurate temperature distribution design and low design efficiency in cleanrooms.

[0005] Technical solution: To achieve the above objectives, the constant temperature cleanroom design method of the present invention includes the following steps:

[0006] S1. Design the cleanroom structure, which includes walls, indoor process equipment, fan filter units (FFU), DCC (Dry Coil Unit), air ducts, return air ducts, and raised floor; then design multiple alternative layout schemes for DCC and air ducts; and obtain parameter data of the design factors of the cleanroom structure.

[0007] S2. Based on the obtained parameter data, set the boundary conditions and material physical parameters for CFD simulation, perform three-dimensional modeling, mesh generation and calculation of the cleanroom structure to obtain the three-dimensional temperature distribution inside the cleanroom.

[0008] S3. Based on the indoor three-dimensional temperature distribution, select key sections, perform section segmentation, obtain temperature distribution cloud maps on the corresponding sections, evaluate the temperature of each area of ​​the key sections, and determine the degree to which the temperature value of each area exceeds the design range; if no temperature value exceeds the target temperature design range, proceed to step S5; otherwise, proceed to step S4; if it is found that the temperature value of an area exceeds the temperature design range, record the degree to which the temperature value of the corresponding area exceeds the design range. The degree to which the temperature value exceeds the design range includes the maximum deviation and minimum deviation of the temperature extreme value from the design range, as well as the deviation area ratio of the area where the temperature value exceeds the design range to the total area of ​​the area.

[0009] S4. Based on the degree to which the temperature values ​​of each region exceed the design range, select the corresponding design optimization factors, optimize the design scheme, and generate multiple corresponding design schemes.

[0010] S4.1, when This is denoted as the first level of deviation, which represents a severe deviation from the design range; when This is recorded as the second level of deviation, which represents a moderate deviation from the design range; when... This is recorded as the third level of deviation, which is a slight deviation from the design range.

[0011] S4.2 Screening based on design factors For each design optimization factor, the Morris method is used to conduct a global sensitivity analysis of the design optimization factors. The mean temperature in the region is used as the evaluation index to obtain the percentage importance of each factor to the regional temperature. According to the percentage importance, the design optimization factors are divided into three corresponding deviation levels: large deviation level, medium deviation level and small deviation level.

[0012] S4.2 For the first level of deviation, select design optimization factors with large deviation level to optimize the design scheme; for the second level of deviation, select design optimization factors with medium deviation level to optimize the design scheme; for the third level of deviation, select design optimization factors with small deviation level to optimize the design scheme, generate multiple corresponding design schemes, and return to step S2 to re-perform CFD simulation to obtain the indoor three-dimensional temperature distribution.

[0013] S5. Select the generated design schemes according to the comparison index parameters and output the optimal design scheme.

[0014] Optionally, in step S1, the cleanroom structure includes walls, indoor process equipment, fan filter units (FFUs), direct current coil (DCC) units, air ducts, return air ducts, and a raised floor. The area between the fan filter units (FFUs) and the roof at the upper ceiling is the upper mezzanine, where the air ducts are arranged. The area between the raised floor and the structural floor is the lower mezzanine, where the direct current coil (DCC) units are arranged. The return air duct is located at the side wall. The area enclosed by the fan filter units (FFUs), return air ducts, raised floor, and walls is the clean production area, where airflow is filtered by the fans. The unit FFU provides power, and the airflow is from top to bottom to achieve uniform distribution of clean air and orderly circulation of indoor air. The function of the lower mezzanine is to accommodate the air pushed out by the fan-filtered unit FFU from the clean room. The pushed-out air then becomes return air, passes through the return air duct on the side, and re-enters the upper mezzanine to complete the air circulation in the clean room. The fresh air inlet is inserted into the return air duct at a certain height above the structural floor. The fresh air treated by the fresh air handling unit is delivered to the return air duct through the fresh air inlet and mixed with the return air. The air duct includes fresh air duct, return air duct, and exhaust air duct.

[0015] Optionally, the design factors in step S1 include the outline of the walls and indoor process equipment, the dimensional parameters of the walls and indoor process equipment, the location parameters of the walls and indoor process equipment, the ceiling area, the ceiling height, the lower mezzanine height, the upper mezzanine height, the target temperature design range, the outdoor temperature, the area of ​​the fan filter unit (FFU) arrangement, the fan filter unit (FFU) wind speed, the height of the fresh air inlet, any type of dry coil DCC arrangement, any type of duct arrangement, the segmented wind speed of the fan filter unit (FFU), the roof insulation layer material, the roof insulation layer thickness, the exterior wall insulation layer material, the exterior wall insulation layer thickness, the raised floor opening ratio, the heat generation of the indoor process equipment, and the heat generation of the indoor personnel.

[0016] Optionally, the boundary conditions in step S2 include inlet wind speed, inlet temperature, outlet pressure conditions, and wall boundary conditions, and the material physical parameters include indoor air physical parameters and dry coil DCC heat exchange parameters.

[0017] Optionally, step S3 specifically includes the following steps:

[0018] S3.1 Select a plane at a height of 0.8m above the raised floor as the first critical section; select a longitudinal section that can cover the location of at least one indoor process equipment as the second critical section, where the longitudinal section is a section parallel to the air supply direction; select a transverse section that can cover the location of at least one indoor process equipment as the third critical section, where the transverse section is a section perpendicular to the air supply direction; the final selected critical sections shall include at least the first critical section, and one or two of the second and third critical sections, and obtain the temperature distribution cloud map on the corresponding sections;

[0019] S3.2. Divide the selected critical section according to the critical section average division method, return air duct division method, outward expansion division method, or temperature measuring point working height division method;

[0020] S3.3 Read the temperature values ​​in each area of ​​the key section and determine whether any temperature value exceeds the target temperature design range of the cleanroom. If no temperature value exceeds the target temperature design range, proceed to step S5; otherwise, proceed to step S4.

[0021] If it is found that the temperature value of a region exceeds the temperature design range, the degree to which the temperature value of the corresponding region exceeds the design range is recorded. The degree to which the temperature value exceeds the design range includes the maximum deviation and minimum deviation of the extreme temperature values ​​from the design range, as well as the percentage of the area in a region where the temperature value exceeds the design range relative to the total area of ​​that region.

[0022] ,

[0023] in This refers to the maximum temperature within a region. This refers to the minimum temperature within a region. The maximum value within the target temperature design range. The minimum value within the target temperature design range;

[0024] Calculate the percentage of a region where the temperature exceeds the design range out of the total area of ​​that region; this percentage is the deviation area. , For the area where the temperature value exceeds the design range, This represents the total area of ​​the region.

[0025] Optionally, the method for dividing the return air duct in step S3.2 refers to dividing different areas based on their distance from the return air duct, with the width of the area closest to the return air duct being... , L represents the cross-sectional length of the area to be divided, which is the room length of the critical cross-section area minus the length of the return air duct; the width of the remaining areas can be determined by geometric indices. The final width of the region is The final area width The width value is the largest, making the area near the return air duct the narrowest, and the remaining areas gradually widen;

[0026] The outward expansion method refers to dividing different areas based on the outward expansion distance of the 2D projection area of ​​the indoor process equipment on the critical section. The closer the equipment is to the 2D projection area on the critical section, the shorter the outward expansion distance; the farther the equipment is from the 2D projection area on the critical section, the longer the outward expansion distance.

[0027] The method of dividing the working height of temperature measuring points refers to dividing different areas based on the distance from the working height of the temperature measuring point. The area closest to the working height of the temperature measuring point is the narrowest, and the remaining areas gradually become wider.

[0028] Optionally, step S4.2 specifically includes the following steps:

[0029] S4.2.1、 The design optimization factors are divided into two types: continuous variables and discrete variables. The parameter variation range and optional arrangement of the design optimization factors are determined according to the actual application.

[0030] S4.2.2 Determine the number of levels and number of sampling paths Latin hypercube sampling was used to generate There are 10 sampling paths, each containing 100 sampling paths. Group parameter combinations generate sampling matrices, total Group parameter combinations;

[0031] S4.2.3. Input each set of parameters in the sampling matrix as simulation parameters into the cleanroom CFD model, run the simulation until convergence, and obtain the mean temperature of the area. ;

[0032] S4.2.4, for the first The first factor and the second Path, , ; in the On the path, find only the factors The two consecutive sampling points that changed; let the parameter sets of these two points be respectively and The corresponding output is and ; Calculation factors The basic effects along this path ;

[0033] ,

[0034] As factors The step size in this change is based on the number of levels. calculate, Typically standardized as ;

[0035] For each factor ,from From the path Basic effect value Calculate the mean and standard deviation ,

[0036] ,

[0037] ,

[0038] in This represents the average of the basic effects;

[0039] S4.2.5 Calculate the importance index of each factor. And calculate the percentage of importance of each factor relative to the total importance of all factors. ;

[0040] ,

[0041] ,

[0042] S4.2.6, Using K-means clustering to determine the percentage of importance for all factors. Cluster analysis yielded three clustering results, corresponding to three levels of bias: large bias, medium bias, and small bias, respectively.

[0043] Optionally, the comparison index parameters in step S5 include one or more of the following: the average temperature stability parameter of the critical section, the temperature extreme value of the critical section, and the optimization cost of the scheme. The average temperature stability parameter... The calculation formula is: , For the first Temperature stability parameters at key cross sections , For the first The difference between the temperature maximum and minimum values ​​at a key cross section; The number of critical sections;

[0044] The smaller the average temperature stability parameter, the larger the temperature extreme value of the critical section, the lower the optimization cost, and the better the design scheme.

[0045] In one embodiment, the constant temperature cleanroom design system of the present invention includes:

[0046] The solution data acquisition module is used to design the cleanroom structure, which includes walls, indoor process equipment, fan filter units (FFU), dry coil DCC, air ducts, return air ducts, and raised floors; it then designs multiple selectable layout schemes for the dry coil DCC and air ducts; and acquires parameter data of the design factors of the cleanroom structure.

[0047] The simulation model building and calculation module is used to set the boundary conditions and material physical parameters of the CFD simulation based on the acquired parameter data, and to perform three-dimensional modeling, mesh generation and calculation of the cleanroom structure to obtain the three-dimensional temperature distribution inside the cleanroom.

[0048] The simulation result evaluation module is used to select key sections based on the three-dimensional temperature distribution in the room, perform section segmentation, obtain temperature distribution cloud maps on the corresponding sections, evaluate the temperature of each area of ​​the key section, and determine the degree to which the temperature value of each area exceeds the design range. If no temperature value exceeds the target temperature design range, the process proceeds to step S5; otherwise, it proceeds to step S4. If the temperature value of an area exceeds the temperature design range, the degree to which the temperature value of the corresponding area exceeds the design range is recorded. The degree to which the temperature value exceeds the design range includes the deviation of the maximum and minimum temperature extreme values ​​from the design range, as well as the percentage of the area in a certain region where the temperature value exceeds the design range relative to the total area of ​​that region.

[0049] The scheme optimization and adjustment module is used to select corresponding design optimization factors based on the degree to which the temperature values ​​of each region exceed the design range, optimize the design scheme, and generate multiple corresponding design schemes.

[0050] when This is denoted as the first level of deviation, which represents a severe deviation from the design range; when This is recorded as the second level of deviation, which represents a moderate deviation from the design range; when... This is recorded as the third level of deviation, which is a slight deviation from the design range.

[0051] Screening based on design factors For each design optimization factor, the Morris method is used to conduct a global sensitivity analysis of the design optimization factors. The mean temperature in the region is used as the evaluation index to obtain the percentage importance of each factor to the regional temperature. According to the percentage importance, the design optimization factors are divided into three corresponding deviation levels: large deviation level, medium deviation level and small deviation level.

[0052] For the first level of deviation, design optimization factors with large deviation levels are selected to optimize the design scheme; for the second level of deviation, design optimization factors with medium deviation levels are selected to optimize the design scheme; for the third level of deviation, design optimization factors with small deviation levels are selected to optimize the design scheme, generating multiple corresponding design schemes. Based on the multiple design schemes, CFD simulation is performed again to obtain the three-dimensional temperature distribution of the indoor environment.

[0053] The scheme comparison and output module is used to filter the multiple generated design schemes according to the comparison index parameters and output the optimal design scheme.

[0054] In one embodiment, the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to execute and implement the constant temperature cleanroom design method as described above.

[0055] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0056] This invention establishes a simulation model of a cleanroom, establishes detailed simulation result analysis and temperature design effect judgment of the scheme, and can accurately quantify the degree of temperature distribution in the cleanroom under different schemes, reflect the impact of different schemes and measures on the temperature distribution in a timely manner, and make adjustments based on the simulation results. Compared with traditional methods, it can better realize the dynamic design of high-precision constant temperature laboratory.

[0057] This invention establishes a multi-dimensional analysis of the influence factors on temperature and their corresponding measures, as well as the percentage importance of various design optimization factors to temperature distribution. Based on the degree of deviation and the percentage importance, design optimization factors are selected, which not only improves adjustment efficiency but also optimizes resource allocation and ensures the stability and adaptability of the design.

[0058] This invention optimizes and adjusts designs by selecting design optimization factors that are appropriate to the degree of deviation. Compared with traditional methods that rely on the designer's experience, it can provide more flexible and diverse design solutions to meet different user needs. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the cleanroom structure in this invention;

[0060] Figure 2 This is a schematic diagram of the return air duct division method in this invention;

[0061] Figure 3 This is a schematic diagram of the outward expansion division method in this invention;

[0062] Figure 4 This is a diagram showing the division of key cross-sectional areas and temperature conditions in this invention;

[0063] Figure 5 This is a temperature diagram of a key section in scheme a of the present invention;

[0064] Figure 6 This is a temperature diagram of a key section in scheme b of the present invention;

[0065] Figure 7 This is a temperature diagram of a key section in scheme c of the present invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0067] Example 1: As Figure 1 As shown, a method for designing a constant temperature cleanroom according to the present invention includes the following steps:

[0068] S1. Design the cleanroom structure, which includes walls, indoor process equipment, fan filter units (FFU), DCC (Dry Coil Unit), air ducts, return air ducts, and raised floor; then design multiple alternative layout schemes for DCC and air ducts; and obtain parameter data of the design factors of the cleanroom structure.

[0069] The cleanroom comprises walls 1, indoor process equipment, fan filter units (FFUs) 2, DCC (Digital Curve Coils) 3, ducts 4, return air ducts 5, and a raised floor 6. The area between the fan filter units (FFUs) and the roof 7 at the upper ceiling is the upper mezzanine 8, where the ducts are arranged. The area between the raised floor and the structural floor is the lower mezzanine 9, where the DCC (Digital Curve Coils) are arranged. The return air duct is located along the side walls. The area enclosed by the fan filter units (FFUs), return air ducts, raised floor, and walls is the clean production area 10, where airflow is controlled by the fan filter units. The FFU provides power, and the airflow is from top to bottom to achieve uniform distribution of clean air and orderly circulation of indoor air. The lower mezzanine serves to contain the air pushed out by the FFU from the cleanroom. The pushed-out air then becomes return air, passing through the side return air duct and re-entering the upper mezzanine to complete the air circulation in the cleanroom. The fresh air inlet 11 is inserted into the return air duct at a height H from the structural floor. Fresh air treated by the fresh air handling unit is delivered to the return air duct through the fresh air inlet and mixed with the return air. The air duct includes a fresh air duct, a return air duct, and an exhaust air duct.

[0070] Design factors include the outline of the walls and indoor process equipment, the dimensional parameters of the walls and indoor process equipment, the location parameters of the walls and indoor process equipment, the ceiling area, ceiling height, lower mezzanine height, upper mezzanine height, target temperature design range, outdoor temperature, fan filter unit (FFU) layout area, fan filter unit (FFU) air velocity, fresh air inlet height, any type of dry coil DCC layout, any type of duct layout, segmented air velocity of the fan filter unit (FFU), roof insulation material, roof insulation thickness, exterior wall insulation material, exterior wall insulation thickness, raised floor opening ratio, heat generation of indoor process equipment, and heat generation of indoor personnel. The cleanroom illustrated in this example has the following indoor temperature design control requirements: The interior ceiling height is 4.7m, the lower mezzanine height is 6m, the upper mezzanine height is 8.3m, and the DCC is arranged in an L-shape in the lower mezzanine.

[0071] S2. Based on the obtained parameter data, set the boundary conditions and material physical parameters for CFD simulation, perform three-dimensional modeling, mesh generation, and calculation of the cleanroom to obtain the three-dimensional temperature distribution inside; the boundary conditions include inlet air velocity, inlet temperature, outlet pressure conditions, and wall boundary conditions, and the material physical parameters include indoor air physical parameters and dry coil DCC heat exchange parameters.

[0072] S3. Based on the indoor three-dimensional temperature distribution, select key sections, perform section segmentation, obtain temperature distribution cloud maps on the corresponding sections, evaluate the temperature of each area of ​​the key sections, and determine the degree to which the temperature value of each area exceeds the design range; if no temperature value exceeds the target temperature design range, proceed to step S5; otherwise, proceed to step S4; if it is found that the temperature value of an area exceeds the temperature design range, record the degree to which the temperature value of the corresponding area exceeds the design range. The degree to which the temperature value exceeds the design range includes the maximum deviation and minimum deviation of the temperature extreme value from the design range, as well as the deviation area ratio of the area where the temperature value exceeds the design range to the total area of ​​the area.

[0073] S3.1 Select a plane at a height of 0.8m above the raised floor as the first critical section; select a longitudinal section that can cover the location of at least one indoor process equipment as the second critical section, where the longitudinal section is a section parallel to the air supply direction; select a transverse section that can cover the location of at least one indoor process equipment as the third critical section, where the transverse section is a section perpendicular to the air supply direction; the final selected critical sections shall include at least the first critical section, and one or two of the second and third critical sections, and obtain the temperature distribution cloud map on the corresponding sections;

[0074] S3.2. Divide the selected critical section according to the critical section average division method, return air duct division method, outward expansion division method, or temperature measuring point working height division method;

[0075] The method of dividing the return air duct refers to dividing different areas based on their distance from the return air duct, with the width of the area closest to the return air duct being [missing information]. , L represents the cross-sectional length of the area to be divided, which is the room length of the critical cross-section area minus the length of the return air duct; the width of the remaining areas can be determined by geometric indices. The final width of the region is The final area width The width value is the largest, making the area closest to the return air duct the narrowest, with the remaining areas gradually widening. In this embodiment, the return air duct is the convergence point in the cleanroom airflow organization. In this area, the air may not be fully mixed with the fresh air and is directly drawn away before sufficient heat exchange with the DCC, resulting in uneven temperature distribution. Therefore, when dividing the cross-sectional area, the objective indicator of "distance from the return air duct" is used, with the area closer to the duct being more densely packed. At the same time, auxiliary support areas that do not need to meet the temperature design control requirements are ignored, and only the controlled areas of the cleanroom are processed. When the length of the overall area is close to the overall length of the cross-section, the last area is shortened or extended to ensure that the overall area length is exactly the same as the cross-sectional length. In this embodiment, the cross-sectional length of the area to be divided is approximately L = 60m (room length - return air duct). The width of the area closest to the return air duct is taken as 10m. Therefore, the cross section can be divided into at least 4 regions with widths of 10, 13, 16.9, and 20.1 mm respectively.

[0076] The outward expansion method refers to dividing different regions based on the outward expansion distance of the 2D projection area of ​​the indoor process equipment on the critical section. The closer to the 2D projection area of ​​the indoor process equipment on the critical section, the shorter the outward expansion distance; the farther away from the 2D projection area of ​​the indoor process equipment on the critical section, the longer the outward expansion distance. For example, some semiconductor plants often designate a 0.3m outward expansion of the equipment projection as a critical control volume, focusing on the environmental parameters within this volume. Therefore, when dividing the cross-sectional regions, the 2D projection polygon of the equipment on the cross-section can be listed; using the 0.3m outward expansion as the base region, other regions are gradually divided, and regions farther away from the equipment projection can be divided more sparsely.

[0077] The method of dividing temperature measurement points at working height refers to dividing different areas based on their distance from the working height of the temperature measurement point. The area closest to the working height is the narrowest, and the remaining areas gradually widen. For example, ISO 14644-3:2019 Cleanrooms and Associated Controlled Environments – Part 3: Test Methods specifies that temperature monitoring points in cleanrooms should be located at the working height. Therefore, when dividing the cross-sectional areas, the working surface at the working height, as the focus of indoor monitoring, should be densely divided in its vicinity to allow for precise analysis of parameters within the area; while areas farther from the working surface can be sparsely divided to ensure implementation efficiency.

[0078] In this embodiment, based on the owner's requirements, a plane at a height of 0.8m above the raised floor is selected as the key section. The length of this key section is divided into two equal parts, and the width of the section is divided into two equal parts, thereby dividing the space of the section into four regions, such as... Figure 4 The diagram shows the key cross-sectional area division and temperature conditions in this embodiment.

[0079] S3.3 Read the temperature values ​​in each area of ​​the key section and determine whether any temperature value exceeds the target temperature design range of the cleanroom. If no temperature value exceeds the target temperature design range, proceed to step S5; otherwise, proceed to step S4.

[0080] If it is found that the temperature value of a region exceeds the temperature design range, the degree to which the temperature value of the corresponding region exceeds the design range is recorded. The degree to which the temperature value exceeds the design range includes the maximum deviation and minimum deviation of the extreme temperature values ​​from the design range, as well as the percentage of the area in a region where the temperature value exceeds the design range relative to the total area of ​​that region.

[0081] ,

[0082] in This refers to the maximum temperature within a region. This refers to the minimum temperature within a region. The maximum value within the target temperature design range. The minimum value within the target temperature design range;

[0083] Calculate the percentage of a region where the temperature exceeds the design range out of the total area of ​​that region; this percentage is the deviation area. , For the area where the temperature value exceeds the design range, This represents the total area of ​​the region.

[0084] S4. Based on the degree to which the temperature values ​​of each region exceed the design range, select the corresponding design optimization factors, optimize the design scheme, and generate multiple corresponding design schemes.

[0085] S4.1, when This is denoted as the first level of deviation, which represents a severe deviation from the design range; when This is recorded as the second level of deviation, which represents a moderate deviation from the design range; when... This is recorded as the third level of deviation, which is a slight deviation from the design range.

[0086] S4.2 Screening based on design factors The Morris method was used to conduct a global sensitivity analysis of the design optimization factors, using the mean temperature in the region as the evaluation index. The percentage importance of each factor to the regional temperature was obtained, and the design optimization factors were divided into three deviation levels according to the percentage importance: large deviation level, medium deviation level, and small deviation level. The design optimization factors include outdoor temperature, fan filter unit (FFU) wind speed, FFU layout area, roof insulation layer thickness, fresh air inlet height, DCC layout, raised floor opening ratio, external wall insulation thickness, and duct layout.

[0087] Step S4.2 specifically includes the following steps:

[0088] S4.2.1、 The design optimization factors are divided into two types: continuous variables and discrete variables. The parameter variation range and layout options of the design optimization factors are determined based on the actual application situation; for continuous variables such as FFU wind speed, the variation range is 0.25-0.55m / s; for discrete variables such as DCC layout, there are 4 layout options.

[0089] S4.2.2 Determine the number of levels and number of sampling paths In this embodiment, the number of discrete variable values ​​and the balance between computational accuracy and simulation quantity are considered when selecting... and ;

[0090] S4.2.3, Based on the number of levels and number of sampling paths Latin hypercube sampling was used to generate 10 sampling paths, each path containing Group parameter combinations generate sampling matrices, total Group parameter combinations;

[0091] S4.2.4. Input each set of parameters in the sampling matrix as simulation parameters into the cleanroom CFD model, run the simulation until convergence, and obtain the mean temperature of the area. ;

[0092] S4.2.5, for the first The first factor and the second Path, , ; in the On the path, find only the factors The two consecutive sampling points that changed; let the parameter sets of these two points be respectively and The corresponding output is and ; Calculation factors The basic effects along this path ;

[0093] ,

[0094] As factors The step size in this change is based on the number of levels. calculate, Typically standardized as ;

[0095] For each factor ,from From the path Basic effect value Calculate the mean and standard deviation As shown in Table 1;

[0096] ,

[0097] ,

[0098] in This represents the average of the basic effects;

[0099] S4.2.6 Calculate the importance index of each factor. And calculate the percentage of importance of each factor relative to the total importance of all factors. ;

[0100] ,

[0101] ,

[0102] S4.2.7, Using K-means clustering to determine the percentage of importance for all factors. Cluster analysis yielded three clustering results, corresponding to three levels of bias: large bias, medium bias, and small bias, as shown in Table 2.

[0103] Table 1. Percentage of Importance of Nine Design Optimization Factors

[0104]

[0105] Table 2. Three Deviation Levels

[0106]

[0107] The final optimization library of design optimization factors is shown in Table 3.

[0108] Table 3 Optimization Library

[0109]

[0110] S4.3 For the first level of deviation, select design optimization factors with large deviation level to optimize the design scheme; for the second level of deviation, select design optimization factors with medium deviation level to optimize the design scheme; for the third level of deviation, select design optimization factors with small deviation level to optimize the design scheme, generate multiple corresponding design schemes, and return to step S2 to re-perform CFD simulation to obtain the indoor three-dimensional temperature distribution.

[0111] This invention employs a strategy of selecting adjustment factors based on the percentage of deviation and importance, which can gradually adjust the temperature distribution effect of the design scheme closer to the target value. Furthermore, in specific implementation, factors with a high percentage of importance are prioritized, allowing for a rapid response and significant improvement of the target variable when the temperature distribution deviation is large. When the deviation is moderate or small, factors with medium and low percentages of importance are adjusted for fine-tuning, avoiding over-adjustment. After adjustment, parameters are re-read and results are evaluated, achieving dynamic evaluation and adjustment. The next adjustment factor can be dynamically selected based on the effect of each adjustment, ensuring the efficiency and adaptability of the adjustment process. This strategy not only improves adjustment efficiency but also optimizes resource allocation, ensuring the stability and adaptability of the system. For example, in the embodiment, the degree to which the temperature value of a certain area exceeds the design range is specifically defined as the deviation of the extreme temperature value from the design range. The proportion of the area where the temperature exceeds the design control range. Its deviation The area of ​​temperature deviation is relatively large. Therefore, it is believed that the temperature value deviates significantly from the design range, and factors with a high percentage of importance in the optimization library should be selected, that is, factors that have a significant impact on the temperature of the clean room, such as outdoor temperature and FFU wind speed.

[0112] S5. Select the optimal design scheme from the multiple generated design schemes according to the comparison index parameters. The comparison index parameters include one or more of the following: average temperature stability parameter of the critical section, temperature extreme value of the critical section, and scheme optimization cost. The average temperature stability parameter... The calculation formula is: , For the first Temperature stability parameters at key cross sections , For the first The difference between the temperature maximum and minimum values ​​at a key cross section; The number of critical sections;

[0113] The smaller the average temperature stability parameter, the larger the temperature extreme value of the critical section, the lower the optimization cost, and the better the design scheme.

[0114] Therefore, when making a comparison, one can calculate and compare the economic benefits based solely on the costs of the measures required by the proposed solution, and select the solution with the lowest required cost, thus making a local comparison and selection of the best option; or one can calculate all the index parameters, conduct a comprehensive comparison of the technical and economic aspects of the proposed solutions, and select the overall optimal solution.

[0115] For example, Scheme A, which considers outdoor temperature as a factor, employs outdoor spraying in area 4 to reduce surface temperature compared to the original scheme. After implementing Scheme A, the highest temperature in area 4, the critical section, is 22.2℃, meeting the temperature design requirements. Figure 5 As shown; Scheme b, which selects the FFU wind speed factor, adds measures to control the FFU wind speed in region 4 compared to the original scheme. After implementing Scheme b, the highest temperature in region 4 of the critical section is 22.24℃, which meets the temperature design requirements. Figure 6 As shown in the diagram; Scheme c, compared to the original scheme, adopts measures such as outdoor spraying in area 4 to reduce surface temperature and increasing FFU wind speed in area 4. After implementing scheme c, the highest temperature in the critical section of area 4 is 22.16℃, which meets the temperature design requirements. Figure 7 As shown.

[0116] In this embodiment, the average temperature stability parameter of the cross-section is used as an indicator for scheme comparison. The temperature stability parameter measures the relationship between the temperature data fluctuation range and the temperature design control target range: the greater the fluctuation, the higher the value; conversely, the lower the value. Therefore, the value directly indicates the stability of the temperature distribution. By selecting the scheme with a lower stability parameter, a more stable temperature distribution in the designed cleanroom can be ensured, and the cleanroom system can operate more reliably.

[0117] In this embodiment, the range of cleanroom temperature control is... The value is 0.6. In scheme a, the maximum temperature of the cross-section is 22.2°C and the minimum temperature is 21.96°C, therefore its average temperature stability parameter is 0.6. In scheme b, the maximum temperature of the cross-section is 22.24°C and the minimum temperature is 21.96°C, therefore its average temperature stability parameter is... In scheme c, the maximum temperature of the cross-section is 22.16°C and the minimum temperature is 21.97°C. Therefore, its average temperature stability parameter is... Therefore, in this embodiment, scheme c has the smallest average temperature stability parameter, indicating that the indoor temperature distribution is more stable and the cleanroom system operates more reliably under this design scheme. Therefore, scheme c is selected as the output scheme, and its relevant design parameters are output.

[0118] Example 2: The design system of the present invention includes:

[0119] The solution data acquisition module is used to design the cleanroom structure, which includes walls, indoor process equipment, fan filter units (FFU), dry coil DCC, air ducts, return air ducts, and raised floors; it then designs multiple selectable layout schemes for the dry coil DCC and air ducts; and acquires parameter data of the design factors of the cleanroom structure.

[0120] The cleanroom comprises walls, indoor process equipment, fan filter units (FFUs), direct current coil (DCC) units, ductwork, return air ducts, and a raised floor. The area between the FFUs and the ceiling is the upper mezzanine, where the ductwork is located. The area between the raised floor and the structural floor is the lower mezzanine, where the direct current coil (DCC) units are located. The return air ducts are located along the side walls. The area enclosed by the FFUs, return air ducts, raised floor, and walls constitutes the clean production area, where airflow is enhanced by the FFUs. Powered by airflow from top to bottom, the system ensures uniform distribution of clean air and orderly circulation of indoor air. The lower mezzanine serves to contain the air pushed out by the fan filter unit (FFU) from the cleanroom. This pushed-out air then acts as return air, passing through the side return air duct and re-entering the upper mezzanine to complete the cleanroom air circulation. Fresh air inlets are inserted into the return air duct at a certain height above the structural floor. Fresh air treated by the fresh air handling unit is delivered through the fresh air inlets to the return air duct to mix with the return air. The ductwork includes fresh air ducts, return air ducts, and exhaust ducts.

[0121] Design factors include the outline of the walls and indoor process equipment, the dimensional parameters of the walls and indoor process equipment, the location parameters of the walls and indoor process equipment, the ceiling area, the ceiling height, the lower mezzanine height, the upper mezzanine height, the target temperature design range, the outdoor temperature, the area of ​​the fan filter unit (FFU) arrangement, the fan filter unit (FFU) air velocity, the height of the fresh air inlet, the arrangement of any type of dry coil DCC, the arrangement of any type of duct, the segmental air velocity of the fan filter unit (FFU), the roof insulation material, the roof insulation thickness, the exterior wall insulation material, the exterior wall insulation thickness, the raised floor opening ratio, the heat generation of the indoor process equipment, and the heat generation of the indoor personnel.

[0122] The simulation model building and calculation module is used to set the boundary conditions and material physical parameters of the CFD simulation based on the acquired parameter data, and to perform three-dimensional modeling, mesh generation and calculation of the cleanroom structure to obtain the three-dimensional temperature distribution inside the cleanroom. The boundary conditions include inlet air velocity, inlet temperature, outlet pressure conditions and wall boundary conditions, and the material physical parameters include indoor air physical parameters and dry coil DCC heat exchange parameters.

[0123] The simulation result evaluation module is used to select key sections based on the three-dimensional temperature distribution in the room, perform section segmentation, obtain temperature distribution cloud maps on the corresponding sections, evaluate the temperature of each area of ​​the key section, and determine the degree to which the temperature value of each area exceeds the design range. If no temperature value exceeds the target temperature design range, the module proceeds to the scheme comparison and output module; otherwise, the module proceeds to the scheme optimization and adjustment module. If the temperature value of an area exceeds the temperature design range, the module records the degree to which the temperature value of the corresponding area exceeds the design range. The degree to which the temperature value exceeds the design range includes the deviation of the maximum and minimum temperature extreme values ​​from the design range, as well as the percentage of the area in a certain region where the temperature value exceeds the design range relative to the total area of ​​that region.

[0124] Specifically, a plane at a height of 0.8m above the raised floor is selected as the first critical section; a longitudinal section that can cover the location of at least one indoor process equipment is selected as the second critical section, wherein the longitudinal section refers to the section parallel to the air supply direction; a transverse section that can cover the location of at least one indoor process equipment is selected as the third critical section, wherein the transverse section refers to the section perpendicular to the air supply direction; the final selected critical sections include at least the first critical section, and one or two of the second and third critical sections are selected, and temperature distribution cloud maps on the corresponding sections are obtained.

[0125] The selected key sections are divided according to the return air duct division method, the outward expansion division method, or the temperature measuring point working height division method;

[0126] The method of dividing the return air duct refers to dividing different areas based on their distance from the return air duct, with the width of the area closest to the return air duct being [missing information]. , L represents the cross-sectional length of the area to be divided, which is the room length of the critical cross-section area minus the length of the return air duct; the width of the remaining areas can be determined by geometric indices. The final width of the region is The final area width The width value is the largest, making the area near the return air duct the narrowest, and the remaining areas gradually widen;

[0127] The outward expansion method refers to dividing different areas based on the outward expansion distance of the 2D projection area of ​​the indoor process equipment on the key section. The closer the equipment is to the 2D projection area on the key section, the shorter the outward expansion distance; the farther the equipment is from the 2D projection area on the key section, the longer the outward expansion distance.

[0128] The method of dividing the working height of temperature measuring points refers to dividing different areas based on the distance from the working height of the temperature measuring point. The area closest to the working height of the temperature measuring point is the narrowest, and the remaining areas gradually become wider.

[0129] Read the temperature values ​​in each area of ​​the key section and determine whether any temperature value exceeds the target temperature design range of the cleanroom. If no temperature value exceeds the target temperature design range, proceed to step S5; otherwise, proceed to step S4.

[0130] If it is found that the temperature value of a region exceeds the temperature design range, the degree to which the temperature value of the corresponding region exceeds the design range is recorded. The degree to which the temperature value exceeds the design range includes the maximum deviation and minimum deviation of the extreme temperature values ​​from the design range, as well as the percentage of the area in a region where the temperature value exceeds the design range relative to the total area of ​​that region.

[0131] ,

[0132] in This refers to the maximum temperature within a region. This refers to the minimum temperature within a region. The maximum value within the target temperature design range. The minimum value within the target temperature design range;

[0133] Calculate the percentage of a region where the temperature exceeds the design range out of the total area of ​​that region; this percentage is the deviation area. , For the area where the temperature value exceeds the design range, This represents the total area of ​​the region.

[0134] The scheme optimization and adjustment module is used to select corresponding design optimization factors based on the degree to which the temperature values ​​of each region exceed the design range, optimize the design scheme, and generate multiple corresponding design schemes.

[0135] when This is denoted as the first level of deviation, which represents a severe deviation from the design range; when This is recorded as the second level of deviation, which represents a moderate deviation from the design range; when... This is recorded as the third level of deviation, which is a slight deviation from the design range.

[0136] Screening based on design factors The Morris method was used to conduct a global sensitivity analysis of the design optimization factors, using the mean temperature in the region as the evaluation index to obtain the percentage importance of each factor to the regional temperature. The design optimization factors were divided into three deviation levels according to the percentage importance: large deviation level, medium deviation level, and small deviation level. The design optimization factors include outdoor temperature, fan filter unit (FFU) wind speed, FFU layout area, roof insulation layer thickness, fresh air inlet height, DCC layout, raised floor opening ratio, external wall insulation thickness, and duct layout.

[0137] Specifically: The design optimization factors are divided into two types: continuous variables and discrete variables. The parameter variation range and layout options of the design optimization factors are determined based on the actual application situation; for continuous variables such as FFU wind speed, the variation range is 0.25-0.55m / s; for discrete variables such as DCC layout, there are 4 layout options.

[0138] Determine the number of levels and number of sampling paths In this embodiment, the number of discrete variable values ​​and the balance between computational accuracy and simulation quantity are considered when selecting... and ;

[0139] Based on level number and number of sampling paths Latin hypercube sampling was used to generate 10 sampling paths, each path containing Group parameter combinations generate sampling matrices, total Group parameter combinations;

[0140] Each set of parameters in the sampling matrix is ​​used as simulation parameters and input into the cleanroom CFD model. The simulation is run until convergence to obtain the mean temperature of the area. ;

[0141] For the The first factor and the second Path, , ; in the On the path, find only the factors The two consecutive sampling points that changed; let the parameter sets of these two points be respectively and The corresponding output is and ; Calculation factors The basic effects along this path ;

[0142] ,

[0143] As factors The step size in this change is based on the number of levels. calculate, Typically standardized as ;

[0144] For each factor ,from From the path Basic effect value Calculate the mean and standard deviation ;

[0145] ,

[0146] ,

[0147] in This represents the average of the basic effects;

[0148] Calculate the importance index of each factor And calculate the percentage of importance of each factor relative to the total importance of all factors. ;

[0149] ,

[0150] ,

[0151] Based on the K-means clustering method, all importance percentages were analyzed. Cluster analysis yielded three clustering results, corresponding to three levels of bias: large bias, medium bias, and small bias, respectively.

[0152] For the first level of deviation, design optimization factors with large deviation levels are selected to optimize the design scheme; for the second level of deviation, design optimization factors with medium deviation levels are selected to optimize the design scheme; for the third level of deviation, design optimization factors with small deviation levels are selected to optimize the design scheme, generating multiple corresponding design schemes. Based on the multiple design schemes, CFD simulation is performed again to obtain the three-dimensional temperature distribution in the room.

[0153] The scheme comparison and output module is used to filter multiple generated design schemes according to comparison index parameters and output the optimal design scheme. These comparison index parameters include one or more of the following: average temperature stability parameter of the critical section, temperature extreme values ​​of the critical section, and scheme optimization cost. The average temperature stability parameter... The calculation formula is: , For the first Temperature stability parameters at key cross sections , For the first The difference between the temperature maximum and minimum values ​​at a key cross section; The number of critical sections;

[0154] The smaller the average temperature stability parameter, the larger the temperature extreme value of the critical section, the lower the optimization cost, and the better the design scheme.

[0155] Example 3: A computer-readable storage medium of the present invention stores computer instructions that are used to cause a processor to execute and implement the design method of a constant temperature cleanroom as described above.

Claims

1. A method for designing a constant-temperature cleanroom, characterized in that, Includes the following steps: S1. Design the cleanroom structure, which includes walls, indoor process equipment, fan filter units (FFU), DCC (Dry Coil Unit), air ducts, return air ducts, and raised floor; then design multiple alternative layout schemes for DCC and air ducts; and obtain parameter data of the design factors of the cleanroom structure. S2. Based on the obtained parameter data, set the boundary conditions and material physical parameters for CFD simulation, perform three-dimensional modeling, mesh generation and calculation of the cleanroom structure to obtain the three-dimensional temperature distribution inside the cleanroom. S3. Based on the three-dimensional temperature distribution in the room, select key sections, divide the sections, obtain the temperature distribution cloud map on the corresponding sections, evaluate the temperature of each area of ​​the key sections, and determine the degree to which the temperature value of each area exceeds the design range. If no temperature value exceeds the target temperature design range, proceed to step S5; otherwise, proceed to step S4. If it is found that the temperature value of a region exceeds the temperature design range, the degree to which the temperature value of the corresponding region exceeds the design range is recorded. The degree to which the temperature value exceeds the design range includes the maximum deviation and minimum deviation of the extreme temperature values ​​from the design range, as well as the percentage of the area in a region where the temperature value exceeds the design range relative to the total area of ​​that region. Step S3 specifically includes the following steps: S3.1 Select a plane at a height of 0.8m above the raised floor as the first critical section; select a longitudinal section that can cover the location of at least one indoor process equipment as the second critical section, where the longitudinal section is a section parallel to the air supply direction; select a transverse section that can cover the location of at least one indoor process equipment as the third critical section, where the transverse section is a section perpendicular to the air supply direction; the final selected critical sections shall include at least the first critical section, and one or two of the second and third critical sections, and obtain the temperature distribution cloud map on the corresponding sections; S3.

2. Divide the selected critical section according to the critical section average division method, return air duct division method, outward expansion division method, or temperature measuring point working height division method; The method for dividing the return air duct in step S3.2 refers to dividing different areas based on their distance from the return air duct, with the width of the area closest to the return air duct being... , L represents the cross-sectional length of the area to be divided, which is the room length of the critical cross-section area minus the length of the return air duct; the width of the remaining areas can be determined by geometric indices. The final width of the region is The final area width The width value is the largest, making the area near the return air duct the narrowest, and the remaining areas gradually widen; The outward expansion method refers to dividing different areas based on the outward expansion distance of the 2D projection area of ​​the indoor process equipment on the key section. The closer the equipment is to the 2D projection area on the key section, the shorter the outward expansion distance; the farther the equipment is from the 2D projection area on the key section, the longer the outward expansion distance. The method of dividing the working height of temperature measuring points refers to dividing different areas based on the distance from the working height of the temperature measuring point. The area closest to the working height of the temperature measuring point is the narrowest, and the remaining areas gradually become wider. S3.3 Read the temperature values ​​in each area of ​​the key section and determine whether any temperature value exceeds the target temperature design range of the cleanroom. If no temperature value exceeds the target temperature design range, proceed to step S5; otherwise, proceed to step S4. If it is found that the temperature value of a region exceeds the temperature design range, the degree to which the temperature value of the corresponding region exceeds the design range is recorded. The degree to which the temperature value exceeds the design range includes the maximum deviation and minimum deviation of the extreme temperature values ​​from the design range, as well as the percentage of the area in a region where the temperature value exceeds the design range relative to the total area of ​​that region. ， in This refers to the maximum temperature within a region. This refers to the minimum temperature within a region. The maximum value within the target temperature design range. The minimum value within the target temperature design range; Calculate the percentage of a region where the temperature exceeds the design range out of the total area of ​​that region; this percentage is the deviation area. , For the area where the temperature value exceeds the design range, This represents the total area of ​​the region. S4. Based on the degree to which the temperature values ​​of each region exceed the design range, select the corresponding design optimization factors, optimize the design scheme, and generate multiple corresponding design schemes. S4.1, when This is denoted as the first level of deviation, which represents a severe deviation from the design range; when This is recorded as the second level of deviation, which represents a moderate deviation from the design range; when... This is recorded as the third level of deviation, which is a slight deviation from the design range. S4.2 Screening based on design factors For each design optimization factor, the Morris method is used to conduct a global sensitivity analysis of the design optimization factors. The mean temperature in the region is used as the evaluation index to obtain the percentage importance of each factor to the regional temperature. According to the percentage importance, the design optimization factors are divided into three corresponding deviation levels: large deviation level, medium deviation level and small deviation level. S4.3 For the first level of deviation, select design optimization factors of the large deviation level to optimize the design scheme; for the second level of deviation, select design optimization factors of the medium deviation level to optimize the design scheme. For the third level of deviation, select the design optimization factor with small deviation level, optimize the design scheme, generate multiple corresponding design schemes, and return to step S2 to re-perform CFD simulation to obtain the indoor three-dimensional temperature distribution. S5. Select the generated design schemes according to the comparison index parameters and output the optimal design scheme.

2. The constant temperature cleanroom design method according to claim 1, characterized in that: The cleanroom structure in step S1 includes walls, indoor process equipment, fan filter units (FFU), dry coils (DCC), air ducts, return air ducts, and raised floors. The area between the fan filter units (FFU) and the roof at the upper ceiling is the upper mezzanine, and the air ducts are arranged in the upper mezzanine. The area between the raised floor and the structural floor is the lower mezzanine, where the DCC dry coil is located. The return air duct is located on the side wall. The area enclosed by the fan filter unit (FFU), the return air duct, the raised floor, and the wall is the clean production area. The airflow is powered by the fan filter unit (FFU) and flows from top to bottom to achieve uniform distribution of clean air and orderly circulation of indoor air. The function of the lower mezzanine is to accommodate the air pushed out by the fan filter unit (FFU) from the clean room. The pushed-out air then serves as return air, passing through the return air duct on the side and re-entering the upper mezzanine to complete the air circulation within the clean room. The fresh air inlet is inserted into the return air duct at a certain height above the structural floor. Fresh air treated by the fresh air handling unit is delivered to the return air duct through the fresh air inlet and mixed with the return air. The air duct includes a fresh air duct, a return air duct, and an exhaust air duct.

3. The constant temperature cleanroom design method according to claim 2, characterized in that: The design factors mentioned in step S1 include the outline of the walls and indoor process equipment, the dimensional parameters of the walls and indoor process equipment, the location parameters of the walls and indoor process equipment, the ceiling area, the ceiling height, the lower mezzanine height, the upper mezzanine height, the target temperature design range, the outdoor temperature, the area of ​​the fan filter unit (FFU) arrangement, the fan filter unit (FFU) air velocity, the height of the fresh air inlet, any type of dry coil DCC arrangement, any type of duct arrangement, the segmented air velocity of the fan filter unit (FFU), the roof insulation material, the roof insulation thickness, the exterior wall insulation material, the exterior wall insulation thickness, the raised floor opening ratio, the heat generation of the indoor process equipment, and the heat generation of the indoor personnel.

4. The constant temperature cleanroom design method according to claim 3, characterized in that: The boundary conditions in step S2 include inlet wind speed, inlet temperature, outlet pressure conditions, and wall boundary conditions. The material physical parameters include indoor air physical parameters and dry coil DCC heat exchange parameters.

5. The constant temperature cleanroom design method according to claim 1, characterized in that: Step S4.2 specifically includes the following steps: S4.2.1、 The design optimization factors are divided into two types: continuous variables and discrete variables. The parameter variation range and optional arrangement of the design optimization factors are determined according to the actual application. S4.2.2 Determine the number of levels and number of sampling paths Latin hypercube sampling was used to generate There are 10 sampling paths, each containing 100 sampling paths. Group parameter combinations generate sampling matrices, total Group parameter combinations; S4.2.

3. Input each set of parameters in the sampling matrix as simulation parameters into the cleanroom CFD model, run the simulation until convergence, and obtain the mean temperature of the area. ; S4.2.4, for the first The first factor and the second Path, , ; in the On the path, find only the factors The two consecutive sampling points that changed; let the parameter sets of these two points be respectively and The corresponding output is and ; Calculation factors The basic effects along this path ; ， As factors The step size in this change; For each factor ,from From the path Basic effect value Calculate the mean and standard deviation , ， ， in This represents the average of the basic effects; S4.2.5 Calculate the importance index of each factor. And calculate the percentage of importance of each factor relative to the total importance of all factors. ; ， ， S4.2.6, Using K-means clustering to determine the percentage of importance for all factors. Cluster analysis yielded three clustering results, corresponding to three levels of bias: large bias, medium bias, and small bias, respectively.

6. The constant temperature cleanroom design method according to claim 1, characterized in that: In step S5, the comparison index parameters include one or more of the following: the average temperature stability parameter of the key section, the temperature extreme value of the key section, and the optimization cost of the scheme. Among them, the average temperature stability parameter... The calculation formula is: , For the first Temperature stability parameters at key cross sections , For the first The difference between the temperature maximum and minimum values ​​at a key cross section; The number of critical sections; The smaller the average temperature stability parameter, the larger the temperature extreme value of the critical section, the lower the optimization cost, and the better the design scheme.

7. A design system for a constant temperature cleanroom based on the design method of any one of claims 1 to 6, characterized in that, include: The solution data acquisition module is used to design the cleanroom structure, which includes walls, indoor process equipment, fan filter units (FFU), dry coil DCC, air ducts, return air ducts, and raised floors; it then designs multiple selectable layout schemes for the dry coil DCC and air ducts; and acquires parameter data of the design factors of the cleanroom structure. The simulation model building and calculation module is used to set the boundary conditions and material physical parameters of the CFD simulation based on the acquired parameter data, and to perform three-dimensional modeling, mesh generation and calculation of the cleanroom structure to obtain the three-dimensional temperature distribution inside the cleanroom. The simulation result evaluation module is used to select key sections based on the three-dimensional temperature distribution in the room, perform section segmentation, obtain temperature distribution cloud maps on the corresponding sections, evaluate the temperature of each area of ​​the key section, and determine the degree to which the temperature value of each area exceeds the design range. If no temperature value exceeds the target temperature design range, proceed to step S5; otherwise, proceed to step S4. If it is found that the temperature value of a region exceeds the temperature design range, the degree to which the temperature value of the corresponding region exceeds the design range is recorded. The degree to which the temperature value exceeds the design range includes the maximum deviation and minimum deviation of the extreme temperature values ​​from the design range, as well as the percentage of the area in a region where the temperature value exceeds the design range relative to the total area of ​​that region. The scheme optimization and adjustment module is used to select corresponding design optimization factors based on the degree to which the temperature values ​​of each region exceed the design range, optimize the design scheme, and generate multiple corresponding design schemes. when This is denoted as the first level of deviation, which represents a severe deviation from the design range; when This is recorded as the second level of deviation, which represents a moderate deviation from the design range; when... This is recorded as the third level of deviation, which is a slight deviation from the design range. Screening based on design factors For each design optimization factor, the Morris method is used to conduct a global sensitivity analysis of the design optimization factors. The mean temperature in the region is used as the evaluation index to obtain the percentage importance of each factor to the regional temperature. According to the percentage importance, the design optimization factors are divided into three corresponding deviation levels: large deviation level, medium deviation level and small deviation level. For the first level of deviation, design optimization factors of the large deviation level are selected to optimize the design scheme; for the second level of deviation, design optimization factors of the medium deviation level are selected to optimize the design scheme. For the third level of deviation, design optimization factors with small deviation levels are selected to optimize the design scheme and generate multiple corresponding design schemes. Based on the multiple design schemes, CFD simulation is performed again to obtain the three-dimensional temperature distribution of the room. The scheme comparison and output module is used to filter the multiple generated design schemes according to the comparison index parameters and output the optimal design scheme.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the constant temperature cleanroom design method according to any one of claims 1-6.

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