High and large space building indoor environment performance optimization design method and system based on CFD and multi-objective optimization algorithm

Abstract

The invention discloses an indoor environment performance optimization design method and system for a tall and large space building based on CFD and a multi-objective optimization algorithm, and the method comprises the steps: building a parameterized analysis model through combining with a BIM model, carrying out the digital simulation analysis of an indoor light environment and energy consumption through employing Honeybee Radio and Honeybee Entry plug-ins in Rhino and Grasshopper environments, determining optimization parameters and objectives, and carrying out the optimization of the indoor environment and energy consumption. And a BIM model and an IDF file corresponding to the optimal schemes are exported, the uniformity of the indoor airflow organization is evaluated based on FFD software, and parameters of the optimal schemes are determined. Compared with a traditional indoor environment optimization method, the high and large space building indoor environment performance optimization and evaluation method is efficient, accurate and economical, and has higher accuracy, higher adaptability and wider applicability. Natural lighting, building energy consumption and indoor three-dimensional thermal comfort can be considered at the same time, and optimization of the indoor environment performance of the tall and large space building is achieved.

Description

Technical Field

[0001] The present invention relates to the field of building environment and energy application engineering technology, and in particular to a method for optimizing and evaluating indoor environmental performance of large-space buildings and a digital design system based on computational fluid dynamics (CFD) and a multi-objective optimization algorithm. Background Art

[0002] In the current construction field, the design and optimization of tall and large-space buildings and their energy systems face many challenges. Tall and large-space buildings have complex structures, diverse functions, and special load characteristics. How to comprehensively consider multiple key indicators such as sunlight, ventilation, and energy consumption in the early stage of energy-saving design has become an urgent problem that needs to be solved. There are often conflicts and constraints between these indicators, such as the balance between natural lighting, lighting energy consumption, solar radiation heat, and air conditioning energy consumption. How to balance these goals and find the optimal solution is a difficult point.

[0003] Before a building is put into operation, natural lighting and HVAC system design are particularly critical for tall and large-space buildings. Due to the large space, uneven population density, diverse usage functions, and complex design of building heat sources, ensuring the optimal function of complex systems while ensuring indoor environmental performance has become the top priority for energy-saving optimization of building design. The uniform distribution of air-conditioning system loads, the reasonable configuration of cold and heat sources, and the balance between lighting uniformity and solar radiation heat gain in the design of the enclosure structure are all issues that need to be considered during the design process.

[0004] In order to achieve the performance design and optimization of large-space buildings, the development of building energy consumption simulation technology is relatively mature. At present, EnergyPlus, DOE-2, DeST, DesignBuilder and other software are usually used for building energy consumption simulation analysis, and three-dimensional computational fluid dynamics methods are used to finely evaluate indoor temperature and airflow organization according to load calculation boundaries, providing technical reference for creating high-quality indoor thermal comfort. Secondly, cutting-edge algorithms in computational science have been introduced into the design of new buildings, such as Grasshopper software in Rhino, which optimizes multiple design parameters of new buildings through pre-set goals, making the design decisions of new buildings more reasonable and scientific.

[0005] However, this high-performance optimization method is still relatively lacking in the decision-making field of energy-saving design of tall and large-space buildings, and a relatively mature and complete system has not yet been formed. The above energy consumption and thermal comfort evaluation methods have the following limitations and shortcomings: 1) The energy consumption simulation analysis software is too ideal for the simulation of indoor airflow organization and temperature space in the room. The simulation software assumes that the indoor air is completely mixed during calculation, which is inconsistent with the uneven indoor temperature distribution of actual tall and large-space buildings. The simulation results are quite different from the actual situation; 2) Due to the efficiency of CFD simulation calculations and the difficulty in describing approximate boundary conditions, CFD simulation technology is difficult to be widely used in energy consumption simulation calculations; 3) The invention technology of optimizing the indoor environmental performance of buildings with publicly available multi-objective optimization algorithms is difficult to be used to evaluate the indoor airflow organization and temperature distribution of tall and large-space buildings. Summary of the invention

[0006] The technical problem to be solved by this application is to address the deficiencies and defects of existing indoor performance optimization design and evaluation technologies for large-space buildings, and to propose an indoor environmental performance optimization design method and system for large-space buildings based on CFD and multi-objective optimization algorithms, which can more realistically simulate the indoor airflow organization and temperature space of the room, and is particularly suitable for energy-saving design of large-space buildings.

[0007] At the same time, the present invention can also more accurately identify boundary conditions, achieve more optimized energy consumption simulation calculations, and avoid being restricted by CFD simulation calculation efficiency and the difficulty in describing approximate boundary conditions.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions: A method for optimizing the indoor environmental performance of a large-space building based on CFD and a multi-objective optimization algorithm, characterized by comprising the following steps: S1. Establish a parametric analysis model of the target building and a 3D model of surrounding buildings based on the BIM model; S2, build a digital simulation system for indoor light environment and a digital simulation analysis system for building energy consumption based on Honeybee plug-ins in Rhino and Grasshopper environments; S3. Determine multi-objective optimization parameters and optimization targets in the digital simulation system of indoor light environment and the digital simulation analysis system of building energy consumption: including identifying key energy-saving optimization targets and decomposing them into multiple sub-targets such as energy consumption, thermal comfort, and light environment quality, matching performance indicators for each sub-target, and determining the constraints that each performance indicator must meet; and determining the parameters that have a greater impact on the optimization target as optimization parameters; S4, respectively connecting the multi-objective optimization parameters and optimization objectives determined in step S3 to a multi-objective optimization module, wherein the multi-objective optimization module is configured to perform Pareto solution set optimization in combination with a strength Pareto evolutionary algorithm to obtain multiple optimal solutions; S5. Export the BIM model and IDF file corresponding to the preferred solution, evaluate the uniformity of indoor airflow organization based on FFD software, and determine the parameters of the preferred solution. This includes establishing the corresponding three-dimensional FFD grid model and FFD-Modelica joint simulation model, analyzing the indoor airflow organization of the building of the preferred solution, and evaluating the uniformity of indoor airflow organization.

[0009] In the above technical solution, in step S2, a digital simulation system of the building indoor light environment is built based on the Honeybee Radiance plug-in, and a digital simulation analysis system of the building energy consumption is built based on the Honeybee Energy plug-in.

[0010] In the above technical solution, in step S2, the light environment simulation includes first establishing a Honeybee model, converting the geometric objects Surface, Brep, and Mesh in Rhino into Honeybee objects, and then using the components in the HoneyBee Radiance plug-in to establish a lighting analysis plane based on the Honeybee model, and setting the light environment analysis parameters and simulation parameters before starting the light environment simulation and building energy consumption simulation.

[0011] In the above technical solution, in step S2, the light environment analysis parameters include the simulated time period, light source type, and optical properties of the material.

[0012] In the above technical solution, in step S2, the simulation parameters include simulation accuracy and number of iterations.

[0013] In the above technical solution, in step S2, the building energy consumption simulation includes: given the known building interior layout, building thermal material parameter information and HVAC design scheme, configuring the Honeybee Energy plug-in parameters, including setting the simulation time period, climate conditions, building material properties, personnel activities and equipment usage, and defining the building envelope, rooms, and thermal zones.

[0014] In the above technical solution, in step S3, the window-to-wall ratio, building orientation, external window parameters and shading parameters are selected as optimization parameters, and the building energy consumption index EUI, indoor lighting index UDI and indoor thermal comfort index PMV-PPD are selected as optimization targets.

[0015] In the above technical solution, the preferred solution in step S4 includes selecting three solutions: minimizing building energy consumption EUIMinimum, maximizing indoor lighting DF Maximum, and maximizing indoor thermal comfort ACP Maximum.

[0016] In the above technical scheme, step S5 updates the digital simulation system of the building indoor light environment and the digital simulation analysis system of the building energy consumption in Rhino according to the preferred scheme, and exports the IDF file corresponding to the preferred scheme and the corresponding design parameters; analyzes the indoor airflow organization and temperature distribution based on the FFD software, establishes the corresponding three-dimensional FFD grid model and FFD-Modelica joint simulation model, and analyzes the indoor airflow organization of the building of the preferred scheme based on the joint simulation model to obtain the cloud map of the indoor airflow organization and temperature distribution of the building.

[0017] In the above technical solution, the joint simulation model in step S5 is composed of Spawn, air handling unit and control model, three-dimensional FFD grid model and FFD room model.

[0018] In the above technical solution, in step S5, the building energy consumption digital simulation analysis system and Spawn realize data transmission through the FMU interface, and the three-dimensional FFD grid model and the FFD room model realize data transmission through the FMU interface.

[0019] In the above technical solution, step S5 uses the building wall temperature change curve calculated by the building energy consumption digital simulation analysis system as the dynamic boundary condition input of the FFD room model, and the HVAC system fan air supply volume and air supply temperature as the dynamic input of the FFD room model. In the above technical solution, step S5 analyzes the indoor air flow organization and temperature distribution based on FFD software, establishes a corresponding three-dimensional FFD grid model and a joint simulation model, and the simulation parameter setting of the joint simulation model system is consistent with the parameter setting of the building energy consumption digital simulation analysis system. The indoor air flow organization and HVAC system of the building are simulated, and the best solution is selected according to the indoor air flow organization and temperature distribution cloud map.

[0020] In the above technical solution, step S5 implements joint simulation based on the FMU interface in the Dymola software to analyze the indoor airflow organization of the building of the preferred solution.

[0021] The present invention also provides a storage medium on which a computer program is stored, and when the computer program is executed, it is used to implement the above-mentioned design method.

[0022] Of course, the present invention also provides an electronic device or a digital design processing system, on which a computer program is stored or accessible, and when the computer program is executed, the electronic device or system is used to implement the above-mentioned design method.

[0023] In summary, the present invention relates to a method and system for optimizing and evaluating the indoor environmental performance of large-space buildings based on computational fluid dynamics (CFD) and multi-objective optimization algorithms. By combining the BIM model to establish a parametric analysis model, the Honeybee Radiance and Honeybee Energy plug-ins in the Rhino and Grasshopper environments are used to perform digital simulation analysis of the indoor light environment and energy consumption, determine the optimization parameters and objectives, and apply the multi-objective optimization algorithm to optimize the Pareto solution set.

[0024] Compared with the prior art, the present invention has the following beneficial effects: The invention provides an efficient, accurate and economical method for optimizing and evaluating the indoor environmental performance of large-space buildings. Compared with the traditional indoor environmental optimization method, the invention has higher accuracy, stronger adaptability and wider applicability.

[0025] It can simultaneously consider natural lighting, building energy consumption and indoor three-dimensional thermal comfort to optimize the indoor environmental performance of tall and large-space buildings. It analyzes and controls the environmental performance indicators of local personnel activity areas in tall and large-space buildings, reducing the simulation error of the coordinated optimization of lighting, energy consumption and thermal comfort in existing energy consumption simulation technologies.

[0026] Compared with traditional indoor environment optimization methods, the present invention has higher accuracy, stronger adaptability and wider applicability. It can be used not only in the design stage of new buildings, but also in the renovation and upgrading of existing buildings. It can provide scientific and systematic optimization solutions for architectural design to achieve comprehensive improvement of indoor environmental performance.

[0027] The present invention can also evaluate the indoor airflow organization and temperature distribution uniformity of buildings. It is not limited by the efficiency of CFD simulation calculations and can describe the reality of the difficulty of approximating boundary conditions. It can efficiently calculate and approach the degree of actual conditions, efficiently select the best light environment and energy consumption design scheme, provide designers with a reliable design basis, and promote efficient innovation and optimization of energy-saving and livable design of large-space buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 It is a flow chart of a method for optimizing indoor environmental performance of large-space buildings based on CFD and multi-objective optimization algorithm according to an embodiment of the present invention; Figure 2 A parametric model diagram is established based on Grasshopper in an embodiment of the present invention; Figure 3 This is a parametric modeling flow chart based on Grasshopper according to an embodiment of the present invention; Figure 4 This is a Honeybee modeling flow chart in an embodiment of the present invention; Figure 5 A diagram of a light environment analysis system established for an embodiment of the present invention; Figure 6 This is a daylight coefficient distribution diagram of an office hall according to an embodiment of the present invention; Figure 7 This is a diagram of parameter settings for the office lobby enclosure structure according to an embodiment of the present invention; Figure 8 This is a diagram of the internal heat source arrangement of office hall lighting, equipment, personnel, etc. according to an embodiment of the present invention; Fig. 9 A diagram showing the staff, equipment, lighting and air conditioning schedule of an office hall according to an embodiment of the present invention; Fig.10 A diagram of a digital simulation analysis system for building energy consumption established for an embodiment of the present invention; Fig.11 This is a temperature variation curve diagram of the inner surface of the exterior wall of the office hall in different directions according to the embodiment of the present invention; Fig.12 This is the indoor thermal comfort distribution diagram of the office hall in the embodiment of the present invention from 8h to 22h throughout the year; Fig.13 is the calculated Pareto front solution diagram; Fig.14 A three-dimensional FFD grid model of an office lobby is established for the embodiment of the present invention; Fig.15 A joint simulation model according to an embodiment of the present invention; Fig.16 This is a cloud diagram of indoor airflow organization and temperature distribution in a building according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0031] Example 1 The present invention, implemented according to the technical solution of the present invention, relates to a method for optimizing the design or evaluation of indoor environmental performance of large-space buildings based on CFD and multi-objective optimization algorithms. The detailed process of the method of the present invention is as follows: Figure 1 As shown, the method comprises the following steps: 1) Establish a parametric analysis model of the target building and a 3D model of surrounding buildings based on the BIM model; 2) In the Rhino and Grasshopper environments, a digital simulation analysis system for indoor light environment and energy consumption was built based on the Honeybee Radiance and Honeybee Energy plug-ins to simulate the lighting and building energy consumption of the building respectively.

[0032] Specifically, sub-step 1 includes: building a Honeybee model, converting geometric objects (Surface, Brep, Mesh, etc.) in Rhino into Honeybee objects (Honeybee Surfaces and Honeybee Zones); Specifically, sub-step 2 includes: using the components in the HoneyBee plug-in, establishing a lighting analysis plane based on the Honeybee model in sub-step 1, and setting relevant parameters of the light environment analysis, including the simulated time period, light source type, optical properties of the material, etc., and setting simulation parameters, such as simulation accuracy, number of iterations, etc., and then running the building indoor light environment digital simulation system; Specifically, sub-step 3 includes: given the known building interior layout, building thermal material parameter information, and HVAC design plan, configure the Honeybee Energy plug-in parameters, including setting the simulated time period, climate conditions, building material properties, personnel activities, and equipment usage, which usually includes defining the building envelope, rooms, thermal zones, etc.; 3) Determine the optimization parameters and optimization objectives.

[0033] Specifically, sub-step 1 includes: according to the background, purpose and expected effect of the building design, identify the key energy-saving goals that need to be optimized, and decompose them into multiple sub-goals such as energy consumption, thermal comfort, and light environment quality. Select or determine appropriate performance indicators for each sub-goal, which should be measurable and can accurately reflect the target status. At the same time, determine the constraints that the above indicators must meet during the optimization process, such as building regulations, environmental requirements, and usage functions.

[0034] Specifically, sub-step 2 includes: selecting relevant parameters that affect the use function and comfort of the building, such as the building's thermal insulation performance, heat preservation performance, lighting performance, etc. According to the design requirements and objectives, select the parameters that have a greater impact on the optimization target as the optimization parameters. These parameters should be adjustable, and their changes can significantly affect the optimization target. Determine the physical constraints of the parameters according to the building's design specifications, safety standards, etc. Set the initial values ​​of the parameters according to the design requirements and constraints. These initial values ​​should be as close to the actual values ​​as possible and meet all constraints.

[0035] 4) Multi-objective optimization parameter setting and optimization analysis: Connect the indoor light environment and energy consumption digital simulation analysis system to the multi-objective optimization module (optimization variable group, optimization target group). When there is no error in the entire system, enter the operation interface and set relevant parameters, among which the elite ratio (Elitism) is set to 50%; the gene mutation probability (Mut.Probability) is set to 0.01; the gene mutation degree (Mutation Rate) is set to 0.5; the crossover probability (CrossoverRate) takes the software default value of 0.8; the population size (PopulationSize) is set to 30; the termination operation algebra (Max.Generations) of the genetic operation takes the default value of 0. Combined with the strength Pareto evolutionary algorithm (SPEA2), the Pareto solution set is optimized; 5) According to the selected optimization scheme, update the Rhino model, export the IDF file and corresponding design parameters corresponding to the preferred scheme, establish a three-dimensional FFD grid model and an FFD-Modelica joint simulation model, implement joint simulation based on the FMU interface in the Dymola software, analyze the indoor airflow organization of the building of the preferred scheme, obtain the indoor airflow organization and temperature distribution cloud map of the building, and evaluate the best scheme.

[0036] Example 2 An example is given below to illustrate the effectiveness and reliability of the method of the present invention. The specific implementation steps are as follows: 1) Based on the existing building BIM model, a parametric analysis model of the target building is established in Rhino and Grasshopper environments; Specifically, the office building is located in Wuhan, Hubei Province, with a total construction area of ​​276,855.8 m², 42 floors above ground and 3 floors underground. The calculation and analysis process introduced in this article is applied to the optimization design of the lobby of the office building. The lobby has a construction area of ​​2162.2 m² and a net height of 11.6 m², which is a typical tall space building. In Grasshopper, Honeybee components are used to define the walls and exterior windows of the building. The window-to-wall ratios of the east, south, west and north directions of the building are recorded as W-WE, W-WS, W-WW and W-WN respectively. The parametric model based on Grasshopper and its process are as follows: Figure 2 , 3 shown.

[0037] 2) In the Rhino and Grasshopper environments, build energy consumption digital simulation analysis and indoor light environment systems based on Honeybee Radiance and Honeybee Energy plug-ins, and perform lighting simulation analysis and building energy consumption simulation analysis on the building respectively.

[0038] Specifically, sub-step 1 includes: based on the established parametric model, converting the geometric objects in Rhino into Honeybee objects. The conversion process is as follows: Figure 4 As shown; Specifically, sub-step 2 includes: using the components in the HoneyBee-Radiance plug-in to establish a lighting analysis plane, the lighting analysis plane selects a plane at a height of 0.9m from the ground, the grid cell size is set to 0.9m, and test points are generated on the plane where the daylight factor (DF) needs to be calculated. The sky model selects the CIE full cloudy model, and the established light environment analysis system is as follows: Figure 5 As shown, the number of bounces (ambient bounces) of the ambient light calculation in the radiance_par_ parameter is 2, the anti-aliasing level (anti-aliasing) is 0.1, the diffuse reflection sampling size (ambientdetail) is set to 2048, and the number of bounces (specular reflections / refractions) of the reflection / refractions calculation is set to 64. Run the digital simulation system of the building indoor light environment to ensure that the system has no errors, and get the daylight coefficient distribution as shown below Figure 6 shown.

[0039] Specifically, sub-step 3 includes: configuring Honeybee Energy plug-in parameters (meteorological parameters, envelope parameters, lighting, equipment, personnel load, etc.) based on the building design drawings and equipment layout, building thermal material parameter information and HVAC preliminary design plan. Wuhan City, where the office building is located, belongs to the hot summer and cold winter area (III A), and the meteorological file of the area where the building is located uses the CSWD meteorological data source. The exterior wall of the office building adopts aerated concrete block wall with a heat transfer coefficient of 0.66 W / m².K; the exterior window adopts double-layer Low-E insulated metal profile sealed frame with a heat transfer coefficient of 2.1 W / m².K; the roof adopts reinforced concrete roof with a heat transfer coefficient of 0.42 W / m².K. The envelope structure parameters are set as follows Figure 7 As shown in the figure, the indoor occupancy density is 0.2 people / m² (7h-19h); the equipment load is 15 W / m² (7h-19h); the lighting load is 10 W / m² (7h-19h), and the internal heat sources such as lighting, equipment and personnel are as follows: Figure 8 The relevant timetable is shown in Fig. 9 As shown in the figure, the indoor design parameters of the office building lobby are 26℃ in summer and 20℃ in winter. The digital simulation analysis system for building energy consumption is established as follows: Fig.10 As shown, run the building energy consumption simulation analysis system to ensure that the system has no errors. According to the outdoor meteorological file, when the maximum outdoor temperature is 38.75℃ (July 31), the corresponding building air-conditioned room's inner surface temperature change curves of the exterior walls in different directions are as follows: Fig.11 As shown; the indoor thermal comfort distribution of the building throughout the year is as follows Fig.12 As shown; the building EUI value is 178kWh / m².

[0040] 3) Determine the optimization parameters and optimization objectives.

[0041] Based on the existing multi-objective parameter research, in this operation, the comprehensive impact of the building window opening ratio and four key parameters that may affect it are evaluated: orientation, external window type, shading and wall enclosure structure. The multi-objective optimization method is applied to determine the optimal window opening ratio for different building orientations. The window-to-wall ratio of the building facade in the east (W-WE), south (W-WS), west (W-WW), and north (W-WN) directions is taken as the independent variable, and the window-to-wall ratio varies between 0.3 and 0.7.

[0042] 4) Multi-objective optimization parameter setting and optimization analysis: Connect the indoor light environment and energy consumption digital simulation analysis system to the multi-objective optimization module. When there is no error in the entire system, double-click the 0ctopus battery to enter the setting interface. The specific setting method is as follows: The default value of the elite ratio (Elitism) is 50%; the possibility of each gene to produce a mutation (Mut.Probability) is generally 1 / (size+1), and is set to 0.05 in this case; the degree of gene mutation (Mutation Rate) is set to 0.5; the crossover probability (CrossoverRate) is generally 0.4~0.99, and the default value is 0.8 in this case; the population size (PopulationSize) is generally 20~100, and is set to 30 in this case; the default value of the termination operation generation (Max.Generations) of the genetic operation is 0; Record Interval, Save Interval and MAX.Eval.Time are not modified. Combined with the intensity Pareto evolutionary algorithm (SPEA2), click the start button to optimize the Pareto solution set, including minimizing building energy consumption (EUIMinimum), maximizing indoor lighting (DF Maximum) and maximizing indoor thermal comfort (ACP Maximum). The Pareto frontier solution set obtained is as follows Fig.13 shown. Fig.13 The unit of energy use intensity (EUI_Min) is KWh / (m².a), and the unit of human thermal comfort evaluation index (PMV_Max) and daylight factor (DF) is 1.

[0043] After calculation, after a limited number of optimization iterations, the daylight factor (DF) changes in the range of 25.59% to 37.59%, the building energy use intensity (EUI) changes in the range of 140.11KW·h / ㎡ to 190.36KW·h / ㎡, and the indoor thermal comfort percentage (ACP) changes in the range of 18.88% to 30.83%. The design schemes corresponding to EUI Minimum, ACP Maximum and DF Maximum are recorded as Case 1, Case 2, and Case 3 respectively. The exterior window design parameters corresponding to Case 1, Case 2, and Case 3 are shown in Table 1.

[0044] Table 1

[0045] 5) In order to evaluate the indoor temperature distribution uniformity of the above scheme, the indoor airflow organization and temperature distribution are analyzed based on FFD software, and the corresponding three-dimensional FFD grid model is established (such as Fig.14 as shown) and co-simulation models (as shown Fig.15 as shown).

[0046] Spawn-of-EnergyPlus (Spawn for short) is the next generation model-based engineering (BEM) engine. It uses functional mock-up interface (FMI) for equation-based modeling and co-simulation, enabling Spawn to unify BEM and control processes. The overall joint simulation model consists of Spawn, air handling unit and its control model, three-dimensional FFD grid model and FFD room model. The building energy consumption simulation analysis system and Spawn realize data transmission through FMU interface, and the three-dimensional FFD grid model and FFD room model realize data transmission through FMU interface. At the same time, the building wall temperature change curve calculated by the energy consumption digital simulation analysis system is used as the dynamic boundary condition input of the FFD room model, and the air supply volume and air supply temperature of the HVAC system fan are used as the dynamic input of the FFD room model. The system simulation parameter settings are consistent with the building energy consumption simulation system parameter settings, and the building indoor air flow organization and HVAC system are simulated. Fig.16 As shown in the figure, from top to bottom, the situations of Case 1, Case 2, and Case 3 are shown respectively). The optimal solution is selected according to the indoor airflow organization and temperature distribution cloud diagram. The indoor airflow organization distribution corresponding to Case 2 in this project is the most uniform, so the window-to-wall ratio design adopts the parameters of Case 2.

[0047] The embodiments described above are part of the embodiments of the present application, rather than all of the embodiments. The detailed description of the embodiments of the present application is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

Claims

1. A method for optimizing the indoor environmental performance of large-space buildings based on CFD and multi-objective optimization algorithm, characterized in that The following steps are involved: S1. Establish a parametric analysis model of the target building and a 3D model of surrounding buildings based on the BIM model; S2. Build a digital simulation system for indoor light environment and a digital simulation analysis system for building energy consumption based on Honeybee plug-ins in Rhino and Grasshopper environments to simulate light environment and building energy consumption; S3. Determine multi-objective optimization parameters and optimization goals in the digital simulation system of indoor light environment and the digital simulation analysis system of building energy consumption: including identifying key energy-saving optimization goals and decomposing them into multiple sub-goals such as energy consumption, thermal comfort, and light environment quality, matching performance indicators for each sub-goal, and determining the constraints that each performance indicator must meet; and determining the parameters that have a greater impact on the optimization target as optimization parameters; S4, respectively connecting the multi-objective optimization parameters and optimization objectives determined in step S3 to a multi-objective optimization module, wherein the multi-objective optimization module is configured to perform Pareto solution set optimization in combination with a strength Pareto evolutionary algorithm to obtain multiple optimal solutions; S5. Export the BIM model and IDF file corresponding to the preferred solution, evaluate the uniformity of indoor airflow organization based on FFD software, and determine the parameters of the preferred solution.

2. The indoor environment performance optimization design method for large-space buildings based on CFD and multi-objective optimization algorithm according to claim 1 is characterized in that In step S2, a digital simulation system of the building's indoor light environment is built based on the Honeybee Radiance plug-in, and a digital simulation analysis system of the building's energy consumption is built based on the Honeybee Energy plug-in.

3. The indoor environment performance optimization design method for large-space buildings based on CFD and multi-objective optimization algorithm according to claim 1 is characterized in that In step S2, the light environment simulation includes first establishing a Honeybee model, converting the geometric objects Surface, Brep, and Mesh in Rhino into Honeybee objects, and then using the components in the HoneyBee Radiance plug-in to establish a lighting analysis plane based on the Honeybee model, and setting the light environment analysis parameters and simulation parameters before starting the light environment simulation and building energy consumption simulation.

4. The indoor environment performance optimization design method for large-space buildings based on CFD and multi-objective optimization algorithm according to claim 2 is characterized in that In step S2, the light environment analysis parameters include the simulated time period, light source type, and optical properties of the material.

5. The indoor environment performance optimization design method for large-space buildings based on CFD and multi-objective optimization algorithm according to claim 1 is characterized in that In step S2, the building energy consumption simulation includes: given the known building interior layout, building thermal material parameter information and HVAC design scheme, configuring the Honeybee Energy plug-in parameters, including setting the simulation time period, climate conditions, building material properties, personnel activities and equipment usage, and defining the building envelope, rooms, and thermal zones.

6. The indoor environment performance optimization design method for large-space buildings based on CFD and multi-objective optimization algorithm according to claim 1 is characterized in that In step S3, the window-to-wall ratio, building orientation, exterior window parameters and shading parameters are selected as optimization parameters, and the building energy consumption index EUI, indoor lighting index UDI and indoor thermal comfort index PMV-PPD are selected as optimization targets.

7. The indoor environment performance optimization design method for large-space buildings based on CFD and multi-objective optimization algorithm according to claim 1 is characterized in that The preferred scheme in step S4 includes selecting three schemes: minimizing building energy consumption EUIMinimum, maximizing indoor daylighting DF Maximum, and maximizing indoor thermal comfort ACP Maximum.

8. The indoor environment performance optimization design method for large-space buildings based on CFD and multi-objective optimization algorithm according to claim 1 is characterized in that Step S5 updates the digital simulation system of the building indoor light environment and the digital simulation analysis system of the building energy consumption in Rhino according to the preferred solution, and exports the IDF file corresponding to the preferred solution and the corresponding design parameters; analyzes the indoor airflow organization and temperature distribution based on the FFD software, establishes the corresponding three-dimensional FFD grid model and the FFD-Modelica joint simulation model, and analyzes the indoor airflow organization of the building of the preferred solution based on the joint simulation model to obtain the cloud map of the indoor airflow organization and temperature distribution of the building.

9. The indoor environment performance optimization design method for large-space buildings based on CFD and multi-objective optimization algorithm according to claim 1 is characterized in that Step S5 uses the building wall temperature change curve calculated by the building energy consumption digital simulation analysis system as the dynamic boundary condition input of the FFD room model, and the HVAC system fan air supply volume and air supply temperature as the dynamic input of the FFD room model.

10. A digital design processing system, on which a computer program is stored, which, when executed, is used to implement the indoor environmental performance optimization design method for large-space buildings based on CFD and multi-objective optimization algorithm as claimed in any one of claims 1 to 9.

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