Parameter optimization method and product of high computational efficiency multilayer window structure containing phase change material

Abstract

The application provides a parameter optimization method and product of a high-computing-efficiency phase-change-material-containing multi-layer window structure, and relates to the field of computer-aided design. The parameter optimization method comprises the following steps: using a pre-established building load calculation model to calculate building load data according to meteorological parameters, building envelope structure parameters, and personnel and equipment operation parameters; constructing a benchmark dynamic simulation model of the phase-change-material-containing multi-layer window and a full-life-cycle carbon emission evaluation function; determining initial data of the phase-change-material-containing multi-layer window structure, calling the benchmark dynamic simulation model and the full-life-cycle carbon emission evaluation function to perform simulation calculation, and using a preset optimization algorithm to optimize the simulation calculation result; iteratively performing the simulation calculation and the optimization calculation until an iteration completion condition is met; and outputting the optimization result meeting the iteration completion condition as an optimization completion parameter. The scheme of the application significantly improves design efficiency and accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer-aided design, and in particular to a high-computational-efficiency parameter optimization method for a multi-layer window structure containing phase change materials and a product. BACKGROUND

[0002] Building load is greatly affected by window structure, and excellent window structure can greatly improve the energy-saving performance of buildings. In the current engineering building window structure design, the window structure parameters of different building types and different geographical locations are different, and designers often need to study the design of certain specific parameters to obtain better system performance. In particular, for windows containing phase change materials, their structure is more complex than ordinary windows, and there are more related variable parameters, so the design process needs more optimization.

[0003] The process of determining multiple parameters of the window structure with building load as the objective function can use a transient system simulation tool (such as TRNSYS software). This tool can simulate building energy consumption, but it lacks targeted optimization methods. When multiple variables are involved, only one variable can be corrected at a time, and the variable value is constantly updated to try to optimize the objective function. However, in reality, when a variable changes, other variables also become non-optimal variables, which requires constant adjustment. The parameter optimization process is very time-consuming and is prone to local optimization, and cannot achieve global optimization.

[0004] For the parameter optimization of the above-mentioned transient system simulation tool, some optimization algorithm tools are also proposed in the prior art, such as GenOpt and JEPlus. However, these optimization algorithm tools still have limitations: on the one hand, they only support built-in optimization algorithms, and users cannot easily extend or introduce new algorithms; on the other hand, for complex simulation programs involving external link files of the transient system simulation tool, the processing capacity of these tools is limited. SUMMARY

[0005] One object of the present application is to overcome at least one of the deficiencies in the prior art and provide a high-computational-efficiency parameter optimization method for a multi-layer window structure containing phase change materials and related products.

[0006] A further object of the present application is to efficiently complete the parameter optimization of the multi-layer window structure containing phase change materials and meet the design needs of various scenarios.

[0007] Another further object of the present application is to more comprehensively reflect the influence of the window structure on the environment and provide a more scientific basis for building design and decision-making.

[0008] One aspect of the present application provides a parameter optimization method for a multi-layer window structure containing phase change materials, comprising:

[0009] The building load data is calculated by using a pre-established building load calculation model according to meteorological parameters, building envelope parameters, and personnel and equipment operation parameters;

[0010] A benchmark dynamic simulation model containing a phase change material multi-layer window and a life cycle carbon emission evaluation function are constructed in a transient system simulation tool based on the building load data;

[0011] Initial data of the phase change material multi-layer window structure are determined, and the initial data include to-be-optimized parameters and optimization ranges and initial values of the to-be-optimized parameters;

[0012] The initial data are simulated and calculated by calling the benchmark dynamic simulation model and the life cycle carbon emission evaluation function, and a preset optimization algorithm is used to optimize the simulation and calculation results;

[0013] The optimized results are used as inputs of the benchmark dynamic simulation model and the life cycle carbon emission evaluation function to perform iterative simulation and optimization calculation until an iteration completion condition is met;

[0014] The optimized results meeting the iteration completion condition are output as optimization completion parameters.

[0015] Optionally, the step of optimizing the simulation and calculation results by using the preset optimization algorithm includes:

[0016] An optimization program for executing the optimization algorithm is called, and a setting interface of the optimization program is used to obtain a path of the benchmark dynamic simulation model, an input data path, an output data path, and optimization variables, the optimization variables including initial values, minimum values, maximum values, and minimum intervals of to-be-optimized parameters required for optimization calculation;

[0017] The optimization program scans the benchmark dynamic simulation model through the path of the benchmark dynamic simulation model, obtains input data through the input data path, and stores optimization calculation results and intermediate data into a file in the output data path.

[0018] Optionally, the optimization algorithm includes a particle swarm optimization algorithm with an inertia weight, and an iteration formula of the particle swarm optimization algorithm with the inertia weight is:

[0019] ,

[0020] In the iteration formula, X represents a position vector of a particle, d represents a dimension of to-be-optimized parameters of a window structure, V represents a velocity vector of the particle, w represents an inertia weight, c1 and c2 are respectively used to adjust steps of an individual optimal position and a global optimal position, r1 and r2 are random number sequences independent of each other in (0, 1), p best,d represents an individual optimal value, and g best,d represents a global optimal value.

[0021] Optionally, the step of optimizing the simulation result using a preset optimization algorithm comprises:

[0022] initializing the particle swarm, randomly generating position vectors and velocity vectors of all particles, and initializing the individual optimal position p of each particle with the initial position of the particle best,d initializing the global optimal position g with the optimal position corresponding to the fitness value of all initial positions best,d ;

[0023] calculating the fitness value, for each particle, comparing the corresponding fitness value with the optimal position p experienced by the particle best,d , if the fitness value corresponding to the new position is larger, updating the local optimal value p best,d ; for each particle, comparing the corresponding fitness value with the global optimal position g best,d , if the fitness value corresponding to the new position is larger, updating the global optimal value g best,d ;

[0024] updating the particle velocity and position in real time according to the iteration formula;

[0025] adjusting the inertia weight, gradually reducing the inertia weight w according to a preset decreasing strategy;

[0026] iterating until the iteration calculation meets the iteration completion condition, outputting the optimal solution, and the iteration completion condition comprises: the iteration number reaches a preset maximum iteration number or the fitness value reaches a preset lower limit value.

[0027] Optionally, the decreasing formula of the decreasing strategy is:

[0028] ,

[0029] wherein, w max and w min are the initial and final inertia weights, and iter is the maximum iteration number.

[0030] Optionally, the calculation formula of the fitness value is:

[0031]

[0032] wherein, P1, P2 are the building load and the carbon emission related to the phase change material multilayer window in the building life cycle of the system simulation, d represents the window structure parameter dimension, X i,1 , X i,2 , X i,3 …X i,d are the optimal parameter combinations under different targets.

[0033] Optionally, the meteorological parameters include: outdoor air dry-bulb temperature, wind speed, relative humidity, and solar radiation.

[0034] The building load data include: heating heat load.

[0035] The parameters to be optimized include: phase change latent heat, phase change extrapolation starting point temperature, phase change material layer thickness, and air spacing layer thickness.

[0036] Optionally, the life cycle carbon emission evaluation function is used to calculate the life cycle carbon emission of the multi-layer window structure, and the calculation formula is:

[0037] ,

[0038] In the formula, GHC LCA is the life cycle carbon emission of the multi-layer window structure, GHC WH is the carbon emission of the multi-layer window structure in the materialization stage, GHC YX is the carbon emission of the multi-layer window structure in the operation stage, and GHC CC is the carbon emission of the multi-layer window structure in the demolition and recycling stage.

[0039] According to another aspect of the present application, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of any of the above-mentioned parameter optimization methods for a multi-layer window structure containing phase change materials.

[0040] According to still another aspect of the present application, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the above-mentioned parameter optimization methods for a multi-layer window structure containing phase change materials.

[0041] According to still another aspect of the present application, there is also provided a computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the steps of any of the above-mentioned parameter optimization methods for a multi-layer window structure containing phase change materials.

[0042] The parameter optimization method for a multi-layer window structure containing phase change materials of the present application improves the accuracy and efficiency of the design of a multi-layer window structure containing phase change materials, simulates and calculates the initial data by calling a benchmark dynamic simulation model and a life cycle carbon emission evaluation function, and uses a preset optimization algorithm to optimize the simulation and calculation results, so that the benchmark dynamic simulation model and the optimization algorithm are iteratively optimized, and the complex parameters of a multi-layer window structure containing phase change materials can be automatically completed. Compared with the existing window structure design which often relies on experience and trial and error, the present application can quickly and accurately find the optimal window structure parameters, thereby significantly improving the design efficiency and accuracy.

[0043] Further, the parameter optimization method of the phase change material-containing multi-layer window structure of the present application interfaces the optimization program of the optimization algorithm with the benchmark dynamic simulation model of the transient system simulation tool, so that data interaction can be conveniently performed, and the iteration calculation efficiency is improved.

[0044] Still further, the parameter optimization method of the phase change material-containing multi-layer window structure of the present application not only considers the carbon emission of the phase change material-containing multi-layer window structure in the operation stage, but also comprehensively evaluates the carbon emission of the window structure in the materialization stage and the demolition stage. This life cycle evaluation method can more comprehensively reflect the influence of the window structure on the environment, and provides a more scientific basis for building design and decision-making.

[0045] Still further, the parameter optimization method of the phase change material-containing multi-layer window structure of the present application uses the particle swarm algorithm with inertia weight to perform parameter optimization, and the optimization algorithm has the characteristics of strong global search capability and fast convergence speed, and can effectively find the optimal solution in a complex parameter space.

[0046] Still further, the parameter optimization method of the phase change material-containing multi-layer window structure of the present application can flexibly adjust the to-be-optimized parameters, optimization range and initial value of the phase change material-containing multi-layer window structure according to actual project requirements, is suitable for different types of buildings and climate conditions, has strong flexibility and applicability, and makes the scheme of the present application have a wide application prospect and can meet the needs of different regions and different building types.

[0047] The above and other objects, advantages and features of the present application will become more apparent from the following detailed description of some embodiments thereof, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0048] Some specific embodiments of the present application will be described in detail below with reference to the accompanying drawings, which are shown by way of example and not limitation. The same reference numbers in the drawings indicate the same or similar components or parts. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0049] Figure 1 is a schematic diagram of the parameter optimization method of the phase change material-containing multi-layer window structure according to an embodiment of the present application;

[0050] Figure 2 is a schematic block diagram of the benchmark dynamic simulation model in the parameter optimization method of the phase change material-containing multi-layer window structure according to an embodiment of the present application;

[0051] Figure 3 is a schematic diagram of the iteration optimization of the optimization algorithm in the parameter optimization method of the phase change material-containing multi-layer window structure according to an embodiment of the present application;

[0052] Figure 4 is a schematic diagram of a computer program product according to an embodiment of the present application;

[0053] Figure 5 is a schematic diagram of a computer readable storage medium according to an embodiment of the present application;

[0054] Figure 6 is a schematic block diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] Those skilled in the art should understand that the embodiments described below are only a part of the embodiments of the present application, and are not intended to limit the protection scope of the present application. Based on the embodiments provided by the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor shall fall within the protection scope of the present application.

[0056] Embodiments of the present application first provide a parameter optimization method of a phase change material containing multilayer window structure. The phase change material containing multilayer window structure is an energy-saving window structure that uses the latent heat absorbed / released by the phase change material in the solid-liquid or solid-solid transition process to adjust the indoor temperature fluctuation. The core is to store solar energy or environmental heat through phase change latent heat, reduce building energy consumption and improve thermal comfort. The multilayer window structure has multiple interlayer structures, such as phase change material layer and air spacing layer. The former fills phase change material and is used for storing / releasing heat energy, and the latter is used for enhancing the heat insulation capacity.

[0057] Figure 1 is a schematic diagram of a parameter optimization method of a phase change material containing multilayer window structure according to an embodiment of the present application. The parameter optimization method of the phase change material containing multilayer window structure can generally include:

[0058] In step S101, the building load data is calculated according to the meteorological parameters, the building envelope parameters, and the personnel equipment operation parameters by using a pre-established building load calculation model.

[0059] The building load calculation model can be selected according to the characteristics of the region where the building is located. The meteorological parameters can include outdoor air dry bulb temperature, wind speed, relative humidity, and solar radiation, which can be a meteorological data file (.epw file). The building envelope parameters are used to describe the building envelope, which can be included in the building model file (.b18) file. The personnel equipment operation parameters are used to describe the influencing factors of the building load, such as personnel activities and the use of various equipment (such as lighting equipment). The building load data can include heating heat load.

[0060] In step S102, a benchmark dynamic simulation model of the phase change material multi-layer window and a life cycle carbon emission evaluation function are constructed in a transient system simulation tool based on building load data. The transient system simulation tool is used to simulate the energy consumption and system operation effect of the building under different working conditions based on the building load data obtained from meteorological parameters, building envelope parameters, and personnel and equipment operation parameters. The transient system simulation tool can use TRNSYS software.

[0061] The life cycle carbon emission evaluation function is used to calculate the carbon emission of the phase change material multi-layer window structure in the whole life cycle. The whole life cycle can include the materialization stage, the operation stage and the demolition stage of the phase change material multi-layer window. The carbon emission of the materialization stage of the phase change material multi-layer window includes the carbon emission in the production, transportation and processing and installation stages of the window; the carbon emission of the operation stage of the phase change material multi-layer window includes the carbon emission of the window in the daily operation stage of the building and the carbon emission of the window in the maintenance and repair stage in the use stage; the carbon emission of the demolition stage of the phase change material multi-layer window includes the carbon emission in the window demolition process and the carbon emission in the waste disposal.

[0062] The life cycle carbon emission evaluation function is used to calculate the carbon emission of the phase change material multi-layer window structure in the whole life cycle, and the calculation formula is:

[0063] ,

[0064] In the formula, GHC LCA is the carbon emission of the multi-layer window structure in the whole life cycle, GHC WH is the carbon emission of the multi-layer window structure in the materialization stage, GHC YX is the carbon emission of the multi-layer window structure in the operation stage, and GHC CC is the carbon emission of the multi-layer window structure in the demolition and recycling stage.

[0065] In step S103, initial data of the phase change material multi-layer window structure are determined, and the initial data include to-be-optimized parameters and optimization ranges and initial values thereof. The to-be-optimized parameters can include: latent heat of phase change L of the phase change material multi-layer window structure, phase change extrapolation starting point temperature Tm, thickness x PCM of the phase change material layer, and thickness x Air of the air spacing layer.

[0066] In step S104, the initial data are simulated and calculated by calling the benchmark dynamic simulation model and the life cycle carbon emission evaluation function, and a preset optimization algorithm is used to optimize the simulation and calculation results.

[0067] Step S105, the result of the optimization is taken as the input of the reference dynamic simulation model and the whole life cycle carbon emission evaluation function for iterative simulation calculation and optimization calculation until the iteration completion condition is met. The iteration completion condition can be that the iteration number reaches the preset maximum iteration number or the fitness value is lower than the preset lower limit value.

[0068] The step of using the preset optimization algorithm to optimize the simulation calculation result can include: calling an optimization program for executing the optimization algorithm, obtaining the path of the reference dynamic simulation model, the input data path, the output data path and the optimization variable by using the setting interface of the optimization program, the optimization variable including: the initial value, the minimum value, the maximum value and the minimum interval of the optimization of the required parameter participating in the optimization calculation; scanning the reference dynamic simulation model by the optimization program through the path of the reference dynamic simulation model, obtaining the input data through the input data path; and storing the optimization calculation result and the intermediate data into the file in the output data path.

[0069] The optimization program can be an optimization software for the TRNSYS software, and the optimization algorithm includes a particle swarm algorithm with inertia weight. The algorithm parameters used in the iterative optimization calculation can include: population size, self-learning factor c1, social learning factor c2, maximum iteration number iter, weight inertia initial value w max and weight inertia final value w min .

[0070] The optimization algorithm includes a particle swarm algorithm with inertia weight, and its iteration formula is:

[0071] ,

[0072] In the iteration formula, X represents the position vector of the particle, d represents the dimension of the window structure to be optimized, V represents the velocity vector of the particle, w represents the inertia weight, c1 and c2 are used to adjust the step of the individual optimal position and the global optimal position respectively, r1 and r2 are random number sequences in (0, 1) independent of each other, p best,d represents the individual optimal value, g best,d represents the global optimal value.

[0073] Step S105, output the optimization result meeting the iteration completion condition as the optimization completion parameter.

[0074] Figure 2is a schematic block diagram of a reference dynamic simulation model in a parameter optimization method of a multilayer window structure containing phase change material according to an embodiment of the present application. The building simulation model 200 can include a meteorological parameter import module 201, an optimization parameter setting module 202, an external surface convective heat transfer coefficient processing module 203, a multilayer window module containing phase change material 204, a building load calculation module 205, and a carbon emission calculation module 206. The building simulation model 200 also defines the parameter transmission relationship lines between the above-mentioned modules.

[0075] The meteorological parameter import module 201 outputs the outdoor air dry bulb temperature and wind speed to the external surface convective heat transfer coefficient processing module 203. The meteorological parameter import module 201 outputs the outdoor air dry bulb temperature, relative humidity, wind speed, solar radiation, and other meteorological parameters to the building load calculation module 205. The optimization parameter setting module 202 outputs the window structure parameters to be optimized to the multilayer window module containing phase change material 204. The external surface convective heat transfer coefficient processing module 203 outputs the external surface convective heat transfer coefficient to the multilayer window module containing phase change material 204. The multilayer window module containing phase change material 204 outputs the heat transfer coefficient, solar heat gain coefficient, shading coefficient, and size of the window to the building load calculation module 205 and the carbon emission calculation module 206.

[0076] Figure 3 is a schematic diagram of iterative optimization of an optimization algorithm in a parameter optimization method of a multilayer window structure containing phase change material according to an embodiment of the present application. The step of using a preset optimization algorithm to optimize the simulation calculation results can include:

[0077] Step S301, initialize the particle swarm, randomly generate the position vector and velocity vector of all particles, and initialize the individual optimal position p of the particle with the initial position of the particle best,d Initialize the global optimal position g with the best position corresponding to the fitness value of all initial best,d ;

[0078] Step S302, calculate the fitness value, for each particle, compare the corresponding fitness value with the optimal position p best,d experienced by the particle, if the fitness value corresponding to the new position is larger, update the local optimal value p best,d ; for each particle, compare the corresponding fitness value with the global optimal position g best,d , if the fitness value corresponding to the new position is larger, update the global optimal value g best,d ;

[0079] Step S303, update the particle velocity and position in real time according to the iteration formula;

[0080] Step S304, adjusting the inertia weight, gradually reducing the inertia weight w according to a preset decreasing strategy;

[0081] Step S305, loop iteration until the iteration calculation meets the iteration completion condition, the iteration completion condition including: the iteration number reaches a preset maximum iteration number or the fitness value reaches a preset lower limit value.

[0082] Step S306, outputting the optimal solution.

[0083] In the above iteration process, the decreasing formula of the decreasing strategy is:

[0084] ,

[0085] In the formula, w max and w min are the initial and final inertia weights respectively, and iter is the maximum iteration number.

[0086] Optionally, the calculation formula of the fitness value is:

[0087]

[0088] In the formula, P1 and P2 are the building load and the carbon emission related to the phase change material-containing multi-layer window in the whole life cycle of the building simulated by the system, d represents the window structure parameter dimension, and X i,1 , X i,2 , X i,3 …X i,d are the optimal parameter combinations under different targets.

[0089] Taking a building in Lhasa, Tibet Autonomous Region as an example, the process of using the method of the embodiment is introduced. According to the environment of the region where the building is located and the characteristics of the building, a benchmark building model is selected, meteorological parameters (such as typical meteorological year parameters), building envelope parameters, personnel and equipment operation parameters and initial parameters of the phase change material-containing multi-layer window structure are determined, and a TRNSYS building system model for optimizing the phase change material-containing multi-layer window structure parameters is built.

[0090] The benchmark dynamic simulation model of the multi-layer window with phase change material and the multi-objective evaluation function model of the carbon emission of the multi-layer window with phase change material in the whole life cycle of the building are established in TRNSYS. The building load includes the heating load. The carbon emission of the multi-layer window with phase change material in the whole life cycle includes the carbon emission in the materialization stage, the operation stage and the demolition stage of the multi-layer window with phase change material. The carbon emission in the materialization stage includes the carbon emission in the production, transportation and installation stages of the window. The carbon emission in the operation stage includes the carbon emission of the window in the daily operation stage of the building and the carbon emission of the window in the maintenance and repair stage. The carbon emission in the demolition stage includes the carbon emission in the demolition process and the carbon emission in the waste disposal.

[0091] The paths of the specified simulation program and related files are respectively set in the setting window of the optimization program (for example, the intelligent optimization algorithm software based on TRNSYS) for executing the optimization algorithm, including the path of the main directory of the TRNSYS simulation program, the input file path and the output file path. The input file path contains the file of the simulation input data. The output file path is used for storing the simulation results and intermediate data.

[0092] The intelligent optimization algorithm software can also configure the variable setting window. The variable setting window is used for completing the selection and configuration of the variables required for optimization. After the designer triggers the setting operation by clicking the variable selection button, the software can automatically scan the physical system established in the input file and identify all independent input variables. The variable setting window can also be used to select specific variables participating in the optimization simulation and set the initial value, minimum value, maximum value and minimum interval of optimization in the optimization process. After the related parameters of the input variables are set, all the input variables are displayed in the working path window.

[0093] When the four-parameter joint optimization condition of the window body is selected, the latent heat of phase change of the phase change material layer in the three-layer window structure with phase change material , the extrapolation starting point temperature of phase change , the thickness and the thickness of the air layer are cooperatively optimized.

[0094] Table 1: Parameter setting of window body optimization condition

[0095]

[0096] The optimization target and the particle swarm algorithm parameters with inertia weight are determined in the algorithm parameter setting window.

[0097] The intelligent optimization algorithm software and the TRNSYS are interacted through data to complete the iteration optimization of the processes of steps S301 to S305 in the above embodiment. The intelligent optimization algorithm software executes the particle swarm optimization algorithm with inertia weight to automatically input the updated parameters into the TRNSYS through the interface for re-simulation. When the maximum number of iterations is reached or the improvement of the population fitness value is lower than the set threshold, the optimization is terminated and the optimal parameters of the window structure are output.

[0098] The parameter optimization method of the phase change material-containing multi-layer window structure in the above embodiment improves the accuracy and efficiency of the design of the phase change material-containing multi-layer window structure, simulates and calculates the initial data by calling the benchmark dynamic simulation model and the full life cycle carbon emission evaluation function, and uses the preset optimization algorithm to optimize the simulation calculation result. The benchmark dynamic simulation model and the optimization algorithm are iteratively optimized, and the complex parameters of the phase change material-containing multi-layer window structure can be automatically completed. Compared with the existing window structure design which often relies on experience and trial and error, the optimal window structure parameters can be quickly and accurately found by the present application, thereby significantly improving the design efficiency and accuracy. The present application can flexibly adjust the to-be-optimized parameters, optimization range and initial value of the phase change material-containing multi-layer window structure according to actual project requirements, is suitable for different types of buildings and climate conditions, has strong flexibility and applicability, and has a wide application prospect, and can meet the needs of different regions and different building types.

[0099] The embodiment also provides a computer program product 80, a computer readable storage medium 820, and a computer device 830. Figure 4 is a schematic diagram of a computer program product 40 according to an embodiment of the present application, Figure 5 is a schematic diagram of a computer readable storage medium 50 according to an embodiment of the present application, Figure 6 is a schematic block diagram of a computer device 60 according to an embodiment of the present application.

[0100] The computer program product 40 includes a computer program 41, which, when executed by a processor 42, implements the steps of any of the parameter optimization methods of the phase change material-containing multi-layer window structure described above. The computer readable storage medium 50 has the computer program 41 stored thereon, and the computer program 41, when executed by the processor 42, implements the steps of any of the parameter optimization methods of the phase change material-containing multi-layer window structure described above. The computer device 60 can include a memory 61, a processor 62, and a computer program 41 stored on the memory 61 and running on the processor 62.

[0101] Computer program 41, which can also comprise programs 41 for instructing the computer, comprises program instructions. The program 41 can be in machine readable format, such as for example source code, object code, interpreted code, etc. The aforementioned program 41 can be distributed over network coupled computer systems so that the program instructions can be executed from a computer memory located in a storage device coupled to a computer system.

[0102] Computer program 41 can perform its operation on a user's computer entirely, partially, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform the aspect of the present application.

[0103] For the description of the present embodiment, computer program product 40 is the relevant product containing computer program 41.

[0104] For the description of the present embodiment, computer readable storage medium 50 is a tangible device that can retain and store computer program 41, which can be any medium that can contain, store, communicate, propagate or transport the program 41 for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer readable storage medium 50 include the following: portable computer diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, and any suitable combination of the above, or any other medium of the moment.

[0105] At this point, those skilled in the art will appreciate that although exemplary embodiments of the present application have been shown and described herein, many other modifications and / or alterations to the present application can be devised in light of the teachings provided herein. It is therefore understood that the scope of the present application should be interpreted in accordance with the principles of the present application and the full scope of equivalents, modifications, and alterations to the present application should be considered to fall within the scope of the present application.

[0106] Those skilled in the art will understand that the embodiments described below are merely exemplary of the present application and are not intended to limit the scope of the present application in any way. Based on the teachings provided herein, others will be able to ascertain additional embodiments of the present application without undue experimentation, and without departing from the scope of the present application. Accordingly, the scope of the present application should be construed in accordance with the principles of the present application and the full scope of equivalents, modifications, and alterations to the present application should be considered to fall within the scope of the present application.

Claims

1. A method for parameter optimization of a multilayer window structure containing a phase change material, characterized in that The method comprises the following steps: using a pre-established building load calculation model to calculate building load data according to meteorological parameters, building envelope parameters, and personnel equipment operation parameters; based on the building load data, constructing a benchmark dynamic simulation model containing a phase change material multi-layer window and a full life cycle carbon emission evaluation function in a transient system simulation tool, the benchmark dynamic simulation model comprising: a meteorological parameter import module, an optimization parameter setting module, an external surface convective heat transfer coefficient processing module, a phase change material multi-layer window module, a building load calculation module, and a carbon emission calculation module, wherein the meteorological parameter import module outputs outdoor air dry bulb temperature and wind speed to the external surface convective heat transfer coefficient processing module; the meteorological parameter import module outputs outdoor air dry bulb temperature, relative humidity, wind speed, and solar radiation to the building load calculation module; the optimization parameter setting module outputs window structure parameters to be optimized to the phase change material multi-layer window module; the external surface convective heat transfer coefficient processing module outputs the external surface convective heat transfer coefficient to the phase change material multi-layer window module; the phase change material multi-layer window module outputs the heat transfer coefficient, solar heat gain coefficient, shading coefficient, and size of the window to the building load calculation module and the carbon emission calculation module, respectively; determining initial data of the phase change material multi-layer window structure, the initial data comprising parameters to be optimized, optimization ranges, and initial values; calling the benchmark dynamic simulation model and the full life cycle carbon emission evaluation function to simulate and calculate the initial data, and using a pre-set optimization algorithm to optimize the simulation results; iteratively simulating and optimizing the results after optimization as input of the benchmark dynamic simulation model and the full life cycle carbon emission evaluation function until the iteration completion condition is met; outputting the optimization results meeting the iteration completion condition as the optimization completion parameters.

2. The method for parameter optimization of phase change material containing multi-layer window structures according to claim 1, wherein, The step of using the pre-set optimization algorithm to optimize the simulation results comprises: calling an optimization program for executing the optimization algorithm, and using a setting interface of the optimization program to obtain the path of the benchmark dynamic simulation model, the input data path, the output data path, and the optimization variables, wherein the optimization variables comprise initial values, minimum values, maximum values, and minimum intervals of the required parameters participating in the optimization calculation; scanning the benchmark dynamic simulation model through the path of the benchmark dynamic simulation model by the optimization program, obtaining the input data through the input data path, and storing the optimization calculation results and intermediate data into the file in the output data path.

3. The method for parameter optimization of phase change material containing multilayer window structures according to claim 2, wherein, The optimization algorithm comprises a particle swarm optimization algorithm with inertia weight, and the iteration formula is: ， In the iteration formula, X represents the position vector of the particle, d represents the parameter dimension to be optimized of the window structure, V represents the velocity vector of the particle, w represents the inertia weight, c1 and c2 are respectively used to adjust the step of the individual optimal position and the global optimal position, r1 and r2 are mutually independent random number sequences in (0, 1), p best,d represents the individual optimal value, g best,d represents the global optimal value.

4. The method for parameter optimization of phase change material containing multi-layer window structures according to claim 3, wherein, The step of using the pre-set optimization algorithm to optimize the simulation results comprises: Initialize the particle swarm, randomly generate the position vector and velocity vector of all particles, and initialize the individual optimal position p of the particle with the initial position of the particle best,d Initialize the global optimal position g with the best position corresponding to the fitness value of all initial positions best,d ; The fitness value is calculated, for each particle, the corresponding fitness value is compared with the optimal position p best,d experienced by the particle, if the corresponding fitness value of the new position is larger, the local optimal value p best,d is updated; for each particle, the corresponding fitness value is compared with the global optimal position g best,d experienced by the particle, if the corresponding fitness value of the new position is larger, the global optimal value g best,d is updated; updating the particle velocity and position in real time according to the iteration formula; adjusting the inertia weight, and gradually reducing the inertia weight w according to a pre-set decreasing strategy; performing cyclic iteration until the iteration calculation meets the iteration completion condition, and outputting the optimal solution, wherein the iteration completion condition comprises that the iteration number reaches a pre-set maximum iteration number or the fitness value reaches a pre-set lower limit value.

5. The method of claim 4, wherein, the decreasing formula of the decreasing strategy is: ， where w max and w min are the initial and final inertia weights, respectively, and iter is the maximum number of iterations.

6. The method of claim 4, wherein, the formula of the fitness value is: ， In the formula, P1, P2 are the building load simulated by the system and the carbon emission related to the phase change material multilayer window in the whole life cycle of the building, d represents the dimension of the window structure parameter, X i,1 , X i,2 , X i,3 …X i,d is the optimal parameter combination under different targets.

7. The method of claim 1, wherein, the meteorological parameters include: outdoor air dry-bulb temperature, wind speed, relative humidity, solar radiation; the building load data include: heating heat load; the parameters to be optimized include: phase change latent heat, phase change extrapolation starting point temperature, phase change material layer thickness, air gap layer thickness.

8. The method of claim 1, wherein, The full life cycle carbon emission evaluation function is used to calculate the carbon emission of the full life cycle of the multi-layer window structure, and the calculation formula is: , wherein GHC LCA is the total carbon emissions for the full life cycle of the multi-layer window structure, GHC WH is the carbon emissions for the materialization phase of the multi-layer window structure, GHC YX is the carbon emissions for the operation phase of the multi-layer window structure, GHC CC is the carbon emissions for the demolition and recycling phase of the multi-layer window structure.

9. A computer program product comprising a computer program, characterized in that, the computer program is executed by the processor to implement the steps of the method of claim 1 to 8.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-9. the processor executes the computer program to implement the steps of the method of claim 1 to 8.

Images