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

Abstract

The invention provides a high-computational-efficiency parameter optimization method for a multi-layer window structure containing a phase-change material and a product, and relates to the field of computer aided design. The parameter optimization method comprises the following steps: calculating to obtain building load data by using a pre-established building load calculation model according to meteorological parameters, building envelope parameters and personnel and equipment operation parameters; constructing a reference dynamic simulation model of the multi-layer window containing the phase-change material and a full-life-cycle carbon emission evaluation function; determining initial data of the phase change material multilayer window structure, calling the reference dynamic simulation model and the full life cycle carbon emission evaluation function to perform simulation calculation, and using a preset optimization algorithm to perform optimization on a simulation calculation result; iterating the analog calculation and the optimization calculation until an iteration completion condition is met; and outputting an optimization result meeting an iteration completion condition as an optimization completion parameter. According to the scheme, the design efficiency and accuracy are remarkably improved.

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 drawbacks 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, including to-be-optimized parameters and their optimization ranges and initial values;

[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 calculation results;

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

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

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

[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 benchmark dynamic simulation model is scanned by the optimization program through the path of the benchmark dynamic simulation model, and input data are obtained through the input data path; and the optimization calculation results and intermediate data are stored in a file in the output data path.

[0018] Optionally, the optimization algorithm comprises 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 the 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 fitness value corresponding to the new position 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 fitness value corresponding to the new position 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 respectively, and iter is the maximum iteration number.

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

[0031] F = max [P1 (X 1,1 , X 1,2 , X 1,3 …X 1,d ), P2 (X 2,1 , X 2,2 , X 2,3 …X 2,d )]

[0032] In the formula, P1 and P2 are building load and carbon emission of the multi-layer window structure containing phase change material in the whole life cycle of the building simulated by the system, d represents the dimension of the window structure parameter, X i,1 , X i,2 , X i,3 …X i,d are the optimized 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 carbon emission of the multi-layer window structure in the whole life cycle, and the calculation formula is:

[0037] GHC LCA = GHG WH + GHG YX + GHG CC ,

[0038] 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.

[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 parameter optimization methods for a multi-layer window structure containing phase change material.

[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 parameter optimization methods for a multi-layer window structure containing phase change material.

[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 parameter optimization methods for a multi-layer window structure containing phase change material.

[0042] The parameter optimization method of the phase change material-containing multi-layer window structure of the present application improves the accuracy and efficiency of the design of the phase change material-containing multi-layer window structure, calls a benchmark dynamic simulation model and a full life cycle carbon emission evaluation function to perform simulation calculation on the initial data, and uses a preset optimization algorithm to optimize the simulation calculation result, and the benchmark dynamic simulation model and the optimization algorithm are iteratively optimized, so that the complex phase change material-containing multi-layer window structure parameters 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 performs interface optimization between the optimization program of the optimization algorithm and the benchmark dynamic simulation model of the transient system simulation tool, so that data interaction can be conveniently performed, and the iterative 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 running stage, but also comprehensively evaluates the carbon emission of the window structure in the materialization stage and the demolition stage. This full 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 a particle swarm algorithm with inertia weight to perform parameter optimization, and the optimization algorithm has the characteristics of strong global search ability and fast convergence speed, and can effectively find the optimal solution in the 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 exemplary and not limiting. The same reference numerals in the drawings denote the same or similar components or parts. Those skilled in the art should understand that the drawings are not necessarily drawn to scale. In the drawings:

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

[0051] Figure 3 is a schematic diagram of an iterative optimization of an optimization algorithm in a parameter optimization method of a 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, rather than all the embodiments of the present application, and are intended to explain the technical principles of the present application, rather than 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 should 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 multi-layer window structure. The phase change material containing multi-layer window structure is an energy-saving window structure that uses the latent heat absorbed / released by phase change material in the solid-liquid or solid-solid transition process to regulate indoor temperature fluctuations. The core is to store solar energy or environmental heat through phase change latent heat, reduce building energy consumption and improve thermal comfort. The multi-layer window structure has multiple sandwich structures, such as phase change material layer and air spacing layer. The former fills phase change material for storing / releasing heat energy, and the latter is used to enhance the heat insulation capacity.

[0057] Figure 1 is a schematic diagram of a parameter optimization method of a phase change material containing multi-layer window structure according to an embodiment of the present application, which can generally include:

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

[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, 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 and equipment operation parameters are used to describe the influencing factors of building load such as personnel activity, use of various equipment (such as lighting equipment) in the building. The building load data can include heating heat load.

[0060] In step S102, a benchmark dynamic simulation model containing 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 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 multi-layer window containing phase change material. Among them, the carbon emission of the materialization stage of the multi-layer window containing phase change material includes the carbon emission of the production, transportation and processing and installation stage of the window; the carbon emission of the operation stage of the multi-layer window containing phase change material includes the carbon emission of the window in the daily operation stage of the building and the carbon emission of the maintenance and repair of the window in the use stage; the carbon emission of the demolition stage of the multi-layer window containing phase change material includes the carbon emission in the window demolition process and the carbon emission of waste disposal.

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

[0063] GHC LCA = GHG WH + GHG YX + GHG CC ,

[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] Step S103, determine the initial data of the phase change material multi-layer window structure, the initial data including the to-be-optimized parameters and the optimization range and initial value thereof. The to-be-optimized parameters can include: the phase change latent heat L of the phase change material multi-layer window structure, the phase change extrapolation starting point temperature Tm, the phase change material layer thickness x PCM , the air spacing layer thickness x Air .

[0066] Step S104, call the benchmark dynamic simulation model and the full life cycle carbon emission evaluation function to perform simulation calculation on the initial data, and use a preset optimization algorithm to perform optimization on the simulation calculation result.

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

[0068] The step of using a preset optimization algorithm to optimize the simulation calculation result can include: calling an optimization program for executing the optimization algorithm, using the 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 variable, the optimization variable including: the initial value, the minimum value, the maximum value, and the minimum interval of the required parameters participating in the optimization calculation; scanning the benchmark dynamic simulation model by the optimization program through the path of the benchmark dynamic simulation model, obtaining the input data through the input data path; and storing the optimization calculation result and 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 optimization 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 optimization algorithm with inertia weight, and the iteration formula thereof is:

[0071]

[0072] In the iteration formula, X represents the position vector of the particle, d represents the dimension of the to-be-optimized parameters 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 random number sequences independent of each other in (0, 1), p best,d represents the individual optimal value, and 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 2 is a schematic block diagram of a reference dynamic simulation model in a parameter optimization method of a multilayer window structure containing a 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 a 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 a 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 a phase change material 204. The multilayer window module containing a 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 a phase change material according to an embodiment of the present application. The step of using a preset optimization algorithm to optimize the simulation calculation result 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 values best,d ;

[0078] Step S302, calculate the fitness value, for each particle, compare 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, 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, updating 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, cyclic 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] F = max [P1(X 1,1 , X 1,2 , X 1,3 …X 1,d ), P2(X 2,1 , X 2,2 , X 2,3 …X 2,d )]

[0088] In the formula, P1 and P2 are the building load and the related carbon emission of the phase change material-containing multi-layer window in the building life cycle 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 features, a benchmark building model is selected, meteorological parameters (such as typical meteorological year parameters), building envelope structure parameters, personnel 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 in the daily operation stage of the window and the carbon emission in the maintenance and repair stage of the window. 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 L of the phase change material layer, the phase change extrapolation starting point temperature T m , the thickness x PCM of the phase change material layer and the thickness x Air of the air layer in the three-layer window structure with phase change material are selected for collaborative optimization.

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

[0095] Optimization parameter Unit Optimization range Reference value Optimization step Latent heat of phase change L J / g [0.1,300] 85.1 0.01 Phase transition extrapolated onset temperature T m ]]> ℃ [0,40] 21.6 0.01 Phase change material layer thickness x PCM ]]> mm [6,24] 12 0.1 Air gap thickness x Air ]]> mm [6,24] 12 0.1

[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 optimization parameters, optimization range and initial value of the phase change material containing multi-layer window structure can be flexibly adjusted according to the actual project requirements, which is suitable for different types of buildings and climate conditions, has strong flexibility and applicability, and makes the scheme of the present application have 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] The computer program 41 for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages.

[0102] The computer program 41 may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform various aspects of the present invention, electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuits.

[0103] In the description of this embodiment, the computer program product 40 is a related product including the computer program 41 .

[0104] For the purposes of the description of this embodiment, the computer-readable storage medium 50 is a tangible device capable of retaining and storing the computer program 41, and can be any device that can contain, store, communicate, propagate, or transmit the program 41 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium 50 include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the foregoing.

[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 not in accordance with the embodiments described herein.

[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. Based on the embodiments provided herein, any other embodiments obtained by those skilled in the art without creative effort should fall within the scope of the present application.

Claims

1. A parameter optimization method for a multi-layer window structure containing phase change material, characterized in that include: Use the pre-established building load calculation model to calculate the building load data based on meteorological parameters, building envelope parameters, and personnel and equipment operation parameters; Based on the building load data, a benchmark dynamic simulation model of multi-layer windows containing phase change materials and a full life cycle carbon emission evaluation function are constructed in a transient system simulation tool; Determining initial data of the phase change material multi-layer window structure, wherein the initial data includes parameters to be optimized, their optimization ranges, and initial values; Calling the benchmark dynamic simulation model and the full life cycle carbon emission evaluation function to simulate the initial data, and optimizing the simulation calculation results using a preset optimization algorithm; The optimization result is used as the input of the benchmark dynamic simulation model and the full life cycle carbon emission evaluation function to perform iterative simulation calculation and optimization calculation until the iteration completion condition is met; Output the optimization results that meet the iteration completion conditions as the optimization completion parameters.

2. The parameter optimization method of the multi-layer window structure containing phase change material according to claim 1, wherein: The step of optimizing the simulation calculation results using a preset optimization algorithm includes: Invoking an optimization program that executes the optimization algorithm, and using a setting interface of the optimization program to obtain the path, input data path, output data path, and optimization variables of the benchmark dynamic simulation model, where the optimization variables include: initial values, minimum values, maximum values, and minimum optimization intervals of parameters required for optimization calculation; 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 the optimization calculation results and intermediate data in the file of the output data path.

3. The parameter optimization method of the multi-layer window structure containing phase change material according to claim 2, wherein: The optimization algorithm includes a particle swarm optimization algorithm with inertia weights, and its iteration formula is: In the iterative formula, X represents the position vector of the particle, d represents the dimension of the window structure parameter to be optimized, V represents the velocity vector of the particle, w represents the inertia weight, c1 and c2 are used to adjust the individual optimal position and the global optimal position step size respectively, r1 and r2 are 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 parameter optimization method of the multi-layer window structure containing phase change material according to claim 3, wherein: The step of optimizing the simulation calculation results using a preset optimization algorithm includes: Initialize the particle swarm, randomly generate the position vectors and velocity vectors of all particles, and use the initial position of the particle to initialize the single optimal position p of the particle best,d , initialize the global optimal position g with the best position of all initial corresponding fitness values best,d ; Calculate the fitness value. For each particle, compare 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, the local optimal value p is updated. best,d ; For each particle, compare the corresponding fitness value with the global optimal position g best,d The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the global optimal value g is updated. best,d ; Update particle velocity and position in real time according to the iterative formula; Adjust the inertia weight and gradually reduce the inertia weight w according to the preset decreasing strategy; The loop is iterated until the iterative calculation satisfies the iteration completion condition and the optimal solution is output. The iteration completion condition includes: the number of iterations reaches a preset maximum number of iterations or the fitness value reaches a preset lower limit.

5. The parameter optimization method of the multi-layer window structure containing phase change material according to 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 parameter optimization method of the multi-layer window structure containing phase change material according to claim 4, wherein: The calculation formula of the fitness value is: F=max[P1(X 1,1 、X 1,2 、X 1,3 …X 1,d ),P2(X 2,1 、X 2,2 、X 2,3 …X 2,d )] Where P1 and P2 are the building loads simulated by the system and the carbon emissions related to the multi-layer windows containing phase change materials during the entire life cycle of the building, d represents the dimension of the window structure parameters, and X i,1 、X i,2 、X i,3 …X i,d Optimization parameter combinations for different objectives.

7. The parameter optimization method of a multi-layer window structure containing phase change material according to claim 1, wherein: The meteorological parameters include: outdoor air dry bulb temperature, wind speed, relative humidity, and solar radiation; 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, and air spacer layer thickness.

8. The parameter optimization method of a multi-layer window structure containing phase change material according to claim 1, wherein: The full life cycle carbon emission evaluation function is used to calculate the carbon emissions of the multi-layer window structure over its full life cycle, and the calculation formula is: GHC LCA =GHG WH +GHG YX +GHG CC , Where GHC LCA is the carbon emissions of the multi-layer window structure over its entire life cycle, GHC WH is the carbon emission of the multi-layer window structure in the physical and chemical stage, GHC YX is the carbon emission of the multi-layer window structure during operation, GHC CC is the carbon emission of the multi-layer window structure during the dismantling and recycling stage.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the parameter optimization method of the multi-layer window structure containing phase change material according to any one of claims 1 to 8 are realized.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the parameter optimization method of the multi-layer window structure containing phase change material according to any one of claims 1 to 8.

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