Enclosure structure heat storage efficiency evaluation method, device and equipment and storage medium

Abstract

The invention relates to the technical field of building thermal engineering, solves the problems of inaccurate and incomplete evaluation of the heat storage performance of a building envelope and poor environmental adaptability in the prior art, and provides a method, a device and equipment for evaluating the heat storage efficiency of the building envelope and a storage medium to prepare a target test piece containing a functional material; carrying out a cross-characteristic change temperature interval experiment by utilizing the dynamic thermal and humid climate wind tunnel to obtain nonlinear thermal response data; constructing an enthalpy method discrete numerical model, and performing parameter inversion by taking the minimum root-mean-square error of theoretical surface heat flow and actually measured heat flow as a target to obtain thermophysical parameters and an effective specific heat capacity curve; and constructing a thermal network model based on the inversion parameters, and simulating hourly heat flow of each node of the test piece, so as to calculate a multi-dimensional heat storage efficiency evaluation index of the enclosure structure. According to the method, the high-precision wind tunnel experiment, the nonlinear heat transfer model and the inversion algorithm are coupled, the dynamic heat storage performance of the enclosure structure is accurately quantified, and an accurate and practical solution is provided for evaluating the heat storage efficiency of the functional enclosure structure.

Description

Technical Field

[0001] This invention relates to the field of building thermal technology, and in particular to a method, apparatus, equipment and storage medium for evaluating the thermal storage efficiency of building envelopes. Background Technology

[0002] With the development of building energy-saving technologies, composite building envelopes containing functional materials such as phase change materials (PCMs) have attracted widespread attention due to their excellent heat storage and temperature regulation performance. However, existing methods for evaluating building thermal performance are mostly based on static or linear assumptions, making it difficult to accurately reflect the nonlinear thermophysical property changes of functional composite building envelopes within the temperature range of characteristic changes.

[0003] For example, Chinese patent CN110470693A discloses a method for comparative characterizing the temperature regulation performance of phase change materials. This patent places the test sample and a control sample into two identical sealed test chambers, subjecting them to the same heating or cooling conditions. The temperature change curves of the two chambers are then compared to qualitatively characterize the temperature regulation performance of the phase change material. This method can intuitively demonstrate the temperature regulation effect of phase change materials through simple comparative experiments and has the advantage of being easy to operate.

[0004] However, this method can only provide qualitative or semi-quantitative comparative conclusions and cannot output quantitative indicators such as daytime dynamic heat storage and latent heat utilization efficiency, making it difficult to directly use for accurate calculation and design optimization of building energy consumption. Furthermore, based on simple air temperature comparisons, this method cannot handle the nonlinear behavior of functional materials where heat capacity changes abruptly by tens of times within a range of characteristic variations, nor can it reflect the impact of material sequence changes on heat storage performance. In addition, this method can only be tested in a single heating or cooling environment, and cannot reproduce the dynamic boundaries of multi-parameter coupling under real climatic conditions, resulting in poor environmental adaptability.

[0005] Therefore, there is an urgent need for a quantitative evaluation method that can accurately identify the nonlinear thermal properties of the building envelope and comprehensively assess its thermal storage efficiency under dynamic conditions. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a method, apparatus, equipment and storage medium for evaluating the thermal storage performance of building envelopes, in order to solve the problems that the prior art cannot accurately describe the nonlinear changes in the thermal capacity of composite building envelopes, and has a single evaluation dimension and poor environmental adaptability.

[0007] In a first aspect, embodiments of the present invention provide a method for evaluating the thermal storage efficiency of an enclosure structure, comprising: preparing a target specimen based on the enclosure structure to be tested; conducting an experiment on the target specimen across a temperature range of characteristic changes using preset dynamic operating conditions for the target specimen, and acquiring nonlinear thermal response data of the target specimen during the experiment, the nonlinear thermal response data including measured surface heat flow; constructing an enthalpy-based discrete numerical model for the target specimen, and constructing a parameter inversion optimization problem with the goal of minimizing the root mean square error between the theoretical surface heat flow calculated by the enthalpy-based discrete numerical model and the measured surface heat flow; solving the parameter inversion optimization problem to invert and obtain target thermal property parameters and effective specific heat capacity data, and processing the target thermal property parameters and effective specific heat capacity data using a preset thermal network model to simulate hourly heat flow data of each node of the target specimen; and calculating a multi-dimensional thermal storage efficiency evaluation index for the enclosure structure to be tested based on the hourly heat flow data of each node of the target specimen.

[0008] As an optional implementation, the enclosure structure to be tested includes a functional material layer; the preparation of the target specimen based on the enclosure structure to be tested includes: preparing a composite enclosure structure specimen including a functional material layer, and pre-embedding radiation sensors on the inner and outer surfaces and inside of the composite enclosure structure specimen to obtain the target specimen; determining the suspension height of the radiation sensor according to the angle coefficient between the photosensitive surface of the radiation sensor and the surface of the target specimen to ensure that the radiation received by the radiation sensor comes from the surface of the target specimen.

[0009] As an optional implementation, the step of conducting cross-temperature range experiments on the target specimen using preset dynamic conditions and acquiring nonlinear thermal response data of the target specimen during the experiment includes: constructing a dynamic hot and humid climate wind tunnel experimental platform, and conducting the cross-temperature range experiments on the target specimen under the preset dynamic conditions; in the preset dynamic conditions, the air temperature change includes linear change waveforms, sinusoidal waveforms, or dynamic changes based on actual meteorological data, and the range of the air temperature change covers and exceeds the characteristic change temperature range of the building envelope under test, so as to excite the nonlinear thermal response of the target specimen.

[0010] As an optional implementation, the step of constructing an enthalpy-based discrete numerical model for the target specimen, and aiming to minimize the root mean square error between the theoretical surface heat flux calculated by the enthalpy-based discrete numerical model and the measured surface heat flux, constitutes a parameter inversion optimization problem, including: discretizing the target specimen along the thickness direction into multiple control volume nodes; for each control volume node, establishing a discretized energy control equation based on the principle of energy conservation, wherein the energy control equation characterizes the relationship between the rate of change of the specific enthalpy of the corresponding control volume node with time and the heat conduction flow between adjacent control volume nodes; using a preset piecewise function to correlate the specific enthalpy of each control volume node with temperature, wherein the preset piecewise function characterizes the nonlinear change characteristics of the specific enthalpy of the enclosure structure under test with temperature within the characteristic temperature range; and constructing the enthalpy-based discrete numerical model based on the energy control equation and the preset piecewise function.

[0011] As an optional implementation, the method for evaluating the thermal storage efficiency of the building envelope further includes: constructing a thermal network model for the target specimen, and determining the convective heat transfer boundary conditions and radiative heat transfer boundary conditions of the outer surface of the target specimen, as well as determining the convective heat transfer boundary conditions of the inner surface of the target specimen; wherein, the convective heat transfer boundary conditions of the outer surface of the target specimen are determined based on the convective heat transfer coefficient calculated from the convective heat transfer and the temperature difference of the outer surface of the target specimen during the cross-characteristic temperature variation range experiment; the radiative heat transfer boundary conditions of the outer surface of the target specimen are determined based on the long-wave radiation heat transfer coefficient calculated from the radiative heat flux and the temperature difference of the outer surface of the target specimen during the cross-characteristic temperature variation range experiment; the convective heat transfer boundary conditions of the inner surface of the target specimen are determined based on the preset indoor air temperature and the convective heat transfer coefficient of the inner surface of the target specimen.

[0012] As an optional implementation, constructing the thermal network model for the target specimen includes: discretizing the target specimen into multiple nodes along the thickness direction, each node representing a control volume with concentrated heat capacity; assigning thermal property parameters to each node according to its material type to establish the thermal conductivity relationship of each node, wherein the specific heat capacity of the node is described by a nonlinear function of the effective specific heat capacity data changing with temperature; based on the thermal conductivity relationship of each node, establishing the thermal resistance connecting adjacent nodes and coupling the heat capacity characteristics to establish a heat capacity element for each node; and constructing the thermal network model based on multiple heat capacity elements.

[0013] As an optional implementation, after constructing the thermal network model for the target specimen, the method further includes: inputting typical meteorological year data into the thermal network model to drive the dynamic heat transfer simulation of the thermal network model under typical meteorological conditions; determining the solar radiation heat gain boundary conditions of the outer surface of the target specimen, wherein the solar radiation heat gain boundary conditions of the outer surface of the target specimen are determined based on the hourly solar irradiance in the typical meteorological year data and the absorptivity of the outer surface of the target specimen to solar radiation; the convective heat transfer boundary conditions of the outer surface of the target specimen are also determined based on the hourly wind speed and air temperature parameters in the typical meteorological year data; and the radiative heat transfer boundary conditions of the outer surface of the target specimen are also determined based on the effective sky temperature parameters in the typical meteorological year data.

[0014] Secondly, embodiments of the present invention provide a device for evaluating the thermal storage efficiency of building envelopes, comprising: The experimental testing module is used to prepare target specimens based on the enclosure structure under test, conduct cross-temperature range experiments on the target specimens using preset dynamic working conditions for the target specimens, and acquire nonlinear thermal response data of the target specimens during the experiment, including measured surface heat flow. The simulation calculation module is used to construct an enthalpy-based discrete numerical model for the target specimen, and to construct a parameter inversion optimization problem with the goal of minimizing the root mean square error between the theoretical surface heat flow calculated by the enthalpy-based discrete numerical model and the measured surface heat flow. The simulation calculation module is also used to solve the parameter inversion optimization problem, invert the target thermal property parameters and effective specific heat capacity data, and use a preset thermal network model to process the target thermal property parameters and effective specific heat capacity data to simulate the hourly heat flow data of each node of the target specimen. The performance evaluation module is used to calculate multi-dimensional thermal storage performance evaluation indicators for the building envelope under test based on the hourly heat flow data of each node of the target specimen.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein when the computer program instructions are executed by the processor, any one of the embodiments in the first aspect is implemented.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement any of the embodiments of the first aspect.

[0017] In summary, the beneficial effects of the present invention are as follows: This invention provides a method, apparatus, equipment, and storage medium for evaluating the thermal storage efficiency of building envelopes. First, a target specimen is prepared based on the building envelope to be tested. Using preset dynamic operating conditions for the target specimen, experiments are conducted on the specimen across a temperature range exhibiting characteristic changes, and nonlinear thermal response data of the target specimen during the experiment is acquired. This nonlinear thermal response data includes measured surface heat flow. Next, an enthalpy-based discrete numerical model is constructed for the target specimen. With the objective of minimizing the root mean square error between the theoretical surface heat flow calculated by the enthalpy-based discrete numerical model and the measured surface heat flow, a parameter inversion optimization problem is constructed. By solving the parameter inversion optimization problem, the target thermal property parameters and effective specific heat capacity data are obtained. A preset thermal network model is then used to process the target thermal property parameters and effective specific heat capacity data, thereby simulating the hourly heat flow data of each node of the target specimen. Finally, based on the hourly heat flow data of each node of the target specimen, multi-dimensional thermal storage efficiency evaluation indicators for the building envelope to be tested can be calculated.

[0018] By coupling high-precision climate wind tunnel experiments, nonlinear heat transfer mechanism models, and parameter inversion algorithms, the dynamic heat storage performance of the building envelope under test can be accurately quantified under real and complex coupled heat transfer boundaries. This effectively solves the three major pain points of existing technologies: inaccurate measurement, incomplete evaluation, and poor environmental adaptability. It provides an accurate, comprehensive, and practical solution for evaluating the heat storage efficiency of building envelopes containing phase change materials or other functional materials. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, and these are all within the protection scope of the present invention.

[0020] Figure 1 This is a flowchart illustrating the method for evaluating the thermal storage efficiency of the building envelope in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the thermal energy storage performance evaluation device for the building envelope in this embodiment of the invention; Figure 3 This is a schematic diagram of the structure of an electronic device in one embodiment of the present invention. Detailed Implementation

[0021] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the invention.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0023] Firstly, because existing building envelopes generally contain phase change materials (PCMs) or other functional materials, current evaluation methods cannot accurately assess the nonlinear and dynamic thermal characteristics of the building envelope, and suffer from problems such as limited evaluation dimensions and insufficient environmental adaptability. This invention provides a method for evaluating the thermal storage efficiency of building envelopes, aiming to offer a comprehensive evaluation method integrating experimental excitation, parameter inversion, dynamic simulation, and multi-index evaluation. This method can achieve accurate and comprehensive quantitative evaluation of the thermal storage efficiency of the building envelope under test, such as functional composite building envelopes, providing a reliable basis for subsequent energy-saving design and optimization.

[0024] Please see as follows Figure 1 As shown, the method for evaluating the thermal storage efficiency of the building envelope includes the following steps S101 to S105: Step S101: Prepare the target specimen based on the enclosure structure to be tested.

[0025] The building envelope under test may include a functional material layer, which may include PCM and / or ordinary materials. The target specimen is prepared by fabricating a composite building envelope specimen including a functional material layer and embedding radiation sensors on the inner and outer surfaces and inside the composite building envelope specimen.

[0026] The suspension height of the radiation sensor is determined by the angle coefficient between the photosensitive surface of the radiation sensor and the surface of the target specimen, so as to ensure that the radiation received by the radiation sensor comes from the surface of the target specimen.

[0027] In the specific implementation process, a composite enclosure structure specimen containing a functional material layer is prepared as the target specimen. The size of the target specimen can be determined according to the size during the wind tunnel test, for example, 300mm×300mm, and the thickness is determined according to the actual structure of the enclosure. The structure of the target specimen from the outside to the inside can be as follows: finishing layer, PCM layer, base wall, and inner plaster layer.

[0028] During the preparation of the target specimen, T-type thermocouples with an accuracy of ±0.1℃ can be pre-embedded at the center of the inner and outer surfaces of the specimen to measure the temperature of the inner and outer surfaces. Heat flow meters with an accuracy of ±0.5W / m² are installed at the center of the inner and outer surfaces to measure the conductive heat flow through the surface of the target specimen. Short-wave and long-wave radiation sensors are suspended above the outer surface of the target specimen to measure the short-wave and long-wave radiation heat flow received and reflected by the outer surface of the target specimen.

[0029] The suspension height of the radiation sensor can be determined based on the angle factor between the photosensitive surface of the radiation sensor and the surface of the target specimen. The angle factor represents the proportion of radiation energy emitted from the photosensitive surface of the radiation sensor that reaches the surface of the target specimen, and its calculation formula is as follows: in, The angle coefficient, Let be the photosensitive surface area of ​​the radiation sensor. The target specimen surface area, , These are the angles between the normals to the two surfaces and the line connecting them. The distance between the two surface elements is denoted as .

[0030] By adjusting the suspension height to make the angle factor approach 1, it is ensured that the radiation received by the radiation sensor mainly comes from the surface of the target specimen.

[0031] Step S102: Using preset dynamic conditions for the target specimen, conduct experiments on the target specimen across the characteristic change temperature range, and obtain the nonlinear thermal response data of the target specimen during the experiment.

[0032] Specifically, a dynamic hot and humid climate wind tunnel test platform can be constructed first, and the target specimen can be tested across the characteristic change temperature range under preset dynamic conditions. In the preset dynamic conditions, the air temperature change includes linear change waveform, sine waveform or dynamic change based on actual meteorological data. The range of air temperature change covers and exceeds the characteristic change temperature range of the building envelope under test, so as to stimulate the nonlinear thermal response of the target specimen.

[0033] The prepared target specimen is installed in the specimen slot of the dynamic hot and humid climate wind tunnel. The thermocouples, heat flow meters, and radiometers are checked to ensure that they are within their metrological validity period and that they are connected normally. The upper surface of the target specimen is flush with the lower surface of the test section. The gaps around the specimen are sealed with aluminum foil tape. Based on the size of the target specimen and the calculation results of the aforementioned angle coefficient formula, the long and short wave radiation sensors are suspended at a specified height above the center of the target specimen.

[0034] The preset dynamic operating conditions include setting the air temperature in the dynamic thermal and humid climate wind tunnel test section. The preset dynamic operating conditions follow a dynamic change curve, and the range of change of this curve within one experimental cycle must completely cover and exceed the entire temperature range of the target functional material layer. The air temperature change in the preset dynamic operating conditions can be a linear change, a sinusoidal fluctuation, or a dynamic fluctuation based on actual meteorological data, thereby providing a sufficiently strong thermal driving force to trigger significant latent heat exchange.

[0035] At the same time, other climate parameters such as solar radiation, wind speed, relative humidity, and effective sky temperature also need to be dynamically controlled. Inside the target specimen, i.e., in the air-conditioned chamber, a relatively stable air condition must be maintained, with a significant temperature difference from the temperature range across the characteristic variation.

[0036] For example, in this embodiment of the invention, the characteristic temperature range of the PCM is 25℃~35℃, therefore the air temperature variation range is set to 20℃~40℃, completely covering and exceeding the characteristic temperature range. The air temperature variation can be represented by a sine wave, and the specific sine wave can be found using the following formula: in, The average temperature can be 30℃. The amplitude can be 10℃; The period can be 24 hours. This sinusoidal fluctuation in air temperature change dynamics can provide sufficient thermal driving force to stimulate the latent heat exchange of the PCM.

[0037] Simultaneously, other climate parameters such as solar radiation intensity, wind speed, relative humidity, and effective sky temperature are controlled. The indoor temperature of the target specimen is maintained at a constant 20°C through an air-conditioned chamber, creating a significant temperature difference with the characteristic temperature range.

[0038] Before the experiment, check that all sensors are properly connected, start the wind tunnel system and run it to the initial operating point, i.e., the air temperature is 20℃, and maintain a stable state for at least 30 minutes. The standard for determining this stable state is that the heat flow meter reading is stable and the fluctuation range of all temperature sensors is less than ±0.2℃.

[0039] Once a stable state is established, the preset dynamic operating condition program is started, and the high-speed data acquisition system is simultaneously activated to continuously record the following data at 1-minute intervals: Drive parameters: air temperature, relative humidity, wind speed, solar radiation irradiance, effective sky temperature setpoint and measured values ​​in the wind tunnel; Response parameters: outer surface temperature of the target specimen, inner surface temperature of the target specimen, thermal flux conducted on the outer surface of the target specimen, heat flux on the inner surface of the target specimen, shortwave radiation received and reflected by the outer surface of the target specimen, and longwave radiation received and reflected by the outer surface of the target specimen. Internal state parameters of the target specimen: temperature of key points inside the PCM layer.

[0040] The aforementioned dynamic hot and humid climate wind tunnel experiment should last for a complete cycle, i.e., 24 hours, and include sufficient time before steady state.

[0041] After the experiment, the collected raw data were processed as follows to obtain the nonlinear thermal response data of the target specimen during the experiment. First, the net solar radiation heat gain and net longwave radiation heat gain of the target specimen's outer surface were calculated. The net solar radiation heat gain of the target specimen's outer surface can be calculated using the following formula: In the formula, To allow the outer surface of the target specimen to receive shortwave radiation, The target specimen reflects short-wave radiation from its outer surface.

[0042] The net long-wave radiation heat gain on the outer surface of the target specimen can be calculated using the following formula: In the formula, To allow the outer surface of the target specimen to receive long-wave radiation, The target specimen reflects long-wave radiation from its outer surface.

[0043] Based on the transient heat balance equation of the target specimen's outer surface, the convective heat transfer is calculated. For the surface of a dry specimen, the latent heat transfer can be considered zero, according to the transient heat balance equation of the outer surface: in, The net solar radiation heat gain on the outer surface of the target specimen. The net long-wave radiation heat gain of the outer surface of the target specimen. For convective heat transfer, The heat flux is the thermal flux that is introduced or extracted through the outer surface of the target specimen, directly measured by a heat flow meter.

[0044] From the above equation, the convective heat transfer can be obtained: Therefore, the long-wave radiation heat transfer coefficient and the convection heat transfer coefficient can be calculated hourly. The long-wave radiation heat transfer coefficient can be obtained by referring to the following formula: In the formula, The long-wave radiation heat transfer coefficient is... The net long-wave radiation heat gain of the outer surface of the target specimen. The target specimen's outer surface temperature, The air temperature in the wind tunnel.

[0045] The convective heat transfer coefficient can be referenced by the following formula: In the formula, The long-wave radiation heat transfer coefficient is... For convective heat transfer, The target specimen's outer surface temperature, The air temperature in the wind tunnel.

[0046] The dataset obtained from the above processing, especially the dynamic correspondence between the heat flux conducted on the outer surface and the outer surface temperature of the target specimen, constitutes a complete dataset containing the nonlinear thermal response characteristics of the target specimen, namely, nonlinear thermal response data. The nonlinear thermal response data includes measured surface heat flux, which will be used as the target data for subsequent parameter inversion.

[0047] Step S102 aims to design specific preset dynamic working conditions, namely, to control the outer surface of the target specimen to experience a dynamic temperature boundary condition with a specific rate and amplitude of change that spans the temperature range of the changing characteristics of the enclosure structure under test, so as to fully excite and record the nonlinear thermal response of the target specimen containing the functional material layer, and provide key data for subsequent parameter identification.

[0048] Step S103: Construct an enthalpy-based discrete numerical model for the target specimen, and construct a parameter inversion optimization problem with the goal of minimizing the root mean square error between the theoretical surface heat flow calculated by the enthalpy-based discrete numerical model and the measured surface heat flow.

[0049] Specifically, to construct the discrete numerical model of the enthalpy method, the target specimen can be discretized into multiple control volume nodes along the thickness direction. For each control volume node, a discretized energy control equation is established based on the principle of energy conservation. The energy control equation characterizes the relationship between the rate of change of the specific enthalpy of the corresponding control volume node with time and the heat conduction and heat flow between adjacent control volume nodes. A preset piecewise function is used to correlate the specific enthalpy of each control volume node with temperature. The preset piecewise function characterizes the nonlinear change characteristics of the specific enthalpy of the building envelope under test with temperature within the characteristic temperature range. Based on the energy control equation and the preset piecewise function, the discrete numerical model of the enthalpy method is constructed.

[0050] For example, assuming the total thickness of the target specimen is L, it can be divided into N control volume nodes, each with a thickness of L. ( Let L be a positive number and N be a positive integer. Then for each control node... Based on the principle of energy conservation, the discretized energy control equations are established as follows: in, For control body nodes Material density, For control body nodes exist Enthalpy at time, For time step, For control body nodes and The interfacial thermal conductivity between them Control Node exist Temperature at any moment For control body nodes and The distance between nodes, For control body nodes and The node spacing between adjacent control volume nodes. This energy control equation characterizes the rate of change of the specific enthalpy of the control volume nodes over time as equal to the net heat transfer flux between adjacent control volume nodes.

[0051] Next, the specific enthalpy of each control volume node is correlated with temperature using a preset piecewise function. For control volume nodes of non-phase change materials, the specific enthalpy and temperature have a linear relationship, satisfying the following relationship: in, Specific enthalpy of the volumetric node for non-phase change materials. For isobaric specific heat capacity, For temperature, It is a constant.

[0052] For the control volume node of a phase change material, a piecewise linear function can be used to describe the relationship between its specific enthalpy and temperature to characterize the latent heat effect of the phase change, i.e., satisfying the following relationship: in, and These are the specific heat capacities of the phase change material in its solid and liquid states, respectively. For latent heat of phase transition, and These are the phase transition initiation temperature and the phase transition termination temperature, respectively.

[0053] The piecewise linear function described above characterizes the nonlinear variation of the specific enthalpy of the functional material layer with temperature within the temperature range of characteristic changes. That is, the rate of change of specific enthalpy increases significantly within the temperature range of characteristic changes, reflecting the absorption or release of latent heat.

[0054] Within the temperature range of characteristic changes, a preset piecewise function significantly increases the rate of change of specific enthalpy with temperature, reflecting the latent heat absorbed or released by the functional material layer when its characteristics change. Based on this preset piecewise function relationship, the nonlinear variation curve of the effective specific heat capacity of the functional material layer with temperature over a wide temperature range can be output during the subsequent parameter inversion process. The curve exhibits peak characteristics within the temperature range of characteristic changes, which is used to characterize the abrupt change behavior of the effective specific heat capacity of the functional material layer.

[0055] Based on the aforementioned energy control equations and the piecewise relationship between specific enthalpy and temperature, a complete enthalpy-based discrete numerical model is constructed. This model can simulate the heat transfer behavior of the target specimen under dynamic boundary conditions. By inputting a set of assumed functional material layer parameters and actual boundary conditions into the enthalpy-based discrete numerical model, the model simulates the thermal response data of the target specimen, i.e., outputs the theoretical surface heat flow variation curve based on the simulation.

[0056] After constructing the discrete numerical model using the enthalpy method, it is necessary to construct a parameter inversion optimization problem. Specifically, the thermal properties of each layer of material in the target specimen can be defined as a parameter vector to be identified. For example, the parameter vector to be identified may include: the thermal conductivity and specific heat capacity of ordinary material layers; and the thermal conductivity, solid specific heat capacity, liquid specific heat capacity, initial temperature, end temperature of property change, and latent heat of property change of PCM layers.

[0057] Next, with the objective of minimizing the root mean square error between the theoretical surface heat flux calculated by the enthalpy-based discrete numerical model and the measured surface heat flux collected in step S102, the following parameter inversion optimization problem is constructed: in, This represents the total number of steps in the time series. For the first The time corresponding to each time step Let be the parameter vector to be identified. To measure the surface heat flow, The theoretical surface heat flow can be obtained by simulation calculation using the enthalpy method discrete numerical model.

[0058] Step S104: Solve the parameter inversion optimization problem, invert the target thermal property parameters and effective specific heat capacity data, and use the preset thermal network model to process the target thermal property parameters and effective specific heat capacity data to simulate the hourly heat flow data of each node of the target specimen.

[0059] After constructing the parameter inversion optimization problem, a global optimization algorithm, such as a genetic algorithm, particle swarm optimization, or Bayesian inversion algorithm, can be used to solve the problem. Taking the genetic algorithm as an example, the specific solution steps can be briefly summarized as follows (A1-A5): A1: Randomly generate an initial population, with each individual representing a set of parameters to be inverted, to achieve initialization; A2: For each individual, substitute the theoretical surface heat flux into the enthalpy method discrete numerical model and calculate the fitness value according to the above objective function; A3: Select superior individuals based on fitness values, perform crossover and mutation operations, and generate a new generation of population; A4: Repeat steps A2-A3 for fitness evaluation and genetic operations until convergence conditions are met, such as the maximum number of iterations or the change in the objective function value being less than a pre-set threshold. A5: Outputs the optimal parameter set.

[0060] The optimal parameter set obtained from the inversion includes the thermophysical parameters of each material layer of the target specimen, i.e., the target thermophysical parameters. Based on this, the nonlinear effective specific heat capacity curves of the functional material layers over a wide temperature range can also be output. The effective specific heat capacity is defined by the derivative of specific enthalpy with respect to temperature as follows: in, The effective specific heat capacity is the derivative of specific enthalpy with respect to temperature. The density of the functional material layer, It represents the instantaneous effective specific heat capacity of the functional material layer at a specific temperature.

[0061] The aforementioned effective specific heat capacity is precisely described by the derivative of specific enthalpy with respect to temperature, which describes the nonlinear relationship between the effective specific heat capacity of the functional material layer and temperature. Within the temperature range of characteristic variation, The significant peak value vividly illustrates the pattern of latent heat absorption or release during the change of properties of the functional material layer.

[0062] After obtaining the target thermal property parameters and effective specific heat capacity data through inversion, the target thermal property parameters and effective specific heat capacity data are processed using a preset thermal network model, which enables the simulation of hourly heat flow data at each node of the target specimen.

[0063] However, prior to this, it is necessary to construct a thermal network model for the target specimen and determine the convective and radiative heat transfer boundary conditions for the outer surface of the target specimen, as well as the convective heat transfer boundary conditions for the inner surface of the target specimen. Specifically, the convective heat transfer boundary conditions for the outer surface of the target specimen are determined based on the convective heat transfer coefficient calculated from the convective heat transfer during the experiment across the characteristic temperature range; the radiative heat transfer boundary conditions for the outer surface of the target specimen are determined based on the long-wave radiation heat transfer coefficient calculated from the radiative heat flux during the experiment across the characteristic temperature range; and the convective heat transfer boundary conditions for the inner surface of the target specimen are determined based on the preset indoor air temperature and the convective heat transfer coefficient of the inner surface of the target specimen.

[0064] The convective heat transfer boundary conditions of the inner surface of the target specimen are determined by establishing a convective heat transfer relationship between the inner surface and the indoor air, based on the indoor air temperature setpoint and the inner surface convective heat transfer coefficient. The preset indoor air temperature can be set to a constant value according to the building's function or dynamically set according to a preset work schedule. In one optional embodiment, the preset indoor air temperature can be set to a constant value of 24°C according to the building's function.

[0065] To construct the thermal network model of the target specimen, the specimen can be discretized into multiple nodes along its thickness direction, with each node representing a control volume with concentrated heat capacity. Thermal property parameters are assigned to each node based on its material type to establish the thermal conductivity relationship. The specific heat capacity of each node is described using a nonlinear function of the effective specific heat capacity data changing with temperature. Based on the thermal conductivity relationship of each node, the thermal resistance connecting adjacent nodes is established and coupled with the heat capacity characteristics to create a heat capacity element for each node. The thermal network model is then constructed based on these multiple heat capacity elements.

[0066] For example, the construction of a thermal network model may include the following steps: The target specimen is discretized into multiple nodes along the thickness direction, for example... Each node represents a control volume with concentrated heat capacity. Thermal property parameters are assigned to each node based on its material type, and these parameters may include thermal conductivity, density, and specific heat capacity. The node specific heat capacity can be obtained as a nonlinear function of the effective specific heat capacity data obtained in step S104 as a function of temperature. To describe, that is .

[0067] Based on the temperature difference and thermal conductivity between each node, in adjacent nodes and The thermal resistance is defined between the two points, and can be referred to the following formula: In the formula, For nodes and Thermal resistance between them For nodes thermal conductivity, For nodes thermal conductivity, For nodes thickness, For nodes The thickness. This thermal resistance describes the heat transfer from the node. Passed to node The degree of obstruction.

[0068] Based on the heat storage capacity of each node, a corresponding heat capacity element is assigned to it: in, For nodes Material density, For nodes Effective specific heat capacity data, For nodes thickness, The target specimen unit area.

[0069] The aforementioned thermal capacity element describes the ability to store or release heat when the node temperature changes.

[0070] By coupling the aforementioned thermal resistance with the thermal capacity element, a thermal network model describing the transient heat transfer behavior of the target specimen can be constructed.

[0071] After constructing a thermal network model for the target specimen, typical meteorological year data can be input into the thermal network model to drive the dynamic heat transfer simulation of the thermal network model under typical meteorological conditions. Correspondingly, the solar radiation heat gain boundary conditions on the outer surface of the target specimen can also be determined.

[0072] The solar radiation heat gain boundary conditions on the outer surface of the target specimen are determined based on the hourly solar irradiance data from a typical meteorological year and the absorptivity of the target specimen's outer surface to solar radiation. For example, the solar radiation heat gain heat flux on the outer surface of the target specimen can be obtained by calculating the product of the hourly solar irradiance data from a typical meteorological year and the absorptivity of the target specimen's outer surface to solar radiation.

[0073] As an optional implementation method, the convective heat transfer boundary conditions of the outer surface of the target specimen can also be determined based on hourly wind speed and air temperature parameters from typical meteorological year data. For example, the convective heat transfer coefficient calculated in step S102 from the convective heat transfer and surface temperature difference can be used, combined with hourly wind speed and air temperature from typical meteorological year data, to construct the convective heat transfer relationship between the outer surface and the outdoor air, thereby determining the convective heat transfer boundary conditions of the outer surface of the target specimen.

[0074] As an optional implementation method, the radiative heat transfer boundary conditions of the outer surface of the target specimen can also be determined based on the effective sky temperature parameter in typical meteorological year data. For example, the long-wave radiative heat transfer coefficient calculated in step S102 by the radiative heat flux and the surface temperature difference can be used, combined with the effective sky temperature in typical meteorological year data, to construct the long-wave radiative heat transfer relationship between the outer surface and the sky, thereby determining the radiative heat transfer boundary conditions of the outer surface of the target specimen.

[0075] Typical meteorological year data is input into the thermal network model. This typical meteorological year data can include hourly air temperature, solar irradiance, wind speed, and effective sky temperature. This data serves as an external excitation to drive the dynamic heat transfer simulation of the thermal network model under typical meteorological conditions.

[0076] Starting from the initial moment, the solution is iteratively solved according to a time step, which can be 1 hour. Within each time step, the following calculations are performed: Based on the current temperature of each node, the heat flow through the thermal resistance of each node is calculated using the following formula. In the formula, For nodes The temperature at time t, For nodes The temperature at time t, For nodes and Thermal resistance between them.

[0077] For each node The net heat flow is calculated using the following formula: In the formula, For nodes The heat conduction heat flow, For nodes The heat flux is conductive. For boundary nodes, convection, radiation, and other boundary heat fluxes need to be superimposed.

[0078] The rate of temperature change at each node is calculated using the following formula, based on the net inflow heat flow and node heat capacity: After obtaining the temperature change rate for each node, the node temperature can be updated using the following formula: In the formula, for Time Node temperature, for Time Node temperature, For time step.

[0079] By recording the instantaneous heat flux values ​​passing through each node interface within the current time step, the hourly heat flux data and hourly temperature data of each discrete node of the target specimen are obtained after iteratively solving the entire simulation time period.

[0080] Step S105: Based on the hourly heat flow data of each node of the target specimen, calculate the multi-dimensional heat storage efficiency evaluation index for the building envelope under test.

[0081] Based on the hourly heat flow data obtained from the dynamic simulation in step S104 above, a systematic quantitative evaluation system can be constructed and calculated. This quantitative evaluation system includes: daytime dynamic net heat storage, material layer heat storage contribution, functional layer efficiency, orientation sensitivity coefficient, and heat storage-release difference.

[0082] The daytime dynamic net heat storage index quantifies the total net heat stored per unit area by the building envelope during typical daytime periods dominated by heat gain factors such as solar radiation. This daytime dynamic net heat storage can be expressed using the following formula: in, The total number of material layers. These are the start and end times of the daytime period, taking the daytime period from 8:00 to 18:00 as an example. This represents the total cumulative heat flow per hour from 8:00 to 18:00. For nodes At any moment The instantaneous heat flow. If the daytime dynamic net heat storage is positive, it indicates heat storage; if the daytime dynamic net heat storage is negative, it indicates heat release. The larger the daytime dynamic net heat storage value, the stronger the heat storage capacity of the building envelope, and the more significant its effect on stabilizing indoor temperature fluctuations and storing and delaying the release of solar energy.

[0083] Regarding the heat storage contribution of material layers, this index reveals the relative proportion of each structural layer in the overall thermal inertia of the building envelope. The heat storage contribution of this material layer can be expressed by the following formula: in, , This indicates that node i belongs to the j-th material layer. For the first The accumulated heat storage of the material layer. Taking absolute values ​​is used to compare the heat storage or release capabilities of each material layer. By analyzing the heat storage contribution of each material layer, it can be determined whether the PCM layer has played its expected dominant heat storage role, or whether other material layers have made a greater actual contribution. Designers can optimize the amount and / or type of materials used in the building envelope based on the heat storage contribution of each material layer. For example, adjusting the thickness of the PCM layer or changing the order of the material layers can achieve a more efficient and economical heat storage design.

[0084] Regarding the functional layer efficiency, this indicator directly reflects the degree of matching between the material property variation range and the actual dynamic boundary conditions, as well as the working status of the functional layer in the overall structure. The functional layer efficiency can be expressed by the following formula: in, This refers to the amount of energy exchanged by the functional material layer through its specific functional mechanism under actual dynamic working conditions. This represents the maximum theoretical energy exchange that the functional material layer can achieve under ideal conditions.

[0085] The functional layer efficiency rate is calculated as the ratio of the actual energy exchange of the functional layer to its theoretical maximum energy exchange. A low functional layer efficiency rate may indicate a mismatch between material selection and the building's operating environment, or that the structural form of the functional layer limits its effectiveness, such as improper inter-layer placement or excessive contact thermal resistance. The functional layer efficiency rate provides designers with clear optimization directions.

[0086] The orientation sensitivity coefficient quantifies the rate of change in daytime thermal energy storage efficiency of the same building envelope under different orientations due to differences in solar radiation intensity and incident angle. This orientation sensitivity coefficient can be expressed using the following formula: in, For reference orientation, such as south, This represents other orientations such as east, west, and north. The larger the absolute value of the orientation sensitivity coefficient, the more significant the impact of orientation on performance. The orientation sensitivity coefficient directly supports the differentiated design of solar buildings based on their orientation. For example, south-facing buildings can utilize structures with strong heat storage capacity, such as thick Trombe walls or walls containing PCM, to maximize solar energy storage; while north-facing or east-west-facing buildings can focus on optimizing insulation to reduce heat loss, thereby achieving refined control of the building's overall energy performance.

[0087] The heat storage-release difference reflects whether the building envelope is net heat storage or net heat release within a specific evaluation period. This heat storage-release difference can be expressed using the following formula: The physical meaning of the difference between the positive and negative heat flux integrals is the absolute value of the difference, representing the net heat storage and release. When the difference is greater than 0, it indicates that the building envelope has net heat storage during that period; when the difference is less than 0, it indicates that the building envelope has net heat release during that period. Combined with the analysis of building usage patterns, the contribution of the building envelope to maintaining the long-term thermal balance of the rooms can be assessed. This is an important reference for analyzing the risk of overheating or overcooling in rooms and optimizing the operation strategy of the air conditioning system.

[0088] The method for evaluating the thermal storage efficiency of building envelopes provided in this invention has the following advantages compared to existing evaluation methods: First, existing evaluation methods are typically based on static or linear assumptions, treating the material's heat capacity as a constant. This fails to address the nonlinear behavior of the building envelope under test, where the heat capacity changes abruptly by tens of times within a temperature range where properties change. This invention designs dynamic operating conditions spanning this temperature range to fully stimulate the latent heat exchange of the building envelope. Combined with a parameter inversion algorithm based on an enthalpy-based discrete numerical model, and aiming to minimize the root mean square error between theoretical and measured surface heat flow, it can accurately invert the nonlinear effective specific heat capacity curve of the functional material layer over a wide temperature range. This curve exhibits a significant peak within the temperature range where properties change, intuitively characterizing the nonlinear abrupt change in the effective specific heat capacity of the functional material layer with temperature, thus solving the technical problem of inaccurate measurement using traditional methods.

[0089] Second, existing technologies rely on a single evaluation dimension. For example, thermal inertia indices like D and heat storage coefficients like S are based on static superposition calculations and cannot reflect the impact of material layer sequence variations on actual heat storage performance. This invention constructs a complete system from dynamic experimental excitation and nonlinear parameter inversion to multi-index quantitative evaluation, proposing a series of quantitative indicators such as daytime dynamic net heat storage, material layer heat storage contribution, functional layer efficiency, orientation sensitivity coefficient, and heat storage / release difference. Among these, the material layer heat storage contribution reveals the relative proportion of each structural layer in thermal inertia, guiding the optimization of PCM layer positions; the functional layer efficiency directly reflects the degree of matching between the material property variation range and actual dynamic boundary conditions, as well as the working state of the functional layers within the overall structure, truly achieving a leap from single-dimensional to comprehensive evaluation.

[0090] Third, existing comparative testing methods can only be conducted in a single heating or cooling environment, failing to reproduce real-world climatic conditions involving multiple coupled parameters such as solar radiation and wind speed changes, resulting in poor environmental adaptability. This invention relies on an all-weather dynamic thermal and humid climate wind tunnel experimental platform, which can accurately reproduce real meteorological parameters such as air temperature, humidity, radiation, and wind speed, and verifies the boundary heat transfer coefficient through wind tunnel experiments. Based on this, a high-fidelity dynamic thermal network model is constructed, combined with typical meteorological year data, capable of simulating the hourly temperature and heat flow distribution of each node of the building envelope under any time period and orientation, making the evaluation results closer to the actual building environment.

[0091] Fourth, the present invention proposes a method to directly quantify the total net heat storage of the building envelope during the dominant solar radiation period, providing a core basis for the thermal storage design of solar buildings. The orientation sensitivity coefficient supports differentiated design based on orientation, such as enhanced heat storage for south-facing buildings and optimized insulation for north-facing buildings. The difference between heat storage and release can assist in analyzing room thermal balance and air conditioning operation strategies. These evaluation indicators transform abstract thermal performance into intuitive quantitative parameters, providing reliable decision support for the optimized selection of energy-saving materials and structural design.

[0092] In summary, this invention systematically solves the three major pain points of existing technologies—inaccurate measurement, incomplete evaluation, and poor environmental adaptability—through experimental excitation, parameter inversion, dynamic simulation, and multi-dimensional evaluation. It provides an accurate, comprehensive, and practical solution for evaluating the thermal storage performance of functional building envelopes containing functional material layers.

[0093] Secondly, based on the same inventive concept, embodiments of the present invention provide a device for evaluating the thermal storage efficiency of building envelopes, as shown in the following example. Figure 2 As shown, the thermal energy storage performance evaluation device for the building envelope may include: The experimental testing module 201 is used to prepare target specimens based on the enclosure structure to be tested, and to conduct experiments on the target specimens across the characteristic change temperature range using preset dynamic working conditions for the target specimens, and to obtain nonlinear thermal response data of the target specimens during the experiment. The nonlinear thermal response data includes measured surface heat flow. The simulation calculation module 202 is used to construct an enthalpy-based discrete numerical model for the target specimen, and to construct a parameter inversion optimization problem with the goal of minimizing the root mean square error between the theoretical surface heat flow calculated by the enthalpy-based discrete numerical model and the measured surface heat flow. The simulation calculation module 202 is also used to solve the parameter inversion optimization problem, invert the target thermal property parameters and effective specific heat capacity data, and use the preset thermal network model to process the target thermal property parameters and effective specific heat capacity data to simulate the hourly heat flow data of each node of the target specimen. The performance evaluation module 203 is used to calculate multi-dimensional heat storage performance evaluation indicators for the building envelope under test based on the hourly heat flow data of each node of the target specimen.

[0094] Since the thermal efficiency evaluation device for building envelopes described in this embodiment is an electronic device used to implement the thermal efficiency evaluation method for building envelopes in this embodiment of the invention, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the thermal efficiency evaluation method for building envelopes in this embodiment of the invention. Therefore, how the electronic device implements the method in this embodiment of the invention will not be described in detail here. Any electronic device used by those skilled in the art to implement the thermal efficiency evaluation method for building envelopes in this embodiment of the invention falls within the scope of protection of this invention.

[0095] Thirdly, based on the same inventive concept, combined with Figure 3 The method for evaluating the thermal storage efficiency of the building envelope described in this embodiment of the invention can be implemented by electronic devices. Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown.

[0096] The electronic device may include a processor and a memory storing computer program instructions.

[0097] Specifically, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement embodiments of the present invention.

[0098] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0099] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated communication signals and carrier waves.

[0100] The processor reads and executes computer program instructions stored in the memory to implement any of the building envelope thermal energy storage performance evaluation methods in the above embodiments.

[0101] In one example, the electronic device may also include a communication interface and a bus. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0102] The communication interface is mainly used to enable communication between various modules, devices, units and / or equipment in the embodiments of the present invention.

[0103] A bus, including hardware, software, or both, couples components of an electronic device together. For example, and not limitingly, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. While specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0104] Fourthly, based on the same inventive concept and in conjunction with the method for evaluating the thermal performance of the building envelope in the above embodiments, the present invention can be implemented using a computer-readable storage medium. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any one of the methods for evaluating the thermal performance of the building envelope in the first aspect described above.

[0105] In summary, the method, apparatus, equipment, and storage medium for evaluating the thermal storage efficiency of building envelopes provided in this invention, by designing dynamic operating conditions spanning a temperature range of characteristic changes, fully stimulates the latent heat exchange of the functional material layer. Combined with a parameter inversion algorithm based on an enthalpy-based discrete numerical model, and aiming to minimize the root mean square error between theoretical and measured surface heat flow, it can accurately invert the nonlinear effective specific heat capacity curve of the functional material layer over a wide temperature range. This curve exhibits a significant peak within the temperature range of characteristic changes, intuitively depicting the nonlinear abrupt change in the effective specific heat capacity of the functional material layer with temperature, thus solving the technical problem of inaccurate measurement using traditional methods.

[0106] Multi-dimensional thermal energy storage performance evaluation indicators include daytime dynamic net heat storage, thermal energy storage contribution of material layers, functional layer efficiency utilization, orientation sensitivity coefficient, and thermal storage-release difference. Among them, the thermal energy storage contribution of material layers can reveal the relative proportion of each structural layer in thermal inertia and guide the optimization of PCM layer positions; the functional layer efficiency utilization directly reflects the degree of matching between the material property variation range and the actual dynamic boundary conditions, as well as the working status of the functional layer in the overall structure, truly realizing a leap from single evaluation to comprehensive evaluation.

[0107] Utilizing an all-weather dynamic thermal and humid climate wind tunnel experimental platform, real meteorological parameters such as air temperature, humidity, radiation, and wind speed can be accurately reproduced, and the boundary heat transfer coefficient can be verified through wind tunnel experiments. Based on this, a high-fidelity dynamic thermal network model is constructed, which, combined with typical meteorological year data, can simulate the hourly temperature and heat flow distribution of each node of the building envelope under any time period and any orientation, making the evaluation results closer to the actual building environment.

[0108] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0109] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0111] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0112] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0113] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0114] The above are merely specific embodiments of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for evaluating the thermal storage efficiency of building envelope, characterized in that, include: Target specimens were prepared based on the building envelope structure to be tested; Using preset dynamic working conditions for the target specimen, experiments are conducted on the target specimen across a temperature range of characteristic changes, and nonlinear thermal response data of the target specimen during the experiment are obtained, including measured surface heat flow. An enthalpy-based discrete numerical model is constructed for the target specimen, and a parameter inversion optimization problem is constructed with the goal of minimizing the root mean square error between the theoretical surface heat flow calculated by the enthalpy-based discrete numerical model and the measured surface heat flow. Solve the parameter inversion optimization problem to obtain the target thermal property parameters and effective specific heat capacity data. Then, use a preset thermal network model to process the target thermal property parameters and effective specific heat capacity data to simulate the hourly heat flow data of each node of the target specimen. Based on the hourly heat flow data of each node of the target specimen, a multi-dimensional thermal storage efficiency evaluation index for the building envelope under test is calculated.

2. The method for evaluating the thermal storage efficiency of building envelopes according to claim 1, characterized in that, The enclosure structure to be tested includes a functional material layer; the preparation of the target specimen based on the enclosure structure to be tested includes: A composite enclosure structure specimen including a functional material layer is prepared, and radiation sensors are pre-embedded on the inner and outer surfaces and inside the composite enclosure structure specimen to obtain the target specimen; The suspension height of the radiation sensor is determined based on the angle coefficient between the photosensitive surface of the radiation sensor and the surface of the target specimen, so as to ensure that the radiation received by the radiation sensor comes from the surface of the target specimen.

3. The method for evaluating the thermal storage efficiency of building envelopes according to claim 1, characterized in that, The step of conducting experiments on the target specimen across a temperature range of characteristic changes using preset dynamic operating conditions and acquiring nonlinear thermal response data of the target specimen during the experiment includes: A dynamic hot and humid climate wind tunnel experimental platform was constructed, and the target specimen was subjected to the cross-characteristic change temperature range experiment under the preset dynamic working conditions. In the preset dynamic operating conditions, the air temperature change includes linear change waveform, sine waveform, or dynamic change based on actual meteorological data. The range of the air temperature change covers and exceeds the characteristic change temperature range of the building envelope under test.

4. The method for evaluating the thermal storage efficiency of building envelopes according to claim 1, characterized in that, The process involves constructing an enthalpy-based discrete numerical model for the target specimen, and minimizing the root mean square error between the theoretical surface heat flow calculated by the enthalpy-based discrete numerical model and the measured surface heat flow. A parameter inversion optimization problem is then constructed, including: The target specimen is discretized into multiple control volume nodes along the thickness direction; For each of the control volume nodes, a discretized energy control equation is established based on the principle of energy conservation. The energy control equation characterizes the relationship between the rate of change of the specific enthalpy of the corresponding control volume node over time and the heat conduction and heat flow between adjacent control volume nodes. The specific enthalpy of each control volume node is correlated with temperature using a preset piecewise function. The preset piecewise function characterizes the nonlinear variation of the specific enthalpy of the enclosure structure under test with temperature within the characteristic variation temperature range. Based on the energy control equation and the preset piecewise function, the enthalpy-based discrete numerical model is constructed.

5. The method for evaluating the thermal storage efficiency of building envelopes according to claim 1, characterized in that, Also includes: A thermal network model for the target specimen is constructed, and the convective heat transfer boundary conditions and radiative heat transfer boundary conditions of the outer surface of the target specimen and the convective heat transfer boundary conditions of the inner surface of the target specimen are determined. The convective heat transfer boundary condition of the outer surface of the target specimen is determined based on the convective heat transfer coefficient calculated from the convective heat transfer and the temperature difference of the outer surface of the target specimen during the cross-characteristic temperature range experiment. The radiative heat transfer boundary condition of the outer surface of the target specimen is determined based on the long-wave radiative heat transfer coefficient calculated from the radiative heat flow and the temperature difference of the outer surface of the target specimen during the cross-characteristic temperature range experiment. The convective heat transfer boundary conditions of the inner surface of the target specimen are determined based on the preset indoor air temperature and the convective heat transfer coefficient of the inner surface of the target specimen.

6. The method for evaluating the thermal storage efficiency of building envelopes according to claim 5, characterized in that, The construction of the thermal network model for the target specimen includes: The target specimen is discretized into multiple nodes along the thickness direction, and each node represents a control body with concentrated heat capacity. Thermophysical parameters are assigned to each node according to its material type to establish the thermal conductivity of each node. The specific heat capacity of the node is described by a nonlinear function of the effective specific heat capacity data as a function of temperature. Based on the thermal conductivity of each node, the thermal resistance connecting adjacent nodes is established and coupled with the thermal capacity characteristics, and a thermal capacity element for each node is established. The thermal network model is then constructed based on multiple thermal capacity elements.

7. The method for evaluating the thermal storage efficiency of building envelopes according to claim 5, characterized in that, After constructing the thermal network model for the target specimen, the method further includes: Typical meteorological year data are input into the heat network model to drive the dynamic heat transfer simulation of the heat network model under typical meteorological conditions; The solar radiation heat gain boundary conditions of the outer surface of the target specimen are determined based on the hourly solar irradiance in the typical meteorological year data and the absorptivity of the outer surface of the target specimen to solar radiation. The convective heat transfer boundary conditions on the outer surface of the target specimen are also determined based on the hourly wind speed and air temperature parameters in the typical meteorological year data. The radiative heat transfer boundary conditions of the outer surface of the target specimen are also determined based on the effective sky temperature parameters in the typical meteorological year data.

8. A device for evaluating the thermal storage efficiency of building envelope, characterized in that, For implementing the method as described in any one of claims 1 to 7, the thermal energy storage performance evaluation device for the building envelope comprises: The experimental testing module is used to prepare target specimens based on the enclosure structure under test, conduct cross-temperature range experiments on the target specimens using preset dynamic working conditions for the target specimens, and acquire nonlinear thermal response data of the target specimens during the experiment, including measured surface heat flow. The simulation calculation module is used to construct an enthalpy-based discrete numerical model for the target specimen, and to construct a parameter inversion optimization problem with the goal of minimizing the root mean square error between the theoretical surface heat flow calculated by the enthalpy-based discrete numerical model and the measured surface heat flow. The simulation calculation module is also used to solve the parameter inversion optimization problem, invert the target thermal property parameters and effective specific heat capacity data, and use a preset thermal network model to process the target thermal property parameters and effective specific heat capacity data to simulate the hourly heat flow data of each node of the target specimen. The performance evaluation module is used to calculate multi-dimensional thermal storage performance evaluation indicators for the building envelope under test based on the hourly heat flow data of each node of the target specimen.

9. An electronic device, characterized in that, include: At least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, The method as described in any one of claims 1-7 is implemented when the computer program instructions are executed by the processor.

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