Method for predicting thermal impact of energy wall based on thermal bridge strength gradient under combined heat and moisture

Abstract

The present application relates to a kind of energy wall thermal influence prediction methods based on thermal bridge intensity gradient under heat and humidity coupling, constructs energy wall geometric model, adds multi-physics field module, and is coupled by heat and humidity multi-physics field module, obtains heat and humidity coupling transient numerical model;After initialization, mesh is divided, the temperature, humidity of grid node is obtained, energy wall outlet temperature is predicted, heat exchange capacity is calculated, indoor side wall surface temperature, relative humidity, heat flow density, wet flow density, thermal bridge effect intensity are predicted;Data monitoring line is added in the central axis of indoor side wall surface, thermal bridge intensity gradient is predicted, and then the maximum influence length area of thermal bridge effect is predicted.The present application is based on heat and humidity coupling transmission principle, can accurately predict the heat exchange capacity of energy wall, improve the accuracy of energy wall heat exchange capacity evaluation result in water-containing humid stratum;At the same time, clear quantitative index is proposed for the degree of influence of indoor side, provides reliable support for engineering design, optimization and risk assessment.

Description

Technical Field

[0001] This invention relates to the field of energy utilization technology in underground buildings, specifically to a method for predicting the thermal impact of energy walls based on thermal bridge intensity gradient under thermal-humid coupling. Background Technology

[0002] An energy wall is a novel underground energy structure that combines a buried pipe heat exchanger with a diaphragm wall. By pre-embedding pipes with circulating fluid within the diaphragm wall, and utilizing heat exchange between the wall and the surrounding soil, heating, cooling, and energy storage can be achieved in underground buildings, effectively regulating building energy in a green manner. Energy walls require no additional land resources, significantly reducing the initial investment for ground source heat pump installation and construction, and have broad application prospects in underground buildings.

[0003] To ensure the safe, efficient, and long-term operation of energy walls and meet the heating and cooling needs of underground buildings, assessing their heat exchange capacity is crucial, providing a reliable design basis for structural optimization. However, during operation, energy walls are in direct or indirect contact with the indoor environment of underground buildings. Their heat transfer and moisture transfer processes are coupled, and the temperature gradient affects the penetration intensity of wet components. Furthermore, the latent heat changes from wet component migration affect the temperature field distribution. This complex coupling effect directly impacts the accuracy of heat exchange capacity assessment. If only a pure heat transfer model is used for single heat transfer simulation without fully considering the heat-moisture coupling effect, the assessment results will deviate significantly from actual operating conditions, failing to accurately determine the extent of the energy wall's impact on the indoor thermal environment. Considering moisture migration, the transfer of wet components in the medium leads to energy changes in the migration direction. This energy change caused by moisture migration, along with the energy change caused by direct heat transfer, jointly affects the energy change of the medium, causing changes in state characterization parameters (temperature and relative humidity). In addition, changes in moisture components also alter thermophysical parameters (thermal conductivity, specific heat capacity, density, etc.), affecting the heat and mass transfer processes. Therefore, ignoring wet migration will affect the final accuracy of the prediction results. According to analysis, such as... Figure 10 The predicted heat flow results for the indoor sidewalls, considering and not considering moisture transfer, deviated by more than 10.9%.

[0004] Therefore, it is necessary to propose new measures to overcome the above-mentioned shortcomings. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the thermal impact of an energy wall based on the thermal bridge intensity gradient under thermal-humid coupling, so as to solve the problem of inaccurate evaluation results when performing a single heat transfer simulation based solely on a pure heat transfer model.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for predicting the thermal impact of an energy wall based on the thermal bridge intensity gradient under thermal-humid coupling is provided, the method comprising:

[0008] Construct a geometric model of the energy wall, add a physical field module for pipe heat transfer, a physical field module for building material heat transfer, and a physical field module for building material moisture transport, and couple the physical field modules for building material heat transfer and building material moisture transport through a multi-physical field module to obtain a transient numerical model of thermal-humidity coupling.

[0009] Determine the material's thermal and moisture properties, environmental parameters, pipeline parameters, and boundary parameters, and input them into the thermal-moisture coupling transient numerical model for initialization settings;

[0010] The thermal-humidity coupled transient numerical model is meshed, and the temperature and humidity of the mesh nodes are obtained.

[0011] Based on the temperature and humidity of the grid nodes, the outlet temperature of the energy wall is predicted, and the heat exchange capacity is calculated in combination with the inlet temperature of the energy wall.

[0012] Based on the temperature and humidity of the grid nodes, predict the indoor sidewall temperature, indoor sidewall relative humidity, indoor sidewall heat flux density, indoor sidewall moisture flux density, and indoor sidewall thermal bridge effect intensity.

[0013] Add a data monitoring line on the central axis of the indoor side wall, and predict the thermal bridge intensity gradient on the data monitoring line based on the temperature and humidity of the grid nodes.

[0014] Based on the thermal bridge intensity gradient on the data monitoring line, the maximum length region affected by the thermal bridge effect is predicted.

[0015] Furthermore, the energy wall geometric model includes a continuous concrete wall domain, a buried pipe heat exchanger, a concrete floor slab domain, an underground surrounding rock medium domain, and an underground building interior space.

[0016] Furthermore, the material's thermo-hygroscopic properties include the moisture content of the surrounding underground rock. Moisture content of concrete Moisture content of insulation materials Moisture content of cement mortar Moisture diffusion coefficient Steam permeability coefficient Liquid water conductivity Material dry density Specific heat capacity of material dry volume Equivalent thermal conductivity Thermal conductivity of pipe walls ;

[0017] Environmental parameters include the initial indoor temperature. Indoor relative humidity Initial temperature of underground surrounding rock Relative humidity of underground surrounding rock ;

[0018] Pipe parameters include pipe inner diameter Pipe wall thickness ;

[0019] Boundary parameters include the wall heat transfer coefficient. Floor heat transfer coefficient Ceiling heat transfer coefficient Wall mass transfer coefficient Floor mass transfer coefficient Ceiling mass transfer coefficient Model far boundary thermal and humid state parameters Flow rate of medium inside the pipeline Pipe inlet temperature .

[0020] Furthermore, the thermal-humidity coupled transient numerical model is meshed, and the temperature and humidity of the mesh nodes are determined, including:

[0021] Add a grid component;

[0022] Set the grid cell size;

[0023] Select a specific face or edge structure to set the mesh size and refinement;

[0024] Build a grid;

[0025] Add research components;

[0026] Set up transient study steps;

[0027] Set the transient time unit and output time step;

[0028] Click "Calculate" and wait for the calculation to complete.

[0029] Add a dataset and select the desired monitoring domain;

[0030] Define the status parameters of the monitoring area, including temperature and humidity;

[0031] Obtain the state variables;

[0032] Output and save the state values.

[0033] Furthermore, based on the temperature and humidity of the grid nodes, the energy wall outlet temperature is predicted, including:

[0034] Define the probe and select the pipe outlet end;

[0035] Let the expression for the acquired state parameters be T;

[0036] Calculate and obtain state variables , i.e., the outlet temperature of the energy wall;

[0037] Output and save state variables .

[0038] Further calculations of heat transfer capacity include:

[0039] ;

[0040] in:

[0041] Q represents heat exchange capacity;

[0042] The inlet temperature of the energy wall;

[0043] The density of water;

[0044] The specific heat capacity of the dry bulk medium;

[0045] V is the velocity of the current-carrying medium.

[0046] Furthermore, based on the temperature and humidity of the grid nodes, the indoor sidewall temperature, indoor sidewall relative humidity, indoor sidewall heat flux density, indoor sidewall moisture flux density, and indoor sidewall thermal bridging intensity are predicted, including:

[0047] Predicting indoor sidewall temperatures includes:

[0048] Define the wall probe;

[0049] Acquire probe state variables That is, the indoor side wall temperature;

[0050] Output and save state variables ;

[0051] Predicting the relative humidity of indoor sidewalls includes:

[0052] Define a wall boundary probe and select the corresponding wall boundary on the indoor side;

[0053] The expression for the acquired state parameters is set as follows: ;

[0054] Calculate and obtain state variables This refers to the relative humidity of the indoor side wall surface;

[0055] Output and save state variables ;

[0056] Predicting the heat flux density of the indoor sidewall ,include:

[0057] ;

[0058] in:

[0059] This represents the convective heat transfer coefficient of the corresponding wall surface;

[0060] This corresponds to the temperature of the wall surface;

[0061] This represents the convective mass transfer coefficient corresponding to the wall surface;

[0062] The latent heat of vaporization of water, T is the temperature of the water;

[0063] This represents the relative humidity of the corresponding wall surface;

[0064] The partial pressure of water vapor on the interior side wall;

[0065] The partial pressure of water vapor in indoor air;

[0066] ;

[0067] Predicting the moisture flow density on the indoor sidewall ,include:

[0068] ;

[0069] Predicting the intensity of thermal bridging effect on indoor sidewalls ,include:

[0070] .

[0071] Furthermore, the predicted thermal bridge intensity gradient along the monitoring data line includes:

[0072] ;

[0073] in:

[0074] The thermal bridge intensity gradient;

[0075] The thermal bridge strength of the current grid node;

[0076] The thermal bridge strength of the next grid node;

[0077] The coordinates of the current grid node;

[0078] The coordinates of the next grid node.

[0079] Furthermore, the predicted length region of maximum influence of the thermal bridge effect includes:

[0080] The length L of the region where the thermal bridge effect has the greatest impact is:

[0081] ;

[0082] in:

[0083] For thermal bridge intensity gradients greater than or equal to 0.01 The coordinates;

[0084] The coordinates are the starting point.

[0085] On the other hand, a thermal effect prediction system for an energy wall based on thermal bridge intensity gradient under thermal-humid coupling is provided, the system being used to implement the method, including:

[0086] The model component module is used to construct the geometric model of the energy wall, add the pipe heat transfer physics field module, the building material heat transfer physics field module, and the building material moisture transport physics field module, and couple the building material heat transfer physics field module and the building material moisture transport physics field module through the heat and humidity multi-physics field module to obtain the heat and humidity coupled transient numerical model.

[0087] The initialization module is used to determine the thermal and moisture properties of materials, environmental parameters, pipeline parameters, and boundary parameters, and to input the thermal and moisture coupling transient numerical model for initialization settings;

[0088] The mesh generation module is used to generate a mesh for the thermal-humidity coupled transient numerical model and determine the temperature and humidity of the mesh nodes.

[0089] The heat exchange capacity calculation module is used to predict the energy wall outlet temperature based on the temperature and humidity of the grid nodes, and calculate the heat exchange capacity in combination with the energy wall inlet temperature.

[0090] The first prediction module is used to predict the indoor sidewall temperature, indoor sidewall relative humidity, indoor sidewall heat flux density, indoor sidewall moisture flux density, and indoor sidewall thermal bridge effect intensity based on the temperature and humidity of the grid nodes.

[0091] The second prediction module is used to add data monitoring lines on the central axis of the indoor side wall and predict the thermal bridge intensity gradient on the data monitoring lines based on the temperature and humidity of the grid nodes.

[0092] The third prediction module is used to predict the length region of maximum influence of the thermal bridge effect based on the thermal bridge intensity gradient on the data monitoring line.

[0093] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0094] This invention provides a method for predicting the thermal impact of an energy wall based on the thermal bridge intensity gradient under thermal-humid coupling. Based on the principle of thermal-humid coupling, it accurately obtains the heat transfer capacity of the energy wall and the degree of its impact on the indoor side. For the first time, it proposes a method for calculating the maximum influence length region of the thermal bridge effect based on the thermal bridge intensity gradient in the thermal impact analysis of energy walls, improving the accuracy of the heat transfer capacity assessment results of energy walls in water-bearing and humid strata. Simultaneously, it proposes clear quantitative indicators for the degree of impact on the indoor side, providing reliable support for engineering design, optimization, and risk assessment. Attached Figure Description

[0095] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0096] Figure 1 This is a schematic diagram of the geometric model structure of the energy wall provided in an embodiment of the present invention.

[0097] Figure 2 A schematic diagram of the continuous concrete wall region and the internal embedded pipe heat exchanger of the energy wall geometric model provided in the embodiments of the present invention.

[0098] Figure 3 This is a schematic diagram of the boundary and far boundary of the energy wall geometric model provided in an embodiment of the present invention.

[0099] Figure 4 This is a schematic diagram of the energy wall geometric model meshing implementation scheme provided in an embodiment of the present invention.

[0100] Figure 5 Temperature field distribution diagram of the energy wall geometric model provided in the embodiment of the present invention.

[0101] Figure 6 This is a schematic diagram illustrating the interaction direction of the energy wall on underground buildings, provided as an embodiment of the present invention.

[0102] Figure 7 This is a schematic diagram illustrating the effect of structural thermal bridging in an embodiment of the present invention.

[0103] Figure 8 This is a schematic diagram illustrating an implementation method for a data monitoring line provided in an embodiment of the present invention.

[0104] Figure 9The diagram illustrates the effect of the maximum influence area length provided in this embodiment of the invention.

[0105] Figure 10 This is a comparison chart showing the results of considering thermal and moisture coupling transfer.

[0106] Figure 11 This is a graph showing the heat exchange capacity of the energy wall.

[0107] Figure 12 This is a graph showing the quantification results of the thermal bridge effect on length.

[0108] The diagram is labeled as follows:

[0109] 1-Underground surrounding rock medium zone, 2-Concrete floor slab zone, 3-Concrete continuous wall zone, 4-Underground building interior space, 5-Buried pipe heat exchanger, 6-Wall, 7-Floor, 8-Ceiling, 9-Distant boundary. Detailed Implementation

[0110] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0111] It should be noted that similar reference numerals and letters indicate similar items; therefore, once an item is defined in one embodiment, it does not need to be further defined and explained in subsequent embodiments. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0112] Furthermore, in the description of this invention, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Of course, such terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than those illustrated or described herein.

[0113] It should also be noted that although the order of steps is mentioned in the method description, in some cases, steps may be performed in a different order than that described here, and this should not be interpreted as a restriction on the order of steps.

[0114] This invention provides a method for predicting the thermal impact of an energy wall based on the thermal bridge intensity gradient under thermal-humid coupling. This method is used to quantitatively assess the heat exchange capacity of an open-cut energy wall and its impact on the indoor environment. It addresses the problems of existing technologies that do not fully consider the thermal-humid coupling effect and lack clear quantitative indicators, thus improving the accuracy of energy wall heat exchange capacity assessment results in water-bearing and humid strata and accurately describing the degree of energy wall impact on the indoor thermal environment. Specifically, the method includes the following steps:

[0115] S1: Construct the geometric model of the energy wall, add the physical field modules of pipe heat transfer, building material heat transfer, and building material moisture transport, and couple the physical field modules of building material heat transfer and building material moisture transport through the thermal and moisture multi-physics module, enable the surface latent heat source term, and obtain the thermal and moisture coupled transient numerical model.

[0116] like Figure 1 and Figure 2 Based on the structural principles of the energy wall and the actual dimensions of the energy wall structure in engineering projects, a 1:1 scale geometric model of the energy wall is constructed. This model includes a continuous concrete wall region 3, an embedded tube heat exchanger 5, a concrete floor slab region 2, an underground surrounding rock medium region 1, and an underground building interior space 4. The space enclosed by the concrete floor slab region 2 and the continuous concrete wall region 3 is the underground building interior space 4. The embedded tube heat exchanger 5 is located within the continuous concrete wall region 3. Together, the continuous concrete wall region 3 and the embedded tube heat exchanger 5 constitute the energy wall. The temperature field distribution of the energy wall geometric model is as follows: Figure 5 As shown, the direction of the interaction between the energy wall and underground buildings is as follows: Figure 6 As shown.

[0117] This step was implemented using COMSOL Multiphysics software, constructing a three-dimensional transient numerical model of heat and moisture coupling in an open-cut energy wall, integrating three core physical fields: pipe heat transfer, building material heat transfer, and building material moisture transport. Pipe heat transfer was simplified to a one-dimensional heat exchange calculation along the flow direction to save computational resources; building material heat transfer and moisture transport within the building materials were bidirectionally coupled through a heat and moisture multiphysics module, realizing the interaction mechanism between the temperature and humidity fields. A building material heat transfer physical field module was added to the model tree (…). Add a physical field module for moisture transport in building materials. Add the thermal and humidity multiphysics module ( ).

[0118] COMSOL Multiphysics can also be replaced by simulation software with multiphysics coupling capabilities, such as ANSYS Fluent and ABAQUS. By combining the corresponding heat and humidity module with the pipe heat transfer module, the same evaluation effect can be achieved.

[0119] S2: Determine the material's thermal and moisture properties, environmental parameters, pipeline parameters, and boundary parameters, and input them into the thermal-humidity coupled transient numerical model for initialization settings.

[0120] This step identifies the multidimensional key parameters involved in model prediction, including:

[0121] Material thermal and hygroscopic properties, including the moisture content of the surrounding underground rock. Moisture content of concrete Moisture content of insulation materials Moisture content of cement mortar Moisture diffusion coefficient Steam permeability coefficient Liquid water conductivity Material dry density (Including surrounding rock materials, concrete materials, insulation materials, and cement mortar materials), dry volume specific heat capacity of materials. (Including surrounding rock materials, concrete materials, insulation materials, and cement mortar materials), equivalent thermal conductivity Thermal conductivity of pipe walls The thermal and hydrophysical properties of materials can be obtained through experimental measurements, further improving the accuracy of the model.

[0122] Environmental parameters, including initial indoor temperature Indoor relative humidity Initial temperature of underground surrounding rock Relative humidity of underground surrounding rock For example: Set the parameter name to "Indoor Initial Temperature". The parameter expression is set to 299.13K.

[0123] Pipe parameters, including pipe inner diameter Pipe wall thickness Flow velocity of the medium inside the pipe Specifies the tangential velocity of the pipe. Specifies the type of medium inside the pipe; the default medium is water. Sets the pipe shape in the pipe properties; the default is circular. Specifies that the pipe outlet is a hot outflow.

[0124] Boundary parameters, including wall heat transfer coefficient Floor heat transfer coefficient Ceiling heat transfer coefficient Wall mass transfer coefficient Floor mass transfer coefficient Ceiling mass transfer coefficient Model far boundary thermal and humid state parameters Flow rate of medium inside the pipeline Pipe inlet temperature .

[0125] When initializing the thermal-hygroscopic coupling transient numerical model, the aforementioned material thermal and hydrophysical properties, environmental parameters, pipe parameters, and boundary parameters are input. Simultaneously, the boundary parameters are mapped to the corresponding boundaries of the geometric model, such as... Figure 3 6. Walls, 7. Floor, 8. Ceiling, 9. Far boundary.

[0126] In the material components of the model tree, add material types and set material parameters, specifying the corresponding fields. For example, add concrete material and set the concrete moisture content. Moisture diffusion coefficient Steam permeability coefficient Liquid water conductivity Material dry density Equivalent thermal conductivity Define the continuous wall zone, ceiling zone, and floor zone as the corresponding zones for this material.

[0127] S3: Mesh generation for the thermo-humid coupling transient numerical model (e.g.) Figure 4 This involves obtaining the temperature and humidity of the grid nodes. Specifically, it includes:

[0128] (1) Add a grid component;

[0129] (2) Set the grid cell size;

[0130] (3) Select a specific face or edge structure to set the mesh size and density;

[0131] (4) Construct the grid;

[0132] (5) Add research components;

[0133] (6) Set up transient study steps;

[0134] (7) Set the transient time unit and output time step;

[0135] (8) Click Calculate and wait for the calculation to complete;

[0136] (9) Add a dataset and select the desired monitoring domain;

[0137] (10) Set the status variables of the monitoring area, including temperature and humidity;

[0138] (11) Obtain the state variables;

[0139] (12) Output and save the state variables.

[0140] Add a mesh component in the model tree, selecting either a physics-controlled mesh or a user-controlled mesh as the mesh sequence type. Select or define the mesh size, with predefined types ranging from very coarse to very fine, totaling nine levels. Specifically, you can select specific faces or edge structures to refine the mesh size as needed. Click "Build Mesh" to complete the meshing of the geometric model.

[0141] S4: Predict the outlet temperature of the energy wall based on the temperature and humidity of the grid nodes, and calculate the heat exchange capacity by combining the inlet temperature of the energy wall.

[0142] in:

[0143] Based on the temperature and humidity of the grid nodes, predict the outlet temperature of the energy wall, including:

[0144] (1) Define the probe and select the pipe outlet end;

[0145] (2) Set the expression of the acquired state parameter to T;

[0146] (3) Calculate and obtain the state variables , i.e., the outlet temperature of the energy wall;

[0147] (4) Output and save state variables .

[0148] Calculating heat transfer capacity includes:

[0149] ;

[0150] in:

[0151] Q represents heat exchange capacity;

[0152] The inlet temperature of the energy wall;

[0153] The density of water;

[0154] The specific heat capacity of the dry bulk medium;

[0155] V is the velocity of the current-carrying medium.

[0156] S5: Based on the temperature and humidity of the grid nodes, predict the indoor sidewall temperature, indoor sidewall relative humidity, indoor sidewall heat flux density, indoor sidewall moisture flux density, and indoor sidewall thermal bridge effect intensity.

[0157] in:

[0158] Predicting indoor sidewall temperatures includes:

[0159] (1) Define the wall probe;

[0160] (2) Obtain probe state variables That is, the indoor side wall temperature;

[0161] (3) Output and save state variables ;

[0162] Predicting the relative humidity of indoor sidewalls includes:

[0163] (1) Define the wall boundary probe and select the corresponding wall boundary on the indoor side;

[0164] (2) Set the expression of the acquired state parameter as follows: ;

[0165] (3) Calculate and obtain the state variables This refers to the relative humidity of the indoor side wall surface;

[0166] (4) Output and save state variables ;

[0167] Predicting the heat flux density of the indoor sidewall ,include:

[0168] ;

[0169] in:

[0170] This represents the convective heat transfer coefficient of the corresponding wall surface;

[0171] This corresponds to the temperature of the wall surface;

[0172] This represents the convective mass transfer coefficient corresponding to the wall surface;

[0173] The latent heat of vaporization of water, T is the temperature of the water;

[0174] This represents the relative humidity of the corresponding wall surface;

[0175] The partial pressure of water vapor on the interior side wall;

[0176] P sat0 The partial pressure of water vapor in indoor air;

[0177] ;

[0178] Predicting the moisture flow density on the indoor sidewall ,include:

[0179] ;

[0180] Predicting the intensity of thermal bridging effect on indoor sidewalls ,include:

[0181] .

[0182] S6: Add a data monitoring line on the central axis of the indoor side wall, such as... Figure 8 Based on the temperature and humidity of the grid nodes, predict the thermal bridge intensity gradient on the data monitoring line, including:

[0183] ;

[0184] in:

[0185] The thermal bridge intensity gradient;

[0186] The thermal bridge strength of the current grid node;

[0187] The thermal bridge strength of the next grid node;

[0188] The coordinates of the current grid node;

[0189] The coordinates of the next grid node.

[0190] One data monitoring line can be added, or multiple monitoring lines can be set on the indoor side surface (such as horizontal and vertical cross arrangement) to replace the monitoring line at a single central axis, realize the two-dimensional distribution quantification of thermal bridge effect, and improve the comprehensiveness of impact assessment.

[0191] S7: Based on the thermal bridge intensity gradient on the data monitoring line, predict the region with the maximum influence length of the thermal bridge effect, including:

[0192] The length L of the region where the thermal bridge effect has the greatest impact is:

[0193] ;

[0194] in:

[0195] For thermal bridge intensity gradients greater than or equal to 0.01 The coordinates;

[0196] The coordinates are the starting point.

[0197] This invention, for the first time, achieves bidirectional coupling of heat transfer and moisture transport in building materials during the assessment of the heat exchange capacity and thermal impact of energy walls. It accurately simulates the influence of temperature gradients on the penetration of moist components and the reaction of latent heat of moisture migration on the temperature field using the thermo-humidity module in COMSOL Multiphysics software, overcoming the accuracy limitations of traditional pure heat transfer models. Simultaneously, it innovatively proposes quantitative indices for the thermal bridge intensity gradient G_TBI and the maximum influence length L, combining heat exchange capacity, heat flux / moisture flux density, and TBI to form a complete assessment system, achieving comprehensive and accurate quantification of the energy wall's impact on the indoor side. Furthermore, it clarifies the value range and default values ​​for material, environmental, piping, and boundary parameters, combining industry standards and engineering measurement data to improve the method's versatility and repeatability, facilitating its widespread application in different scenarios.

[0198] This invention establishes the following different operating conditions (as shown in Table 1):

[0199] Table 1

[0200]

[0201] Among them: Case A represents a natural wall (without buried pipe heat exchange function); Case B represents an energy continuous wall that only considers the heat transfer process; Case C represents an energy continuous wall that considers the heat and moisture coupling transfer; Case C1, Case C2, and Case C3 represent working conditions with different inlet operating temperature levels, all belonging to the category of Case C.

[0202] By comparison, the following conclusions can be drawn:

[0203] like Figure 11 The heat transfer rate per unit length of the energy wall gradually decreases to approximately 12.51 W / m in the later stages of stable operation (Case C1). Considering only the pure heat transfer process (Case B), the heat transfer rate per unit length is approximately 10.71 W / m. When the inlet operating temperature is 40 and 45°C, the heat transfer rate per unit length increases to 16.98 and 21.68 W / m, respectively.

[0204] Figure 7 This is a schematic diagram illustrating the structural thermal bridge implementation effect of the energy wall. Figure 9 This is a diagram illustrating the maximum impact area length of the energy wall. (Example:) Figure 12The length of the thermal bridge effect gradually increases with the increase of the inlet operating temperature of the energy wall. In Case C3 (inlet operating temperature of 45°C), L1 increases to 9.52m, which is approximately 1.04 times and 1.07 times that of Case C2 and Case C1 (inlet operating temperatures of 40°C and 35°C, respectively). The gain effect with increasing inlet operating temperature does not seem significant. However, by comparing Case B and Case C1, it can be seen that when the transfer of wet components is ignored, the area affected by the thermal bridge intensity in Case B is underestimated by about 15.5%. In addition, in the natural wall Case A, L1 is only 7.04m at most, which is smaller than that of the continuous energy wall, especially smaller than that of the continuous energy wall considering the heat and moisture coupling transfer, further confirming that the present invention can be used to predict the dynamic heat transfer and thermal effects of energy walls.

[0205] In addition, the present invention also provides a thermal effect prediction system for energy walls based on thermal bridge intensity gradient under thermal-humid coupling, the system being used to implement the above method, comprising:

[0206] The model component module is used to construct the geometric model of the energy wall, add the pipe heat transfer physical field module, the building material heat transfer physical field module, and the building material moisture transport physical field module, and couple the building material heat transfer physical field module and the building material moisture transport physical field module through the heat and humidity multi-physics field module to obtain the heat and humidity coupled transient numerical model, which corresponds to S1 of the above method.

[0207] The initialization module is used to determine the material's thermal and moisture properties, environmental parameters, pipeline parameters, and boundary parameters. It inputs the thermal and moisture coupling transient numerical model for initialization settings, corresponding to S2 of the above method.

[0208] The mesh generation module is used to generate a mesh for the thermal-humidity coupled transient numerical model and determine the temperature and humidity of the mesh nodes, corresponding to S3 of the above method.

[0209] The heat exchange capacity calculation module is used to predict the outlet temperature of the energy wall based on the temperature and humidity of the grid nodes, and calculate the heat exchange capacity in combination with the inlet temperature of the energy wall, corresponding to S4 of the above method.

[0210] The first prediction module is used to predict the indoor sidewall temperature, indoor sidewall relative humidity, indoor sidewall heat flux density, indoor sidewall moisture flux density, and indoor sidewall thermal bridge effect intensity based on the temperature and humidity of the grid nodes, corresponding to S5 of the above method.

[0211] The second prediction module is used to add a data monitoring line on the central axis of the indoor side wall and predict the thermal bridge intensity gradient on the data monitoring line based on the temperature and humidity of the grid nodes, corresponding to S6 of the above method.

[0212] The third prediction module is used to predict the length region of maximum influence of the thermal bridge effect based on the thermal bridge intensity gradient on the data monitoring line, corresponding to S7 of the above method.

[0213] The present invention has the following technical advantages in its implementation:

[0214] 1. Significantly improved assessment accuracy:

[0215] Through two-way coupled thermal and humidity simulation, the interaction mechanism between the temperature field and the humidity field is fully restored. Compared with the traditional pure heat transfer model, the evaluation results of heat transfer capacity are more in line with actual working conditions.

[0216] 2. Comprehensive and accurate quantitative system:

[0217] A five-level quantitative system is established, which includes "heat exchange capacity, heat flux / moisture flux density, thermal bridge strength (TBI), thermal bridge gradient, and maximum influence length," providing comprehensive coverage from performance to impact and supporting engineering design, optimization, and risk assessment.

[0218] 3. Highly practical engineering applications:

[0219] The parameter values ​​are designed with intervals to adapt to different geological conditions, building types and pipe specifications; the model is built 1:1 based on the actual engineering dimensions, and the output results can directly guide engineering operations.

[0220] Those skilled in the art will understand that all or part of the functions of the embodiments of the present invention can be implemented by hardware or by computer programs. When all or part of the functions in the above embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to achieve the above functions. For example, the program can be stored in the memory of a device, and when the program in the memory is executed by the processor, all or part of the above functions can be achieved. In addition, when all or part of the functions in the above embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash drive, or portable hard drive, and can be downloaded or copied to the memory of a local device, or the system of the local device can be updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be achieved.

[0221] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A method for predicting the thermal impact of an energy wall based on the thermal bridge intensity gradient under thermal-humid coupling, characterized in that: The method includes: Construct a geometric model of the energy wall, add a physical field module for pipe heat transfer, a physical field module for building material heat transfer, and a physical field module for building material moisture transport, and couple the physical field modules for building material heat transfer and building material moisture transport through a multi-physical field module to obtain a transient numerical model of thermal-humidity coupling. Determine the material's thermal and moisture properties, environmental parameters, pipeline parameters, and boundary parameters, and input them into the thermal-moisture coupling transient numerical model for initialization settings; The thermal-humidity coupled transient numerical model is meshed, and the temperature and humidity of the mesh nodes are obtained. Based on the temperature and humidity of the grid nodes, the outlet temperature of the energy wall is predicted, and the heat exchange capacity is calculated in combination with the inlet temperature of the energy wall. Based on the temperature and humidity of the grid nodes, predict the indoor sidewall temperature, indoor sidewall relative humidity, indoor sidewall heat flux density, indoor sidewall moisture flux density, and indoor sidewall thermal bridge effect intensity. Add a data monitoring line on the central axis of the indoor side wall, and predict the thermal bridge intensity gradient on the data monitoring line based on the temperature and humidity of the grid nodes. Based on the thermal bridge intensity gradient on the data monitoring line, the maximum length region affected by the thermal bridge effect is predicted.

2. The method for predicting the thermal impact of an energy wall based on thermal bridge intensity gradient under thermal-humid coupling as described in claim 1, characterized in that: The energy wall geometric model includes a continuous concrete wall domain, a buried pipe heat exchanger domain, a concrete floor slab domain, an underground surrounding rock medium domain, and an underground building interior space.

3. The method for predicting the thermal impact of an energy wall based on thermal bridge intensity gradient under thermal-humid coupling as described in claim 2, characterized in that: Material thermal and hygroscopic properties include the moisture content of the surrounding underground rock. Moisture content of concrete Moisture content of insulation materials Moisture content of cement mortar Moisture diffusion coefficient Steam permeability coefficient Liquid water conductivity Material dry density Specific heat capacity of material dry volume Equivalent thermal conductivity Thermal conductivity of pipe walls ; Environmental parameters include the initial indoor temperature. Indoor relative humidity Initial temperature of underground surrounding rock Relative humidity of underground surrounding rock ; Pipe parameters include pipe inner diameter Pipe wall thickness ; Boundary parameters include the wall heat transfer coefficient. Floor heat transfer coefficient Ceiling heat transfer coefficient Wall mass transfer coefficient Floor mass transfer coefficient Ceiling mass transfer coefficient Model far boundary thermal and humid state parameters Flow rate of medium inside the pipeline Pipe inlet temperature .

4. The method for predicting the thermal impact of an energy wall based on thermal bridge intensity gradient under thermal-humid coupling as described in claim 3, characterized in that: The transient numerical model of thermal-humid coupling is meshed, and the temperature and humidity of the mesh nodes are obtained, including: Add a grid component; Set the grid cell size; Select a specific face or edge structure to set the mesh size and refinement; Build a grid; Add research components; Set up transient study steps; Set the transient time unit and output time step; Click "Calculate" and wait for the calculation to complete. Add a dataset and select the desired monitoring domain; Define the status parameters of the monitoring area, including temperature and humidity; Obtain the state variables; Output and save the state values.

5. The method for predicting the thermal impact of an energy wall based on thermal bridge intensity gradient under thermal-humid coupling as described in claim 4, characterized in that: Based on the temperature and humidity of the grid nodes, predict the outlet temperature of the energy wall, including: Define the probe and select the pipe outlet end; Let the expression for the acquired state parameters be T; Calculate and obtain state variables , i.e., the outlet temperature of the energy wall; Output and save state variables .

6. The method for predicting the thermal impact of an energy wall based on thermal bridge intensity gradient under thermal-humid coupling as described in claim 5, characterized in that: Calculating heat transfer capacity includes: ； in: Q represents heat exchange capacity; The inlet temperature of the energy wall; The density of water; The specific heat capacity of the dry bulk medium; V is the velocity of the current-carrying medium.

7. The method for predicting the thermal impact of an energy wall based on thermal bridge intensity gradient under thermal-humid coupling as described in claim 6, characterized in that: Based on the temperature and humidity of the grid nodes, predict the indoor sidewall temperature, indoor sidewall relative humidity, indoor sidewall heat flux density, indoor sidewall moisture flux density, and the intensity of the indoor sidewall thermal bridging effect, including: Predicting indoor sidewall temperatures includes: Define the wall probe; Acquire probe state variables That is, the indoor side wall temperature; Output and save state variables ; Predicting the relative humidity of indoor sidewalls includes: Define a wall boundary probe and select the corresponding wall boundary on the indoor side; The expression for the acquired state parameters is set as follows: ; Calculate and obtain state variables This refers to the relative humidity of the indoor side wall surface; Output and save state variables ; Predicting the heat flux density of the indoor sidewall ,include: ； in: This represents the convective heat transfer coefficient of the corresponding wall surface; This corresponds to the temperature of the wall surface; This represents the convective mass transfer coefficient corresponding to the wall surface; The latent heat of vaporization of water, , The temperature of the water; This represents the relative humidity of the corresponding wall surface; The partial pressure of water vapor on the interior side wall; The partial pressure of water vapor in indoor air; ； Predicting the moisture flow density G on the indoor sidewall sur ,include: ； Predicting the intensity of thermal bridging (TBI) on interior sidewalls includes: 。 8. The method for predicting the thermal impact of an energy wall based on thermal bridge intensity gradient under thermal-humid coupling as described in claim 7, characterized in that: The predicted data monitoring line includes the thermal bridge intensity gradient, including: ； in: The thermal bridge intensity gradient; The thermal bridge strength of the current grid node; The thermal bridge strength of the next grid node; The coordinates of the current grid node; The coordinates of the next grid node.

9. The method for predicting the thermal impact of an energy wall based on thermal bridge intensity gradient under thermal-humid coupling as described in claim 8, characterized in that: The predicted maximum length region affected by thermal bridging includes: The length L of the region where the thermal bridge effect has the greatest impact is: ； in: For thermal bridge intensity gradients greater than or equal to 0.01 The coordinates; The coordinates are the starting point.

10. A thermal effect prediction system for an energy wall based on thermal bridge intensity gradient under thermal-humid coupling, characterized in that: The system is used to implement the method of claim 1, including: The model component module is used to construct the geometric model of the energy wall, add the pipe heat transfer physics field module, the building material heat transfer physics field module, and the building material moisture transport physics field module, and couple the building material heat transfer physics field module and the building material moisture transport physics field module through the heat and humidity multi-physics field module to obtain the heat and humidity coupled transient numerical model. The initialization module is used to determine the thermal and moisture properties of materials, environmental parameters, pipeline parameters, and boundary parameters, and to input the thermal and moisture coupling transient numerical model for initialization settings; The mesh generation module is used to generate a mesh for the thermal-humidity coupled transient numerical model and determine the temperature and humidity of the mesh nodes. The heat exchange capacity calculation module is used to predict the energy wall outlet temperature based on the temperature and humidity of the grid nodes, and calculate the heat exchange capacity in combination with the energy wall inlet temperature. The first prediction module is used to predict the indoor sidewall temperature, indoor sidewall relative humidity, indoor sidewall heat flux density, indoor sidewall moisture flux density, and indoor sidewall thermal bridge effect intensity based on the temperature and humidity of the grid nodes. The second prediction module is used to add data monitoring lines on the central axis of the indoor side wall and predict the thermal bridge intensity gradient on the data monitoring lines based on the temperature and humidity of the grid nodes. The third prediction module is used to predict the length region of maximum influence of the thermal bridge effect based on the thermal bridge intensity gradient on the data monitoring line.

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