A Method for Evaluating the Thermal Insulation Performance of Lightweight Recycled Blocks Based on Thermo-Coupling Simulation

Abstract

The present application relates to a method for evaluating the thermal insulation of lightweight recycled building blocks based on thermal coupling simulation, modeling the recycled building blocks as a heterogeneous multiphase composite, introducing a three-layer structure field of micro-pores, meso-aggregates and macro-blocks; adopting a partition coupling strategy to build a cause-and-effect chain of microstructure variability, heat flow density disturbance and stress field reconstruction, capturing the macroscopic thermal insulation fluctuations caused by thermal deformation; through thermal and force coupling simulation, tracking the thermal stress concentration area caused by temperature difference changes; analyzing the influence of thermal stress migration path on local structure integrity, pore change and crack evolution; building a thermal stress and structure disturbance factor framework to evaluate the influence of local structure deformation on thermal resistance; extracting high-dimensional feature data; establishing a thermal and force feature space; constructing a response surface equation to map the change trajectory of thermal insulation performance in the thermal and force coupling response space; forming a time and structure response composite scoring system to realize real coupling of multiple physical fields and break through the limitations of traditional evaluation.

Description

Technical Field

[0001] This invention relates to a method for evaluating the thermal insulation properties of lightweight recycled blocks based on thermo-coupling simulation. Background Technology

[0002] Currently, in the evaluation of the thermal insulation performance of lightweight recycled blocks, although more and more researchers are beginning to focus on the introduction of thermo-mechanical coupling simulation methods, striving to achieve full-process modeling and performance evaluation from thermal response to structural response through multiphysics analysis, there are still a series of obvious technical bottlenecks and limitations in practical applications. These shortcomings not only limit the engineering applicability of the model, but also affect the reliability and predictive ability of the evaluation results to a certain extent. First, most existing thermo-mechanical coupling simulation methods rely on standard finite element platforms such as COMSOL, ABAQUS, or ANSYS to establish thermo-mechanical joint solution models. Although these platforms have strong modeling capabilities, when dealing with typical heterogeneous, strongly discontinuous materials such as lightweight recycled blocks, which contain a large number of pores and recycled aggregate phase interfaces, their mesh generation and element approximation methods are difficult to truly restore the microstructure of the material. Especially when dealing with foamed regions, interface transition zones, crack tips, and weakly bonded zones, simplification is often necessary due to resolution limitations or numerical stability requirements, leading to distortion in the description of local thermal flow disturbances and stress concentration evolution. Furthermore, the acquisition of material constitutive parameters in thermo-mechanical coupling models often relies on idealized assumptions or extrapolation from finite sample points in experiments. The input data such as thermal expansion coefficient, thermal conductivity, and elastic modulus have significant uncertainties. In actual use, these physical parameters may fluctuate due to changes in humidity, aging, and the amount of recycled components. If the spatial and temporal variability of material physical properties is not fully considered, deviations are likely to accumulate in the simulation, weakening the effectiveness of the prediction performance.

[0003] Secondly, in thermo-mechanical coupling simulations, the description of pore structure evolution and microcrack development typically relies on simplified models, such as phase-field methods and damage mechanics models. However, these models face challenges in applying them to multiphase materials like lightweight recycled blocks. The complex pore morphology, varying scale distribution, and uncontrollable connectivity make it difficult to accurately capture the thermal-crack coupling mechanism. The correlation between crack initiation and the direction of the thermal gradient cannot be precisely fitted, leading to delayed or even misidentified identification when predicting thermally induced damage paths, thus affecting the accurate prediction of overall thermal resistance trends. Furthermore, most current thermo-mechanical coupling simulation studies have not fully considered the nonlinear interaction between the structure and the thermal field under dynamic conditions. Especially in regions with drastic diurnal temperature variations or repeated thermal cycling, stress release and reaccumulation within the material exhibit significant path dependence and historical evolution characteristics. Static or simplified loading models clearly cannot reproduce this real-world behavior, resulting in an underestimation of the material's thermal insulation degradation after long-term service. More notably, existing assessment methods generally lack quantitative means to express the impact of thermal stress-induced structural perturbations on the heat flow path reconstruction process. Although some literature has proposed using thermal bridge coefficients or crack thermal conductivity as substitutes to simulate changes in heat transfer paths, these methods mostly rely on empirical settings and lack the ability to perform data-driven modeling based on microstructural perturbations. They struggle to handle generalization problems under various combinations of structural parameters, resulting in predictive capabilities limited to fixed models and failing to achieve adaptive extension across structures and material systems. Furthermore, there is still a lack of effective linkage mechanisms between thermo-mechanical coupling assessment systems and material design. Current thermo-mechanical coupling simulations are mainly used to assess the performance of existing materials or experimental samples. However, there is a lack of clear feedback paths for how to deduce material composition design, optimize pore structure distribution, and improve interface strength based on performance targets. The assessment results cannot truly impact the material design end, thus turning thermo-mechanical simulation into a post-hoc verification tool rather than a feedforward design guide. Furthermore, current methods lack a unified definition and extraction standard for microstructure perturbation factors. Deformation rate factors, crack evolution indices, and pore reconstruction quantification models are mostly derived from individual research experience. A standardized, universally applicable, and performance-correlated factor framework has not yet been formed, making it difficult to compare or reproduce results from different studies. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the thermal insulation properties of lightweight recycled blocks based on thermo-coupling simulation, thereby addressing some of the drawbacks and shortcomings pointed out in the background art.

[0005] The present invention addresses the aforementioned technical problems by employing the following technical solution: a method for evaluating the thermal insulation performance of lightweight recycled blocks based on thermo-mechanical coupling simulation, comprising: modeling the recycled blocks as a heterogeneous multiphase composite, introducing a three-layer structural field of micropores, meso-aggregates, and macro-blocks; adopting a partitioned coupling strategy to map microstructural parameters, including pore connectivity and aggregate morphology, into local response functions of heat conduction and thermal expansion; constructing a causal chain of microstructural variability → heat flux density perturbation → stress field reconstruction to capture macroscopic thermal insulation performance fluctuations caused by thermal deformation;

[0006] Through thermal-mechanical coupling simulation, we tracked the thermal stress concentration areas caused by temperature difference changes; analyzed the impact of thermal stress migration paths on local structural integrity, porosity changes, and crack evolution; and constructed a framework of thermal stress and structural perturbation factors to evaluate the degree of influence of local structural deformation on thermal resistance.

[0007] Based on the simulation data, high-dimensional feature data including temperature gradient, heat flux, maximum stress, and stress gradient are extracted; a thermal and mechanical feature space is established through principal component analysis and partial least squares regression dimensionality reduction; and a response surface equation is constructed to map the change trajectory of thermal insulation performance in the thermal and mechanical coupled response space.

[0008] The thermal residence time index (TRT-I) is defined to measure the material's ability to maintain a stable temperature range within a unit of time; the thermal and stress coupling stability coefficient (TMS-C) is defined to evaluate the magnitude and recovery ability of structural field disturbances under temperature fluctuations; the two are combined to form a composite scoring system of time and structural response.

[0009] Furthermore, the response surface equation and the composite scoring system serve as the objective function; with aggregate ratio, foaming agent content, and porosity control as design variables, a multi-objective optimization framework is constructed; the reverse search includes a proportioning scheme that satisfies the insulation target, such as insulation time ≥ X hours and TMS-C ≥ Y; thus forming a three-in-one design and control path that integrates performance, structure, and parameters.

[0010] Furthermore, the method for capturing macroscopic thermal insulation performance fluctuations caused by thermal deformation includes:

[0011] S1. Establish a heterogeneous microstructure model of lightweight recycled blocks, divide the block material into multiple microstructure response regions, and assign differentiated local thermal conductivity and thermal expansion coefficients based on the pore connectivity, aggregate distribution density, and interface quality microstructure characteristics of each region, and construct a set of thermo-mechanical coupling input parameters.

[0012] S2. By adopting a partitioned coupling strategy, a multi-physics coupled model of heat and force inside the block is constructed to simulate the joint evolution of the temperature field and stress field, and to track the disturbance path of heat flux density and the resulting local thermal stress concentration phenomenon.

[0013] S3. Analyze the causal relationship between the thermal flow disturbance zone and the stress field reconstruction, and identify the structural response characteristics induced by thermal stress, including the location of microcrack initiation, stress gradient migration trend, and interface failure region.

[0014] S4. Map the local response to macroscopic thermal insulation performance fluctuation parameters, including temperature difference retention capacity, thermal hysteresis time, and thermal stress peak change rate index, and construct a dynamic evaluation model of thermal insulation performance driven by thermal deformation.

[0015] Furthermore, the division of the microstructure response region is based on factors including pore connectivity index, interfacial bonding strength of recycled aggregate, particle size distribution, and cavity distribution morphology, and the modeling parameters are extracted through image reconstruction and material test data; the simulation process of heat flux density perturbation is achieved by setting different heat conduction blocks to track non-uniform heat flux distribution, recording the path bending, accumulation, or scattering effects of heat energy in the structure, and using it as input for subsequent stress analysis.

[0016] In the above scheme, based on the porous composite structure of the material, a response zone division mechanism and a heat flow disturbance tracking mechanism are introduced, and combined with a custom high-order mathematical function model, the coupled characterization of heat conduction path variability and structural disturbance factor is realized.

[0017] The material is divided into multiple microstructure response sub-units, based on the following criteria:

[0018] Pore ​​connectivity index (χ): represents the degree of interconnection of open pores per unit volume; interfacial bonding strength of recycled aggregate (σ) int ): Indicates interfacial bonding ability; particle size distribution variance (Δ d ): Represents the degree of mixing of different particle sizes; cavity geometric deviation coefficient (φ) cav ): Reflects the complexity of the cavity shape.

[0019] The heat flux density perturbation is simulated by setting up irregular heat conduction distribution blocks to capture the bending, accumulation, and scattering path characteristics of heat energy in the material, and these are used as input variables for stress migration simulation.

[0020] To quantify the response trend of the microstructure perturbation region to changes in thermal resistance, the following function is defined:

[0021]

[0022] in:

[0023] Ψ res : Thermal perturbation-induced structural response potential energy function, used to characterize the local thermo-mechanical activation degree of microstructural units (unit: virtual potential energy index); Ω: Represents the integration domain, covering all spatial regions A that divide the response units and the simulation time period t; χ: Pore connectivity index, with a value range of [0,1]; φcav : Cavity geometric deviation coefficient, describing the cavity complexity; α, β: Empirical exponents, controlling the weights of the influence of cavity and connectivity on the response potential energy; The perturbation gradient along the heat flux direction reflects the degree of anomaly in the thermal energy path; δ: thermal perturbation trigger threshold, when... Stress migration occurs when >; κ: thermal disturbance sensitivity factor, controlling the steepness of the activation function; σ int : Aggregate interfacial bonding strength; Δ d : Standard deviation of particle size distribution; γ: Structure weighting coefficient, used to enhance the modulating effect of interface properties on the response; ε: Fine-tuning constant, to prevent the denominator from approaching zero.

[0024] Furthermore, the thermal stress reconstruction process is based on the local thermal expansion difference caused by the temperature gradient, simulating the stress concentration evolution between interfaces, the stress release around the aggregate, and the structural integrity disturbance behavior, and extracting key areas as thermal failure risk points.

[0025] Furthermore, the thermal insulation performance fluctuation parameters include thermal hysteresis time, thermal stability maintenance time, heat flux variation rate, and structural disturbance frequency, which are used to establish a macroscopic thermal insulation performance response surface.

[0026] Furthermore, the method for constructing the framework of thermal stress and structural perturbation factor includes:

[0027] S1. Establish a multiphysics coupling model for lightweight recycled blocks, integrate the heterogeneous microstructure characteristics inside the material in the model, and set thermal and force boundaries to enable dynamic coupling calculation of temperature and stress fields.

[0028] S2. During the simulation, identify the thermal stress migration path, extract the local stress concentration area formed along the path, and identify the structural disturbance characteristics caused by stress migration.

[0029] S3. Based on the thermal stress concentration effect, a structural disturbance factor framework is constructed, which includes: local deformation rate factor, crack evolution factor, pore reconstruction factor and stress disturbance amplitude factor.

[0030] S4. Perform heat flux reconstruction analysis on the structural disturbance region, measure the change in heat flux density before and after the disturbance, and quantify the thermal resistance reduction ratio of the corresponding region.

[0031] Furthermore, the identification of the thermal stress migration path includes tracking the spatial movement trajectory of the stress peak, the direction and rate of change of the stress gradient, and is used to analyze the thermal stress diffusion mechanism; the local deformation rate factor is used to characterize the degree of deformation of the material unit in the local area under thermal stress; the crack evolution factor identifies the microstructure failure path driven by thermal stress by recording the initiation, propagation and connection behavior of microcracks during the simulation process.

[0032] The above scheme achieves quantitative modeling and risk prediction of microstructural damage processes by coupling stress field tracking mechanisms and microstructure response factor extraction mechanisms, specifically including the following steps:

[0033] By using a multiphysics simulation platform, the dynamic diffusion mode of thermal stress is obtained by continuously tracking the stress peak movement trajectory, stress gradient direction change and its rate of change in the thermal stress field during the time evolution process.

[0034] Microstructure perturbation factor extraction: Local deformation rate factor (Λ) d ): Characterizes the instantaneous or cumulative deformation of a material element caused by thermal stress; crack evolution factor (Ξ) c ): By recording the time series of crack initiation, propagation path and connection rate, the reaction path of microcracks on thermal stress migration is revealed; the two constitute the core set of disturbance variables.

[0035] To comprehensively characterize the interaction between thermal stress migration path and structural perturbation factor, the following function is constructed:

[0036]

[0037] in:

[0038] Φ dyn : Dynamic stress-disturbance coupled response function, measuring the activation intensity of microstructures induced by stress migration; Ω: Integral domain, representing the summation region over a specific time t and spatial volume V; Λ d Local deformation rate factor: represents the magnitude of local structural deformation in a unit volume, derived from the rate of change of the displacement field; The derivative of the stress gradient with respect to time describes the degree of abrupt change in the stress concentration region along the stress migration path; Ξ c : Crack evolution factor, expressing the strength of the structural weakening chain formed during the crack initiation and propagation process; Δ θ : The offset angle between the crack propagation direction and the thermal gradient direction; η: Thermally driven crack activation coefficient, which controls the intensity of the crack factor's influence on the overall function; The disturbance frequency factor is used to control the periodic contribution of stress fluctuations to structural disturbances; s σ The spatial distance function of the stress peak displacement path is used to quantify the nonlinearity of the stress peak displacement path; μ,ν: empirical parameters used to control the weights of structural response and stress field disturbance.

[0039] Furthermore, the pore reconstruction factor is used to reflect the changes in the geometric shape and connectivity of local pores under thermal stress, including the transformation of closed pores into open pores and pore expansion phenomena; the stress disturbance amplitude factor is used to quantify the degree of stress fluctuation in a specific local area, which is obtained by analyzing the differences in stress values ​​at multiple times in the same area.

[0040] Furthermore, the thermal resistance attenuation ratio is used to describe the proportion of heat flux reduction per unit area before and after structural disturbance, reflecting the degree of attenuation of the heat conduction path by microstructural changes; the correlation model between the structural disturbance factor and the thermal resistance attenuation ratio is constructed using regression modeling, response surface analysis, or data-driven machine learning methods.

[0041] The thermal insulation performance evaluation method for lightweight recycled blocks based on thermo-mechanical coupling simulation proposed in this invention has significant advantages over traditional single heat conduction evaluation models or empirical experimental approaches in terms of thermophysical modeling depth, accuracy of structural response mechanism capture, and practicality of material optimization. These advantages are specifically reflected in the following:

[0042] By simulating the coupling of heat and force, the stress migration path and structural response behavior of the blocks under the influence of temperature gradient are dynamically tracked. This breaks through the traditional single-field evaluation method that only uses thermal conductivity or steady-state thermal resistance as indicators, and more comprehensively reflects the overall performance of the material's thermal stability and structural adaptability in the actual service environment.

[0043] By constructing response indices such as local deformation rate factor, crack evolution factor, pore reconstruction factor and stress disturbance amplitude factor, the internal microstructure disturbance process of block materials can be quantitatively described. This allows for the early identification of thermally induced damage behaviors such as crack initiation and pore topological reconstruction, thus enabling microscale risk early warning capabilities.

[0044] To address the difficulty in assessing structural micro-damage caused by thermal stress, a novel response index, thermal resistance attenuation ratio, is proposed. This index accurately characterizes the decreasing trend of heat flux per unit area before and after disturbance, providing a quantitative basis for macroscopic thermal resistance fluctuations and compensating for the lack of response of traditional thermal performance models to structural dynamic evolution. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method for evaluating the thermal insulation properties of lightweight recycled blocks based on thermo-coupling simulation, as described in this invention.

[0046] Figure 2 This is a flowchart of the method for capturing macroscopic thermal insulation performance fluctuations caused by thermal deformation according to the present invention.

[0047] Figure 3 The flowchart illustrates the method for constructing the framework of thermal stress and structural disturbance factor in this invention. Detailed Implementation

[0048] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0049] Combined with appendix Figure 1This invention presents a method for evaluating the thermal insulation performance of lightweight recycled building blocks based on thermo-mechanical coupling simulation. It recreates the complex, real-world characteristics of recycled building block materials in numerical simulations, enabling accurate prediction of their thermal insulation performance under real thermo-mechanical coupling conditions. The lightweight recycled building block needs to be modeled as a heterogeneous multiphase composite. The model cannot rely solely on average macroscopic thermal or mechanical parameters; it must fully express the multi-scale structure of the material. Based on this, a three-layer structural field is constructed, including a microporous layer, a meso-aggregate layer, and a macroscopic block layer. The microporous layer primarily reflects the material's pore distribution, connectivity, and pore size statistics. These data are typically obtained through X-ray CT scanning and high-resolution image reconstruction techniques. Subsequently, image processing algorithms such as edge detection, region segmentation, and connected domain analysis are used to quantify the pore morphology and connectivity. The meso-aggregate layer focuses on the size, shape, interface distribution, and density of the recycled aggregate. Data sources can include further layered annotation of scanned images and particle-level image analysis, combined with calibration through physical experiments (such as sieving tests or laser particle size analysis). The macroscopic block layer describes the overall geometry, load boundaries, installation methods, and other boundary layer conditions. Data can be derived from CAD drawings, physical measurements, or structural design specifications. A partitioned coupling strategy is employed during modeling, dividing the overall block into multiple response units based on spatially distributed structural characteristics, with each unit independently assigned physical properties. A parameter mapping mechanism is involved, converting microstructural data such as pore connectivity index (characterizing local thermal insulation) and aggregate morphology factor (controlling the formation of thermal bridges) into response functions such as local thermal conductivity coefficient, thermal diffusivity coefficient, and thermal expansion coefficient through custom mapping rules. This mapping is based on empirical relationships obtained from multiple experiments and backfitting. Simultaneously, parameter regression optimization is performed in the training set using a finite element simulation-measurement comparison method to ensure that the simulated parameters reflect real heat conduction behavior. A complete causal chain is constructed: microstructural variability → heat flux density perturbation → stress field reconstruction. In local microstructural regions (such as densely porous areas and aggregate interface areas), heat flux is perturbed due to abrupt changes in thermal conductivity, including heat flux bending, heat accumulation, or heat leakage paths. Simulations can observe local abrupt changes or directional reconstructions in the heat flux vector field; these anomalies are the potential areas of thermal stress concentration. Due to non-uniform thermal gradients, these regions will induce local thermal stress concentrations. The model uses a thermo-mechanical coupling solver to process the temperature and stress fields simultaneously, deriving the stress reconstruction path. This stress field reconstruction further affects local structural deformation or the occurrence of microcracks, causing changes in local heat conduction performance, which in turn affects the overall thermal insulation performance. In this causal chain, the transmission relationships between all variables are continuous and cannot be captured by static averaging models; they must be obtained in a dynamic, coupled, multi-scale simulation environment.Therefore, the model training process adopts a time stepping method, combined with thermal-mechanical dual load input, to dynamically output data such as heat flux density map, temperature field distribution map, and stress cloud map at multiple time points. Key indicators such as temperature lag time, thermal stability duration, and stress peak drift path are extracted from these results, and finally a functional relationship with macroscopic thermal insulation performance indicators (such as equivalent thermal resistance and heat dissipation rate) is established.

[0050] This method achieves dynamic linkage between the temperature field and the stress field to recreate the actual structural response and heat conduction behavior of masonry blocks in complex thermal environments. It relies on constructing a thermo-mechanical multiphysics model, using a finite element platform such as COMSOL Multiphysics, ABAQUS, or ANSYS to set up a thermally coupled mechanics module. This simulates how internal temperature changes in recycled masonry materials trigger the evolution of thermal stress concentration under specific thermal boundary conditions (such as diurnal temperature variation and thermal shock). The model structure consists of three parts: establishing a realistic microstructural geometric model based on CT scans to obtain information on the pore distribution, aggregate particle boundaries, and interfaces within the masonry block; preprocessing using image processing techniques, including image denoising, binarization, region segmentation, and 3D reconstruction, to extract geometric structural units suitable for simulation; and assigning thermal and mechanical properties, such as thermal conductivity, coefficient of thermal expansion, elastic modulus, and Poisson's ratio, to various parts of the material. These data can be obtained through experimental measurements (thermal conductivity meter, differential scanning calorimeter, micro / nano mechanical testing, etc.) and are standardized and normalized before simulation to ensure data from different sources are of uniform scale and comparable. During the simulation execution phase, by setting steady-state and transient thermal boundaries and applying actual loads (such as self-weight and external constraints), the model begins to calculate the temperature distribution and corresponding thermal stress evolution at different time points. The focus is on how regions of drastic temperature change cause non-uniform reconstruction of the stress field. In the simulation results, thermal stress peaks first appear at material interfaces, pore edges, and aggregate transition zones. By tracing the drift trajectories of these stress concentration points in time and space, a thermal stress migration path map can be formed. These paths not only reveal the fluid nature of stress but also demonstrate its deep coupling with the heterogeneity of the material structure. Furthermore, to analyze the impact of thermal stress migration on the integrity of local structures, the model uses the stress peak migration region as a local analysis unit, extracting the deformation rate, porosity changes, and crack evolution process of the material within this region. Porosity changes can be calculated by comparing the volumetric deformation of elements before and after the simulation, while crack evolution is based on the damage mechanics module settings, simulating the initiation, propagation, and connectivity of microcracks under specific stress thresholds, and recording the trends of their direction, length, and number over time. The method further proposes a thermal stress-structural perturbation factor framework, which constructs an evaluation system through a set of specific indicators. These indicators include a local deformation rate factor (characterizing the magnitude of thermally induced local structural deformation), a crack evolution factor (recording crack propagation rate and morphological changes), a pore reconstruction factor (reflecting changes in the connectivity of local pores under stress), and a stress perturbation amplitude factor (measuring the degree of drastic fluctuations in the stress gradient within the same region). These perturbation factors are all calculated from the data field output by simulation; for example, the deformation rate is derived from the displacement field, the crack propagation path is extracted from the damage field, and the perturbation amplitude is obtained from the gradient map of the stress field.In the data modeling phase, multi-factor regression analysis or data-driven machine learning models (such as random forests or support vector regression) are used to establish a functional relationship between the set of perturbation factors and the equivalent thermal resistance change, thereby achieving predictive modeling of the impact of structural response on thermal performance. During model training, perturbation factors under different structural units are used as input variables, and the thermal resistance change at their corresponding time steps is used as label data to form the training sample set. The training set is generated in batches using a simulation platform under various boundary conditions and microstructure parameter combinations, and then normalized and feature-selected to improve training effectiveness. The trained model will continuously optimize prediction accuracy, and its generalization ability will be verified through cross-validation.

[0051] To achieve quantitative modeling of the thermal insulation performance variation under complex multiphysics responses, the large amount of physical data generated during the thermo-mechanical coupling simulation is transformed into usable mathematical feature vectors. A high-dimensional feature space is established and further reduced in dimensionality to extract the most representative response factors for performance prediction. Thermo-mechanical coupling simulation of lightweight recycled masonry materials is performed using a finite element simulation platform (such as COMSOL Multiphysics or ABAQUS). Temperature boundaries are set under conditions including diurnal temperature difference, alternating hot and cold conditions, steady-state and transient conditions, as well as structural mechanical boundaries induced by self-weight, constraint loads, or thermal expansion. During the simulation, temperature field, heat flux vector field, stress field, and strain field data are collected at multiple time steps. Each simulation output node contains multiple physical quantities, which are extracted automatically by the script, including key indicators such as temperature gradient (i.e., temperature change per unit distance), heat flux magnitude and direction, maximum principal stress value, and spatial gradient of the stress field (i.e., the rate of stress change within the structure). These constitute an initial high-dimensional feature set. These original feature data exist in the form of multidimensional matrices with dimensions reaching hundreds or thousands. Due to the problems of collinearity, redundancy, and noise interference in high-dimensional features, data preprocessing is necessary. First, all features are normalized and scaled to the [0,1] or [-1,1] interval. Principal Component Analysis (PCA) is then used to linearly reduce the dimensionality of the feature matrix, extracting principal components along the direction of maximum variance to reduce the number of variables while retaining key information. Furthermore, to better establish the mapping relationship between input and output, Partial Least Squares Regression (PLSR) is introduced. This algorithm can directly correlate thermal insulation performance indicators (such as equivalent thermal resistance, insulation time, and thermal hysteresis coefficient) while extracting principal components, thus constructing a model with both feature extraction and mapping capabilities. After dimensionality reduction, the resulting principal feature space is used to construct a thermo-mechanical coupled response space. Each point in this space represents the structural response state of the material under a set of temperature and stress states, and the corresponding change in thermal insulation performance can be considered as the response value in this space. To achieve a functional description of the relationship between thermal and mechanical inputs and thermal insulation performance output, response surface modeling is further employed. By setting a polynomial response surface model or a fitting method based on radial basis functions (RBF), a continuous functional relationship, i.e., the response surface equation, is constructed, where thermal insulation performance is the principal characteristic factor. This response surface equation is represented as an analytical trajectory or surface, revealing how thermal insulation performance exhibits a nonlinear fluctuation trend driven by the coupling of thermal gradient, heat flux density, and stress gradient. During training, a large amount of sample data with different thermal boundaries and material structure configurations is generated through simulation as input feature points, and thermal insulation performance indicators are used as output labels. Least squares or regularization fitting is employed to optimize parameters, constructing a response surface function with strong generalization ability.Once the response surface methodology is trained, it can be directly used to quickly predict the thermal insulation performance of blocks under different working conditions, or to optimize design parameters in reverse, i.e., input the target thermal insulation performance and deduce the optimal thermo-mechanical response path or structural design combination.

[0052] To more comprehensively and dynamically reflect the response behavior of block materials to the thermal environment in actual use, two key original indicators are introduced: Thermal Retention Time Index (TRT-I) and Thermo-Mechanical Stability Coefficient (TMS-C). Based on these, a composite scoring system of time and structural response for dynamic working conditions is constructed to achieve a highly timely, sensitive, and multi-dimensional evaluation of thermal insulation performance. To define TRT-I, a thermo-mechanical multiphysics model of lightweight recycled masonry blocks needs to be built in a thermo-mechanical coupling simulation platform (such as COMSOL Multiphysics or ABAQUS). The simulation is run under predefined temperature boundaries (such as diurnal temperature variation and external wall heat flow impact), recording the response of internal temperature changes within the masonry blocks over time. Temperature time-series data is extracted from multiple monitoring points. By setting a reasonable "thermal stability range" (e.g., temperature fluctuation not exceeding ±2℃), the duration for which the material maintains this temperature range per unit time is statistically analyzed, and this time is divided by the total simulation time to obtain the standardized TRT-I value. A larger TRT-I value indicates better thermal stability of the material. During data acquisition, temperature data at each time step is exported as a matrix through node tracking, followed by time axis expansion. Data preprocessing includes missing value imputation, signal smoothing (such as moving average), and outlier handling to ensure the physical rationality of temperature curve changes. Dynamic window statistics are used in TRT-I calculations to improve accuracy. TMS-C calculations, on the other hand, focus on... The basic idea behind simulating the structural perturbation response of materials under temperature fluctuations is to simulate how temperature changes induce stress fluctuations or deformation field changes. Specifically, stress and deformation field data of corresponding time series are extracted under thermally coupled loads. Several key response points or regions are selected, and the stress fluctuation amplitude (e.g., the difference between maximum and minimum stress) and recovery rate per unit time are recorded—that is, whether the material can recover its initial stress distribution state after the temperature returns to normal. TMS-C comprehensively considers two dimensions: the amplitude of the perturbation (how strong the perturbation is) and the recovery capability (whether it can recover after the perturbation). Therefore, in the calculation, the stress-time data of each simulation node must first be normalized, then the fluctuation range is calculated, and the slope of the stable regression trend after the perturbation is solved by a fitting function. TMS-C is then defined as a functional fusion result of the structural perturbation amplitude and recovery rate. In this process, the preprocessing of stress data includes time synchronization, extreme value identification, filtering and denoising, and synthesizing surface indexes using a regional averaging method to improve the representativeness of the overall stability assessment.When constructing a composite scoring system, TRT-I and TMS-C are used as representative variables for thermal stability and structural adaptability, respectively. To ensure the comparability of the two dimensions in the scoring system, TRT-I and TMS-C first need to be normalized and transformed, and then standardized using Min-Max scaling or Z-score. Different indicators are then assigned weights, and weight coefficients are set based on experimental verification or expert experience (e.g., 60% for the thermal dimension and 40% for the mechanical dimension). Finally, a comprehensive score R-Score is generated through weighted linear combination or nonlinear fusion (e.g., weighted harmonic average). This score can be directly used for applications such as thermal insulation performance ranking, material grade classification, performance prediction, and production ratio optimization. During model training, to establish the mapping relationship between TRT-I and TMS-C and the actual structural design parameters, the calculated TRT-I and TMS-C indices can be used as label values. Influencing factors such as porosity, aggregate morphology, thermal conductivity, and elastic modulus can be selected as input feature variables to construct a multi-input-multi-output supervised learning model (such as a multivariate linear regression model or a neural network model). The training set comes from multiple simulation schemes. After feature selection, feature cross-combination, and standardization, modeling training is performed. The predictive ability is evaluated and the structural design parameters are optimized through cross-validation.

[0053] Intelligent, goal-oriented design control of block material performance is achieved by using the response surface equation obtained from thermo-coupling simulation and the composite scoring system as the optimization objective function. A multi-objective optimization framework is then established around structural parameters and thermal insulation performance indicators, and a closed-loop control mechanism integrating performance, structure, and parameters is realized through a reverse search method. Constructing an optimized objective function requires extensive thermo-mechanical coupling simulations. Different combinations of material design parameters (such as aggregate ratio, foaming agent dosage, and overall porosity) are set in the simulation platform. Thermo-mechanical multiphysics simulations are run to extract key data such as temperature field, stress field, and heat flux density. Based on the simulation results, performance response variables corresponding to each design parameter combination are extracted, including insulation time (i.e., the duration the internal temperature is maintained within the comfortable range) and the thermo-mechanical coupling stability coefficient TMS-C (used to describe the stress stability performance of the structure under thermal disturbance). These response variables are then fitted with the input parameters to construct a response surface equation. The response surface model can employ methods such as multinomial regression, support vector regression, radial basis function fitting, or Gaussian process modeling. The specific selection depends on the sample size and the degree of nonlinearity. During modeling, all data must be normalized to prevent differences in characteristic dimensions from affecting modeling accuracy. Cross-validation is used to test the model's generalization ability, selecting the mean squared error or R-squared value. 2As an indicator of fit performance, the generated form is a dual-response objective function: heat preservation time = f4(aggregate ratio, porosity, foaming agent content) and TMS-C = f2(aggregate ratio, porosity, foaming agent content). After constructing the objective function, it is further integrated with a composite scoring system, which consists of the thermal residence time index (TRT-I) and TMS-C. TRT-I is used to measure thermal stability, and TMS-C is used to evaluate structural resilience. The weighted combination of the two forms a comprehensive performance score R-Score, which, together with the response surface function, serves as the target output of the optimization model. Based on this, a multi-objective optimization framework is constructed. The optimization variables are set as controllable block mix proportion parameters, including aggregate ratio (e.g., 30%-60%), foaming agent content (e.g., 5%-20%), and total porosity (e.g., 25%-60%). These variables form an initial sample set through experimental design methods (e.g., Latin hypercube sampling or orthogonal experiments) to construct the optimization domain. The optimization objective is to maximize the holding time and the TMS-C value, while satisfying engineering constraints, such as a holding time of no less than X hours and a TMS-C value no less than a Y threshold. Additional constraints are added, such as material density range and molding process adaptability. The entire optimization problem can be solved using a multi-objective evolutionary algorithm (such as NSGA-II, MOPSO, or multi-objective Bayesian optimization). During the optimization process, the model generates new parameter combinations in each generation, inputs them into the response surface model to predict performance scores, selects and retains the optimal non-dominated solution set that satisfies the objective conditions, and iterates until convergence. The final output is a set of optimal or near-optimal material ratio schemes, each scheme clearly corresponding to its comprehensive performance in TRT-I, TMS-C, and holding time. Users can flexibly choose from these solutions according to their actual application scenarios. The advantage of this optimization process is that it replaces a large number of physical experiments with modeling, significantly saving costs and time, and realizing a performance-driven structural control path.

[0054] Example 1:

[0055] Combined with appendix Figure 1 In this embodiment, a passive house project in a frigid northern region requires the development of a novel lightweight recycled block that meets energy-saving standards (thermal conductivity of insulation layer ≤ 0.20 W / m·K, thermal inertia index > 60 min) while also considering crack resistance and thermal shock resistance. The research team selected a lightweight block made of 30% recycled expanded clay, 50% construction waste sand, and 20% foaming agent as a prototype and conducted a performance evaluation based on thermo-mechanical coupling.

[0056] In phase S1, researchers first used micro-CT scanning to obtain images of the internal microstructure of the blocks, measuring 10cm... 3Sample slices were reconstructed into three-dimensional structural data. Image processing algorithms were used to calculate the local porosity connectivity index (range 0.22–0.61), aggregate bulk density (0.35–0.48), and aggregate-matrix interface uniformity index (0.68–0.91), with each 1 mm... 3 The system was divided into microstructural response regions, forming a total of 1200 response units. The above data were then mapped to local thermal conductivity (ranging from 0.08 W / m·K to 0.18 W / m·K) and thermal expansion coefficient (ranging from 7.2 × 10⁻⁶). -6 / ℃ to 1.5×10 -5 The input parameter tensor ( / ℃) is constructed and imported into the simulation platform as a thermo-coupling input variable.

[0057] In the S2 phase, the research team built a complete thermo-mechanical coupling model on the COMSOL Multiphysics platform. The simulation conditions were set as follows: the external diurnal temperature ranged from -15℃ to +10℃, cycling hourly; the internal temperature remained constant at 20℃; the thermal boundary was set as a combination of radiation and convection boundaries; and the force boundary included the block's self-weight and a 50kPa preload on top. The simulation ran for 48 hours with a 15-minute time step, outputting the temperature field, heat flux vector field, stress field, and deformation field. Visual analysis revealed a significant heat flux disturbance zone near the recycled aggregate aggregation area. Heat flux in this region underwent a "bending and focusing" phenomenon, causing a sharp increase in local thermal stress. The peak stress jumped from 30MPa to 49MPa, and the thermal stress mainly migrated along the aggregate boundary, exhibiting a clear migration path characteristic.

[0058] In the S3 phase, researchers further analyzed the structural response in the thermal stress peak region. Using stress-strain field extraction tools, they assessed the risk of microcracks in the disturbed region and found four microcracks, approximately 2.5 mm in length, forming at the leading edge of the stress concentration path. The angle between the crack propagation direction and the heat flux direction was less than 15°, verifying that thermally induced stress migration was the main driver of crack evolution. Simultaneously, in the weakly bonded boundary region, interface failure occurred earlier than in other regions, with the local deformation rate exceeding the average by 2.7 times, verifying the triggering effect of interface stress reconstruction.

[0059] In phase S4, researchers mapped the aforementioned microscopic response results to macroscopic thermal insulation performance parameters. The temperature stability time period (the duration the internal temperature remained between 18-22℃) was extracted, showing a total maintenance time of 9.8 hours within the first 24-hour cycle, a 16% reduction compared to the crack-free sample. The thermal hysteresis time, the delay in the internal temperature stabilizing after a sudden external change, was measured at 71 minutes, a 12% reduction compared to the ideal model. The peak thermal stress variation rate was then calculated, showing stress fluctuations of ±21 MPa in the cracked region, significantly enhanced compared to the overall average stress fluctuation of ±9 MPa. These results were used as inputs to a dynamic thermal insulation performance model, and regression analysis was used to establish a mapping equation between microstructural perturbation factors and thermal insulation indices.

[0060] Ultimately, the simulation and evaluation process clearly revealed how a series of mechanisms, such as local thermal deformation leading to heat flow disturbance, thermal stress concentration, and microcrack evolution, are gradually transmitted to the dynamic fluctuations of overall thermal insulation performance under real service conditions, providing quantitative guidance for subsequent material optimization. To meet the engineering objectives (insulation time ≥10h, thermal hysteresis time ≥75min, TMS-C ≥0.8), the research team adjusted the aggregate volume ratio to 38%, fine-tuned the foaming agent to 18%, and optimized the compaction process to control the porosity at around 42%. After updating the parameters and resimulating, the peak thermal stress decreased to 42MPa, the number of crack initiation points decreased to 1, and the insulation time was ultimately increased to 10.7 hours, with a thermal hysteresis time reaching 76 minutes, successfully meeting the engineering performance requirements.

[0061] To investigate the influence mechanism of microstructural heterogeneity within lightweight recycled blocks, particularly the causal coupling between thermal flow disturbance paths and structural stress response, a potential energy function model of thermal flow disturbance-induced structural response based on microstructural response sub-region division is established. This model aims to reveal how the local microstructure of the material determines its sensitivity to thermal deformation-induced stress, ultimately driving the dynamic changes in thermal resistance performance.

[0062] First, in the image data acquisition phase, researchers used X-ray micro-CT to image 100mm. 3 3D reconstruction was performed on the block slices, achieving an image resolution of 8μm. The reconstructed three-dimensional pore structure image was then divided into units using machine learning-assisted image segmentation technology. In the segmented structure, a cubic partitioning method was used to divide the overall structure into 400 microstructural response sub-units. For each unit, the following structural parameters were extracted: pore connectivity index χ, calculated from the volume of connected pores / total pore volume, with a measured range of [0.28, 0.71]; and cavity geometric deviation coefficient φ. cav The value is obtained by calculating the deviation between the three-dimensional centroid drift of the cavity and the theoretical regular sphere, with a range of [0.12, 0.46]; the interfacial bonding strength σ of the recycled aggregate is... intThe bond strength, measured in MPa, was calculated using a combination of nanoindentation and interfacial shear tests, ranging from [3.5, 12.6] MPa; the particle size distribution variance Δ... d The particle size is calculated after identifying aggregate edges in the scanned image, with a variance range of [0.002mm]. 2 0.045mm 2 ].

[0063] Substitute the above parameters into the custom heat flux disturbance response function model:

[0064]

[0065] In this simulation, the parameter values ​​were set as follows: α = 1.2, β = 0.9, κ = 0.85, δ = 15 W / m 2 γ = 0.6, ε = 1 × 10 -3 The integration region Ω covers 400 sub-regions, and integration is performed in 5-minute steps over a 24-hour hot load cycle.

[0066] Heat flux perturbation gradient The heat flux vector field is obtained by deriving the heat flux vector field at each time step in the simulation platform, calculating the local heat flux change rate for each sub-region cell according to the main heat flux direction, and approximating its gradient using the difference of Gaussians. In actual measurements, areas with significant disturbances... Between 12 and 34 W / m 2 Within this range, approximately 36% of the sub-regions exceeded the perturbation threshold δ.

[0067] After substituting the data into the formula, integrate over the entire period for all units, and output Ψ. res Spatial distribution of values. The results show that the high-value region of the response potential function (i.e., the region highly sensitive to thermal stress) is mainly concentrated in the ranges where χ>0.6 and φ>0.6. cav >0.35、σ int <6MPa and Δ d >0.03mm 2 The regions in which the function significantly overlaps with the heat flux disturbance path and matches the simulated stress peak migration trajectory by more than 88%, demonstrate that the function has strong predictive and spatial positioning capabilities.

[0068] Regarding the evaluation of output, the research team found Ψ res When the threshold value (selected as 1800 potential energy units) is exceeded in a specific region, the average volatility in the stress field of that region increases to 2.3 times the original value, and the probability of crack formation during subsequent temperature loading increases by 68%, further confirming the logical chain between thermal disturbance leading to structural weakening and decreased thermal resistance.

[0069] By integrating this functional model with a thermo-coupling simulation module, researchers successfully established a complete closed-loop path from obtaining microstructure parameters → identifying local thermal disturbance paths → analyzing stress reconstruction mechanisms → predicting thermal insulation performance. In the subsequent optimization stage, Ψ res Incorporating the design variable sensitivity analysis framework, we found that pore connectivity χ and particle size distribution variance Δ d These are the main control factors. Under the premise of keeping the overall material density unchanged, optimizing these two factors can reduce the peak heat flux disturbance by 21% and increase the structural stability coefficient (TMS-C) by 12%, thereby increasing the heat preservation time from 10.7 hours to 11.6 hours, forming a material microstructure control strategy based on heat flow path response potential energy control.

[0070] After completing the modeling of the microstructure response potential function and identifying high-risk sub-regions, the research team further focused on the thermal stress reconstruction process at the macrostructural level. The goal was to capture stress concentration, aggregate edge instability, and interface structural disturbances caused by local thermal expansion differences due to temperature gradients, in order to accurately identify potential failure risk points under thermo-mechanical coupling and provide precise input for subsequent material optimization. This process began with data acquisition. Building upon the previous thermo-mechanical coupling simulation model, the team further improved the resolution, establishing a three-dimensional thermo-mechanical coupling finite element model of the masonry block specimens. They selected wall elements with dimensions of 300mm × 300mm × 100mm, embedding detailed aggregate distribution and pore structure within them. The model was refined to the interface area around each aggregate and foaming channels, dividing into more than 800,000 elements to ensure accurate tracking of micro-local deformations. The thermal boundary was set as a constant indoor temperature of 20℃ and a sinusoidal loading of outdoor temperature varying from -18℃ to +8℃ due to diurnal variation. The heat conduction boundary simulation included both radiation and forced convection, while the mechanical boundary included the material's self-weight and the compressive stress of the wall structure. The total simulation duration was 72 hours, with a time step of 10 minutes to ensure continuous observation of the gradual evolution of thermal stress with temperature difference. After the simulation began, the research team focused on extracting nodal data from the region with the most significant temperature gradient and based on the material's thermal expansion parameters (experimentally measured at 7.5 × 10⁻⁶). -6 / ℃~1.4×10 -5The difference in expansion response at different locations was calculated using a temperature (°C). It was found that the volume expansion deviation caused by temperature changes in the pore edges and aggregate envelope was significantly higher than that in the matrix region, with a maximum displacement difference of 0.42 mm, resulting in high-gradient stress fluctuations. Further analysis of the stress distribution cloud map revealed a stress peak zone at the aggregate-matrix interface, with stress values ​​soaring from an average of 28 MPa to 61 MPa, significantly exceeding the average stress field fluctuation of the structure. Time-series tracking of these high-stress areas revealed that the stress was not statically distributed but spatially migrated with temperature cycles. This manifested as stress concentration jumps along the aggregate boundary and negative stress fluctuations in stress release zones near some cavities, leading to the cumulative growth of local structural deformation. During data processing, researchers exported spatial-time series data of the stress and temperature fields, performed differential analysis at each time step to obtain stress gradient change rate curves, and then superimposed the stress-displacement functions of nodes near the interface to extract the stress release rate change at the aggregate edges, identifying the transfer path from stress concentration to release.

[0071] To extract high-risk areas for thermal failure, the research team constructed a structural perturbation factor combination model, integrating three core indicators: first, the stress gradient range index, defined as the difference between the maximum and minimum stress values ​​in a local area divided by the average stress; second, the deformation non-uniformity index, i.e., the standard deviation of the deformation difference between local nodes; and third, the thermal expansion stress response ratio, i.e., the change in equivalent stress response caused by a unit temperature gradient. After normalizing all indicators, cluster analysis was performed. Using the K-means clustering algorithm, the entire structure was divided into five response level zones. The high-risk zone at level 5 was mainly concentrated in the aggregate aggregation area, cavity boundaries, and two-phase material interface zone, accounting for 9.3% of the total area. However, 60% of the stress fluctuations before instability originated in these areas. To further quantify the thermal risk, the team used the thermo-stress data from these areas as a training set, inputting it into a logistic regression model and a decision tree model to train a binary classification thermal instability predictor. The prediction accuracy reached 87.2%, providing a strong basis for subsequent structural reinforcement.

[0072] In summary, the modeling of the thermal stress reconstruction process starts from the difference in thermal expansion caused by the temperature gradient. By capturing the location of stress concentration, tracking the stress release path, identifying structural disturbance response factors, and establishing a statistical mapping relationship between microscopic physical characteristics and thermal failure risk, a closed-loop modeling process from simulation data to failure early warning was successfully completed. Finally, in the optimized version of the block structure, the research team introduced interface reinforcing agents to treat high-risk areas, reduced the maximum aggregate particle size to 10 mm, and optimized the foaming agent distribution, resulting in a decrease in maximum stress to 48 MPa, a 42% reduction in local instability rate, and a significant improvement in corresponding thermal insulation performance.

[0073] After completing the microstructural response function modeling and thermal stress reconstruction analysis, the research team further introduced a system of thermal insulation performance fluctuation parameters, centered on thermal hysteresis time, thermal stability maintenance time, heat flux variability rate, and structural disturbance frequency, in order to achieve prediction and rapid optimization decisions for macroscopic thermal insulation performance. Based on this, they established a macroscopic thermal insulation performance response surface model, thereby realizing a quantitative prediction system that inputs structural design parameters and outputs performance response trends. The team selected 12 sets of different block mix proportions (covering different aggregate ratios, foaming agent dosages, and interface reinforcement processes) in a thermo-mechanical coupling simulation platform for full-cycle (72-hour) multiphysics simulation. The simulation results yielded high-dimensional data including temperature field, heat flux vector field, stress field, and displacement field with over 6000 time steps. The data collection points were concentrated in representative areas: the central solid area, the pore aggregation area, the aggregate boundary area, and the weakly bonded interface zone, forming a multi-source time series dataset. Thermal hysteresis time is a key indicator for evaluating the delay in internal temperature response under the influence of external temperature disturbances. Data acquisition is as follows: a trigger threshold is set at each sampling point (e.g., when the external temperature changes by 5°C, the internal temperature response changes by 1°C), and the delay time is recorded as the hysteresis indicator. The average thermal hysteresis time varies from 48 minutes to 97 minutes in different structural designs. Thermal stability maintenance time represents the duration for which the internal temperature remains within the comfortable temperature range (18–24°C). The cumulative time is calculated within the same simulation cycle, and the data range is... The simulation time ranged from 8.2 to 11.4 hours. The heat flux variability rate reflects the intensity of heat flux fluctuations per unit time, calculated by dividing the variance of the heat flux vector by the square of its mean. The measured range was from 0.04 to 0.17; a larger value indicates a more unstable heat conduction path. The structural disturbance frequency was determined by statistically analyzing the number of local stress abrupt changes (e.g., instantaneous stress changes >15%) in the structure and dividing by the total simulation time. The measured frequency ranged from 0.06 times / min to 0.22 times / min, mainly concentrated in areas with poor interfacial bonding or concentrated pores. These data were uniformly normalized, and Z-score standardization was used to compress data of different dimensions to avoid interference between features on the fitting accuracy of the response surface model. Principal component analysis (PCA) was then used to extract the main influencing dimensions. It was found that thermal hysteresis time and thermal stability maintenance time constituted the main thermal dimension factors, accounting for 48% of the total variance, while the heat flux variability rate and structural disturbance frequency mainly reflected the dynamic response capability of the coupled field, accounting for 37% of the total variance. Based on this, using these four parameters as response variables and structural design parameters (aggregate ratio, porosity, foaming agent content, and interfacial strength) as input factors, a four-dimensional response surface function was established. The function form was chosen as a quadratic polynomial response surface model, and the least squares method was used for parameter fitting. The training samples included 12 simulation schemes, each containing the above four response variables and four input variables. After model training, cross-validation was used for evaluation. The average prediction error was controlled within 6%, and the goodness of fit Rfit was [value missing]. 2The accuracy reached 0.92, demonstrating good prediction precision. The resulting macroscopic thermal insulation performance response surface can not only be used for rapid prediction of thermal insulation performance under arbitrary combinations of structural parameters, but can also be embedded in a multi-objective optimization framework to achieve goal-oriented reverse engineering of formulations. For example, if an engineering project requires a thermal hysteresis time ≥80 minutes, a thermal stability maintenance time ≥10 hours, and a heat flux variation rate ≤0.08, the system can use the response surface model to inversely deduce feasible proportion ranges and output multiple near-optimal solutions (such as aggregate ratio 35%–42%, porosity 38%–44%, foaming agent content 16%–19%, and interfacial strength >9MPa). Subsequent experiments verified that its actual thermal insulation performance is superior to existing solutions.

[0074] Example 2:

[0075] Building upon Example 1, after completing the identification of thermal stress reconstruction and the modeling of the thermal insulation performance response surface, the research team further conducted an in-depth analysis of the detailed mechanisms of the structural stability of the blocks under thermal cycling conditions. They introduced a framework of thermal stress and structural disturbance factors to identify, quantify, and predict the weakening effect of microstructural changes caused by local thermal stress on the overall thermal resistance performance.

[0076] In Phase S1, the team first built a three-dimensional multiphysics coupled model of the lightweight recycled blocks in COMSOL Multiphysics, based on the geometric model of the blocks constructed in the previous scans. The model size was 400mm×400mm×100mm, containing a three-phase structure including a recycled ceramsite aggregate region, a foamed pore region, and a cement matrix region. The aggregate diameter distribution was controlled between 6 and 14mm. To reflect the heterogeneity, a custom material module was used to assign different thermal and mechanical properties to each phase, such as thermal conductivity (aggregate: 0.27W / m·K, matrix: 0.42W / m·K, foamed pore region: 0.08W / m·K) and coefficient of thermal expansion (aggregate: 8.5×10⁻⁶). -6 / ℃, matrix: 1.2×10 -5 The model has an elastic modulus ranging from 1.1 to 3.8 GPa. The thermal boundary is set as an indoor constant temperature of 20℃, and an outdoor load of -15℃ to +10℃ diurnal temperature range sinusoidal wave, with each cycle lasting 24 hours, for a total of 72 hours. The force boundary is a vertical self-weight load and a wall-transmitted pressure of 20 kPa. The model calculation step is every 10 minutes, and dynamic coupling solution is performed.

[0077] In the S2 stage, after the simulation is completed, the stress field at each time step is exported. Using the "Extreme Path Tracing" function in COMSOL, the spatial drift of principal stresses over time is analyzed, regions forming stable migration paths are identified, and stress migration trajectories are constructed using the maximum principal stress point at each step. In the simulation results, researchers found that high thermal stress areas gradually expand outward along aggregate boundaries and pore distribution areas, exhibiting obvious "path-based concentration behavior." For example, between 24 and 36 hours, the stress value at the aggregate-matrix interface increases from 28 MPa to 54 MPa, accompanied by structural disturbance responses during the migration process. The team extracted stress data from the nodes in these path regions and tracked their deformation field responses, identifying stress migration-induced structural disturbance regions. Some nodes showed nonlinear abrupt changes in the stress-strain curves, indicating crack initiation.

[0078] In the S3 phase, the team constructed a structural perturbation factor framework and extracted the following four types of perturbation indices through simulation: ① Local Deformation Rate Factor (LDRF), defined as the ratio of the average displacement of nodes in a local area to the overall average displacement. The simulation showed that the average LDRF in the thermal stress concentration area was 1.82, indicating that the deformation in this area was about 82% greater than that in the normal area; ② Crack Evolution Factor (CEF), extracted based on the crack phase field parameters in the damage mechanics module, quantifying the crack length growth rate. In the simulation, high CEF values ​​were concentrated in the aggregate sharp corner area, with a maximum crack propagation rate of 0.26 mm / h; ③ Pore Reconstruction Factor (PRF), based on volume grid tracking of pore morphology changes over time, found that some closed pores connected under thermal stress to form heat conduction channels, with a local PRF of 0.41 (indicating a 41% topological change in pore morphology); ④ Stress Perturbation Amplitude Factor (SDF), which refers to the proportion of stress variation intervals per unit time during thermal loading. This value reached 0.62 in the interface failure area, much higher than the overall average of 0.25. All perturbation factors were normalized and used to construct a perturbation sensitivity distribution map to mark potential thermal instability zones.

[0079] In phase S4, the team used the high-value regions of the aforementioned perturbation factors as the analysis object, extracting heat flux density data before and after simulation. By calculating the rate of change of heat flux per unit time in these regions, they found that the direction of heat flux changed in some areas, with the heat flux density changing from the initial 42 W / m². 2 Reduced to 27W / m 2This resulted in significant heat transfer short circuits or thermal bridge formation. To quantify its impact on thermal resistance, the Thermal Resistance Reduction Ratio (TRDR) was calculated, which compares the equivalent thermal resistance before and after the local disturbance. The measured TRDR reached -23% in a typical disturbance area, meaning the effective thermal resistance in that area decreased by nearly a quarter. Combined with response surface methodology, this reduction in local structural thermal resistance led to a decrease in the overall wall insulation performance with a lag time of approximately 0.8 hours, exceeding the project's set tolerance for energy-saving deviations. Based on the disturbance factor distribution and TRDR analysis results, the research team adopted the following optimization strategies in subsequent block structures: increasing the interfacial bonding strength from 3.6 MPa to 7.4 MPa, controlling the aggregate size to less than 10 mm, and redistributing the foaming agent to make the porosity more uniform. After optimization, simulations showed that the LDRF decreased to 1.23, the SDF decreased by 45%, the local TRDR decreased to -9%, and the thermal stability maintenance time recovered to 10.6 hours, meeting the design requirements.

[0080] The project has progressed to the stage of quantitative modeling of the relationship between the microscopic thermal stress migration path and structural failure response of masonry materials under long-term service conditions. To address the problem of the inability to identify local thermal stress-induced failures in advance, the research team conducted verification simulation experiments based on a stress-disturbance coupling function model, and constructed a function Φ dyn To achieve thermal stress migration path Deep coupling analysis between microstructure response intensity, combined with material microscale response factors, enables the prediction of failure risk.

[0081] First, in the multiphysics simulation platform COMSOL Multiphysics, the team used a previously constructed heterogeneous microstructure 3D model to periodically heat-load lightweight recycled blocks (the external ambient temperature was simulated as a sinusoidal function from -20℃ to +12℃, with a period of 24 hours and a total simulation duration of 72 hours; the thermal boundary was a combined radiation and convection boundary, and the force boundary was vertical self-weight plus horizontal residual stress of 20 kPa). The model incorporated weakly bonded regions and pore accumulation zones at real interfaces. The principal stress field output from the simulation was sampled every 10 minutes. By identifying the positional changes of the principal stress extrema at each time step in 3D space, the stress peak trajectory line s was generated. σ (t), and its nonlinearity (defined as the deviation from the shortest straight path, measured in the range of 8 mm to 26 mm) is calculated using polynomial curve fitting to express the complexity of the migration path.

[0082] Next, we proceed to the disturbance factor extraction stage, specifically the local deformation rate factor Λ. d The displacement field time derivative of each sub-cell mesh is calculated to represent the instantaneous deformation or cumulative displacement induced by thermal stress in the unit volume. In the simulation, the aggregate edges and weak areas at the interface Λ dThe range is 0.08 to 0.35 (indicating that the maximum local deformation rate is 3.5 times the overall average deformation rate); crack evolution factor Ξ c The crack evolution variable is extracted from the phase-field crack module, representing the crack length growth rate per unit time multiplied by the crack propagation direction consistency weight. In actual measurements, the maximum value is 0.18 and the minimum is 0.02. Additionally, the angle Δ between the crack propagation direction and the heat flux gradient direction... θ Distributed between 5° and 60°, it is used to quantify the consistency between crack and main thermal migration path. The smaller the value, the easier it is to form a thermal bridge path, and the larger the value, the more likely it is that crack propagation deviates from thermal stress migration.

[0083] Substitute into the following function:

[0084]

[0085] Among them, μ=1.3, ν=1.8, η=4.5, The above parameters were obtained by analyzing Φ from 15 simulation schemes. dyn The results were obtained through fitting and optimization to the actual failure thermal resistance reduction rate. The integral region Ω covers the simulation volume (including approximately 96,000 elements in the high-stress region) and the 72-hour simulation period.

[0086] After the function is solved, Φ is obtained for each region. dyn The value distribution map shows a distinct high-response band at the aggregate aggregation and pore connectivity, with a maximum value reaching 1480 (unit: normalized potential energy index). Simultaneously, the final crack length in the corresponding region reaches 11.4 mm in the simulation, accompanied by a local heat flux increasing from 38 W / m². 2 Reduced to 24W / m 2 The thermal resistance decreases by -27% compared to TRDR. Further analysis of Φ... dyn The coefficient of determination R is obtained from the fitted curve between the thermal resistance reduction ratio and the thermal resistance reduction ratio. 2 =0.89, indicating that the function has high predictive accuracy in characterizing the structural failure mechanism induced by thermal stress migration.

[0087] Based on the output of this function, the team will increase Φ dyn The area was marked as a thermally weak zone, and attempts were made to control failure through design adjustments: controlling the foaming agent content in the structural design to reduce pore connectivity, and adjusting the interface modification process to reduce σ. int The pressure was increased from 5.6 MPa to 10.1 MPa, resulting in Λ d It dropped to 0.14, Ξ c The value decreased to 0.06, ultimately reducing the Φ value in the high-response region. dyn The peak value decreased by 42%, the crack evolution length was shortened to 4.1 mm, the TRDR decreased to -10%, and the overall heat preservation time was extended by 0.9 hours.

[0088] The project has entered the stage of in-depth modeling of the dynamic evolution mechanism of microstructure. The research team has focused on the influence mechanism of the topological reconstruction process of pore structure and the violent fluctuation behavior of local stress field on the overall thermal insulation performance of the material under thermal stress. Furthermore, they have proposed and implemented two key perturbation factors—pore reconstruction factor and stress perturbation amplitude factor—as the core input parameters of the micro-dynamic response monitoring and performance prediction model in this invention.

[0089] In the simulation modeling phase, researchers used X-ray CT technology to acquire pore structure images of multiple samples with different aggregate ratios, based on the real three-dimensional microstructure of the blocks obtained from previous scans. Each sample was imaged at 100 mm. 3 Volume reconstruction at a resolution of 5 μm was performed. After image segmentation and connectivity analysis, the pore region was extracted, and the surface area, volume, principal axis length, centroid trajectory, and connectivity label of each pore unit were calculated. These characteristic parameters were recorded at multiple time points to characterize dynamic changes. Thermo-coupling simulation was conducted using the structural thermo-coupling module of the COMSOL platform. The same pore structure was simulated under a 72-hour cyclic thermal loading. The thermal boundary was a sinusoidal loading from -18℃ to +15℃ externally, with a fixed internal temperature of 20℃. The structure's self-weight and thermal expansion forces were applied, and nonlinear geometric deformation analysis was enabled to capture the evolution of pores within the material under high temperature differential conditions. In the simulation output, the topological properties of the same pore unit were extracted at each time step, including whether it changed from a closed to a connected pore, whether volume expansion, structural rupture, or merging of adjacent pores occurred, and the state change time, evolution path, and local material stress state were recorded.

[0090] The calculation of the pore remodeling factor does not rely on traditional static porosity indices, but focuses on units where the topological structure undergoes fundamental changes. Each pore is assigned a dynamic label, and the calculation determines whether it transforms from a closed state to a through-pore state during thermal cycling (based on pore wall rupture and connection to adjacent pores), and whether it has experienced key geometric events such as pore diameter expansion exceeding 10% and increased surface complexity (judged by a decrease in sphericity). Finally, the pore remodeling factor is defined in the range of 0 to 1 using a weighted average of topological state change rate, volume change rate, and connectivity change. In the measured samples, the pore remodeling factor reached 0.62 in some high-stress areas, indicating that thermal stress induced drastic pore evolution behavior locally, while the value in normal areas is generally below 0.15. By mapping the pore remodeling factor to the heat flux change region, the research team found that in regions with a remodeling value higher than 0.5, the heat conduction path exhibits significant bending or short-circuiting in the simulation, with a local thermal resistance decrease of more than 20%, verifying the strong interference of dynamic pore structure evolution on heat conduction behavior.

[0091] Meanwhile, the quantification method for the stress perturbation amplitude factor is based on statistical analysis of the principal stress variation data over time output from the simulation. The team deployed multiple monitoring units in the thermal stress concentration area, extracted the principal stress value at each time step, calculated the maximum-minimum difference of the time series at each monitoring point, and divided it by the mean as the perturbation coefficient, defined as the stress perturbation amplitude factor. In actual measurements, it was found that in the central region of the stress migration path, this perturbation factor generally ranged from 0.35 to 0.58, with a local extreme value as high as 0.71, while in the non-perturbation area it was below 0.15. To avoid local high-frequency noise interference, the team used the Savitzky-Golay filtering method to smooth the original stress sequence, then extracted the perturbation rhythm using the sliding window difference method, and further incorporated the perturbation frequency (number of changes per unit time) and perturbation amplitude into the evaluation to obtain the final factor score. Comparative analysis of the spatial mapping of this factor with crack initiation points revealed that over 80% of the high-perturbation areas exhibited stress overload type cracks, and the area superimposed with the pore reconstruction factor became the core risk zone where the material was most prone to thermal instability.

[0092] Ultimately, the research team input the pore reconstruction factor and stress disturbance amplitude factor into the dynamic thermal insulation performance prediction model. By constructing a prediction function through regression analysis with the rate of decrease in thermal resistance, the model was trained under 12 different material design schemes. The average prediction error was controlled within 6.4%, successfully incorporating the microstructure evolution behavior into the macroscopic performance prediction system. In the subsequent material optimization stage, measures such as controlling the particle size distribution of the foaming agent and improving the interface bonding process were taken to reduce the distribution area of ​​high-factor regions, achieving a 31% reduction in local thermal resistance fluctuations, an extension of 0.7 hours in insulation time, and a 47% reduction in crack density.

[0093] Further, these perturbation factors are transformed into quantitative prediction capabilities for macroscopic thermal insulation performance. The thermal resistance attenuation ratio is introduced to measure the degree of heat flux transfer capacity reduction caused by structural perturbation. A data-driven correlation modeling mechanism is constructed to connect microscopic perturbation mechanisms with macroscopic performance evolution trends, thus forming a complete thermodynamic response evaluation chain. The thermal resistance attenuation ratio is defined based on comparative analysis of high-resolution heat flux vector fields in thermodynamic coupling simulations. The research team selects the same spatial unit region before and after structural perturbation (e.g., before and after microcrack formation, before and after pore topology changes) in the simulation model, extracts the average heat flux per unit area, and calculates the heat flux reduction rate according to the formula. This rate is defined as the ratio of the original flux to the flux after perturbation, i.e., the thermal resistance attenuation ratio. Physically, it represents the relative decrease in heat transfer capacity per unit of structural perturbation. During the data acquisition process, the team set up an automated data extraction script in COMSOL to extract the heat flux vector distribution of selected areas (such as crack initiation points, aggregate boundaries, and cavity aggregation zones) every 10 minutes, and converted the vector field into scalar flux density values ​​through numerical integration. The total number of samples reached 8640 regional time nodes.

[0094] To map the microstructure perturbation factor to the thermal resistance attenuation ratio, the team compared three modeling methods: one was multiple linear regression modeling, using the local deformation rate factor (Λ). d ), crack evolution factor (Ξ) c Using the pore reconstruction factor (PRF) and stress disturbance amplitude factor (SDF) as independent variables and the thermal resistance attenuation ratio as the dependent variable, the fitting coefficients were solved using the least squares method, and the regression R was obtained. 2 The R² value is 0.81, indicating that the linear model has some predictive power but is limited by nonlinear perturbation response characteristics. Secondly, response surface methodology (RSM) was used. After dimensionality reduction through principal component analysis (PCA), a quadratic polynomial response surface was constructed with 216 sample points. The input variables were orthogonally sampled. The visualization of the model's output prediction surface showed the changing trend of the thermal resistance reduction ratio under multiple factor combinations, significantly enhancing the interpretability of the interactions between variables. 2 The value was increased to 0.89 and used to optimize the search path in subsequent designs.

[0095] The third approach involves constructing a data-driven machine learning model. Random Forest Regression (RF) and Support Vector Regression (SVR) algorithms are used to establish nonlinear mapping models between the structural perturbation factor and the thermal resistance attenuation ratio, respectively. The dataset is divided into a training set (80%) and a validation set (20%). Five-fold cross-validation is used to evaluate model performance. The random forest model parameters are set to 150 trees and a maximum depth of 8; the SVR model uses a radial basis function (RBF) kernel with a kernel width parameter γ = 0.4 and a penalty factor C = 10. The results show that the random forest model achieves R on the test set. 2 =0.93, mean absolute error (MAE) =0.014, while SVR model R 2 =0.91, slightly worse than RF, but more sensitive to predictions in boundary perturbation regions. Ultimately, the team chose to integrate the outputs of the two models to build a hybrid predictor to improve the robustness of predictions under extreme perturbation scenarios.

[0096] Once established, this model can quickly output the predicted thermal resistance reduction ratio based on any combination of structural perturbation factors, and provides visualization analysis by combining response surface trend plots. In practical engineering applications, this model has been successfully used to evaluate three different block materials with varying proportions. The predicted thermal resistance reduction ratio for scheme A (porosity 38%, aggregate ratio 42%, interface reinforcing agent content 5%) is 0.14, for scheme B it is 0.08, and for scheme C, after optimization, it decreases to 0.05. This is highly consistent with the experimentally measured insulation time, with an error within ±5%, providing a clear basis for final material recommendations.

[0097] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the thermal insulation property of a lightweight recycled block based on thermal coupling simulation, characterized by Comprise the following steps: Model the recycled building blocks as a heterogeneous multiphase composite, introduce a three-layer structure field of micro-pores, meso-aggregates, and macro-blocks; adopt a partition coupling strategy to map the microstructure parameters including pore connectivity and aggregate morphology into local response functions of heat conduction and thermal expansion; build a causal chain of microstructure variability → heat flux disturbance → stress field reconstruction to capture the macroscopic thermal insulation performance fluctuations caused by thermal deformation; Track the thermal stress concentration areas caused by temperature difference changes through thermal and force coupling simulation; Analyze the influence of thermal stress migration path on local structure integrity, pore change, and crack evolution; build a thermal stress and structure disturbance factor framework to evaluate the influence of local deformation on thermal resistance; Based on simulation data, extract high-dimensional characteristic data including temperature gradient, heat flux, maximum stress, and stress gradient; use principal component analysis and partial least squares regression dimension reduction methods to establish a thermal and force characteristic space; Build a response surface equation to map the change trajectory of thermal insulation performance in the thermal and force coupling response space; Define the thermal retention time index TRT-I to measure the ability of the material to maintain a stable temperature interval within a unit of time; define the thermal and stress coupling stability coefficient TMS-C to evaluate the disturbance amplitude and recovery ability of the structure field under temperature fluctuations; combine the two to form a time and structure response composite scoring system.

2. The method for evaluating the thermal insulation property of a light weight recycled block based on coupled thermal-moisture simulation according to claim 1, wherein The response surface equation and the composite scoring system serve as the optimization function objective; use aggregate proportion, foaming agent content, and porosity control as design variables to build a multi-objective optimization framework; search reversely for a proportioning scheme that satisfies the insulation target including insulation time ≥ X hours and TMS-C ≥ Y; form a design and control path that integrates performance, structure, and parameters.

3. The method for evaluating the thermal insulation property of a light weight recycled block based on coupled thermal-moisture simulation according to claim 2, characterized in that The method for capturing macroscopic thermal insulation performance fluctuations caused by thermal deformation comprises: S1, establish a microstructure heterogeneous model of lightweight recycled building blocks, divide the building block material into multiple microstructure response regions, and according to the microstructure characteristic parameters of pore connectivity, aggregate distribution density, and interface quality of each region, give different local thermal conductivity and thermal expansion coefficients, and construct a thermal and force coupling input parameter set; S2, adopt a partition coupling strategy to build a building block internal thermal and force multi-physical field coupling model, simulate the joint evolution of temperature field and stress field, track the disturbance path of heat flux and the local thermal stress concentration phenomenon caused thereby; S3, analyze the causal relationship between the heat flow disturbance area and the stress field reconstruction, identify the structure response characteristics induced by thermal stress, including micro-crack initiation position, stress gradient migration trend, and interface failure area; S4, map the local response to macroscopic thermal insulation performance fluctuation parameters, including temperature difference retention ability, thermal hysteresis time, and thermal stress peak change rate index, and build a dynamic evaluation model of thermal insulation performance driven by thermal deformation.

4. The method for evaluating the thermal insulation property of a light weight recycled block based on coupled thermal-moisture simulation according to claim 3, characterized in that The division of the microstructure response area is based on the pore connectivity index, the recycled aggregate interface bonding strength, the particle size distribution and the cavity distribution morphology, and the modeling parameter extraction is realized through image reconstruction and material test data; the simulation process of the heat flux density disturbance is realized by setting different heat conduction blocks to track the non-uniform heat flux distribution, record the path bending, gathering or scattering effect of heat energy in the structure, and serve as the input of subsequent stress analysis.

5. The method for evaluating the thermal insulation property of a light weight recycled block based on coupled thermal-moisture simulation according to claim 4, wherein The reconstruction process of the thermal stress is based on the local thermal expansion difference caused by the temperature gradient, simulates the stress concentration evolution between interfaces, the stress release around aggregates and the structure integrity disturbance behavior, and extracts key areas as thermal failure risk points.

6. The method for evaluating the thermal insulation property of a light weight recycled block based on coupled thermal-moisture simulation according to claim 5, wherein The thermal insulation performance fluctuation parameters include thermal hysteresis time, thermal stability maintenance time, thermal flow variation rate and structure disturbance frequency, which are used to establish a macro thermal insulation performance response surface.

7. The method of claim 1, wherein the method is characterized by The method for constructing the thermal stress and structure disturbance factor framework comprises: S1, a multi-physical field coupling model of light recycled building blocks is established, the internal heterogeneous microstructure characteristics of the material are comprehensively considered in the model, and the thermal boundary and force boundary are set to realize the dynamic coupling calculation of the temperature field and the stress field; S2, in the simulation process, the thermal stress migration path is identified, the local stress concentration area formed along the path is extracted, and the structure disturbance characteristics caused by stress migration are identified; S3, based on the thermal stress concentration effect, a structure disturbance factor framework is constructed, and the structure disturbance factor includes a local deformation rate factor, a crack evolution factor, a pore reconstruction factor and a stress disturbance amplitude factor; S4, the heat flux reconstruction analysis is performed on the structure disturbance area, the heat flux density change before and after the disturbance occurs is measured, and the thermal resistance weakening ratio of the corresponding area is quantified.

8. The method for evaluating the thermal insulation property of a light weight recycled block based on coupled thermal-moisture analogy according to claim 7, characterized in that The identification of the thermal stress migration path includes tracking the spatial movement trajectory of the stress peak value, the stress gradient change direction and its change rate, which are used to analyze the thermal stress diffusion mechanism; The local deformation rate factor is used to represent the deformation degree of the local area material unit under the action of thermal stress; the crack evolution factor identifies the microstructure damage path driven by thermal stress by recording the cracking, expansion and connectivity behavior of microcracks in the simulation process.

9. The method for evaluating the thermal insulation property of a light weight recycled block based on coupled thermal-moisture analogy according to claim 8, characterized in that The pore reconstruction factor is used to reflect the geometric shape and connectivity state change of local pores under the action of thermal stress, including the phenomenon that closed pores become open pores and pores expand; the stress disturbance amplitude factor is used to quantify the stress fluctuation degree in a specific local area, and the change difference of stress value in the same area at different times is obtained through analysis.

10. The method for evaluating the thermal insulation property of a light weight recycled block based on coupled thermal-moisture simulation according to claim 9, wherein The thermal resistance weakening ratio is used to describe the proportion of the unit area heat flux drop before and after the structure disturbance, and reflects the weakening degree of the microstructure change on the heat conduction path; the correlation model between the structure disturbance factor and the thermal resistance weakening ratio is constructed by using regression modeling, response surface analysis or data-driven machine learning method.

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