Bedding and clothing system three-dimensional heat transfer simulation method, equipment and medium

Abstract

The invention relates to a bedding and clothing system three-dimensional heat transfer simulation method and device and a medium, and the method comprises the steps: respectively obtaining point cloud data of a human body in a lying posture in a naked state, a pajamas state and a quilt dressing state through a three-dimensional scanning technology, and carrying out the post-processing; establishing a bed pillow geometric model based on the body pressure distribution data, determining the contact area of the bed pillow geometric model and the human-bedding system, adjusting the spatial positions of the two parties until the contact areas are matched, and executing intersection Boolean operation of the model contact parts; integrally assembling the processed geometric models of the human body, the bedding, the clothing and the bed pillow, and dividing the body model into body sections; establishing a human body-bedding-environment three-dimensional heat transfer model; the model is verified and optimized based on segmented warm dummy experimental data, and the prediction accuracy of the model is ensured. The influence of bedding and clothing material parameters on skin temperature distribution and a prediction equation can be systematically analyzed by utilizing the established model, and a theoretical basis and technical support are provided for accurate design and optimization of high-performance sleep bedding and clothing.

Description

TECHNICAL FIELD

[0001] The present application relates to the thermal performance design and evaluation of a sleep bedding system, in particular to a three-dimensional heat transfer simulation method, device and medium for a bedding system based on three-dimensional scanning and multi-physics simulation and verified by experiments. BACKGROUND

[0002] Sleep is a core physiological activity for maintaining the homeostasis of human body, and is essential for fatigue recovery and health protection. However, internal factors such as individual health and mental stress, and environmental factors such as noise, light, air temperature and humidity often lead to a decrease in sleep quality. Among the many environmental factors that affect sleep, poor thermal comfort is a key inducement. According to the ISO 7730 standard, thermal comfort refers to the subjective psychological state of satisfaction of the human body in the thermal environment. During sleep, the metabolic rate of the human body decreases significantly, and the body temperature regulation system is more sensitive. When the environmental temperature deviates from the thermal neutral zone of the human body, thermal discomfort is easily induced, which can cause multiple awakenings and interrupt the sleep structure. In this process, the microenvironment formed by the "bedding system" composed of the human body, bedding and bedding is more direct and significant in influencing the thermal comfort of the human body during sleep than the macro environment of the bedroom.

[0003] From the perspective of thermodynamic system, "human body-bedding-environment" is a complex and dynamically coupled system, and its thermal equilibrium state is determined by the body temperature regulation mechanism of the human body, the thermal physical properties of the bedding system, and the thermodynamic characteristics of the environment. The heat and moisture transfer process in this system involves multiple disciplines such as thermal physiology, heat transfer, fluid mechanics and textile material science. The research methods mainly include physical experiments and numerical simulations. Traditional physical experiment methods, such as thermal manikin test and real person experiment, have inherent limitations such as high cost, poor repeatability, and large influence of individual differences. At the same time, physical experiments rely on limited spatial point arrangement of sensors to obtain data, and it is difficult to fully and non-interferingly reveal the three-dimensional dynamic transfer mechanism inside the system.

[0004] With the development of computer technology, numerical simulation methods such as computational fluid dynamics have provided powerful tools for studying heat exchange in the "human-environment" system. However, most existing simulation studies have significant shortcomings when simulating sleep scenarios: First, the models are often oversimplified, failing to fully consider the overall bedding system composed of blankets, mattresses, pillows, and clothing worn during sleep. In particular, they generally ignore the crucial role of the thermal resistance distribution of clothing and blankets in heat transfer during sleep, leading to serious discrepancies between the models and reality. Second, the interface relationship and three-dimensional spatial morphology between the human body and blankets, mattresses, and pillows do not adequately consider the role of heat transfer within the entire system. Many models assume that blankets and clothing have uniform thermal resistance, failing to consider the three-dimensional morphology between the human body and bedding, resulting in poor simulation accuracy. Third, there is a lack of systematic research and clear explanation of the intrinsic mechanisms by which key parameters of the bedding system (such as blanket thickness and thermal conductivity) and environmental parameters (such as ambient temperature and air velocity) work together to influence the three-dimensional thermal field distribution and dynamic changes of the sleep microclimate.

[0005] Therefore, there is an urgent need in this field for a research method that can accurately construct a numerical model containing the complete three-dimensional form of "human body-clothing-environment" and systematically reveal the heat and moisture transfer law of clothing system under the synergistic effect of multiple parameters, so as to provide a reliable theoretical basis and technical support for the optimized design of high-performance and high-comfort clothing products. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the fact that the existing clothing heat transfer simulation technology cannot truly reflect the overall clothing system composed of quilt, mattress, pillow and clothing worn during sleep, and thus cannot accurately simulate the heat transfer between clothing and the human body and environment.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for simulating three-dimensional heat transfer in a clothing system includes the following steps: S1: Using 3D scanning technology, point cloud data of a lying human body in three states—naked, wearing pajamas, and covered with bedding—are obtained respectively. The point cloud data is then reverse engineered and post-processed to construct a human body model and a clothing model. S2: Establish the geometric model of the bed pillow and determine its contact area with the human-clothing system. After adjusting the spatial positions of both parties to match the contact area, perform the intersection Boolean operation to complete the geometric assembly of the contact parts. S3: Assemble the processed human body model, clothing model, pillow and mattress model as a whole, and divide the human body model into multiple body segments according to physiological structure; S4: In a multiphysics simulation environment, set parameters including bedroom environment parameters, clothing properties parameters, and local heat flux parameters of human body during sleep based on the body segment division, couple the heat transfer and fluid flow physical fields, and the heat transfer and radiation physical fields to establish a three-dimensional heat transfer model of "human body-clothing-environment". S5: Based on the measured data obtained from the segmented warm body dummy experiment, the three-dimensional heat transfer model is optimized and verified until its simulation accuracy reaches a preset threshold. S6: Using the validated three-dimensional heat transfer model, simulate and calculate the effect of clothing material properties on skin temperature, and output skin temperature prediction equations or analysis results to guide clothing design.

[0008] The aforementioned method for simulating three-dimensional heat transfer in a clothing system is characterized in that: the reverse engineering post-processing in step S1 includes the following sequentially executed steps: In the point cloud data processing stage, the main task is to filter and optimize the point cloud data, remove unnecessary numerical control, noisy data and overlapping data, and retain the necessary human body and clothing feature points. In the polygon data processing stage, the processed point cloud data is converted into polygon data, so that the human body and clothing are transformed into a geometric model composed of triangular faces. The surface processing stage is used to transform the mesh model into a smooth surface model.

[0009] The aforementioned three-dimensional heat transfer simulation method for a bedding system is characterized in that: the Boolean operation of the intersection between the bed-pillow geometric model and the contact area between the person and the bedding system in step S2 includes: Adjust the spatial position of the human body-clothing model and the bed pillow geometric model so that the surface area of ​​the cut human body model is close to the contact area determined in the body pressure distribution experiment; Perform an intersection Boolean operation on the human-clothing assembly and the mattress-pillow assembly that are in contact, and delete the overlapping volumes inside the model after the operation.

[0010] The aforementioned three-dimensional heat transfer simulation method for a bedding system is characterized in that: in step S3, the processed human body model, bedding model, pillow and mattress model are assembled and aligned in three-dimensional space according to the actual sleeping position, so as to accurately reproduce the complete sleep system composed of "human body-bedding-bed and pillow".

[0011] The aforementioned three-dimensional heat transfer simulation method for a clothing system is characterized in that: in step S3, the human body model is divided into multiple body segments, including but not limited to: head, face, neck, shoulders, chest, abdomen, buttocks, upper arm, forearm, hand, thigh, calf, and foot, which can be combined as needed.

[0012] The aforementioned three-dimensional heat transfer simulation method for a clothing system is characterized in that: the local heat flux parameter of the human body during sleep in step S4 is the dry heat flux density of the human body surface in a thermoneutral state, which is set based on the differences in metabolic rate and blood flow distribution in different segments of the human body. The determination of the dry heat flux density at the body surface includes: Based on the known human physiological thermoregulation model, the total heat flux density and evaporative heat flux density of each local area of ​​the human body under the thermoneutral state of sleep were calculated. The dry heat flux density is the difference between the total heat flux density and the evaporation heat flux density; When the human body partitions of the known human physiological thermoregulation model are inconsistent with the human body partitions of the target warm-body dummy, the calculated heat flux density data is converted to a local heat flux density that matches the partitions of the target warm-body dummy based on the heat conservation and the proportional relationship between the surface areas of each local area.

[0013] The aforementioned three-dimensional heat transfer simulation method for a bedding system is characterized in that: in step S5, the measured data includes at least the local skin temperature of the human body, the temperature of the microspace under the quilt, and the temperature of the outer surface of the quilt; The optimization specifically involves using the measured data as a benchmark and iteratively adjusting the key physical property parameters or boundary conditions in the three-dimensional heat transfer model to control the relative error between the simulated data and the measured data in local areas to within 3%.

[0014] The aforementioned three-dimensional heat transfer simulation method for a clothing system is characterized in that: the skin temperature prediction equation in step S6 is: ; Among them, T sk,i For skin temperature, T a For ambient temperature, v a Let H be the airflow velocity, D be the quilt thickness, k be the quilt thermal conductivity, and the intercept h and regression coefficients a, b, c, d be obtained by using skin temperature as the dependent variable and ambient temperature T. a airflow velocity v a The quilt thickness D and the quilt thermal conductivity k are the independent variables, and the results are obtained through multiple linear fitting.

[0015] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the aforementioned three-dimensional heat transfer simulation method for a clothing system.

[0016] A computer-readable storage medium storing a computer program thereon, characterized in that: when the computer program is executed by a processor, it implements the steps of the aforementioned three-dimensional heat transfer simulation method for a clothing system.

[0017] The beneficial effects of this invention are: 1. This invention constructs a complete geometric model of a sleep system, including "human body - clothing (including clothing and quilt) - bed and pillow", through three-dimensional scanning and reverse engineering, and performs precise spatial Boolean operations on the contact parts. This technical solution completely overcomes the simulation distortion problem caused by existing models ignoring clothing worn during sleep and simplifying the mattress contact interface. In particular, it makes it possible to simulate complex scenarios such as winter sleep with multiple layers of clothing, and provides an unprecedented high-fidelity model foundation for revealing the three-dimensional heat transfer mechanism in real sleep environment. 2. Based on the differences in metabolic rate and blood flow distribution in different parts of the human body, this invention sets non-uniform surface dry heat flux density under thermoneutral state for each of the divided physiological segments (such as head, trunk, limbs, etc.). This thermophysiologically driven parameter setting method enables the model to more realistically simulate the thermoregulatory response of the human body in the sleep state, thereby improving the physiological significance and accuracy of the simulation results from the source. 3. This invention constructs a reliable simulation and prediction tool that has been rigorously validated by experimental data (with errors controlled within 3%) and can be used for quantitative analysis. Through this model, the intrinsic mechanism of interaction between key physical properties such as quilt thickness and thermal conductivity and heat transfer in the sleep microenvironment can be systematically revealed. This provides precise quantitative basis for performance prediction, material selection, and structural optimization of bedding systems, effectively overcoming the traditional R&D model that relies on trial-and-error physical experiments, and significantly improving the development efficiency and design success rate of high-performance bedding products. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the establishment and application of a three-dimensional heat transfer simulation method for a clothing system in an embodiment of the present invention. Figure 2 The three-dimensional heat transfer simulation method for the clothing system in one embodiment of the present invention includes a human body in a natural lying position, a human body in a lying position wearing pajamas, and a complete set of bedding covering the body surface, with the pasted scanning points attached. Figure 3 The three-dimensional point cloud data of a naked human body in a natural lying posture, a human body wearing pajamas, and the outer surface covered by the clothing system are obtained by scanning in one embodiment of the three-dimensional heat transfer simulation method of the clothing system in this invention. Figure 4 This is a model diagram of point cloud data processing in one embodiment of the three-dimensional heat transfer simulation method for clothing systems in this invention. Figure 5 This is a model diagram of the three-dimensional heat transfer simulation method for the clothing system in one embodiment of the present invention after polygon data processing; Figure 6(a) is a diagram of the process of accurate human body surface modeling in one embodiment of the three-dimensional heat transfer simulation method of the clothing system in the present invention. Figure 6(b) is a diagram of the precise surface rendering process of clothing in one embodiment of the three-dimensional heat transfer simulation method of the clothing system in the present invention. Figure 6(c) is a diagram of the precise surface rendering process of the quilt in one embodiment of the three-dimensional heat transfer simulation method of the clothing system in the present invention. Figure 7(a) shows the body pressure distribution measurement site and the measured body pressure distribution map of the three-dimensional heat transfer simulation method of the clothing system in one embodiment of the present invention. Figure 7(b) shows the determination of the relative position and contact area between the human body and the mattress model in one embodiment of the three-dimensional heat transfer simulation method of the clothing system in this invention. Figure 8 This is a diagram showing the segment division and local region division of a combined human body and clothing model in one embodiment of the three-dimensional heat transfer simulation method for clothing systems according to an embodiment of the present invention. Figure 9 This is a geometric model diagram of the "human body-clothing-environment" in one embodiment of the three-dimensional heat transfer simulation method for the clothing system in this invention. Figure 10 This refers to the placement point of the internal temperature sensor in one embodiment of the three-dimensional heat transfer simulation method for the clothing system in this invention. Figure 11 The location of the external temperature sensor is shown in one embodiment of the three-dimensional heat transfer simulation method for the clothing system in this invention. Figure 12 These are the physical experimental measurements and simulated values ​​of the temperature in the microspace beneath the clothing system in one embodiment of the three-dimensional heat transfer simulation method for the clothing system in this invention. Figure 13 The temperature value of the outer surface of the quilt in the physical experiment and numerical simulation of the three-dimensional heat transfer simulation method of the clothing system in one embodiment of the present invention; Figure 14(a) is a trend diagram of the local skin temperature of the human arm, chest, abdomen, waist, buttocks, thigh and calf as a function of the blanket thickness in one embodiment of the three-dimensional heat transfer simulation method of the clothing system in the present invention. Figure 14(b) is a trend diagram of the change of average skin temperature of the human torso with the thickness of the quilt in one embodiment of the three-dimensional heat transfer simulation method of the clothing system in the present invention. Detailed Implementation

[0019] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings. Example 1

[0020] This invention provides a heat transfer simulation method for a clothing system, comprising: S1: Using 3D scanning technology, point cloud data of a lying human body in three states—naked, wearing pajamas, and covered with bedding—are obtained respectively. The point cloud data is then reverse engineered and post-processed to construct a human body model and a clothing model. S2: Based on physical experimental data such as body pressure distribution, establish a geometric model of the bed pillow and determine its contact area with the human-clothing system. Then, adjust the spatial positions of both parties until the contact area matches, and perform an intersection Boolean operation to complete the geometric assembly of the contact parts. S3: Assemble the processed human body model, clothing model, pillow and mattress model as a whole, and divide the human body model into multiple body segments according to physiological structure; S4: In a multiphysics simulation environment, set parameters including bedroom environment parameters, clothing properties parameters, and local heat flux parameters of human body during sleep based on the body segment division, couple heat transfer and fluid flow physical fields, and establish a three-dimensional heat transfer model of "human body-clothing-environment". S5: Based on the measured data obtained from the segmented warm body dummy experiment, the three-dimensional heat transfer model is optimized and verified until its simulation accuracy reaches a preset threshold. S6: Using the validated three-dimensional heat transfer model, simulate and calculate the effect of clothing material properties on skin temperature, and output skin temperature prediction equations or analysis results to guide clothing design.

[0021] The following is a detailed description of this embodiment: like Figure 1 As shown, Figure 1 A flowchart illustrating the establishment of a heat transfer simulation method for a clothing system provided by this invention includes the following steps: S1: Obtain 3D point cloud data of a human body model in a natural lying position, a human body model wearing pajamas, and the outer surface of the entire set of clothing covering the body using non-contact 3D scanning technology. Then, perform reverse engineering post-processing on the point cloud data to construct the human body model and the clothing model.

[0022] In one embodiment, the specific process of obtaining the geometric model using non-contact 3D scanning technology includes: First, attaching scanning positioning point stickers to the surface of a reclining, naked, warm-body dummy to assist the scanner in rapid positioning and data stitching; then, using a high-precision HandySCAN handheld 3D laser scanner (resolution: 0.050 mm, accuracy: 0.030 mm), performing a full-body scan from head to toe and from back to front to obtain a high-fidelity initial human geometric model. The dummy is then subjected to the same positioning point attachment and system scanning process in two scenarios: wearing pajamas and wearing pajamas covered with a blanket. Finally, 3D point cloud data of the outer surface of the naked human body in a natural reclining posture, the human body wearing pajamas, and the human body covered with a blanket system are obtained. Figure 2 The images show a human body in a natural lying position, a human body in a lying position wearing pajamas, and the complete set of clothing covering the body, used for pasting scan points. Figure 3 This is for scanning and acquiring three-dimensional point cloud data of the outer surface of a naked human body in a natural lying posture, a human body wearing pajamas, and a human body covered by a clothing system.

[0023] The obtained .obj point cloud data model is then imported into Geomagic Wrap 2021 reverse engineering software for post-processing to transform the original coarse scan data into an accurate, complete, and usable 3D model, ultimately meeting the computational requirements of finite element analysis. The post-processing includes three stages: point cloud data processing, polygon data processing, and surface processing.

[0024] Point cloud data processing stage: ① Delete "non-connected items" and "external isolated points" to remove point cloud data that is not the target object; ② Reduce scanning noise points caused by minor hand vibrations or dummy movement during the scanning process; ③ Retain necessary human and clothing feature points, delete overlapping point clouds, reduce the number of point clouds, and improve computational efficiency. Use default spacing settings and curvature priority to preserve boundaries; ④ Keep the original data, delete the smallest component, the default maximum number of triangles is 3,200,000, and execute at the highest quality.

[0025] Polygon data processing stage: ① Convert point cloud data into polygon data, so that the human body and clothing can be transformed into a geometric model composed of triangular faces; ② Adjust the coordinate system of the human body and clothing system, with the center of the human body's buttocks as the origin, the height of the human body and clothing as the z-axis, and the cross-sectional direction as the x-axis; ③ Using the flat cutting tool, divide the human body and clothing into two symmetrical parts along the center line of the mannequin, delete the right part, and keep the left part; ④ Select an appropriate curvature to fill the missing holes during the scanning process, forming a complete and closed human body contour; ⑤ Remove the depressions and protrusions on the abdomen, arms, legs and joints of the dummy, as well as the parts where the wrists and ankles connect to the walking poles. Use a polygonal repair tool to repair the secondary holes on the dummy's surface and the rough parts on the clothing surface to form a smooth human body and clothing surface. ⑥ Perform symmetrical processing on the divided left half of the dummy model to generate a complete human body and clothing model; ⑦ Inspect each model for non-manifold edges, highly refractive edges, nails, pinholes, etc., and automatically repair them; ⑧ Reduce the number of triangles to reduce the model size while ensuring model accuracy, thereby improving the efficiency of subsequent numerical simulation operations; relax the mesh to make it smoother.

[0026] Surface processing stage: ① Detect and edit contour lines: Define curvature sensitivity, separator sensitivity, and minimum area to automatically detect contour lines. Adjust the contour lines using the contour line editing tool; ② Surface Patch and Grating Processing: Surface patches are constructed using automatic estimation, and surface patch repair tools are used to correct surfaces with intersecting paths or excessively large angles. Grating tools are used to divide the human body and clothing surfaces into grids. ③ Generate accurate NURBS surfaces; Export the processed human body and clothing surface model as a model file. Figure 4 This is a model diagram after processing point cloud data; Figure 5 Figure 6(a), Figure 6(b), and Figure 6(c) are diagrams of the process of accurate surface rendering.

[0027] S2: Based on physical experimental data, a simplified geometric model of the pillow and mattress is established, and the contact area between the pillow and the human bedding system is calculated. Then, spatial Boolean operations are used to complete the geometric assembly of the contact points, ultimately simulating the sleep contact interface. The solution for the contact area between the pillow / bedroom geometric model and the human bedding system is based on body pressure distribution measurements. The spatial Boolean operations between the mattress and the human model include: adjusting the spatial positions of the human model, bedding model, mattress model, and pillow model so that the surface area of ​​the cut human model is close to the contact area determined in the body pressure distribution experiment; performing an intersection Boolean operation on the human-bedding assembly and the mattress-pillow assembly in contact, and deleting the overlapping volumes within the models after the operation.

[0028] In one embodiment, a corresponding cuboid geometry is created in numerical simulation software based on the actual dimensions (mattress: 160 cm × 220 cm × 20 cm; pillow: 80 cm × 45 cm × 10 cm) as a preliminary model of the mattress and pillow.

[0029] Subsequently, the spatial relative positions of the human-clothing assembly with the mattress and pillow are determined. The process for determining the relative positions of the human model and mattress model and assembling the models is as follows: The contact area between the human body and the mattress is indirectly determined using the body pressure distribution measurement method, and the relative positions of the two are deduced from this.

[0030] First, the body pressure measurement system (BPMS, Tekscan, USA) was laid flat on the mattress surface, and the human body lay flat on the BPMS. Measurements were recorded for 3 minutes at a sampling rate of 8 f / s, with pressure recorded in kPa. The spacing between the sensor matrices on the pressure pad was 1.061 cm. The collected pressure data was used to form a pressure matrix, saved as a .csv file. Excel software was used to calculate the number of matrices representing pressure points, and this number was multiplied by the area of ​​a single sensor matrix to obtain the contact area between the human model and the mattress. This process was repeated three times to determine the final contact area.

[0031] Next, the spatial position of the human body model was adjusted using the movement function in Geomagic Wrap software, so that the lower surface of the human body model was tangent to the xy plane. By adjusting the position of this plane along the positive z-axis, the surface area of ​​the cut human body model was made close to the contact area determined in the body pressure distribution experiment, thus determining the relative position of the human body model and the mattress model. The human-clothing assembly and the mattress-pillow assembly with their spatial positions determined were then imported into COMSOL numerical simulation software. To realistically simulate the contact state during sleep, Boolean difference operations were performed: along the surface of the dummy's back, the volume that overlaps with the human body was subtracted from the geometry of the mattress and pillow, thereby accurately generating the contact interface after pressure and automatically removing all overlapping geometric parts to avoid conflicts during mesh generation.

[0032] To simplify calculations and focus on the heat transfer of the bedding system, this model assumes that the mattress and pillow are thermal insulators, neglecting their own heat transfer effects. Figure 7(a) shows the body pressure distribution measurement site and the measured body pressure distribution diagram, and Figure 7(b) shows the determination of the relative position between the human body and the mattress model and the contact area diagram.

[0033] S3: The high-quality human body model, clothing model, pillow model, and mattress model obtained from the preceding steps are assembled and aligned in three-dimensional space according to the actual sleeping position to accurately reproduce the complete sleep system consisting of "human body-clothing-bed and pillow". The human body model is divided into multiple body segments, including: head, face, neck, shoulders, chest, abdomen, buttocks, upper arm, forearm, hand, thigh, calf, and foot.

[0034] In one embodiment, to facilitate the definition of heat transfer parameters for different segments of the human body in the simulation and to collect and analyze data on the local human body and the microenvironment beneath the blanket, this study, based on Newton's human body surface partitioning method, used the segmentation tool in COMSOL to divide the human body-clothing combination model into torso segments, and also divided the blanket into local regions. The model diagram after segmentation is shown below. Figure 8 As shown.

[0035] S4: In a multiphysics simulation environment, parameters including bedroom environment parameters, clothing properties parameters, and local heat flux parameters for human sleep based on the aforementioned body segment divisions are set. The local heat flux parameters for human sleep are the dry heat flux density of the body surface when the human body is in a thermoneutral state, based on the differences in metabolic rate and blood flow distribution in different body segments.

[0036] To accurately simulate the heat transfer process between the human body and the bedding system during sleep in a bedroom, the heat transfer mechanism of the sleeping human body surface is analyzed: the body surface area exposed to the air exchanges heat with the surrounding air through convection, and the heat exchange efficiency depends on the airflow pattern. The body surface area in contact with the mattress undergoes heat conduction with the mattress; assuming the mattress has extremely high thermal resistance, the effect of conduction heat dissipation will be very limited. In addition, there is also heat energy propagation in the form of electromagnetic radiation between the body surface temperature and the climate chamber wall; when the wall temperature is lower than the body surface temperature, the heat transfer direction is from the body surface to the wall. When calculating the heat flux density of various parts of the human body surface, considering that the evaporative heat dissipation generated by the human body's sweat secretion has a limited role in maintaining thermal balance under the state of thermal comfort during sleep, the heat exchange between the sweat and the environment is mainly in the form of dry heat (such as convection and radiation heat transfer), the simulation only considers the dry heat transfer of the body surface, and does not discuss the heat transfer process generated by the latent heat of vaporization and heat loss through sweating. Therefore, the bedding system model couples the physical fields of heat transfer and fluid flow, and heat transfer and radiation, to establish a three-dimensional heat transfer model of "human body-bedding-environment". The physical fields include solid heat transfer field, fluid heat transfer field, turbulent flow field, radiation heat transfer physical field, and non-isothermal flow physical field.

[0037] In one embodiment, for radiative heat transfer between the human body surface and the surfaces of clothing, mattress, and climate chamber walls, a surface-to-surface (S2S) radiation model coupled with a heat transfer model is used to calculate a surface-to-surface radiative heat transfer model. This radiation model is suitable for describing radiative heat transfer between surfaces within a closed system and does not involve the internal medium, which is consistent with the heat transfer simulation in this case.

[0038] Convective heat transfer was calculated using a non-isothermal flow heat transfer model coupled with the standard k-ε model. The standard k-ε model can effectively simulate fluid flow in fully developed turbulent regions, while empirical functions are used to provide approximate estimates for transition regions with low Reynolds numbers and near-wall surfaces. The thickness of the quilt and clothing was measured using a digital fabric thickness meter (according to GB / T 3820 standard), the mass of fabric samples was measured and density converted using an electronic balance (referring to GB / T 4669-2008 standard), the thermal conductivity was measured using a REFOND thermal conductivity and thermal conductivity meter (according to GB / T 11048-2018 standard), and the specific heat capacity was measured using a DSC differential calorimeter (referring to GB / T 19466-2004 standard). The surface layer and filling core layer were placed together in the equipment crucible for overall measurement according to their actual usage mass ratio. The relevant material property measurements of the bedding system used are shown in Table 1 below, used for setting material properties in the simulation.

[0039] Table 1

[0040] Boundary condition settings include fluid flow variables and boundary thermal variables. The computational domain of this bedding system model is the human body, bedding, mattress, and bedroom environment. Relevant values ​​for the human body, bedding system, mattress surface, bedroom walls, and airflow inlets and outlets need to be defined.

[0041] (1) Human body Based on the surface characteristics of human skin, the emissivity of human skin radiation is set to 0.95. The local heat flux parameter for human sleep is the dry-state heat flux density of the body surface under thermoneutral conditions, set based on the differences in metabolic rate and blood flow distribution across different body segments. The determination of the dry-state heat flux density is based on the Fiala human thermoregulation model, calculating the total heat flux density and evaporative heat flux density of various local areas of the human body under thermoneutral sleep conditions, where the dry-state heat flux density... q dry Total heat flux density q sk With evaporation heat flux density q eskThe difference is that, since the human body partitions in the Fiala thermal regulation model are inconsistent with those in the clothing heat transfer model, the local heat flux density applicable to the human body in the heat transfer model will be calculated based on the ratio of heat conservation to the local body surface area. Table 2 shows the local body surface area S in the Fiala model. i Table 3 gives the local total heat flux density q in the Fiala model. sk,i Evaporation heat flux density q esk,i and dry heat flux density q dry,i The local heat Q is calculated using Equation 1. i Combining the surface area distribution of different parts of the human body in the heat transfer model A i (See Table 4) and Equation 2, the local heat flux density q of the human body in the heat transfer model shown in Table 4 is calculated. i。

[0042] (1) (2).

[0043] Table 2

[0044] Table 3

[0045] Table 4

[0046] (2) Clothing system The quilt and clothing were designed as thin-layer structures, and the material properties of the thin layers were defined based on the fabric performance test results. The outer layer of the quilt and the clothing were both made of cotton fabric, with an emissivity of 0.8.

[0047] (3) Mattress surface Studies have shown that mattresses have extremely high thermal resistance, with heat loss through the mattress accounting for only about 3% of total heat loss. To reduce computational costs, numerical simulations of heat transfer across the human body surface during sleep often treat the mattress as adiabatic, neglecting conductive heat dissipation from the body surface through the mattress. The mattress surface is covered with cotton fabric, and its emissivity is set to 0.8.

[0048] (4) Bedroom walls The temperature of the bedroom walls depends on the surrounding air temperature, which is 12°C. Assuming no heat exchange between the room and the outside, the walls are designed to be insulated. The wall material is defined with an emissivity of 0.05, referencing the characteristics of walls in an artificial climate chamber.

[0049] (5) Airflow inlet and outlet Airflow inlet and outlet settings as follows Figure 9As shown, the simulated indoor wind speed is set to 0.2 m / s. Furthermore, all geometric spatial domains are symmetrically divided, thereby halving the computational load and improving simulation efficiency.

[0050] S5: Based on the measured data obtained from the segmented warm-body dummy experiment, the three-dimensional heat transfer model is optimized and verified until its simulation accuracy reaches a preset threshold. The measured data includes at least the local skin temperature of the human body, the temperature of the micro-space under the blanket, and the temperature of the outer surface of the blanket. Human skin temperature is the primary verification indicator, providing crucial data support for research on human sleep thermal comfort. Secondly, the temperature of the bedding microenvironment during sleep also has a significant impact on heat transfer between the human body and the microenvironment, and is considered in subsequent parameter studies; therefore, it is also a necessary verification indicator.

[0051] In one embodiment, the procedure for a segmented warm-body dummy experiment is as follows: ① Turn on the main switch of the climate chamber, turn off the climate chamber door and the lights inside, and adjust the temperature of the climate chamber to 12 ± 0.5 ℃ and the relative humidity to 60 ± 5%, referring to the daily indoor environment in winter; ② Observe the temperature changes in the climate chamber and wait for the temperature to stabilize at 12 ± 0.5 ℃ and the relative humidity to stabilize at 60 ± 5%; ③ Enter the climate chamber, dress the heated dummy in cotton pajamas, and place it supine on the mattress with its hands at its sides. Use medical tape to secure the temperature sensor probes to five locations: between the arms and torso, on the outer sides of the arms, between the legs, on the outer sides of the legs, and at the feet. Figure 10 As shown, the specific coordinates of the temperature measurement points at each part were measured and recorded simultaneously. Finally, the dummy's limbs and torso were covered with a down comforter. Six temperature measurement points (1, 2, 3, 4, 5, 6) were marked on the outer surface of the comforter using yellow 3D cutting marker tape. Similarly, medical tape was used to fix the temperature sensor's probe at each of the six points. The temperature distribution on the outer surface of the comforter was measured during the experiment. Figure 11 As shown; ④ Close the climate chamber and prohibit personnel from entering or exiting. Observe the temperature changes in the climate chamber and wait for the temperature to stabilize again at 12 ± 0.5 ℃ and the relative humidity to stabilize at 60 ± 5%. ⑤ Open the ThermDAC software that comes with the computer, and set the heating power of each part of the heated mannequin, with an allowable variation rate of ±5%; ⑥ Start the experiment and continuously observe the changes in surface skin temperature of different areas of the warm body dummy in the software. End the experiment when the surface temperature of different areas of the warm body dummy gradually stabilizes, i.e., the rate of change is ≤5%. ⑦ Open the climate chamber door, enter the climate chamber, use an infrared camera to photograph the overall temperature distribution on the outer surface of the comforter, then uncover the down comforter and cover yourself with it again, and turn off the climate chamber door and the interior lights; ⑧ After the surface skin temperature of the warm-body dummy has dropped to room temperature, repeat the above steps for a total of 3 repeated experiments, and take the average of the 3 measurements as the final experimental result.

[0052] To further compare and verify the experimental data with the simulated temperature, the relative error between the simulated value and the average value of the physical experiment was calculated at the end of the experiment. The comparison between the experimental data and the simulated data is shown in Table 5. It can be seen that the relative error of the skin temperature of each local part of the dummy's torso is less than 3%, and the relative error of the arms, chest, abdomen, waist and buttocks is less than 1.5%, which proves that the simulated value and the experimental value have good agreement.

[0053] Table 5

[0054] Figure 12 The figures show the physical experimental measurements and simulated values ​​of the temperature in the microspace beneath the dummy. As can be seen from the figures, except for the area between the feet, where the simulated temperature of the feet is lower than the experimental value by about 2 ℃, the simulated temperatures of the various parts of the dummy's torso are basically consistent with the experimental data, with relative errors of less than 1.5%, indicating that the simulation results and experimental measurements have good consistency. Figure 13 The figure shows the temperature values ​​of the outer surface of the quilt in physical experiments and numerical simulations. As can be seen from the figure, the experimental and simulated temperature values ​​at various measurement points on the outer surface of the quilt are basically consistent, falling between 11.9 ℃ and 13.7 ℃. Overall, the predicted temperature is highest at the central axis of the quilt, and decreases as the temperature moves closer to the outer edge.

[0055] Comparative analysis of experimental and simulated values ​​of human skin temperature, temperature of the microspace under the quilt, and temperature of the outer surface of the quilt shows that the model and the experiment have good agreement. It is believed that the established heat transfer model including the bedding can simulate the heat transfer process between the sleeping human body, bedding, and the environment.

[0056] S6: Using the validated three-dimensional heat transfer model, simulate and calculate the effect of clothing material properties on skin temperature, and output skin temperature prediction equations or analysis results to guide clothing design.

[0057] The following is an example of heat transfer analysis between clothing and the human body based on the clothing system heat transfer simulation method provided in this application: Example 1: Based on a validated three-dimensional heat transfer model of "human body-bedding-environment," the influence of bedding properties on skin temperature is simulated and calculated, and the analysis results are output to guide bedding design. By controlling other variables, a single-factor experiment on quilt thickness is conducted to analyze the impact of changes in a single parameter on human thermal comfort, explore the influence of bedding thermal properties on skin temperature, and reveal the mechanism by which key parameters of the bedding system (such as quilt thickness) affect the three-dimensional heat transfer of the sleep system. Based on a detailed analysis of its heat transfer mechanism, the bedding design scheme is further optimized. The specific implementation method is as follows: Four comforter thickness levels were set: 1 cm, 3 cm, 5 cm, and 7 cm, covering different comforter thicknesses from thin to thick (this thickness is the comforter thickness d measured directly under no pressure), reflecting sleep needs in different seasons and regions (such as differences between North and South China). Ambient temperature, wind speed, and comforter thermal conductivity were set to 12.4 ℃, 0.20 m / s, and 0.03 W / (m·K), respectively. Simulation calculations were performed by changing these parameter values, and the simulation results were analyzed, including data analysis and visualization analysis. The following is an analysis of the influence of comforter thickness and thermal conductivity on heat transfer in the human body during sleep: Figures 14(a) and 14(b) show the trends of local skin temperature and average skin temperature of the torso in the arms, chest, abdomen, waist, hips, thighs, and calves, respectively, as a function of blanket thickness. This section only considers the body areas covered by the blanket, and the average skin temperature of the torso is set as the area-weighted average of the skin temperatures of the seven selected sites in this study. As the blanket thickness increases, the skin temperature in each part of the body gradually increases, and the rate of increase slows down. This indicates that increasing the blanket thickness can improve its thermal resistance, thereby reducing heat loss from the body to the environment and significantly improving the insulation performance, thus enhancing the thermal comfort of the human body; however, this improvement is limited. When the blanket thickness reaches a certain threshold, convection and radiation heat loss become the main heat transfer methods, and further increasing the thickness has limited effect on suppressing convection and radiation heat loss, thus leading to a slower rate of increase in skin temperature. In addition, the figures show that the skin temperature response to blanket thickness varies in different body parts. For example, the skin temperature of the chest and abdomen is higher, while the skin temperature of the arms and calves is lower. This may be because the chest and abdomen are closer to the core area and generate more heat through metabolism, while the limbs (such as arms and calves) are farther from the core area and have a larger exposed area, resulting in faster heat loss. This difference indicates that when designing clothing systems, it is valuable to optimize zoning design for the thermal needs of different body parts, such as adding insulation materials to the limb areas to improve overall thermal comfort.

[0058] The trend of average skin temperature of the torso as the thickness of the quilt increases, as shown in Figure 14(b), is basically consistent with that in Figure 14(a). As the thickness of the quilt increases from 1 cm to 7 cm, the average skin temperature gradually increases from 28.5 ℃ to 31.5 ℃, further verifying the positive correlation between quilt thickness and skin temperature.

[0059] Therefore, through simulation analysis of different quilt thicknesses, the following conclusions can be drawn: When designing bedding systems, the differences in local thermal needs should be considered in a zoned design to balance overall thermal comfort. Optimizing the zoned design for the thermal needs of different body parts is valuable; for example, adding insulation material to the limb areas can improve overall thermal comfort.

[0060] Example 2: Based on a validated three-dimensional heat transfer model of "human body-clothing-environment," the influence of clothing properties on skin temperature is simulated and calculated, and a skin temperature prediction equation is output to guide clothing design. This prediction equation comprehensively considers the ambient temperature T. a airflow velocity v a The effects of blanket thickness (D) and thermal conductivity (k) on local heat transfer during sleep were investigated using a four-factor, four-level parametric simulation with orthogonal design. A skin temperature prediction model was established by performing multiple regression fitting analysis on the simulated skin temperature values ​​and each parameter value. The factor levels are shown in Table 6.

[0061] Table 6

[0062] The average skin temperature T of the torso under 16 sets of corresponding parameter test conditions was obtained by simulating the sleep heat transfer model of "human body-clothing-environment". sk,0 and arm skin temperature T sk,1 Chest skin temperature T sk,2 Abdominal skin temperature T sk,3 Waist skin temperature T sk,4 Buttock skin temperature T sk,5 Thigh skin temperature T sk,6 and lower leg skin temperature T sk,7 Local skin temperature at seven sites. The results of the parametric simulation are shown in Table 7.

[0063] Table 7

[0064] With skin temperature as the dependent variable, ambient temperature T a airflow velocity v aThe thickness of the quilt (D) and the thermal conductivity of the quilt (k) are used as independent variables. Through multiple linear fitting (using existing fitting techniques), the coefficients of each parameter (symbols h, a, b, c, d) of the fitting equations for the average skin temperature of the torso and the skin temperature of various local areas are obtained, as shown in Table 8 below. The skin temperature prediction model is shown in Equation 3. The goodness of fit R for each fitting equation is... 2 All values ​​are greater than 0.99, indicating that the model equations are meaningful and have a good fit.

[0065] Table 8

[0066] (3) Among them, T sk,i For skin temperature, T a For ambient temperature, v a Let D be the airflow velocity, k be the blanket thickness, h be the blanket thermal conductivity, and a, b, c, and d be regression coefficients. Specifically, skin temperature is the dependent variable, and ambient temperature T is the dependent variable. a airflow velocity v a The thickness of the quilt (D) and the thermal conductivity of the quilt (k) are used as independent variables. Through multiple linear fitting, the coefficients of each parameter (including intercept h and regression coefficients a, b, c, d) of the fitting equation for the average skin temperature of the torso and the skin temperature of each local area are obtained.

[0067] The skin temperature prediction model described above can be used to assess thermal comfort during sleep and provide a scientific basis for selecting appropriate bedding systems. The following are specific application methods of the skin prediction model: (1) By solving the skin temperature prediction model, the thermal comfort of the human body under different environmental conditions and clothing parameter combinations can be quantitatively evaluated. The environmental temperature T is used as the parameter. a airflow velocity v a The model inputs the blanket thickness D and the blanket thermal conductivity k; it uses regression equations to calculate the average skin temperature of the torso and the local skin temperature; based on the calculated skin temperature results, combined with thermal comfort standards (such as ISO 7730 or ASHRAE 55), it assesses the thermal comfort status of the human body under specific conditions.

[0068] (2) Solving the skin temperature prediction model can provide theoretical support for the optimized design of bedding systems. First, the ambient temperature, airflow velocity and target skin temperature are determined. Then, the combination of bedding thickness and thermal conductivity that meets the target skin temperature can be derived by solving the prediction model. Alternatively, the bedding material can be specified, and based on the prediction model, the development and design of zoned bedding can be scientifically guided to improve the thermal comfort performance of the product.

[0069] (3) Skin temperature prediction models can provide a scientific basis for the design of indoor environmental control systems. Based on the prediction model, the indoor temperature and wind speed settings can be optimized to ensure the thermal comfort of the human body during sleep. The prediction model can also be used to determine the combination of environmental parameters with the lowest energy consumption, thereby achieving a balance between energy saving and thermal comfort.

[0070] (4) Skin temperature prediction models can also be used in the development of intelligent health monitoring and early warning systems. By combining sensor data, environmental parameters and clothing status are monitored in real time, and skin temperature is calculated using prediction models. When the predicted skin temperature exceeds the comfort range, the system can issue an early warning to prompt the user to adjust the environmental or clothing parameters. Example 2

[0071] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the three-dimensional heat transfer simulation method for the clothing system described in this embodiment. Example 3

[0072] This embodiment provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the three-dimensional heat transfer simulation method for the clothing system described in Embodiment 1.

[0073] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0074] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0078] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for three-dimensional heat transfer simulation of a system of clothing, characterized by: The method comprises the following steps: S1: Obtain point cloud data of a lying human body in three states of nakedness, wearing pajamas and being covered by a quilt through three-dimensional scanning technology, and perform reverse engineering post-processing on the point cloud data to obtain a human body model and a quilt model; S2: Establish a geometric model of a pillow and determine its contact area with the human-quilt system, adjust the spatial positions of both to match the contact area, and perform intersection Boolean operation to complete the geometric assembly of the contact part; S3: Assemble the processed human body model, quilt model, pillow and mattress model as a whole, and divide the human body model into multiple body segments according to physiological structure; S4: In a multi-physical field simulation environment, set parameters including bedroom environment parameters, quilt physical property parameters, and human body sleep local heat flux parameters based on the body segment division, couple heat transfer and fluid flow physical fields, heat transfer and radiation physical fields, and establish a three-dimensional heat transfer model of "human body-quilt-environment"; S5: Based on the measured data obtained from the segmented warm body dummy experiment, optimize and verify the three-dimensional heat transfer model until its simulation accuracy reaches a preset threshold; S6: Use the verified three-dimensional heat transfer model to simulate and calculate the effect of quilt physical property parameters on skin temperature, and output a skin temperature prediction equation or analysis result for guiding quilt design.

2. The three-dimensional heat transfer simulation method of a clothing system according to claim 1, characterized in that: The reverse engineering post-processing in step S1 comprises the following steps executed in sequence: A point cloud data processing stage, mainly for filtering and optimizing the point cloud data, deleting unnecessary numerical control, noise data and overlapping data, and retaining necessary human body and quilt feature points; A polygon data processing stage, for converting the processed point cloud data into polygon data, and converting the human body and quilt into geometric models composed of triangular faces; A surface processing stage, for converting the grid model into a smooth surface model.

3. The three-dimensional heat transfer simulation method of a clothing system according to claim 2, characterized in that: The intersection Boolean operation between the pillow geometric model and the human-quilt system in step S2 comprises: Adjust the spatial positions of the human-quilt model and the pillow geometric model so that the surface area of the cut human body model is similar to the contact area determined in the body pressure distribution experiment; Perform intersection Boolean operation on the human-quilt combination and the mattress-pillow combination in contact, and delete the overlapping volume in the model after operation.

4. The three-dimensional heat transfer simulation method of a clothing system according to claim 1, characterized in that: In step S3, the processed human body model, quilt model, pillow and mattress model are assembled as a whole and the spatial relationship is aligned according to the actual sleeping position, so as to accurately reproduce the complete sleeping system composed of "human body-quilt-pillow".

5. The three-dimensional heat transfer simulation method of a clothing system according to claim 3 or 4, characterized in that: In step S3, the human body model is divided into multiple body segments, including but not limited to head, face, neck, shoulder, chest, abdomen, hips, upper arm, forearm, hand, thigh, lower leg and foot, which can be combined as needed.

6. The three-dimensional heat transfer simulation method of a clothing system according to claim 1, characterized in that: The human body sleep local heat flux parameter in step S4 is the body surface dry heat flux density of the human body in a neutral thermal state based on the metabolic rate and blood flow distribution of each body segment; The determination of the body surface dry heat flux density comprises: Based on a known human physiological thermal regulation model, the total heat flux density and the evaporative heat flux density of each local region of the human body in the sleep thermal neutral state are calculated; The dry heat flux density is the difference between the total heat flux density and the evaporative heat flux density; When the human body partition of the known human physiological thermal regulation model is inconsistent with the human body partition of the target thermal manikin, the calculated heat flux density data is converted to local heat flux density matching the partition of the target thermal manikin based on the proportional relationship of heat conservation and the area of each local body surface.

7. The three-dimensional heat transfer simulation method of a clothing system according to claim 1, characterized in that: In the step S5, the measured data at least includes the local skin temperature of the human body, the temperature of the lower space and the temperature of the outer surface of the quilt. The optimization specifically refers to taking the measured data as the benchmark, adjusting the key physical parameters or boundary conditions in the three-dimensional heat transfer model through iteration, so that the relative error between the local position simulation data and the measured data is controlled within 3%.

8. The three-dimensional heat transfer simulation method of a clothing system according to claim 1, characterized in that: The skin temperature prediction equation in the step S6 is: ； where T sk,i is the skin temperature, T a is the ambient temperature, v a is the air flow rate, D is the quilt thickness, k is the quilt thermal conductivity, and the intercept h and the regression coefficients a, b, c, d are obtained by multiple linear fitting with the skin temperature as the dependent variable and the ambient temperature T a , the air flow rate v a , the quilt thickness D, and the quilt thermal conductivity k as the independent variables.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that: The processor executes the computer program to realize the steps of the three-dimensional heat transfer simulation method of the clothing system according to any one of claims 1 to 8.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the three-dimensional heat transfer simulation method of the clothing system according to any one of claims 1 to 8.

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