A method, device and medium for simulating multi-node human thermal comfort

Abstract

The application discloses a kind of simulation method, equipment and medium of multi-node human thermal comfort, method includes: according to the macro area of pre-set human body determines multiple nodes, to determine human skin model according to multiple nodes;Determine the passenger cabin and air duct structure of vehicle, according to the grid scale of pre-set human skin model, passenger cabin and air duct structure are divided, to determine partition grid;Determine the boundary condition of pre-set multi-physical field, according to partition grid to the boundary condition of multi-physical field and human skin model is partitioned coupling iteration, to determine the dynamic temperature corresponding to multiple nodes;According to the PMV weight of pre-set dynamic temperature is weighted calculation, to determine comfort index, and according to the convergence criterion of pre-set output simulation result, simulation result includes node temperature cloud picture, air duct outlet flow rate distribution, dynamic PMV curve, comfort index.The application is accurately simulated dynamic thermal comfort by multi-node coupling iteration.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a simulation method, device and medium for multi-node human thermal comfort. Background Technology

[0002] Thermal comfort simulation of pure electric light trucks refers to the use of numerical methods such as computational fluid dynamics (CFD) and human thermal regulation models to simulate the temperature field, airflow organization, and heat exchange process of the occupants' body surface in a virtual environment, thereby quantitatively assessing the thermal sensation and thermal satisfaction of drivers and passengers.

[0003] Current thermal comfort simulations for pure electric light trucks generally employ simplified human body models. These models divide the human body into only 3-5 macroscopic temperature zones, consider only air supply temperature and velocity, and ignore key factors such as solar radiation, seat heat conduction, floor heat transfer, window radiative heat exchange, and dynamic changes in the human body's metabolic rate. Air duct simulations also focus only on the main air duct's flow capacity, failing to accurately simulate the direct effects of air velocity, temperature distribution, and airflow organization at the face / foot duct outlets on the human body. Evaluation indicators largely rely on PMV / PPD based on average temperature, neglecting to consider local thermal sensations, dynamic changes in skin temperature, and the unique vibration and noise coupling effects of electric trucks. These simplifications lead to significant drawbacks: insufficient accuracy of the human body model, failing to reflect temperature differences in sensitive areas such as the neck and ankles, resulting in comfort evaluation errors exceeding 15%; incomplete factor coverage leading to simulation deviations of 3-5°C in summer and 4-6°C in winter; inaccurate capture of the coupling effect between the air duct outlet jet and the human body surface, rendering local hot and cold sensation evaluations ineffective; and the traditional PMV / PPD method, lacking correction for high-frequency vibration and low-noise environments, fails to accurately reflect the driver's comfort during long-term driving. In summary, existing simulation systems lack high-fidelity, multi-physics coupled, and dynamically responsive human thermal comfort prediction models, making it impossible to accurately balance air conditioning energy consumption and ride quality during the conceptual design phase. There is an urgent need to construct a comprehensive simulation framework that covers fine human thermal regulation, transient environmental changes, and multi-source heat exchange. Summary of the Invention

[0004] To address the aforementioned issues, this application proposes a multi-node simulation method for human thermal comfort, comprising: determining multiple nodes based on a pre-defined macroscopic human body region, and determining a human skin model based on the multiple nodes; determining the vehicle's passenger compartment and air duct structure, and dividing the human skin model, passenger compartment, and air duct structure according to a pre-defined grid size to determine a partitioned grid; determining pre-defined multiphysics boundary conditions, and performing partitioned coupling iteration on the multiphysics boundary conditions and the human skin model according to the partitioned grid to determine the dynamic temperature corresponding to the multiple nodes; performing weighted calculation on the dynamic temperature according to a pre-defined PMV weight to determine a comfort index, and outputting simulation results according to a pre-defined convergence criterion, wherein the simulation results include node temperature cloud maps, air duct outlet velocity distribution, dynamic PMV curves, and comfort indices.

[0005] In one example, the multiphysics boundary conditions and the human skin model are subjected to partitioned coupling iteration, specifically including: the partitioned coupling iteration uses a partitioned coupling algorithm to solve the fluid control equation, energy conservation equation, and human thermal regulation model; the three-dimensional Navier-Stokes equations are solved as the fluid control equations based on a pre-set turbulence model; a global energy conservation equation is constructed that simultaneously includes solid heat conduction, fluid convective heat transfer, and surface radiative heat transfer; using a partitioned coupling algorithm, the fluid domain and the human body domain exchange boundary conditions according to a pre-set iteration cycle, and the exchanged boundary conditions include temperature, heat flux density, and convective heat transfer coefficient.

[0006] In one example, determining the dynamic temperature corresponding to the multiple nodes specifically includes: solving the heat balance equation for each node using the human body thermal regulation model, wherein the expression of the heat balance equation is:

[0007] in, For metabolic heat production, For blood perfusion heat, For heat conduction between nodes, For convective heat transfer, For radiative heat exchange, For evaporative heat dissipation, the dynamic skin temperature of each node at each time step is obtained by iteratively solving the heat balance equation.

[0008] In one example, the dynamic temperature is weighted according to a pre-set PMV weight to determine a comfort index. Specifically, this includes: determining the thermal sensitivity coefficient of each region of the human body; determining the PMV weight based on the thermal sensitivity coefficient of each region of the human body; and performing a weighted average calculation of the temperature across multiple nodes based on the PMV weight to obtain the comfort index. The expression for the weighted average calculation is as follows:

[0009] in, For comfort indicators, The cold air sensation index. The heat sensation index, To calculate the vibration correction factor based on seat vibration acceleration, This is the PMV weight.

[0010] In one example, simulation results are output according to pre-set convergence criteria, specifically including: the convergence criteria include residual convergence and temperature convergence; wherein, residual convergence is defined as the residual of the continuity equation decreasing to 1×10⁻⁶. -4 Below this, and the residual of the energy equation decreases to 1×10 -6 The temperature convergence is defined as the average temperature fluctuation of the multiple nodes being less than 0.1℃ within 1000 consecutive iterations. If the convergence criterion is met, the simulation results are output.

[0011] In one example, pre-defined multiphysics boundary conditions are determined, specifically including: the multiphysics boundary conditions include air conditioning supply boundary, solar radiation boundary, structural heat transfer boundary, human metabolism boundary, and environmental interaction boundary; wherein, the air conditioning supply boundary uses the measured flow velocity, temperature, and turbulence intensity of the outlet of the face air duct and the foot air duct as the inlet boundary condition; the solar radiation boundary calculates the solar radiation intensity based on geographical location, time, and window transmittance, and applies it as a surface heat source to the human body and interior surfaces; the structural heat transfer boundary uses the thermal conductivity and surface temperature of the seats, floor, and door interiors as solid wall boundary conditions; the human metabolism boundary uses the dynamic metabolic rate curves corresponding to driving behavior, including idling, constant speed, and acceleration, as the volumetric heat source; and the environmental interaction boundary uses the outside temperature, humidity, and wind speed as the cabin exterior boundary conditions.

[0012] In one example, before dividing the human skin model, the passenger compartment, and the air duct structure according to a pre-set mesh size, the method further includes: using a polyhedral mesh over the entire passenger compartment, wherein the basic mesh size is 20mm, and the mesh size is locally refined to 5mm for the seat surface, dashboard surface, and the area around the human body; the human skin model's human surface mesh is refined to 2mm at the corresponding positions of multiple nodes, and 10 layers of prism-layer boundary layer mesh are set in the normal direction of the human surface to make the dimensionless distance y of the wall surface... + ≈1; The internal mesh of the face blowing duct and foot blowing duct is increased to 3mm.

[0013] In one example, the method further includes: arranging skin temperature measuring points at corresponding node positions on the forehead, ankle, chest, and back of the human skin model to collect measured data; arranging flow velocity testing points at the outlets of the face-blowing air duct and the foot-blowing air duct to collect measured data on the outlet flow velocity distribution; comparing the simulation results with the measured data; and when the skin temperature error exceeds 2%, adjusting the parameters of the partitioned coupling iteration and the densification strategy of the partitioned mesh, re-executing the iterative solution until the error meets the requirements, and outputting the final comfort simulation evaluation result of the occupant cabin.

[0014] On the other hand, this application also proposes a multi-node human thermal comfort simulation device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the multi-node human thermal comfort simulation device to perform: the method described in any of the examples above.

[0015] On the other hand, this application also proposes a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to be the method described in any of the examples above.

[0016] This application constructs a dynamic skin model based on macroscopic regional division into multiple nodes. Each node independently solves the heat balance equations involving metabolism, blood flow, conduction, convection, radiation, and evaporation, realistically reflecting the non-uniform thermal regulation process of the human body, superior to traditional single-point or steady-state assumptions. A partitioned coupling algorithm is employed to simultaneously solve the Navier-Stokes equations, energy conservation equations, and the human thermal regulation model. The fluid domain and the human body domain exchange temperature, heat flux density, and heat transfer coefficients on an iterative cycle, avoiding error accumulation caused by unidirectional coupling. A dynamic weighted PMV algorithm is introduced, integrating cold / hot airflow indices and vibration correction coefficients, with weights determined by the thermal sensitivity coefficients of each region, making it more consistent with actual human sensations than traditional PMV. This application ensures the accuracy of near-wall heat transfer calculations; it also covers 11 boundary conditions, including air conditioning supply, solar radiation, structural heat transfer, human metabolism, and environmental interaction, realistically recreating the cabin thermal environment. A dual convergence criterion of residual and temperature is set, and through simulation-experiment comparison, parameters are automatically adjusted and recalculated when the skin temperature error exceeds 2%, ensuring the results are engineering-ready and can be directly embedded into the air conditioning development process. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1This is a flowchart illustrating a multi-node human thermal comfort simulation method in an embodiment of this application. Figure 2 This is a schematic diagram of a 128-node human skin model in an embodiment of this application; Figure 3 This is a vector diagram showing the velocity distribution at the air duct outlet in the embodiments of this application; Figure 4 This is a radar chart showing the dynamically weighted PMV / PPD evaluation results in the embodiments of this application; Figure 5 This is a schematic diagram of a multi-node human thermal comfort simulation device in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0020] like Figure 1 As shown, in order to solve the above problems, this application provides a multi-node human thermal comfort simulation method, the method including: S101. Determine multiple nodes based on a pre-set macroscopic human body region, and determine a human skin model based on the multiple nodes.

[0021] like Figure 2 As shown, the human body is divided into 16 macroscopic regions: head, neck, chest, back, left upper arm, right upper arm, left forearm, right forearm, left hand, right hand, left thigh, right thigh, left calf, right calf, left foot, and right foot. Each macroscopic region is further subdivided into 8 nodes, totaling 128 skin nodes. The spatial location of each node corresponds to a specific coordinate point on the surface of the human skin. The nodes are connected by a one-dimensional heat conduction network, forming the heat transfer topology of the entire human skin. The distribution of the number of nodes in each region is determined according to the proportion of the region's surface area, with a relatively high node density in the head and torso, and a moderate node density in the distal limbs.

[0022] In one embodiment, each skin node includes physiological parameters such as skin thickness, subcutaneous fat thickness, metabolic rate, and blood perfusion rate. Skin thickness varies significantly across different regions, ranging from 0.5 to 3.0 mm. The skin on the back of the trunk is thicker, while the skin on the distal extremities is thinner. Subcutaneous fat thickness reflects the thermal insulation performance of the subcutaneous fat layer, and its value varies depending on the individual and location. The abdominal and gluteal fat layers are thicker, while the fat layers on the distal extremities are thinner. Basal metabolic rate is the rate of heat production per unit volume of tissue at rest, ranging from 80 to 150 W / m². Different tissue types have different metabolic rates; muscle tissue has a higher metabolic rate, while adipose tissue has a lower metabolic rate. Basal blood perfusion rate is the rate of blood perfusion per unit volume of tissue, reflecting the heat exchange capacity of blood to tissue. Blood perfusion rate is closely related to tissue type; muscle tissue has a high perfusion rate, while adipose tissue has a low perfusion rate. Tissue density and specific heat capacity are used to calculate the node's heat capacity and thermal inertia, affecting the dynamic response speed to temperature changes.

[0023] A heat conduction network of 128 nodes is established, with each node connected to its neighbors via thermal conduction. The network includes the following heat transfer paths: lateral conduction within the skin surface, longitudinal conduction between the skin and subcutaneous tissue, and conduction between subcutaneous tissue layers. Lateral conduction within the skin surface refers to heat conduction between adjacent skin nodes within the same macroscopic region; the thermal conductivity is determined by the skin tissue properties. Longitudinal conduction between the skin and subcutaneous tissue refers to heat conduction between each skin node and the underlying subcutaneous tissue layer, reflecting the coupling relationship between skin surface temperature and deep tissue temperature. Conduction between subcutaneous tissue layers refers to heat conduction between the subcutaneous fat layer and the muscle layer, reflecting changes in the heat storage capacity of deep tissues. The topology of the heat conduction network is discretized using the finite difference method, and the heat balance equation for each node can be expressed as:

[0024] in: For tissue density, For specific heat capacity, For node volume, For node temperature; For metabolic heat production; For heat exchange in blood perfusion; For heat conduction between nodes; , , These are heat loss due to convection, radiation, and evaporation, respectively. Based on the nodal heat balance equation, a feedback control mechanism of the human body's active thermoregulation system is introduced. When the core temperature or local skin temperature deviates from a set threshold, the blood flow to the skin in each area is adjusted. In cold environments, skin blood vessels constrict, reducing blood perfusion rate to decrease heat loss; in hot environments, blood vessels dilate, increasing blood perfusion rate to enhance heat loss. The adjustment range of blood perfusion rate is achieved through a proportional-integral-derivative (PID) control algorithm, with the adjustment coefficient calibrated based on human thermophysiological experimental data. When the skin temperature exceeds the sweating threshold (approximately 34-35°C), the sweating mechanism is activated, removing heat through sweat evaporation. The sweating rate is related to the skin temperature overshoot and the rate of warming, and is described using an exponential sweating response function. Evaporative heat loss... The rate of sweating and ambient humidity jointly determine the core temperature. When the core temperature falls below the shivering threshold, a skeletal muscle shivering response is triggered, generating additional heat to maintain the core temperature. This heat generation from shivering is added as an additional component to metabolic thermogenesis. middle.

[0025] S102. Determine the passenger compartment and air duct structure of the vehicle. Divide the human skin model, the passenger compartment and the air duct structure according to the pre-set grid size to determine the partition grid.

[0026] Based on the geometric model, the inner surface of the occupant cabin and the inner wall of the air duct are extracted, and a closed computational fluid domain is constructed. A partitioned collaborative meshing strategy is adopted for the entire occupant cabin, the human body surface and the interior of the air duct. The flow field and temperature field are coupled and transferred between each region through the interface.

[0027] The entire passenger compartment mesh adopts a highly adaptable polyhedral core mesh with a base size of 20mm, which effectively controls the total mesh size. For critical areas with strong convective heat transfer, such as the leading edge of the instrument panel, seat surfaces, inner window walls, and floor, the mesh density is locally reduced to 5mm. The mesh size transition ratio is controlled within 1.2 to ensure smooth transition between coarse and fine mesh areas and avoid numerical pseudo-diffusion caused by abrupt size changes.

[0028] The human body surface mesh surrounds the skin surface region corresponding to 128 nodes, with the surface mesh size refined to 2mm, accurately representing the geometric curvature and local heat flux changes of each node region. Ten prism boundary layer meshes are generated along the normal to the human body surface; the thickness of the first layer is calculated based on the wall function to ensure dimensionless distance. Satisfying low Reynolds number SST The turbulence model requires a solution for the viscous sublayer; the total thickness of the prism layer is about 2~3mm, and the growth rate is set to 1.2 to accurately capture the velocity and temperature gradients on the skin surface.

[0029] The internal mesh of the air duct is made up of 3mm volumetric mesh for the internal fluid domain of the air duct at the blow surface 4 outlet and the blow foot 2 outlet. The focus is on adding local densification in the core area of ​​the outlet jet and the jet shear layer to accurately analyze the jet entrainment and its mixing with the human body surface.

[0030] After mesh generation, a quality check is performed, requiring element skewness <0.85 and orthogonality >0.1. The total number of grid cells in the entire domain is controlled at approximately 22 million, including 15 million for the crew compartment, 5 million for the air ducts, and 2 million for the human body, balancing computational accuracy and solution efficiency.

[0031] S103. Determine the pre-set multiphysics boundary conditions, and perform partitioned coupling iteration on the multiphysics boundary conditions and the human skin model according to the partitioned grid to determine the dynamic temperature corresponding to the multiple nodes.

[0032] In one embodiment, the cabin thermal environment is decomposed into five types of physical boundaries, which are then applied to corresponding grid regions. The air conditioning supply boundary uses measured data as input, such as... Figure 3 As shown, the four outlets of the face-blowing duct and the two outlets of the foot-blowing duct are assigned their respective velocity distributions, temperature values, and turbulence intensities. The turbulence intensity is determined by the ratio of the root mean square of the measured fluctuating velocity to the average flow velocity. The hydraulic diameter is calculated based on the equivalent diameter of the outlet and serves as the velocity inlet boundary condition. The solar radiation boundary is calculated based on the vehicle's latitude and longitude and the current date and time, taking into account the transmittance, reflectance, and absorptivity of the window glass. This converts the direct and diffuse solar radiation entering the passenger compartment into the heat flux density of the inner surfaces of each window, while also considering the absorption and secondary radiation of solar radiation by the interior surfaces. The structural heat transfer boundary assigns the thermal conductivity, density, and specific heat capacity to solid components such as seats, floors, and door trim. Their outer surface temperatures are given through actual measurements or thermal balance calculations, serving as the temperature boundary or convective heat transfer boundary of the solid walls. The human metabolic boundary is based on the idling, constant speed, and acceleration conditions in the driving cycle. The metabolic heat production value at the current moment is read from the preset metabolic rate-time curve and used as the volumetric heat source for each skin node. The environmental interaction boundary uses the outside temperature, relative humidity, and wind speed. The heat exchange between the vehicle body and the external environment is calculated using the convective heat transfer coefficient and radiative heat transfer model of the outer surface of the vehicle body, and is used as the boundary condition for the outer wall of the passenger compartment.

[0033] In one embodiment, a partitioned coupling algorithm is used to jointly solve the fluid domain, solid domain, and human body thermal regulation domain, including fluid domain solution, energy equation coupling, human body thermal regulation model embedding, data transfer, and iteration mechanism.

[0034] Fluid domain solution based on three-dimensional Reynolds-averaged Navier-Stokes equations and SST A turbulence model is used to solve for the velocity, pressure, and temperature distribution within the occupant cabin. The SST model combines... Advantages of accurately capturing the boundary layer in the near-wall region and Its stable characteristics in far-field calculations make it suitable for complex in-cabin flows involving jets, separated flows, and wall constraints.

[0035] The energy equations are coupled by considering convection and heat conduction terms in the fluid domain energy equations and introducing anisotropic heat conduction in the solid domain energy equations. At the same time, the radiative heat flow from the inner surface of the car window is used as an additional source term to achieve energy conservation across the entire domain.

[0036] When embedding the human body thermal regulation model, the 128-node heat balance equation is written as a user-defined function and interacts with the CFD solver in real time. Within each iteration step, the convective, radiative, and evaporative heat transfer of each node is updated based on the current skin surface temperature and the fluid wall heat transfer coefficient.

[0037] The fluid domain and the human body domain exchange boundary conditions every 500 iterations. Specifically, the CFD transmits wall temperature, convective heat transfer coefficient, and radiation temperature to the human body model; the human body model transmits the updated skin surface temperature to the CFD as the wall temperature boundary. Data mapping uses area-weighted interpolation to map the fluid mesh wall physical quantities to the surface regions corresponding to the 128 skin nodes. Sub-relaxation factors (pressure 0.3, momentum 0.7, temperature 0.8) are used to control iteration stability. The solution terminates after the global residual and the human body average temperature fluctuation satisfy the convergence criterion for 1000 consecutive steps.

[0038] In one embodiment, within each coupled iteration step, the following heat balance equation is solved discretely for each of the 128 nodes:

[0039] in, For metabolic heat production, For blood perfusion heat, For heat conduction between nodes, For convective heat transfer, For radiative heat exchange, For evaporative heat dissipation.

[0040] Metabolic heat production ,in Basal metabolic rate (W / m³). The activity intensity factor is 1.0 for idle speed, 1.1 for constant speed, and 1.2 for acceleration. The volume of the node organization.

[0041] Blood perfusion fever ,in For the heat capacity of blood volume, For local blood perfusion rate, Arterial blood temperature, This represents the current temperature of the node.

[0042] inter-node heat conduction , It is determined by the thermal conductivity, contact area and distance between adjacent nodes.

[0043] Convection heat transfer , The wall heat transfer coefficient is provided by CFD solution. The fluid temperature is close to the skin surface.

[0044] Radiative heat transfer , For skin emissivity, The Stefan-Boltzmann constant is... Ambient radiation temperature calculated using the DO model.

[0045] Evaporative cooling , Skin moisture levels are dynamically updated based on a sweating model. It is the latent heat of water vapor. The convective mass transfer coefficient is determined based on the analogy relationship. Conversion, The partial pressure of saturated water vapor. This refers to the relative humidity of the environment.

[0046] The equations are discretized in the time domain using a first-order implicit Euler scheme, with the time step synchronized with the CFD physical step and set to 0.5 s. The solution yields the dynamic skin temperature of each node at each physical moment, providing accurate spatiotemporal distribution data for subsequent comfort evaluation.

[0047] S104. The dynamic temperature is weighted according to the pre-set PMV weight to determine the comfort index, and the simulation results are output according to the pre-set convergence criterion. The simulation results include nodal temperature cloud map, duct outlet velocity distribution, dynamic PMV curve, and comfort index.

[0048] like Figure 4 As shown, after obtaining the dynamic temperature distribution of 128 nodes, a dynamic weighted evaluation model is constructed based on human thermophysiology and thermal comfort theory to quantify comfort. The validity of the solution is judged according to strict convergence criteria, and finally, multidimensional simulation results are output.

[0049] In one embodiment, based on the dynamic temperature of 128 nodes, a weighted average is calculated using the local thermal sensitivity coefficient of the human body. The local thermal sensitivity coefficient is determined according to the contribution rate of skin temperature in each area to overall thermal sensation. For example, the head and neck regions have high thermal sensitivity and dense nerve endings, so a weight of 0.25 is assigned; the chest and back regions, the core trunk, have large heat reserves, so a weight of 0.20 is assigned; the upper limbs are sensitive to convective heat transfer, so a weight of 0.25 is assigned; and the lower limbs, being far from the core, are sensitive to temperature changes, so a weight of 0.30 is assigned. Eight nodes within each macroscopic region are equally weighted. Weighted average temperature. After calculating using the following formula, substitute it into the Fanger comfort equation to solve. At the same time, the percentage of PPD (Programmable Product Dissatisfaction) is calculated based on the PMV (Program Value for Values) prediction.

[0050] To quantify the local thermal sensation index, a cold blowing sensation index is defined for 16 nodes in the blowing area, such as the head, neck, and upper chest. For the foot-blowing area, such as the ankle and instep, eight nodes are defined to determine the heat blowing sensation index. The calculation formula is:

[0051] in, The local neutral temperature threshold. , The extreme discomfort threshold, , This is a local airflow velocity correction factor, which is a normalized version of the flow velocity near the node extracted by CFD. The exponent ranges from 0 to 1, with a larger value indicating a stronger local discomfort.

[0052] The vibration correction factor is extracted from the root mean square value of the triaxial weighted acceleration at the seat rail. (Unit: m / s²). Vibration correction factor. Defined as: when hour, That is, no correction; when hour, ;when hour, The upper limit is set at 0.5. This correction value reflects the interference effect of vibration on human thermal sensation. Vibration can cause changes in local blood flow and increase thermal discomfort, and is included in the comprehensive evaluation in the form of an offset.

[0053] The three components mentioned above have different dimensions and numerical ranges. After range normalization, they are combined into a comprehensive evaluation index, the formula of which is:

[0054] in, For comfort indicators, The cold air sensation index. The heat sensation index, To calculate the vibration correction factor based on seat vibration acceleration, This is the PMV weight.

[0055] Normalize to the 0~1 interval, The closer to 0.5, the more comfortable it is; below 0.3 indicates discomfort due to cold; and above 0.7 indicates discomfort due to heat.

[0056] In one embodiment, multiple convergence criteria are monitored simultaneously during the calculation process to ensure the physical reliability of the solution results, including residual convergence and temperature convergence.

[0057] Regarding residual convergence, the residual of the continuity equation was monitored to decrease to Below, the momentum residuals and energy equation residuals in the three directions decrease to The fact that all residual curves remain flat without significant oscillations indicates that the flow and energy equations have reached numerical stability.

[0058] The temperature convergence invention calculates the arithmetic mean of the skin temperature of all 128 nodes in each iteration step. The fluctuation range of this average value is less than 0.1℃ within 1000 consecutive iteration steps, and the average temperature fluctuation of each region is less than 0.15℃, ensuring that all parts of the human body reach a state of thermal equilibrium.

[0059] A global thermal balance check was performed, and the net difference between the total heat flow inlet and outlet of the computational domain was less than 1% of the total heat exchange, verifying the physical integrity of energy conservation.

[0060] Once the convergence criteria are met, post-processing is automatically executed to generate output content, including a 128-node temperature cloud map, duct outlet velocity distribution, dynamic PMV curve, and comprehensive comfort index.

[0061] The 128-node temperature cloud map maps the temperature values ​​of each node onto the three-dimensional geometric surface of the human body, and uses pseudo-color interpolation to generate a whole-body temperature distribution cloud map, which intuitively displays the thermal stratification characteristics of high-temperature and low-temperature zones.

[0062] The velocity distribution vector map at the duct outlet extracts the velocity vector field of the airflow surface and foot duct outlet section, showing the magnitude, direction, and turbulence intensity distribution of the jet velocity, which is used to evaluate whether the air supply covers the expected human body area.

[0063] The dynamic PMV time curve plots the dynamic weighted PMV value change curve across the entire time period with simulated physical time as the horizontal axis, clearly reflecting the transient comfort evolution during the start-stop of air conditioning or the switching of driving conditions.

[0064] Overall Comfort Index The system outputs the final scalar value and the corresponding comfort level, including comfortable, slightly cold, slightly hot, and locally uncomfortable. It also displays the contribution percentage of four components—PMV, cold air sensation, hot air sensation, and vibration correction—in the form of a radar chart, providing intuitive data support for optimizing air conditioning control strategies.

[0065] In one embodiment, a 4.5t pure electric light truck is used as the implementation object. The passenger compartment has dimensions of 1850×1900×1200mm and is equipped with dual-zone automatic air conditioning and air ducts with 4 outlets for blowing on the face and 2 outlets for blowing on the feet. In the simulation, the total number of meshes in the passenger compartment, air ducts, and human body surface is approximately 22 million. The human body is divided into 128 nodes, with skin thickness of 0.5~3mm and metabolic rate of 80~150W / m². For two typical operating conditions, such as summer (outside temperature 40℃, solar radiation 800W / m²) and winter (outside temperature -10℃, no sunlight), the flow velocity at the surface outlet is set to 2~5m / s, and the flow velocity at the foot outlet is set to 1~3m / s. The results show that the dynamic PMV in summer and winter are 0.3 and -0.2, respectively, both within the comfort range. The local cold / hot blowing sensation indices are 0.12 and 0.08, respectively, indicating no discomfort. The errors between the forehead and ankle node temperatures and the measured values ​​are only 0.86% and 0.93%, respectively, verifying the simulation accuracy. Based on this, optimization suggestions are proposed: adjusting the outlet angle of the face blowing duct by 15° and adding a guide vane to the foot blowing duct can reduce the local blowing sensation index by 30%. In contrast, when using the traditional 5-node model and single-factor simulation, the errors in forehead and ankle temperatures reach 2.9% and 4.3%, respectively, with a PMV of 0.8, indicating excessive heat and a significant deviation from actual subjective feelings, further highlighting the superiority of this application.

[0066] like Figure 5 As shown in the illustration, this application also provides a multi-node human thermal comfort simulation device, including: At least one processor; and, A memory that is communicatively connected to at least one processor; wherein, The memory stores instructions that can be executed by at least one processor to enable a multi-node human thermal comfort simulation device to perform the method as described in any of the embodiments above.

[0067] This application also provides a non-volatile computer storage medium storing computer-executable instructions, which are configured as described in any of the above embodiments.

[0068] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0069] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0070] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0071] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0072] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0073] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0074] 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 embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. 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 1 A 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] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0078] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0079] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0081] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A simulation method for multi-node human thermal comfort, characterized in that, include: Multiple nodes are determined based on a pre-defined macroscopic human body region, and a human skin model is determined based on the multiple nodes. The passenger compartment and air duct structure of the vehicle are determined, and the human skin model, the passenger compartment and the air duct structure are divided according to the pre-set grid size to determine the partition grid; Determine the pre-set multiphysics boundary conditions, and perform partitioned coupling iteration on the multiphysics boundary conditions and the human skin model according to the partitioned grid to determine the dynamic temperature corresponding to the multiple nodes; The dynamic temperature is weighted according to the pre-set PMV weights to determine the comfort index, and the simulation results are output according to the pre-set convergence criteria. The simulation results include nodal temperature cloud map, duct outlet velocity distribution, dynamic PMV curve, and comfort index.

2. The method according to claim 1, characterized in that, The multiphysics boundary conditions and the human skin model are subjected to partitioned coupling iteration, specifically including: The partitioned coupling iteration uses a partitioned coupling algorithm to solve the fluid control equation, energy conservation equation, and human body thermal regulation model; The three-dimensional Navier-Stokes equations are solved based on a pre-set turbulence model and used as the fluid control equations. Construct a global energy conservation equation that simultaneously incorporates solid heat conduction, fluid convective heat transfer, and surface radiative heat transfer; The partitioned coupling algorithm is adopted, and the fluid domain and the human body domain exchange boundary conditions according to a pre-set iteration cycle. The exchanged boundary conditions include temperature, heat flux density and convective heat transfer coefficient.

3. The method according to claim 2, characterized in that, Determining the dynamic temperature corresponding to the multiple nodes specifically includes: The heat balance equation is solved for each node using the human body thermal regulation model. The expression for the heat balance equation is as follows: in, For metabolic heat production, For blood perfusion heat, For heat conduction between nodes, For convective heat transfer, For radiative heat exchange, For evaporative heat dissipation; The dynamic skin temperature of each node at each time step is obtained by iteratively solving the heat balance equation.

4. The method according to claim 1, characterized in that, The dynamic temperature is weighted according to a pre-set PMV weight to determine the comfort index, specifically including: The thermal sensitivity coefficients of different regions of the human body are determined, and the PMV weights are determined based on these coefficients. A temperature-weighted average is then calculated for the multiple nodes based on these PMV weights to obtain a comfort index. The expression for the weighted average calculation is as follows: in, For comfort indicators, The cold air sensation index. The heat sensation index, To calculate the vibration correction factor based on seat vibration acceleration, This is the PMV weight.

5. The method according to claim 1, characterized in that, The simulation results are output according to the pre-set convergence criteria, specifically including: The convergence criteria include residual convergence and temperature convergence. The residual converges to the residual of the continuity equation, which decreases to 1×10. -4 Below this, and the residual of the energy equation decreases to 1×10 -6 Hereinafter, temperature convergence is defined as the average temperature fluctuation of the multiple nodes being less than 0.1℃ within 1000 consecutive iterations. If the convergence criterion is satisfied, the simulation results are output.

6. The method according to claim 1, characterized in that, Determine the pre-set multiphysics boundary conditions, specifically including: The multiphysics boundary conditions include air conditioning supply boundary, solar radiation boundary, structural heat transfer boundary, human metabolism boundary, and environmental interaction boundary. The air supply boundary of the air conditioner uses the measured flow velocity, temperature and turbulence of the outlet of the face blowing duct and the outlet of the foot blowing duct as the inlet boundary conditions. The solar radiation boundary is calculated based on geographical location, time and window transmittance to determine the solar radiation intensity, which is then applied as a surface heat source to the human body and interior surfaces. The heat transfer boundary of the structure uses the thermal conductivity and surface temperature of the seats, floor, and door interior as the solid wall boundary conditions. The human metabolic boundary is based on the dynamic metabolic rate curves corresponding to driving behavior, including idling, constant speed, and acceleration, as a volumetric heat source. The environmental interaction boundary uses the outside temperature, humidity, and wind speed as the external boundary conditions.

7. The method according to claim 1, characterized in that, Before dividing the human skin model, the crew cabin, and the air duct structure according to a pre-set grid size, the method further includes: The entire passenger compartment uses a polyhedral mesh, with a basic mesh size of 20mm and a local mesh size of 5mm for the seat surface, dashboard surface and the area around the human body. The human skin model's surface mesh is refined to 2mm at multiple node locations, and 10 prism-layer boundary layer meshes are set in the normal direction of the human surface, making the dimensionless distance y on the wall surface... + ≈1; The internal mesh of the face blowing duct and foot blowing duct is increased to 3mm.

8. The method according to claim 1, characterized in that, The method further includes: Skin temperature measuring points were arranged at corresponding nodes on the forehead, ankles, chest, and back of the human skin model to collect measured data. Velocity test points were set up at the outlets of the face blowing duct and the foot blowing duct to collect measured data on the outlet velocity distribution. The simulation results are compared with the measured data. When the error in skin temperature exceeds 2%, the parameters of the partition coupling iteration and the encryption strategy of the partition mesh are adjusted, and the iterative solution is re-executed until the error meets the requirements. Finally, the simulation evaluation results of the passenger cabin comfort are output.

9. A multi-node human thermal comfort simulation device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the multi-node human thermal comfort simulation device to perform the method as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to be the method as described in any one of claims 1-8.

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