A multi-component coupled simulation prediction method and related device for temperature and humidity field of an environmental test chamber

Abstract

This invention belongs to the technical field of environmental testing equipment, and relates to a multi-component coupled simulation prediction method and related device for the temperature and humidity field of an environmental test chamber. The method comprehensively considers the actual working characteristics and interactions of core components such as centrifugal fans, evaporators, and heating devices. It introduces mathematical models of the centrifugal fan, heating device, and the evaporator's resistance, heat transfer, and frosting models into the CFD model, overcoming the shortcomings of existing technologies that simplify key components to ideal boundary conditions. This achieves accurate simulation prediction of the internal thermal and humidity environment. The influence of the evaporator on airflow and heat and humidity exchange is quantitatively characterized through resistance, heat transfer, and frosting models, realizing dynamic coupled simulation of airflow resistance, refrigeration heat transfer, and frosting growth processes.

Description

Technical Field

[0001] This invention belongs to the technical field of environmental testing equipment, and relates to a multi-component coupled simulation prediction method and related device for the temperature and humidity field of an environmental test chamber. Background Technology

[0002] With the continuous development of science and technology, the application scenarios of mechanical and electronic products are becoming increasingly widespread, and their status in production and daily life is constantly improving. However, many products are susceptible to environmental factors during actual use, leading to performance degradation or even failure, resulting in economic losses. Therefore, before products are put into production and application, they must undergo environmental adaptability tests such as high temperature, low temperature, and vibration to verify their core performance stability under extreme conditions, thereby improving safety and reliability during actual service. To meet these testing needs, researchers have developed various environmental testing equipment. Among them, environmental test chambers, as devices that can provide a stable and controllable testing environment, are widely used in fields such as automotive manufacturing, biomedicine, and electronic information. To accurately assess the stability and reliability of products, multiple repeated tests are usually conducted in environmental test chambers to ensure the statistical validity of the experimental results. However, such repeated testing not only consumes a lot of manpower and resources but also significantly prolongs the product development cycle. In recent years, the development of computer science and fluid simulation technology has provided new technical paths to solve the above problems. Numerical simulation methods based on computational fluid dynamics can be used to simulate and analyze the airflow organization and distribution characteristics and heat and mass transfer processes inside an environmental test chamber during the design phase. This allows for the evaluation of the influence of different design parameters and operating conditions on the temperature and humidity field inside the environmental test chamber, providing a theoretical basis for product optimization design.

[0003] Current simulation studies of environmental test chambers primarily focus on the macroscopic fluid motion and temperature and humidity distribution within the chamber's working chamber. In the modeling process, to reduce computational complexity, simplified models are often used to replace key components. This is mainly reflected in the following: First, the centrifugal fan, as the core power source for airflow circulation, is often simplified to an inlet boundary condition with a fixed wind speed. This approach makes it difficult to accurately reproduce the actual pressure loss, turbulence intensity changes, and the true airflow range caused by fan speed, blade angle, and outlet structure, resulting in a discrepancy between the simulated flow field distribution and the actual operating conditions of the physical prototype.

[0004] Secondly, in terms of temperature and humidity control, existing single-flow-field modeling methods typically presuppose the cooling effect of the evaporator and the thermal effect of the heating device as fixed thermal boundaries. However, the actual heat transfer efficiency of the evaporator is affected by the refrigerant flow state, heat transfer area, and surface temperature distribution; its convective heat transfer with the surrounding airflow is a dynamic process. Similarly, the power density distribution and local thermal effect of the heating device can induce secondary flows, altering the local flow field structure. This approach, which simplifies the complex heat transfer process to static boundary conditions, ignores the dynamic thermo-humid coupling between the evaporator, heating device, and flow field, and therefore cannot accurately simulate the temporal and spatial co-evolution of the temperature and humidity field and airflow organization.

[0005] In summary, due to the lack of consideration for the actual working characteristics and interactions of multiple components such as centrifugal fans, evaporators, and heating devices, existing technologies cannot accurately predict the dynamic formation process of the internal thermal and humid environment of the environmental test chamber during the design phase. Summary of the Invention

[0006] To address the problem that existing technologies struggle to accurately predict the dynamic formation process of the internal thermal and humidity environment of an environmental test chamber during the design phase, this invention provides a method and related apparatus for coupled simulation prediction of temperature and humidity fields in an environmental test chamber. This method enables precise simulation and dynamic modeling of the internal thermal and humidity environment under the coupled action of multiple components such as centrifugal fans, evaporators, and heating devices. This prediction method allows for accurate prediction of the spatial distribution and dynamic characteristics of the internal temperature and humidity fields during the product design phase, providing designers with reliable performance evaluation data. This significantly shortens the new product development cycle, reduces R&D costs, and solves the technical challenges of insufficient accuracy and inadequate consideration of complex operating conditions such as multi-component coupling and frosting in existing simulation methods.

[0007] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber, comprising: Input the operating conditions of the environmental test chamber into the CFD model, conduct simulation experiments, and output the temperature and humidity field data inside the environmental test chamber. The CFD model includes a geometric model of the environmental test chamber, which includes the chamber's working space, air duct, centrifugal fan, evaporator, and heating device. The CFD model also includes mathematical models of the centrifugal fan, heating device, evaporator resistance model, heat exchange model, and frosting model. The operating conditions include temperature and humidity within the chamber's working space, centrifugal fan speed, and inlet and outlet pressure difference of the chamber's working space.

[0008] Preferably, the evaporator is simplified to a porous medium, and the resistance model of the evaporator is: Air-side pressure drop data of the evaporator at different initial wind velocities were obtained through simulation. An air-side pressure drop-velocity curve was fitted. Based on the porous medium momentum equation and the air-side pressure drop-velocity curve, the drag coefficient of the porous medium under frost-free conditions at the initial moment was calculated. When the accumulated frost thickness obtained in the frosting model reached a preset threshold, the porous medium parameters were adaptively corrected according to the drag coefficient and frost thickness of the porous medium under frost-free conditions at the initial moment. These porous medium parameters included porosity. ε Drag coefficient.

[0009] Furthermore, the drag coefficient includes a viscous drag coefficient and an inertial drag coefficient; the porosity of the porous medium at the current time step is corrected. ε Viscous resistance coefficient D and inertial drag coefficient C The formula for calculating 2 is:

[0010] in, D Let m be the viscous drag coefficient at the current time step. -2 ; C 2 represents the inertial drag coefficient at the current time step, m -1 ; ε The porosity of the porous medium at the current time step; A f The surface area of ​​the heat exchanger fins is m. 2 ; The porosity of the porous medium at the initial moment; The current time step frost layer thickness; V Let m be the volume of the evaporator. 3 .

[0011] Furthermore, the heat transfer model of the evaporator is as follows: (1) Assume the refrigerant outlet enthalpy. h out It then calls the frosting model to calculate the frost thickness at the current time step and calculates the heat transfer based on the heat transfer equation. Q evap Heat transfer coefficient K and logarithmic mean temperature difference ΔT ; (2) Based on hout1=hin+KAΔT / mr, the actual refrigerant outlet enthalpy value is obtained. h out1 Where hin is the inlet enthalpy of the refrigerant, A is the total internal area of ​​the heat exchange tubes in the evaporator, and mr is the refrigerant mass flow rate; when the actual outlet enthalpy of the refrigerant... h out1 Compared with the assumed refrigerant outlet enthalpy h outThe calculation converges when the relative error is less than a preset threshold, and the heat exchange is obtained. Q evap Proceed to step (3), otherwise return to step (1); (3) Heat exchange Q evap The equivalent transformation is a dynamic negative volume heat source, which is then added to the governing equations of the CFD model. The calculation formula for the dynamic negative volume heat source is shown below:

[0012] in, S h W is the dynamic negative volume heat source for the evaporator. m -3 ; Q evap The heat exchange capacity of the evaporator is W; V Let m be the volume of the evaporator. 3 .

[0013] Furthermore, the frosting model of the evaporator is as follows: (1) At each time step, read the evaporator wall temperature and the current frost layer parameters; the frost layer parameters include frost layer thickness and frost layer density; (2) If the evaporator wall temperature meets the frosting condition, calculate and update the frost layer parameters, calculate the mass source term caused by frost and apply the mass source term to the control equation in the CFD model; if the frosting condition is not met, keep the frost layer parameters unchanged. (3) After the frost layer parameters are updated, the frost simulation of the current time step is completed, and the updated frost layer parameters are used as the initial input for the next time step.

[0014] Furthermore, the formula for calculating the quality source term is:

[0015] in, S m For the mass source item of the evaporator, kg m -3 s -1 ; The mass flow rate of water vapor in the air transferred to the evaporator surface and condensed into frost, in kg. s -1 ; h m Let m be the convective mass transfer coefficient. s -1 ; A The phase interface area is m 2 ; hW is the convective heat transfer coefficient. m -2 K -1 ; ρ v,∞ and ρv,sat These represent the density of water vapor in the mainstream air and the density of saturated water vapor at the interface, respectively, in kg. m -3 ; Le It is a Lewis number.

[0016] Furthermore, the formulas for calculating frost thickness and frost density are as follows:

[0017] in, and The frost thicknesses at the current time step and the previous time step are respectively, in meters; and The frost density at the current time step and the previous time step are respectively, in kg. m -3 ; Δt Let be the time step, in seconds; T wall and T air These are the evaporator surface temperature and the air temperature, respectively, in °C; J is the specific heat capacity of air. kg -1 K -1 .

[0018] Preferably, the mathematical model of the centrifugal fan is: The impeller area of ​​the centrifugal fan is set as the rotating domain, using a rotating reference system; the fan casing and inlet / outlet areas are set as the stationary domain, using a stationary reference system; data exchange between the rotating and stationary domains is achieved through an interface.

[0019] In a second aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber as described above.

[0020] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber as described above.

[0021] Compared with the prior art, the present invention has the following beneficial effects: The environmental test chamber temperature and humidity field coupling simulation prediction method of the present invention has the following advantages: (1) Multi-component coupling: The present invention comprehensively considers the real working characteristics and interaction relationships of core components such as centrifugal fan, evaporator, and heating device. The CFD model introduces the mathematical model of centrifugal fan, the mathematical model of heating device, and the resistance model, heat transfer model and frost model of evaporator. This overcomes the defect of simplifying key components to ideal boundary conditions in the prior art and realizes accurate simulation prediction of the thermal and humid environment inside the chamber. (2) Evaporator fine simulation: The influence of evaporator on air flow and heat and humidity exchange is quantitatively characterized by resistance model, heat transfer model and frost model, realizing dynamic coupling simulation of airflow resistance, refrigeration heat exchange and frost growth process. The prediction method of the present invention can accurately predict the spatial distribution law and dynamic change characteristics of the temperature and humidity field inside the environmental test chamber in advance during the product design stage, providing designers with reliable performance evaluation basis, greatly shortening the new product development cycle, reducing R&D costs, and solving the technical problems of insufficient accuracy of existing simulation methods and insufficient consideration of the influence of complex working conditions such as multi-component coupling and frost.

[0022] Furthermore, the heat transfer model and the frosting model accurately describe the cooling effect of the evaporator on the air and the phase change mass loss during the frosting process by applying dynamic negative volume heat source and mass source terms respectively in the evaporator region, thereby achieving a refined simulation of the thermal and humid state of the air side.

[0023] Furthermore, the resistance model is based on the resistance characteristic curve of the evaporator, which is transformed into porous media parameters. The porous media parameters are dynamically adjusted according to the real-time changes in the thickness and density of the frost layer, so that the simulation of flow resistance is more in line with the actual physical process.

[0024] Furthermore, this invention employs a multiple reference frame method to simulate the centrifugal fan. The rotational domain (i.e., the impeller region) of the centrifugal fan is set as an independent rotational reference frame, and given the same speed and direction of rotation as the actual operating conditions. Meanwhile, the fan casing and inlet / outlet flow channels remain as stationary reference frames. Data exchange between the rotational domain and the stationary domain is achieved through the interface. This method can realistically reproduce the driving effect of the fan impeller rotation on the airflow inside the casing and accurately capture the non-uniform distribution of the velocity field and the deflection characteristics of the outlet flow field caused by the rotational effect. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of the environmental test chamber temperature and humidity field coupling simulation prediction method of the present invention.

[0027] Figure 2 This is the mathematical model of the finned tube evaporator of the present invention.

[0028] Figure 3 This is the resistance model for the finned tube evaporator of the present invention.

[0029] Figure 4 This is a heat exchange model of the finned tube evaporator of the present invention.

[0030] Figure 5 This is a frosting model of the finned tube evaporator of the present invention.

[0031] Figure 6 This is the physical model of the environmental test chamber for the upward and downward feeding of this invention.

[0032] Figure 7 This is a comparison between the predicted and actual temperatures at the measuring points of the environmental test chamber in this embodiment of the invention.

[0033] Figure 8 This is a comparison between the predicted and actual relative humidity values ​​at the measuring points of the environmental test chamber in this embodiment of the invention.

[0034] Figure 9 This is a comparison of temperature fluctuation and uniformity in the top-feed and bottom-return environmental test chamber in this embodiment of the invention.

[0035] Figure 10 This is a comparison of temperature fluctuation and uniformity in the top-feed and bottom-return environmental test chamber in this embodiment of the invention.

[0036] Figure 11 The cooling characteristic curve of the top-feed and bottom-return environmental test chamber in this embodiment of the invention is shown. Detailed Implementation

[0037] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0038] It should be noted that the process equipment or apparatus not specifically mentioned in the following embodiments are all conventional equipment or apparatus in the art.

[0039] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Furthermore, unless otherwise stated, the numbering of each method step is merely a convenient tool for identifying each method step, and not intended to limit the order of the method steps or define the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0040] like Figure 1 As shown, the calculation process of the coupled simulation prediction method for temperature and humidity fields in an environmental test chamber proposed in this invention includes the following steps: constructing a geometric model of the environmental test chamber, constructing a mathematical model of the evaporator, constructing a mathematical model of the centrifugal fan, constructing a mathematical model of the heating device, discretizing the computational domain, setting boundary conditions and material properties, setting solver parameters, and evaluating the temperature and humidity uniformity and dynamic characteristics of the environmental test chamber based on simulation results. Through the above steps, accurate modeling and dynamic simulation of the internal thermal and humidity environment under the coupled action of multiple components such as the centrifugal fan, evaporator, and heating device can be achieved. This prediction method can predict the spatial distribution and dynamic changes of the temperature and humidity fields inside the test chamber in advance during the product design stage, providing designers with a reliable basis for performance evaluation.

[0041] Specifically, the environmental test chamber temperature and humidity field coupled simulation prediction method includes the following steps: Step S1: Construct the geometric model of the environmental test chamber First, based on the actual structural dimensions of the environmental test chamber, a complete geometric model including the chamber's working space, air ducts, centrifugal fan, evaporator, and heating device is constructed to ensure that the model is consistent with the core structure of the actual equipment. To reduce the computational load, improve solution efficiency, and ensure simulation accuracy, the geometric model needs to be simplified. Specific simplification methods include: simplifying the grid structure of the shelves in the chamber into a porous medium model or a solid plate, ensuring that the equivalent heat transfer area of ​​the shelves is consistent with the actual area; and geometrically simplifying the small structures of the evaporator and heating device (such as fixing brackets, terminals, lead holes, and other non-core heat transfer components), retaining their main heat transfer areas to ensure that the heat transfer characteristics do not deviate significantly. The simplified geometric model must meet the requirements of mesh generation and numerical solution, while also conforming to the actual airflow path and heat transfer characteristics.

[0042] Step S2: Constructing a mathematical model for the finned tube evaporator Taking a finned tube evaporator as an example, the finned tube evaporator, as a core component of the environmental test chamber, has a significant impact on the uniformity of the internal flow field and temperature and humidity field. The finned tube evaporator is simplified as a porous medium. To quantify its influence, a mathematical model needs to be constructed. This model must simultaneously encompass the resistance model, heat transfer model, and frosting model. The evaporator is defined as a key component that combines airflow resistance (porous medium), a dynamic negative volume heat source for refrigeration and heat absorption, and a mass source for frosting phase change. Due to the complex structure and dense arrangement of finned tubes in the evaporator, full-size geometric modeling and mesh generation would pose a significant challenge to computational resources and time. Therefore, while ensuring the accuracy of temperature and humidity field prediction, the simplified model described above is adopted to improve computational efficiency.

[0043] The calculation process of the evaporator mathematical model is as follows: Figure 2 As shown, the specific steps are as follows: (1) Input structural parameters and thermodynamic parameters. Structural parameters include: tube length, inner and outer diameter, number of tube rows, tube row spacing, fin thickness and fin spacing on the finned tube, etc. Thermodynamic parameters include: air-side inlet temperature, atmospheric pressure, mass flow rate, evaporation pressure, inlet dryness, etc. (2) Establish the resistance model of the evaporator. Select an appropriate pressure drop correlation, calculate the windward velocity based on the air volume, solve the air-side pressure drop, fit the air-side pressure drop-velocity curve, and then solve the porosity, viscous drag coefficient and inertial drag coefficient of the porous medium at the initial moment. Update the above three parameters in real time according to the frost layer thickness, and add the above parameters to the Fluent porous medium model settings; (3) Establish a heat transfer model for the evaporator. Select an appropriate heat transfer correlation, obtain the heat transfer through simulation calculation, convert it into a corresponding dynamic negative volume heat source, and add it to the control equations of the CFD model; (4) Establish a frosting model for the evaporator. Determine whether the frosting conditions are met based on the evaporator wall temperature, air dew point temperature, and freezing point temperature: if the frosting conditions are not met, operate according to normal parameters, that is, keep the frost layer thickness and density of the previous time step unchanged, and keep the resistance coefficient and porosity of the porous medium unchanged; if the frosting conditions are met, start the frosting kinetic model, calculate the frost layer thickness and density, calculate the mass source terms caused by frosting, and add the corresponding mass source terms to the control equations of the CFD model; (5) Import the parameters (porous medium parameters, dynamic negative volume heat source, mass source term) that characterize the evaporator resistance characteristics, refrigeration effect and frosting process into the CFD model and carry out global calculation.

[0044] The simulation process for the evaporator resistance model is as follows: Figure 3 As shown, the specific steps are as follows: (1) Input structural parameters and thermodynamic parameters. Structural parameters include: tube length, inner and outer diameter, number of tube rows, tube row spacing, fin thickness and fin spacing on the finned tube, etc. Thermodynamic parameters include: air-side inlet temperature, relative humidity, mass flow rate and atmospheric pressure, etc. (2) Based on the air inlet temperature, atmospheric pressure and relative humidity, retrieve the air density under the current conditions, and calculate the windward velocity using the air mass flow rate, density and the windward area of ​​the evaporator. (3) Calculate the air pressure drop at different wind velocities, fit the air pressure drop to the wind velocity as a quadratic curve, and calculate the porosity of the porous medium at the initial moment. ε 0. Inertial drag coefficient C 2,0 Viscous resistance coefficient D 2,0 ; (4) When the frost layer thickness accumulates to the preset threshold, the porosity, inertial drag coefficient and viscous drag coefficient of the porous medium at the current moment are corrected and updated to accurately reflect the influence of frost layer accumulation on airflow resistance.

[0045] In the evaporator resistance model, the initial velocity is the fitted evaporator windward velocity. v air pressure drop ΔP The relationship is determined by fitting a quadratic function curve without a constant term to Δ. P = f ( v) The functional relationship (Formula (2)) is used in conjunction with the momentum equation of porous media (as shown in Formula (1)) to calculate the inertial drag coefficient of porous media under frost-free conditions at the initial moment. C 2,0 With viscous resistance coefficient D 0.

[0046]

[0047] in, C 2,0 Let m be the initial inertial drag coefficient. -1 ; D 0 represents the initial viscous drag coefficient, m -2 ; n The thickness of the porous medium is in meters (m). μ Pa is the dynamic viscosity of air. s, The density of air, kg m -3 .

[0048] When the accumulated frost thickness reaches a preset threshold, it significantly alters the flow characteristics of the finned tube evaporator. At this point, it is necessary to adaptively modify the equivalent simplified porous media parameters to accurately reflect the impact of frost accumulation on airflow resistance. The porosity of the porous media at the current time step after modification is then determined. ε Viscous resistance coefficient D and inertial drag coefficient C The calculation of 2 is shown in formulas (3) to (5).

[0049]

[0050] in, D Let m be the viscous drag coefficient at the current time step. -2 ; C 2 represents the inertial drag coefficient at the current time step, m -1 ; ε The porosity of the porous medium at the current time step; A f The surface area of ​​the fin is m. 2 ; The porosity of the porous medium at the initial moment; The current time step frost layer thickness; V Let m be the volume of the evaporator. 3 .

[0051] The simulation process of the heat transfer model of the evaporator is as follows: Figure 4 As shown, the specific steps are as follows: (1) Input the core structural parameters such as tube length, tube diameter, tube spacing, and fin geometry of the finned tube, set the type of refrigerant and inlet dryness, pressure and flow rate mr, and configure the boundary conditions of air-side inlet temperature, humidity and wind speed. (2) Assuming the refrigerant outlet enthalpy h out It then calls the frosting model to calculate the frost thickness at the current time step and calculates the heat transfer based on the heat transfer equation. Q evap Heat transfer coefficient K and logarithmic mean temperature difference ΔT ; (3) Back-calculate the actual refrigerant outlet enthalpy value h out1 Based on the refrigerant's inlet dryness, pressure, and type, the refrigerant inlet enthalpy hin can be retrieved via software. The actual refrigerant outlet enthalpy hout1 = hin + KAΔT / mr, where A is the total internal area of ​​the heat exchange tubes in the evaporator, and mr is the refrigerant mass flow rate. When the actual refrigerant outlet enthalpy... h out1 Compared with the assumed refrigerant outlet enthalpy h outWhen the relative error is less than the preset threshold (0.01), the calculation converges, and step (4) is performed. When the iteration error is not satisfied (i.e., the relative error is greater than or equal to the preset threshold), the refrigerant outlet enthalpy value needs to be re-assumed. h out ; (4) Output the heat exchange after completing the simulation. Q evap A complete dataset including refrigerant and air outlet state parameters was generated, and the refrigeration and heat transfer effects of the evaporator were simulated.

[0052] The refrigeration heat transfer effect of the evaporator is numerically characterized using the dynamic negative volume heat source method, and the obtained evaporator heat transfer value is... Q evap The heat source is equivalently transformed into a dynamic negative volume heat source and uniformly distributed according to the volume of the evaporator simulation calculation domain to accurately simulate the continuous heat exchange process and cooling effect between the refrigerant and the air. To ensure that the flow-heat coupling simulation process is consistent with the actual physical mechanism, the intensity of this dynamic negative volume heat source needs to be adaptively adjusted according to the dynamic changes of the air inlet thermodynamic conditions and refrigerant state parameters. Its specific calculation expression is shown in formula (6):

[0053] in, S h W is the dynamic negative volume heat source for the evaporator. m -3 ; Q evap The heat exchange capacity of the evaporator is W; V Let m be the volume of the evaporator. 3 .

[0054] The complete simulation process of the evaporator frosting model is as follows: Figure 5 As shown, the specific steps are as follows: (1) Within each time step, the system first reads core parameters such as evaporator wall temperature and air temperature, as well as the current frost thickness and density; (2) The frosting condition is determined. If the evaporator wall temperature meets the conditions of being less than 0℃ and less than the air dew point temperature, the mass source term caused by frosting is calculated and the mass source term is applied to the control equation in the CFD model. The frost thickness and frost density are calculated and updated simultaneously. If the above conditions are not met, the frost parameters (frost thickness and frost density) are kept unchanged. (3) After the update is completed, the frost simulation of the current time step is completed. The next time step will use the updated frost parameters as the initial input to realize the continuous dynamic simulation of frost growth.

[0055] This process can effectively reflect physical phenomena such as airflow blockage and heat exchange performance degradation caused by frost accumulation, and ultimately realize the fully coupled modeling and integrated simulation of evaporator flow resistance, refrigeration heat exchange and frost phase change process, further improving simulation accuracy and engineering adaptability.

[0056] The frosting process is simulated using the evaporator surface temperature. T _wall air dew point temperature T _dew With the comparison of freezing point temperature as the core, the conditions for determining the onset of frost are clarified. At the same time, a frost dynamics model is constructed to accurately calculate the real-time changes in frost thickness and frost mass. By applying a mass source term to the control equation of the CFD model, the calculation formulas are shown in (7)~(9), which quantitatively characterizes the gas phase mass loss of water vapor condensing into frost phase and realizes accurate simulation of the phase change process. Simultaneously, based on the real-time changes in frost thickness and density, the calculation of frost thickness and density is shown in formulas (10)~(12), and the porosity and resistance coefficient of the porous medium region of the evaporator are dynamically updated. The updated porosity and resistance coefficient are calculated with reference to formulas (3)~(5).

[0057]

[0058] in, S m For the mass source item of the evaporator, kg m -3 s -1 ; The mass flow rate of water vapor in the air transferred to the evaporator surface and condensed into frost, in kg. s -1 ; h m Let m be the convective mass transfer coefficient. s -1 ; A The phase interface area is m 2 ; h W is the convective heat transfer coefficient. m -2 K -1 ; ρ v,∞ and ρv,sat These represent the density of water vapor in the mainstream air and the density of saturated water vapor at the interface, respectively, in kg. m -3 ; Le It is a Lewis number. and The frost thicknesses at the current time step and the previous time step are respectively, in meters; and The frost density at the current time step and the previous time step are respectively, in kg. m -3 ; Δt Let be the time step, in seconds; T wall and T air These are the evaporator surface temperature and the air temperature, respectively, in °C. For the specific heat capacity of air, J kg -1 K -1 .

[0059] Step S3: Construct a mathematical model of the centrifugal fan A multi-reference frame method is employed to simulate the centrifugal fan. Specifically, the rotating domain (impeller region) of the centrifugal fan is set as an independent fluid region using a rotating reference frame; the fan casing and inlet / outlet regions are set as stationary domains using a stationary reference frame; data exchange between the rotating and stationary domains is achieved through an interface, ensuring continuous transmission of flow field parameters. This method accurately simulates the driving effect of the centrifugal fan's rotational motion on the flow field within the casing, reproducing the actual air delivery characteristics of the centrifugal fan. The rotational speed of the rotating domain is set according to the actual rotational speed of the centrifugal fan, and the rotation direction is set according to the impeller rotation direction.

[0060] Step S4: Construct a mathematical model of the heating device The heating device area is divided into several sub-regions. Specifically, the simplified model divides the flow channel above the evaporator (i.e., the heating device area) into several rectangular sub-regions of the same shape and size. Based on the actual power distribution of the heating wires, a corresponding volumetric heat source is applied to each sub-region to ensure that the heat distribution of the heating device is consistent with the actual operating conditions. This simplified model allows for accurate simulation of the heating device's heating characteristics and its impact on the temperature field inside the chamber while maintaining computational efficiency. The distribution of the volumetric heat source needs to be considered in conjunction with the arrangement density and power of the heating wires, employing either uniform or non-uniform distribution methods.

[0061] Step S5: Discretize the computational domain Based on the simplified geometric model of the environmental test chamber, a fluid computational domain is established, encompassing the chamber's working space, air ducts, the rotating and stationary domains of the centrifugal fan, the porous media region of the evaporator, and the sub-region of the heating device. To balance simulation accuracy and computational efficiency, a hybrid mesh generation method is adopted. The core heat exchange and flow regions, such as the centrifugal fan impeller and evaporator, are meshed with higher density, while non-core regions such as the chamber's working space and air ducts are meshed with relatively sparse meshes. After mesh generation, the mesh quality is checked to ensure that indicators such as mesh orthogonality and distortion meet the requirements for numerical solution (typically, mesh orthogonality ≥ 0.5, distortion ≤ 0.3), avoiding a decrease in the reliability of simulation results due to poor mesh quality. During mesh discretization, the continuous fluid computational domain is divided into several discrete control volumes, laying the foundation for subsequent numerical solutions.

[0062] Step S6: Setting Boundary Conditions and Material Properties Based on the actual operating conditions of the environmental test chamber, boundary conditions and material property parameters of each component were reasonably set to ensure that the simulation process closely matches reality. Specifically, the boundary conditions include: the six sides of the environmental test chamber are set as wall boundary conditions, taking into account the thermal conductivity of the external insulation material and natural convection heat transfer with the surrounding environment, and setting corresponding ambient temperatures and natural convection heat transfer coefficients; the observation window on the chamber is set as a constant heat flux boundary, with the heat flux density determined based on the anti-frost heating power to ensure that the observation window is free of frost and does not affect experimental observation; the rotating surface of the centrifugal fan is set as a rotating wall surface, and the speed is set according to actual operating parameters; the top air outlet on the chamber is set as a pressure inlet boundary, and the bottom return air outlet is set as a pressure outlet boundary, conforming to the actual airflow organization of the environmental test chamber.

[0063] Material properties include the density, specific heat capacity, and thermal conductivity of each solid material (shell, insulation material, evaporator copper tubes, fins, heating wires, etc.), as well as the density, dynamic viscosity, specific heat capacity, and thermal conductivity of fluids (air, refrigerant).

[0064] Step S7: Set solver parameters Select appropriate solver parameters based on simulation requirements, specifically including: selecting a pressure-based solver suitable for solving flow fields of low-speed incompressible fluids; selecting a corresponding turbulence model (such as a Realizable k-ε low Reynolds number turbulence model) to accurately simulate complex turbulent flow in an environmental test chamber; enabling the energy equation in the governing equations to achieve coupled solution of the flow field and temperature and humidity fields; selecting a suitable algorithm for pressure-velocity coupled solution to ensure the stability and convergence of the solution; and discretizing the momentum, turbulent kinetic energy, turbulent dissipation rate, and energy equations using a second-order upwind scheme to improve the accuracy of numerical calculations.

[0065] During the solution process, a convergence criterion for the iterative residuals is set (usually the residuals converge to 10). -6 Simultaneously, the temperature, humidity, and velocity changes at characteristic points within the environmental test chamber are monitored to ensure a stable solution process without divergence. Through coupled solution, the velocity field and temperature and humidity field distributions inside the environmental test chamber are finally obtained, providing data support for subsequent temperature and humidity field assessment.

[0066] Step S8: Evaluate the uniformity and dynamic variation characteristics of the temperature and humidity field of the environmental test chamber based on the simulation results. By combining the velocity, temperature, and humidity field data obtained from simulation, a comprehensive evaluation of the temperature and humidity field uniformity and dynamic variation characteristics of the environmental test chamber is conducted. The evaluation includes analysis of temperature and humidity uniformity and fluctuation under steady-state conditions, as well as analysis of cooling rate under dynamic conditions. Through comprehensive analysis, the temperature and humidity field characteristics of the environmental test chamber under steady-state and dynamic operating conditions can be systematically understood, providing quantitative basis for structural optimization, airflow organization improvement, and control strategy calibration of the environmental test chamber.

[0067] Example This embodiment uses a top-supply, bottom-return environmental test chamber as the simulation verification object. The core components of this environmental test chamber mainly include the chamber body, evaporator, heating device, centrifugal fan, and air ducts (supply air duct and return air duct). A geometric model is established after simplifying its physical structure using Space Claim software, such as... Figure 6 As shown, the working space inside the environmental test chamber is a rectangular cavity measuring 800mm (length) × 700mm (width) × 900mm (height), with shelves installed inside, on which loads are placed. An observation window is located at the front of the cavity. The supply air duct, return air duct, evaporator, heating device, and centrifugal fan are all arranged in the external space at the rear of the chamber. The environmental test chamber adopts an airflow organization form of top supply and bottom return, circulating along the wall. The specific flow process is as follows: air first flows in from the bottom inlet of the return air duct on the left, flows through the evaporator and heating device for dehumidification and heating, and then, driven by the centrifugal fans arranged on the left and right sides of the upper rear of the chamber, is sent into the working space inside the chamber through the supply air duct. The air entering the chamber flows along the wall and finally flows out from the rectangular outlet located at the bottom of the chamber. The feasibility and prediction accuracy of the temperature and humidity field coupling simulation prediction method described in this invention are verified through this typical model.

[0068] 1) Geometric model of the environmental test chamber Based on the actual dimensions of the top-supply, bottom-return environmental test chamber, a complete geometric model was constructed, including the top supply air duct, bottom return air duct, working space, centrifugal fan, evaporator, heating device, and shelves. During simplification, the shelf grid structure was simplified to an equivalent porous medium plate, and small non-core structures such as evaporator fixing brackets and heating wire fixing terminals were eliminated. The heat exchange area of ​​the evaporator, the fan impeller, and the core area of ​​the air duct flow were retained. The air duct inlet and outlet are seamlessly connected to the working space of the chamber, conforming to the top-supply, bottom-return airflow direction.

[0069] 2) Evaporator Mathematical Model According to the method described in step S2 of this invention, the mathematical model of the finned tube evaporator includes a resistance model, a heat transfer model, and a frosting model. The dynamic simulation of fluid-heat-mass coupling is achieved through the three models and their coupling effect.

[0070] The specific modeling process is as follows: Resistance Model: The evaporator geometry is defined, with core parameters including: copper tube outer diameter 9.52mm, fin thickness 0.15mm, fin spacing 2.0mm, 9 rows of tubes (10 tubes per row), tube spacing 25mm, row spacing 21.65mm, and an ascending staggered arrangement. Different thermodynamic boundary conditions are set for the refrigerant and air side. Simulations are used to obtain air-side pressure drop data at different initial wind velocities, which are then fitted to an air-side pressure drop-velocity curve. Based on the porous medium momentum equation and the air-side pressure drop-velocity curve, the viscous drag coefficient of the porous medium under frost-free conditions at the initial moment is calculated. D 0 and inertial drag coefficient C 2,0 When the frost layer parameters accumulate to a preset threshold, the viscous resistance coefficient and inertial resistance coefficient of the porous medium are adaptively corrected based on the resistance coefficient and frost layer thickness of the porous medium under the frost-free condition at the initial moment, so as to restore the airflow blockage and heat exchange attenuation characteristics caused by frost, thereby constructing the resistance model of the evaporator.

[0071] Heat transfer model: through Figure 4 The simulation process calculates the heat exchange of the evaporator and converts it into a dynamic negative volume heat source, which is then uniformly distributed across the evaporator region and applied to the corresponding computational domain. The intensity of the dynamic negative volume heat source is dynamically and adaptively adjusted according to the air inlet parameters and refrigerant state to simulate the continuous cooling effect of the refrigerant on the air.

[0072] Frosting Model: The frost formation conditions are determined based on the evaporator wall temperature, air dew point temperature, and freezing point temperature. The real-time frost growth rate is calculated based on the frost kinetic model, and a mass source term is applied to the control equation to characterize the gas phase mass loss of water vapor condensing into frost.

[0073] 3) Centrifugal fan and heating device model The centrifugal fan has a rated speed of 1450 rpm. It uses a multiple reference system to divide the rotating and stationary domains, with the interface located at the outlet of the fan casing. The heating device has a power of 6 kW and is divided into 4 equal sub-regions along the air duct flow direction. The volume heat source is distributed according to the principle of uniform heating to meet the uniform air supply requirements of the top supply and bottom return air duct.

[0074] 4) Boundary conditions and mesh solution settings The enclosure walls are designed for an ambient temperature of 27℃ and a convective heat transfer coefficient of 10 W·m. - ²·K - ¹, The heat flux density of the observation window is 328 W·m - ², the supply air duct inlet and the bottom return air duct outlet are respectively set as pressure inlet and pressure outlet boundaries, conforming to the top-supply and bottom-return airflow circulation logic; a hybrid mesh generation is adopted, with finer meshes in the fan and evaporator areas, and the overall mesh orthogonality is 0.55, meeting the quality requirements; a Realizable k-ε low Reynolds number turbulence model is selected, and the SIMPLE algorithm is used to enable the energy and component transport equations, completing the fully coupled fluid-heat-mass solution, with the iterative residuals converging to 10. -6 .

[0075] 5) Simulation results Using the method described in this invention, under the conditions of a set temperature of 85°C, relative humidity of 85%, centrifugal fan speed of 1450 rpm, and inlet / outlet pressure difference (i.e., the pressure difference between the inlet of the supply air duct and the outlet of the bottom return air duct) of 200 Pa, the changing trends of the temperature and relative humidity at the central measuring point over a 30-minute period were compared and verified. The comparison results are as follows: Figure 7 , Figure 8 As shown, within a statistically defined 30-minute period, the maximum absolute error between the simulated temperature prediction and the actual value is ≤0.7℃, and the average absolute error is only 0.48℃, which is significantly lower than the industry's allowable error range of ±1℃; the maximum absolute error between the simulated relative humidity prediction and the actual value is ≤1.12%RH, and the average absolute error is 0.51%RH, indicating that the prediction method has high calculation accuracy and reliability.

[0076] In addition to the comparison of the above operating conditions, the method described in this invention is used to compare the temperature and humidity fluctuations and uniformity for the following five operating conditions. In all five conditions, the centrifugal fan speed is 1450 rpm, the inlet and outlet pressure difference (i.e., the pressure difference between the inlet of the supply air duct and the outlet of the bottom return air duct) is 200 Pa, and the corresponding temperatures and humidity levels are 20℃ and 50%, 40℃ and 80%, 55℃ and 98%, 60℃ and 20%, and 85℃ and 20%, respectively. The comparison of temperature fluctuations and uniformity is as follows: Figure 9As shown, the maximum absolute error of temperature fluctuation is 0.30℃, and the average absolute error is 0.146℃; the maximum absolute error of temperature uniformity is 0.28℃, and the average absolute error is 0.168℃. A comparison of humidity fluctuation and uniformity is also provided. Figure 10 As shown, the maximum absolute error of humidity fluctuation is 0.89%, and the average absolute error is 0.514%; the maximum absolute error of humidity uniformity is 0.47%, and the average absolute error is 0.206%. The temperature and humidity related indicators predicted using the method described in this invention have small and uniform errors, and the model has high reliability.

[0077] Based on this, to further investigate the dynamic cooling performance of the top-supply, bottom-return environmental test chamber, the above CFD model was used to conduct no-load (unloaded) numerical simulation tests. The test parameters were set as follows: the initial supply air temperature was 85℃, which was reduced to the target temperature of -55℃ at a rate of 10℃ / min, and then kept constant after reaching the target temperature; the temperature data of the measuring point at the center of the chamber was recorded every 0.5 minutes. Its cooling characteristic curve is shown below. Figure 11 As shown, the actual cooling rate of the enclosure calculated according to the standard is 9.74℃ / min, and the relative error with the set standard cooling rate of the air supply is only 2.6%.

[0078] The environmental test chamber temperature and humidity field coupled simulation prediction method provided by this invention comprises the following steps: construction and reasonable simplification of the geometric model of the environmental test chamber, construction of mathematical models for each key component, discretization of the model mesh, setting of boundary conditions and solver parameters, and then completion of the fluid-heat-mass fully coupled numerical solution and verification and evaluation of simulation results, forming a complete simulation prediction closed loop. This method accurately realizes the refined simulation of the fluid-heat-mass three-phase coupling characteristics of the evaporator, the rotational motion characteristics of the centrifugal fan, and the power distribution of the heating device, effectively balancing modeling efficiency and simulation prediction accuracy. In a classic test chamber model verification, under typical operating conditions of 85℃ and 85% relative humidity, the maximum absolute errors between the simulated and actual values ​​of temperature and relative humidity are ≤0.7℃ and ≤1.12%RH, respectively, significantly better than the industry allowable error standards. This confirms that the method can accurately predict the distribution law and dynamic change characteristics of the temperature and humidity field inside the test chamber, providing reliable theoretical support for the structural design of environmental test chamber products, effectively shortening the product development cycle, and possessing engineering application value.

[0079] The prediction method of this invention has the following advantages: (1) Evaporator heat-fluid-mass three-phase coupled modeling method. By simplifying the finned tube evaporator into a porous medium region, and jointly constructing a resistance model, a heat transfer model and a frosting model, dynamic coupled simulation of airflow resistance, refrigeration heat transfer and frosting growth process is realized; among them, the frosting model determines the frosting initiation based on the wall temperature and dew point temperature, and accurately simulates the influence of frosting on flow and heat transfer performance through dynamic correction of porosity and resistance coefficient driven by mass source term and frost thickness.

[0080] (2) Simplification and integrated modeling strategy for key components. The multi-reference frame method is used to simulate the rotational motion of the centrifugal fan. Combined with the regional volume heat source distribution method of the heating device, and by simplifying the non-core structures (such as the shelf grid and evaporator support) in the geometric model to preserve performance, an integrated fluid computing domain covering the fan, evaporator, heating device and air duct is constructed, which significantly improves the computing efficiency while ensuring the simulation accuracy.

[0081] (3) Fully Coupled Simulation Method for Temperature and Humidity Field Based on Multi-Component Coupling. The above-mentioned porous medium and phase change model of evaporator, fan rotation model, and heat source model of heating device are integrated. Combined with hybrid mesh generation, pressure-based solver, low Reynolds number turbulence model and energy-component transport equation, a fully coupled numerical solution process of flow-heat-mass is formed to achieve high-precision prediction of temperature and humidity field inside environmental test chamber under steady-state and dynamic conditions. The error is significantly better than the industry standard through typical working conditions.

[0082] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not omitted in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0083] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of an industrial control system controller configuration parameter readback method.

[0084] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be Random Access Memory (RAM) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the industrial control system controller configuration parameter readback method in the above embodiments.

[0085] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A multi-component coupled simulation and prediction method for the temperature and humidity field of an environmental test chamber, characterized in that, include: Input the operating conditions of the environmental test chamber into the CFD model, conduct simulation experiments, and output the temperature and humidity field data inside the environmental test chamber. The CFD model includes a geometric model of the environmental test chamber, which includes the chamber's working space, air duct, centrifugal fan, evaporator, and heating device. The CFD model also includes mathematical models of the centrifugal fan, heating device, evaporator resistance model, heat exchange model, and frosting model. The operating conditions include temperature and humidity within the chamber's working space, centrifugal fan speed, and inlet and outlet pressure difference of the chamber's working space.

2. The multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber according to claim 1, characterized in that, The evaporator is simplified as a porous medium, and the resistance model of the evaporator is: Air-side pressure drop data of the evaporator at different initial wind velocities were obtained through simulation. An air-side pressure drop-velocity curve was fitted. Based on the porous medium momentum equation and the air-side pressure drop-velocity curve, the drag coefficient of the porous medium under frost-free conditions at the initial moment was calculated. When the accumulated frost thickness obtained in the frosting model reached a preset threshold, the porous medium parameters were adaptively corrected according to the drag coefficient and frost thickness of the porous medium under frost-free conditions at the initial moment. These porous medium parameters included porosity. ε Drag coefficient.

3. The multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber according to claim 2, characterized in that, The drag coefficient includes the viscous drag coefficient and the inertial drag coefficient; the porosity of the porous medium at the current time step after correction. ε Viscous resistance coefficient D and inertial drag coefficient C The formula for calculating 2 is: in, D Let m be the viscous drag coefficient at the current time step. -2 ; C 2 represents the inertial drag coefficient at the current time step, m -1 ; ε The porosity of the porous medium at the current time step; A f The surface area of ​​the heat exchanger fins is m. 2 ; The porosity of the porous medium at the initial moment; The current time step frost layer thickness; V Let m be the volume of the evaporator. 3 .

4. The multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber according to claim 2, characterized in that, The heat transfer model of the evaporator is as follows: (1) Assume the refrigerant outlet enthalpy. h out It then calls the frosting model to calculate the frost thickness at the current time step and calculates the heat transfer based on the heat transfer equation. Q evap Heat transfer coefficient K and logarithmic mean temperature difference ΔT ; (2) Based on hout1=hin+KAΔT / mr, the actual refrigerant outlet enthalpy value is obtained. h out1 Where hin is the inlet enthalpy of the refrigerant, A is the total internal area of ​​the heat exchange tubes in the evaporator, and mr is the refrigerant mass flow rate; when the actual outlet enthalpy of the refrigerant... h out1 Compared with the assumed refrigerant outlet enthalpy h out The calculation converges when the relative error is less than a preset threshold, and the heat exchange is obtained. Q evap Proceed to step (3), otherwise return to step (1); (3) Heat exchange Q evap The equivalent transformation is a dynamic negative volume heat source, which is then added to the governing equations of the CFD model. The calculation formula for the dynamic negative volume heat source is shown below: in, S h W is the dynamic negative volume heat source for the evaporator. m -3 ; Q evap The heat exchange capacity of the evaporator is W; V Let m be the volume of the evaporator. 3 .

5. The multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber according to claim 2, characterized in that, The frosting model of the evaporator is as follows: (1) At each time step, read the evaporator wall temperature and the current frost layer parameters; the frost layer parameters include frost layer thickness and frost layer density; (2) If the evaporator wall temperature meets the frosting condition, calculate and update the frost layer parameters, calculate the mass source term caused by frost and apply the mass source term to the control equation in the CFD model; If the conditions for frosting are not met, keep the frosting parameters unchanged; (3) After the frost layer parameters are updated, the frost simulation of the current time step is completed, and the updated frost layer parameters are used as the initial input for the next time step.

6. The multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber according to claim 5, characterized in that, The formula for calculating the quality source term is: in, S m For the mass source item of the evaporator, kg m -3 s -1 ; The mass flow rate of water vapor in the air transferred to the evaporator surface and condensed into frost, in kg. s -1 ; h m Let m be the convective mass transfer coefficient. s -1 ; A The phase interface area is m 2 ; h W is the convective heat transfer coefficient. m -2 K -1 ; ρ v,∞ and ρv,sat These represent the density of water vapor in the mainstream air and the density of saturated water vapor at the interface, respectively, in kg. m -3 ; Le It is a Lewis number.

7. The multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber according to claim 5, characterized in that, The formulas for calculating frost thickness and frost density are as follows: in, and The frost thicknesses at the current time step and the previous time step are respectively, in meters; and The frost density at the current time step and the previous time step are respectively, in kg. m -3 ; Δt Let be the time step, in seconds; T wall and T air These are the evaporator surface temperature and the air temperature, respectively, in °C; J is the specific heat capacity of air. kg -1 K -1 .

8. The multi-component coupled simulation prediction method for the temperature and humidity field of an environmental test chamber according to claim 1, characterized in that, The mathematical model of the centrifugal fan is as follows: The impeller area of ​​the centrifugal fan is set as the rotating domain, using a rotating reference system; the fan casing and inlet / outlet areas are set as the stationary domain, using a stationary reference system; data exchange between the rotating and stationary domains is achieved through an interface.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the multi-component coupled simulation prediction method for the temperature and humidity field of the environmental test chamber as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-component coupled simulation prediction method for the temperature and humidity field of the environmental test chamber as described in any one of claims 1 to 8.

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