Condensation phase change numerical simulation method and device, electronic equipment and storage medium

Abstract

The application relates to a condensation phase change numerical simulation method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a basic numerical framework based on a Lee model; the Lee model comprises a phase change mass source term and a phase change energy source term; obtaining gas-liquid equilibrium phase diagram data of a non-ideal binary mixture, and constructing a phase equilibrium explicit correlation between the component content and the saturation temperature of each component; according to the volatility difference of each component, the saturation temperature of each component is matched; in the iteration process of the condensation phase change numerical simulation, the real-time component content of the calculation grid unit in each iteration time step is obtained; the real-time saturation temperature of the corresponding component is determined based on the phase equilibrium explicit correlation and the real-time component content, and the phase change mass source term and the phase change energy source term of the corresponding component are corrected based on the real-time saturation temperature. By applying the technical scheme of the application, the distortion problem of the phase change source term of the non-ideal binary mixture can be solved, and the accuracy and stability of the condensation process are improved.

Description

Technical Field

[0001] This application relates to the field of phase change numerical simulation technology, specifically to a method, apparatus, electronic device, and storage medium for condensation phase change numerical simulation. Background Technology

[0002] Condensation phase change is a key process in chemical engineering, brewing, and energy industries. The condensation of multi-component mixtures is significantly more difficult to simulate than that of single-component systems due to characteristics such as selective condensation and temperature glide. Water-ethanol binary mixtures are typical working fluid systems in brewing and distillation processes, and numerical simulation of their condensation process has significant engineering value for equipment optimization and energy consumption control.

[0003] Currently, numerical simulations of condensation processes typically employ the Lee model or a scheme that dynamically corrects for the saturation temperature of a single component. However, these schemes cannot accurately reflect the selective condensation of non-ideal binary mixtures. Furthermore, the Lee model sets the saturation temperature to a fixed value, and dynamically correcting for the saturation temperature of a single component may lead to distortion of the phase change source terms, resulting in low accuracy and stability of the condensation process. Summary of the Invention

[0004] In view of the above problems, the embodiments of this application provide a numerical simulation method, apparatus, electronic device and storage medium for condensation phase change, which can solve the problem of phase change source term distortion caused by fixed saturation temperature during the condensation process of non-ideal binary mixtures, and improve the accuracy and stability of the condensation process.

[0005] According to one aspect of the embodiments of this application, a numerical simulation method for condensation phase change is provided, applied to non-ideal binary mixtures, the method comprising: A basic numerical framework based on the Lee model is constructed; wherein, the Lee model includes a phase change mass source term and a phase change energy source term; Obtain the gas-liquid equilibrium phase diagram data of the non-ideal binary mixture, and construct an explicit phase equilibrium correlation between the component content of each component and the saturation temperature. Based on the differences in volatility of each component, a differentiated saturation temperature is matched for each component; During the iterative process of numerical simulation of condensation phase change, the real-time component content of the computational grid cells is obtained in each iteration time step; The real-time saturation temperature of the corresponding component is determined based on the phase equilibrium explicit correlation and the real-time component content, and the phase change mass source term and phase change energy source term of the corresponding component are corrected based on the real-time saturation temperature.

[0006] In some embodiments of this application, the non-ideal binary mixture includes a first component and a second component, and the volatility of the first component is less than that of the second component; Construct explicit phase equilibrium correlations between the component content of each component and the saturation temperature, including: The gas-liquid equilibrium phase diagram data are fitted using polynomial fitting, and explicit phase equilibrium correlations for the first component and the second component are constructed respectively; wherein, the independent variable of the explicit phase equilibrium correlation is the gas phase mole fraction or the liquid phase mole fraction, and the dependent variable is the saturation temperature; During the fitting process, the order of the polynomial fitting corresponding to the first component is less than the order of the polynomial fitting corresponding to the second component.

[0007] In some embodiments of this application, the polynomial fitting order corresponding to the first component is 3rd order, and the polynomial fitting order corresponding to the second component is 6th order.

[0008] In some embodiments of this application, matching differentiated saturation temperatures to each component based on their volatility differences includes: To match the dew point temperature of the non-ideal binary mixture to the first component; To match the bubble point temperature of the non-ideal binary mixture to the second component.

[0009] In some embodiments of this application, the dew point temperature and the bubble point temperature are smoothed in a preset component gradient region or gas-liquid interface region of the non-ideal binary mixture; wherein, The preset component gradient region includes an azeotropic composition region, and the smoothing process includes at least one of smooth interpolation, weighted average, and piecewise function processing.

[0010] In some embodiments of this application, the iterative process of numerical simulation of condensation phase change further includes: The mole fraction of instantaneous gas phase components in each computational grid cell is extracted using a custom function; Based on the phase equilibrium explicit correlation and the mole fraction of the instantaneous gas phase components, update the real-time saturation temperature of each component at the current time step. Based on the updated real-time saturation temperature and the Lee model, the corresponding instantaneous phase transition source terms are determined and transiently coupled; wherein, the instantaneous phase transition source terms include instantaneous phase transition mass source terms and instantaneous phase transition energy source terms.

[0011] In some embodiments of this application, the non-ideal binary mixture is water-ethanol or water-ethylene glycol.

[0012] According to another aspect of the embodiments of this application, a numerical simulation apparatus for condensation phase change is provided, applied to non-ideal binary mixtures, the apparatus comprising: The module is used to construct a basic numerical framework based on the Lee model; wherein the Lee model includes a phase change mass source term and a phase change energy source term; the gas-liquid equilibrium phase diagram data of the non-ideal binary mixture is obtained, and an explicit phase equilibrium correlation between the component content of each component and the saturation temperature is constructed. The matching module is used to match different saturation temperatures for each component based on the differences in volatility. The acquisition module is used to acquire the real-time component content of the computational grid cells in each iteration time step during the iterative process of numerical simulation of condensation phase change. The correction module is used to determine the real-time saturation temperature of the corresponding component based on the phase equilibrium explicit correlation and the real-time component content, and to correct the phase change mass source term and phase change energy source term of the corresponding component based on the real-time saturation temperature.

[0013] According to another aspect of the embodiments of this application, an electronic device is provided, comprising: Controller; The memory is used to store one or more programs, which, when executed by the controller, enable the controller to implement the numerical simulation method for condensation phase change described above.

[0014] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program, when run on a condensation phase change numerical simulation device / electronic device, causes the condensation phase change numerical simulation device / electronic device to perform the steps of the condensation phase change numerical simulation method as described above.

[0015] In the numerical simulation method for condensation phase change provided in this application, an accurate simulation of the condensation process of a non-ideal binary mixture is achieved by constructing a basic numerical framework based on the Lee model and introducing an explicit phase equilibrium correlation driven by gas-liquid equilibrium phase diagram data. First, gas-liquid equilibrium phase diagram data of the non-ideal binary mixture is obtained, and an explicit phase equilibrium correlation between component content and saturation temperature is constructed, solving the problem of traditional complex thermodynamic iterative calculations. Then, based on the volatility differences of each component in the non-ideal binary mixture, saturation temperatures are differentially matched for different components, allowing low-volatility components and high-volatility components to participate in phase change calculations separately. During the iterative process of numerical simulation, the component content within the computational grid cells is extracted in real time, and the real-time saturation temperature of each component at the current moment is dynamically determined using the constructed explicit phase equilibrium correlation. The phase change mass source term and phase change energy source term in the Lee model are then corrected based on the determined real-time saturation temperature. The technical solution of this application, based on the deep coupling of phase diagram data-driven and improved Lee models, constructs a closed loop from basic framework building, explicit correlation construction of phase equilibrium, differentiated temperature matching to transient source term correction. This enables the phase change driving force to adaptively adjust with the transient changes in component concentration, thereby effectively solving the problem of phase change source term calculation deviation caused by using fixed saturation temperature or ideal solution. It avoids the loss of temperature glide effect and distortion of selective condensation behavior when simulating non-ideal systems, and improves the calculation accuracy, convergence stability and applicability of numerical simulation of condensation phase change in the condensation process of non-ideal binary mixtures, thus improving the accuracy and stability of the condensation process.

[0016] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the numerical simulation method for condensation phase change provided in this application.

[0019] Figure 2 This is a flowchart illustrating an embodiment of the iterative process of condensation phase change numerical simulation in the condensation phase change numerical simulation method provided in this application.

[0020] Figure 3 A schematic diagram of the computational grid cells in the numerical simulation method for condensation phase change provided in this application.

[0021] Figure 4 Temperature distribution cloud map at different ethanol inlet concentrations in the numerical simulation method for condensation phase change provided in this application.

[0022] Figure 5 The cloud map shows the distribution of the mole fraction of ethanol in the gas phase at different ethanol inlet concentrations in the numerical simulation method for condensation phase change provided in this application.

[0023] Figure 6 The cloud map shows the distribution of the ethanol liquid phase mole fraction under different ethanol inlet concentrations in the numerical simulation method for condensation phase change provided in this application.

[0024] Figure 7 A schematic diagram of an embodiment of the numerical simulation apparatus for condensation phase change of this application is shown.

[0025] Figure 8 A schematic diagram of an embodiment of the electronic device provided in this application is shown. Detailed Implementation

[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0027] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0028] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0029] In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0030] First, a brief overview of the background of this application is provided below: Condensation phase change is a key process in chemical production, energy and power, and brewing distillation industries, widely used in heat exchanger design, separation tower optimization, and energy consumption control. During the condensation of multi-component mixtures, due to differences in volatility and intermolecular interactions, complex thermodynamic behaviors such as selective condensation, temperature glide, and non-azeotropic / azeotropic characteristics are exhibited. Numerical simulations in related technologies are typically based on Computational Fluid Dynamics (CFD) platforms, using volume of fluid (VOF) models to track the gas-liquid interface and combining them with the Lee model to describe mass transfer and energy exchange between phases. When dealing with multi-component systems, the saturation temperature is usually determined based on Raoult's law or the ideal solution assumption, or activity coefficient models such as NRTL (Non-Random Two-Liquid) are used to calculate the gas-liquid equilibrium relationship, thereby obtaining the extensive thermodynamic variables required for phase change to simulate the condensation heat and mass transfer process.

[0031] However, due to the strong nonlinear coupling between component partial pressures and saturation temperatures in non-ideal systems, and their dynamic changes with component concentrations, numerical simulation methods using related techniques struggle to accurately reflect the dynamic evolution. For example, when dealing with non-ideal conditions such as significant temperature glide, azeotropic enrichment, and selective condensation, the transient responses of the component and temperature fields are often not accurately captured, leading to deviations in the predictions of condensation initiation points, phase transition rates, and intensity distributions, resulting in low accuracy and stability during numerical simulations.

[0032] In view of this, to address the aforementioned problems, this application proposes a numerical simulation method for condensation phase transitions to solve the problem of phase transition source term distortion caused by a fixed saturation temperature during the condensation process of non-ideal binary mixtures, thereby improving the accuracy and stability of the condensation process. The execution entity of the condensation phase transition numerical simulation method can be a terminal device, a server, or other processing device with computing capabilities. The terminal device can be user equipment (UE), a computer, a mobile device, a user terminal, a terminal, a personal digital assistant (PDA), a computing device, etc.

[0033] In some embodiments, the numerical simulation method for condensation phase change can be implemented by a processor calling computer-readable instructions stored in memory. The numerical simulation method for condensation phase change in this embodiment can be applied to non-ideal binary mixtures such as water-ethanol or water-ethylene glycol; for ease of overall explanation, the following embodiments in this application all use water-ethanol as an example.

[0034] Please see as follows Figure 1 As shown, the numerical simulation method for condensation phase change in this embodiment includes the following steps: S1. Construct a basic numerical framework based on the Lee model.

[0035] The construction of the basic numerical framework refers to establishing a mathematical model system to characterize the gas-liquid two-phase flow and heat and mass transfer processes of non-ideal binary mixtures. This basic numerical framework is typically based on the VOF gas-liquid two-phase flow model, using phase volume fractions to characterize the gas-liquid phase distribution within the computational grid cells, and includes mass conservation equations, momentum conservation equations, energy conservation equations, and phase fraction transport equations. The Lee model, as the computational model in this basic numerical framework characterizing mass transfer and phase change during condensation, introduces phase change mass source terms and phase change energy source terms into the continuity equation and energy equation, respectively, to characterize interphase mass transfer, heat transfer, and interfacial phase change behavior. The phase change mass source term characterizes the rate of mass change per unit volume per unit time due to phase change, while the phase change energy source term characterizes the impact of latent heat released or absorbed during the phase change process on the temperature field.

[0036] like Figure 2 The diagram shows a schematic of the computational domain meshing, representing the discretized spatial structure upon which the numerical simulation relies. The computational grid cells are the carriers for calculating the source terms of the Lee model, and the density distribution of the grid directly affects the accuracy of phase transition interface capture. By constructing a basic framework for the Lee model containing dual source terms, it is easier to embed dynamic saturation temperatures subsequently, ensuring synchronous iteration and data consistency of the component fields, temperature fields, flow fields, and phase transition fields.

[0037] S2. Obtain the gas-liquid equilibrium phase diagram data of the non-ideal binary mixture and construct an explicit phase equilibrium correlation between the component content of each component and the saturation temperature.

[0038] Among them, the gas-liquid equilibrium phase diagram data refers to the measured data or high-precision thermodynamic data of the content of gas phase components or liquid phase components and the corresponding saturation temperature of the target non-ideal binary mixture under a specific pressure. For non-ideal binary mixtures such as water-ethanol, the gas-liquid phase equilibrium deviates significantly from Raoult's law due to intermolecular interactions, exhibiting obvious temperature glide and azeotropic characteristics. Therefore, it is necessary to rely on measured phase diagram data rather than the assumption of an ideal solution. The phase equilibrium diagram data under normal pressure is shown in Table 1 below. The data in Table 1 can be obtained based on the DECHEMA gas-liquid equilibrium database.

[0039] Table 1: Phase Equilibrium Diagram Data of Water-Ethanol Mixtures under Normal Pressure

[0040] An explicit phase equilibrium correlation refers to a method of transforming discrete phase diagram data into a continuous functional expression through fitting. The independent variable is the mole fraction of the component, and the dependent variable is the saturation temperature. For an explicit phase equilibrium correlation, firstly, saturation temperature and component content data pairs covering the entire concentration range are extracted. Then, polynomial fitting, spline interpolation, or regression algorithms are used to determine the fitting coefficients, thereby obtaining the equation. Explicit equations in the form of equations. For example, for water-ethanol, based on the mole fraction of ethanol in the gas phase at normal pressure. With saturation temperature The relationship between the phase equilibrium and water components is analyzed. A sixth-order polynomial fitting formula is constructed to characterize the phase equilibrium properties of the ethanol component, or a cubic polynomial fitting formula is constructed to characterize the properties of the water component. Through the embodiments of this application, the constructed explicit phase equilibrium correlation establishes a fast mapping channel from component content to saturation temperature, thereby improving computational efficiency, avoiding simulation failures caused by non-convergence of thermodynamic model iterations, and ensuring simulation accuracy in non-azeotropic, azeotropic, and low-concentration regions.

[0041] S3. Based on the differences in volatility of each component, match different saturation temperatures for each component.

[0042] The difference in volatility refers to the ability of different components in a non-ideal binary mixture to evaporate or condense under the same conditions, manifested as differences in boiling point or saturated vapor pressure. In water-ethanol, ethanol has a lower saturation temperature and is the more volatile component, while water has a higher saturation temperature and is the less volatile component.

[0043] In this embodiment, a corresponding phase transition temperature is matched to each component based on its physicochemical properties. For example, for non-volatile components, the dew point temperature is matched as the driving temperature for their condensation phase transition to reflect their condensation tendency in the bulk gas phase; for volatile components, the bubble point temperature is matched as the driving temperature for their phase transition to suit their phase transition characteristics at the gas-liquid interface or liquid film region. By differentiating the saturation temperature according to the volatility differences of the components in this embodiment, the selective condensation phenomenon caused by different volatility in non-ideal mixtures can be realistically reproduced, effectively solving the problem of misjudgment of the phase transition initiation point caused by the fixed saturation temperature assumption.

[0044] S4. During the iterative process of numerical simulation of condensation phase change, obtain the real-time component content of the computational grid cell in each iteration time step.

[0045] In this context, the iterative process of numerical simulation of condensation phase change refers to the calculation process in which the CFD solver repeatedly solves the governing equations according to the time step until a steady state is reached or the transient simulation is completed. Combined with, for example... Figure 3 The diagram shows a computational grid cell. Within each iteration time step, the mole fraction of the gas or liquid phase components in each computational grid cell is dynamically extracted at the current moment. Because the condensation process involves continuous component separation and enrichment, the mixture composition within the grid cell evolves continuously with time and space. This is achieved through a User Defined Function (UDF) interface. At the end of each iteration step of the CFD solver, the instantaneous mole fraction of the gas phase components in each grid cell is automatically read and stored. For example, at a certain time step, the molar fraction of ethanol vapor phase in a certain grid cell may change from the initial 0.5 to 0.45. This real-time data will be collected and transmitted to the saturation temperature calculation module, providing direct input conditions for subsequent real-time saturation temperature updates. This ensures that the phase change model can respond to fluctuations in component concentration in the flow field in a timely manner, thereby collecting unsteady physical characteristics such as component stratification and local enrichment during the condensation process.

[0046] S5. Determine the real-time saturation temperature of the corresponding component based on the phase equilibrium explicit correlation and the real-time component content, and correct the phase change mass source term and phase change energy source term of the corresponding component based on the real-time saturation temperature.

[0047] Specifically, by substituting the aforementioned real-time component content into the constructed phase equilibrium explicit correlation, the dynamic saturation temperature of each component at the current time and current grid position is calculated. Then, the calculated real-time saturation temperature is used. Replace the fixed saturation temperature parameter in the source term formula of the Lee model and recalculate the values ​​of the phase change mass source term and the phase change energy source term.

[0048] In this embodiment, the size of the source term in the Lee model depends on the local temperature. With saturation temperature The difference, saturation temperature The dynamic update directly alters the phase transition driving force, thereby adjusting the phase transition rate and intensity. For example, when a decrease in ethanol concentration in a region leads to an increase in its corresponding real-time saturation temperature, if the local temperature remains constant, the subcooling decreases, and the calculated ethanol condensation mass source term decreases accordingly, thus automatically suppressing the condensation rate in that region, characterizing the difficulty in condensation at low concentrations. Figures 4 to 6 As shown, temperature distribution cloud maps, ethanol vapor phase mole fraction distribution cloud maps, and ethanol liquid phase mole fraction distribution cloud maps are displayed for different ethanol inlet concentrations, respectively, characterizing the simulation results after real-time correction: presenting the temperature slip trajectory along the flow direction and the nonlinear distribution of ethanol concentration in the gas and liquid phases, demonstrating that the embodiments of this application can accurately reproduce the azeotropic composition enrichment and temperature slip effect of non-ideal systems, realizing the coupling between components, temperature, and phase transition, effectively avoiding calculation distortion caused by saturation temperature lag or distortion, and improving the reliability and applicability of numerical simulation.

[0049] In some embodiments, systematic extraction and analysis of simulation results can verify the condensation principle of non-ideal binary mixtures. In this embodiment, physical quantities such as temperature field, gas and liquid phases, liquid volume fraction, condensation heat transfer coefficient, condensate component ratio, and temperature slip (i.e., inlet and outlet temperature difference) are extracted during the simulation process. After extracting these physical quantities, the difference between the total inlet mass flow rate and the sum of the total outlet gas and liquid mass flow rates is calculated. If the ratio of this difference to the total inlet mass flow rate is less than a preset tolerance, it indicates that the calculation result satisfies conservation.

[0050] In some embodiments, the preset tolerance may be, but is not limited to, 0.1%, 0.2%, 0.3%, etc., and is set according to the actual application scenario. This is only an example and is not specifically limited.

[0051] Based on the extracted physical quantity data, the condensation heat and mass transfer patterns under different inlet component concentrations, flow rates, temperatures, and wall cooling conditions are analyzed. By observing the component concentration cloud map near the gas-liquid interface, selective condensation of volatile components such as ethanol preferentially enriches in the liquid phase can be identified. The quantitative analysis results in the embodiments of this application provide accurate data support and theoretical basis for process optimization of non-ideal solution condensation processes.

[0052] In the above embodiments, by constructing a basic numerical framework based on the Lee model and introducing explicit phase equilibrium correlations driven by gas-liquid equilibrium phase diagram data, accurate simulation of the condensation process of non-ideal binary mixtures is achieved. First, gas-liquid equilibrium phase diagram data of the non-ideal binary mixture is acquired, and explicit phase equilibrium correlations between component content and saturation temperature are constructed, solving the problem of traditional complex thermodynamic iterative calculations. Then, based on the differences in volatility of each component in the non-ideal binary mixture, saturation temperatures are differentially matched for different components, allowing low-volatility components and high-volatility components to participate in phase transition calculations separately. During the iterative process of numerical simulation, the component content within the computational grid cells is extracted in real time, and the real-time saturation temperature of each component at the current moment is dynamically determined using the constructed explicit phase equilibrium correlations. The phase transition mass source term and phase transition energy source term in the Lee model are then corrected based on the determined real-time saturation temperature. The technical solution of this application, based on the deep coupling of phase diagram data-driven and improved Lee models, constructs a closed loop from basic framework building, explicit correlation construction of phase equilibrium, differentiated temperature matching to transient source term correction. This enables the phase change driving force to adaptively adjust with the transient changes in component concentration, thereby effectively solving the problem of phase change source term calculation deviation caused by using fixed saturation temperature or ideal solution. It avoids the loss of temperature glide effect and distortion of selective condensation behavior when simulating non-ideal systems, and improves the calculation accuracy, convergence stability and applicability of numerical simulation of condensation phase change in the condensation process of non-ideal binary mixtures, thus improving the accuracy and stability of the condensation process.

[0053] In some embodiments, based on the construction of explicit correlations for phase equilibrium in the foregoing embodiments, this application further includes embodiments for differentiated polynomial fitting of components with different volatileities in a non-ideal binary mixture, to solve the problem that a single fitting order cannot simultaneously account for the smooth curves of low-volatility components and the drastic nonlinear changes of high-volatility components. Embodiments of this application further include at least the following steps: The gas-liquid equilibrium phase diagram data were fitted using polynomial fitting, and explicit correlations of phase equilibrium for the first and second components were constructed respectively.

[0054] The non-ideal binary mixture includes a first component and a second component, with the volatility of the first component being lower than that of the second component. The first component refers to the component with a higher saturation temperature and relatively low volatility during condensation, such as water in a water-ethanol mixture. The second component refers to the component with a lower saturation temperature and relatively high volatility during condensation, such as ethanol in a water-ethanol mixture. The curve of the saturation temperature as a function of mole fraction for the low-volatility first component is relatively flat, exhibiting weak nonlinear characteristics. The curve of the saturation temperature as a function of mole fraction for the high-volatility second component, approaching the azeotropic composition region, shows a large curvature and dense inflection points, exhibiting nonlinear characteristics.

[0055] During the fitting process, the polynomial fitting order corresponding to the first component is lower than that corresponding to the second component. Polynomial fitting refers to using numerical analysis such as the least squares method to map the experimentally measured gas-liquid equilibrium phase diagram data points into a continuous polynomial function. The independent variable can be either the gas phase mole fraction or the liquid phase mole fraction. Under constant pressure, there is a definite correspondence between the gas and liquid phase compositions and the saturation temperature; the dependent variable is the saturation temperature under that composition. In this embodiment, since the phase equilibrium curve of the first component is flat, a lower-order polynomial is used to accurately characterize its temperature change trend; since the phase equilibrium curve of the second component changes drastically near the azeotropic point, a higher-order polynomial is required to capture its complex nonlinear transition characteristics and avoid azeotropic point position shifts or temperature slip calculation errors. For example, taking water-ethanol as an example, if the gas phase mole fraction is selected... As the independent variable, for the first component, water, a third-order polynomial is used. To perform a fitting, for example, the explicit correlation involved for the first component, water, is: This is to cover the smooth temperature changes of water over a wide concentration range; for the second component, ethanol, a sixth-order polynomial is used. To perform a fitting, for example, the explicit correlation involving the ethanol of the second component is: This ensures that the rapid decline and gradual rise of the curve can be accurately reproduced as the ethanol mole fraction changes from 0 to azeotropic composition and then to pure ethanol.

[0056] In some embodiments, the polynomial fitting order of the first component and the polynomial fitting order of the second component are set according to the actual application scenario. In the embodiments of this application, the polynomial fitting order of the first component is 3rd order and the polynomial fitting order of the first component is 6th order. These are all illustrative examples and are not specifically limited.

[0057] In the above embodiments, the aforementioned differentiated polynomial fitting strategy eliminates the calculation deviation of phase change driving force caused by fitting error, enabling the improved Lee model to adaptively respond to the phase change characteristics of different components at different concentrations, thereby accurately predicting the dynamic evolution of component stratification, local enrichment, and temperature field during unsteady condensation.

[0058] In some embodiments, for matching differentiated saturation temperatures to each component based on the differences in volatility, the present application embodiments further include at least the following steps: To match the dew point temperature of the non-ideal binary mixture to the first component; To match the bubble point temperature of the non-ideal binary mixture for the second component.

[0059] The dew point temperature refers to the temperature at which a gas-phase mixture begins to condense into the first droplet under the current pressure. Its value depends on the mole fraction and partial pressure of each component in the gas phase. The saturation temperature of the first component is set as the dew point temperature of a non-ideal binary mixture, serving as the thermodynamic basis for the transition of the less volatile component from the bulk gas phase to the liquid phase. The bubble point temperature refers to the temperature at which a liquid-phase mixture begins to produce the first bubble under the current pressure, and its value is mainly affected by the liquid phase composition. The saturation temperature of the second component is set as the bubble point temperature of a non-ideal binary mixture, characterizing the phase transition properties of the more volatile component at the gas-liquid interface or in the liquid film region.

[0060] In this embodiment, during the condensation process, the less volatile components in the bulk gas phase have higher partial pressures and are more likely to reach saturation. The dew point temperature accurately reflects the phase change driving force of this component in the gas phase environment. A custom function reads the instantaneous gas phase mole fraction within the computational grid in real time, substitutes it into the explicit gas-liquid equilibrium correlation constructed based on phase diagram data, calculates the current dew point temperature, and assigns it to the saturation temperature of the first component. Volatile components tend to accumulate at the gas-liquid interface or on the liquid phase side. Phase change behaviors such as evaporation or recondensation depend on the thermodynamic state of the liquid phase, and the bubble point temperature accurately reflects the saturation conditions of this component in the liquid phase environment. During the numerical simulation iteration, based on the instantaneous liquid phase mole fraction within the computational grid cells, the current bubble point temperature is determined using the explicit phase equilibrium correlation and updated to the saturation temperature of the second component.

[0061] For example, for water-ethanol, when the molar fraction of water in the gas phase is 0.6, the dew point temperature calculated according to the fitting formula is 85°C, which is the saturation temperature of the water component in the current iteration step. When the molar fraction of ethanol in the liquid phase is 0.8, the bubble point temperature is 79°C, which is the reference temperature for calculating the phase change source term of the ethanol component.

[0062] In the above embodiments, by matching the dew point temperature to the first component and the bubble point temperature to the second component, the problem that the single saturated temperature model cannot distinguish the timing of component phase transitions is solved. The temperature glide phenomenon and component stratification effect during the condensation process are effectively collected. At the same time, the phase transition mass source term and energy source term of multiple components can be controlled separately in the same computational grid cell, thereby improving the accuracy and stability of numerical simulation of the condensation process of non-ideal binary mixtures.

[0063] In some embodiments, the method further includes: In the preset component gradient region or gas-liquid interface region of a non-ideal binary mixture, the dew point temperature and bubble point temperature are smoothed.

[0064] The preset component gradient region refers to the spatial region in the mixture where the component concentration changes drastically. It may, but is not limited to, including the azeotropic region, depending on the actual application scenario. This is merely an illustrative example and not a specific limitation. During the condensation of water-ethanol, the azeotropic region exhibits unique thermodynamic properties, where the dew point temperature and bubble point temperature of water-ethanol coincide. If calculations are performed using independently constructed explicit correlations for water and ethanol, the difference in polynomial fitting orders may lead to non-physical numerical jumps or discontinuities in the calculated dew point and bubble point temperatures within the azeotropic point neighborhood. This can cause severe oscillations in the phase change mass and energy source terms, triggering divergence in the CFD solver. Therefore, in the preset component gradient region or gas-liquid interface region of a non-ideal binary mixture, it is necessary to smooth the dew point and bubble point temperatures to eliminate numerical singularities and ensure the stability of the iterative process.

[0065] Smoothing processes include at least one of smooth interpolation, weighted averaging, and piecewise function processing. When using smooth interpolation, a transition segment connecting the dew point temperature curve and the bubble point temperature curve is constructed within a preset neighborhood of the azeotropic composition region (e.g., the ethanol mole fraction range of 0.88 to 0.90). This ensures the continuity of the first derivative of the temperature field within the preset neighborhood of the azeotropic composition region, thus achieving a smooth temperature transition. When using weighted averaging, weighting coefficients are set according to the local component gradient or distance from the azeotropic point. The dew point temperature and bubble point temperature are then linearly or non-linearly superimposed according to the weights. This results in a differentiated temperature being used far from the azeotropic point, while the dew point temperature and bubble point temperature gradually converge and smoothly merge as the azeotropic point approaches. When using piecewise function processing, a threshold range centered on the azeotropic component is used. Outside the range, the saturation temperature of each component is called, while within the range, a corresponding smooth transition function is used.

[0066] In the above embodiments, combined with the aforementioned determined differential saturation temperature, the smoothing mechanism for the preset component gradient region and gas-liquid interface region preserves the complex phase change thermodynamic characteristics of multi-component mixtures and eliminates singularities in numerical calculations. At the same time, the smoothing of dew point temperature and bubble point temperature ensures that the saturation temperature remains continuous and smooth throughout the entire computational domain, thereby ensuring the stability of the phase change source term calculation and improving the convergence speed and stability of the CFD solver.

[0067] In some embodiments, the iterative process of numerical simulation of condensation phase change further includes performing a transient coupling update step to address the problem of saturation temperature becoming disconnected from phase change source terms due to lag in component content extraction under the traditional static framework. Embodiments of this application further include at least S41 to S43: S41. Extract the mole fraction of instantaneous gas phase components in each computational grid cell using a custom function; S42. Based on the phase equilibrium explicit correlation and the mole fraction of the instantaneous gas phase components, update the real-time saturation temperature of each component at the current time step. S43. Based on the updated real-time saturation temperature and Lee phase transition model, determine the corresponding instantaneous phase transition source terms and perform transient coupling.

[0068] Here, the user-defined function (UDF) refers to the user-defined function embedded in the computational fluid dynamics solver. The UDF acts as a data interface, directly accessing the CFD solver's memory variables within each iteration time step to read the gas phase component distribution state within the current computational grid cell in real time. The instantaneous mole fraction of the gas phase components is obtained by solving the multi-component transport equations, reflecting the dynamic changes in local component concentrations caused by selective condensation during the condensation process.

[0069] In this embodiment, the user-defined function (UDF) is triggered at the beginning of each iteration loop, traversing all computational grids involved in the phase transition and extracting the gaseous mole fractions xi(t) of ethanol and water at the current moment. For example, at a certain iteration step t=n, if ethanol preferentially condenses within a near-wall grid cell, the UDF will promptly capture the transient change in the gaseous mole fraction of ethanol within that cell, decreasing from 0.89 to 0.85, without waiting for the entire time step to end.

[0070] The instantaneous gas phase component mole fraction xi(t) is collected and substituted into the previously constructed explicit phase equilibrium correlation to recalculate the real-time saturation temperature Tsat(t) corresponding to the current time step. In this embodiment, for water-ethanol, the extracted instantaneous ethanol gas phase mole fraction is substituted into the sixth-order polynomial fitting formula, and the instantaneous water gas phase mole fraction is substituted into the third-order polynomial fitting formula to calculate the saturation temperatures of water and ethanol at that time and location, respectively.

[0071] Transient coupling refers to adding the calculated transient phase change mass source term and transient phase change energy source term to the source term list of the continuity equation and energy equation, participating in the residual convergence calculation of the current time step. In this embodiment, when the real-time saturation temperature Tsat(t) is higher than the local temperature T, condensation is determined to have occurred, generating a mass source term with reduced gas phase and increased liquid phase, and an energy source term releasing latent heat. For example, during the unsteady condensation initiation stage, as the component concentration near the wall changes rapidly, the real-time updated saturation temperature will adjust the phase change driving potential in a timely manner, thereby dynamically regulating the condensation rate.

[0072] The above embodiments solve the problem of phase change source term calculation deviation caused by asynchronous data updates, enabling the simulation process to accurately collect the temperature slip evolution over time, the dynamic enrichment of azeotropic components on the wall, and the condensation start-up characteristics under unsteady conditions, effectively supporting the analysis and optimization of complex non-ideal binary mixture condensation processes.

[0073] In some embodiments, during the execution of the aforementioned transient coupling update step, to ensure the accuracy and efficiency of the numerical solution, it is necessary to perform fine mesh generation and reasonable solver configuration on the computational domain. In this embodiment, after completing the modeling of the computational domain, structured or unstructured mesh generation is performed, and mesh independence verification is conducted to eliminate the influence of mesh size on the calculation results. For example, multiple sets of meshes with different densities, such as coarse, medium, and fine meshes, can be generated, and simulations can be performed to compare the differences in physical quantities such as wall heat flux density and outlet component concentration. When the deviation of the calculation results between two adjacent sets of meshes is less than a preset threshold, it indicates that the current mesh density meets the calculation accuracy requirements, and a medium-density mesh can be used for subsequent simulations. In the gas-liquid interface region, due to the large component and temperature gradients, it is necessary to locally refine the mesh, for example, by using adaptive mesh refinement or manually setting the boundary layer mesh, to ensure the accuracy of interface acquisition.

[0074] In some embodiments, the preset threshold may be, but is not limited to, 0.5%, 1%, 2%, etc., and is set according to the actual application scenario. This is only an illustrative example and is not specifically limited.

[0075] In this embodiment, the inlet adopts a velocity inlet boundary, requiring specification of the inlet velocity magnitude, direction, turbulence intensity, and mass fraction or mole fraction of each component; the wall adopts an isothermal condensation surface boundary, setting a fixed temperature value below the dew point temperature of the mixture to drive condensation; the outlet adopts a pressure outlet boundary, setting the outlet back pressure. Simultaneously, auxiliary boundary conditions such as no-slip wall conditions and adiabatic symmetry are added to fully define the constraints of the physical problem.

[0076] Based on the above embodiments, a CFD solver adapted to both steady-state and unsteady-state conditions is configured. Regarding the discretization scheme, the pressure equation is discretized using the standard scheme; the momentum equation is discretized using a first-order upwind scheme to ensure stability in the early stages of iteration; and the energy equation and component transport equation are discretized using a second-order upwind scheme to improve computational accuracy. The pressure / velocity coupling uses the SIMPLEC algorithm, and the introduction of a pressure correction equation effectively accelerates the convergence process. By changing parameters such as the inlet ethanol mole fraction, inlet flow rate, fluid temperature, and wall cooling temperature, multi-condition numerical simulations can be conducted to systematically analyze the condensation heat and mass transfer characteristics of non-ideal binary mixtures under different operating conditions.

[0077] In the above embodiments, the non-ideal binary mixtures include water-ethanol or water-ethylene glycol. The water-ethanol binary mixture is a non-ideal mixture with the lowest azeotropic point; its gas-liquid equilibrium curve shows equal gas and liquid phase compositions at the azeotropic point, and exhibits a significant temperature glide in the non-azeotropic region. The water-ethylene glycol binary mixture is a non-ideal mixture with a large difference in boiling point; the two components have a large difference in volatility, making them prone to component stratification and selective condensation during condensation. For example, when applied to condenser simulation in the baijiu brewing process, water-ethanol is selected as the working fluid. The 6th and 3rd order differentiated polynomial fitting formulas constructed in the aforementioned embodiments are used to accurately characterize the saturation temperature changes of ethanol and water at different concentrations, thereby collecting azeotropic enrichment phenomena. When applied to heat pump working fluid simulation, if the working fluid is water-ethylene glycol, the corresponding phase diagram data is replaced, and the differentiated saturation temperature matching of the embodiments of this application is used to solve the simulation distortion problem of high-boiling-point components being difficult to condense.

[0078] In some embodiments, the non-ideal binary mixture may also be water-methanol, acetone-ethanol, etc., selected according to the actual application scenario. This is only an illustrative example and is not specifically limited.

[0079] The following describes an embodiment of the condensation phase change numerical simulation apparatus of this application, which can be used to execute the condensation phase change numerical simulation method in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the condensation phase change numerical simulation method described above in this application.

[0080] Please see Figure 7 As shown, Figure 7 A schematic diagram of an embodiment of the numerical simulation apparatus for condensation phase change applied to electronic devices according to this application is shown. Please refer to... Figure 7 As shown, the condensation phase change numerical simulation device 600 includes a construction module 610, a matching module 620, an acquisition module 630, and a correction module 640; Module 610 is used to construct a basic numerical framework based on the Lee model; wherein the Lee model includes a phase change mass source term and a phase change energy source term; the gas-liquid equilibrium phase diagram data of the non-ideal binary mixture is obtained, and an explicit phase equilibrium correlation between the component content of each component and the saturation temperature is constructed. Matching module 620 is used to match different saturation temperatures for each component based on the differences in volatility of each component; The acquisition module 630 is used to acquire the real-time component content of the computational grid cells in each iteration time step during the iterative process of numerical simulation of condensation phase change. The correction module 640 is used to determine the real-time saturation temperature of the corresponding component based on the phase equilibrium explicit correlation and the real-time component content, and to correct the phase change mass source term and phase change energy source term of the corresponding component based on the real-time saturation temperature.

[0081] It should be noted that the condensation phase change numerical simulation device 600 provided in the above embodiments and the condensation phase change numerical simulation method provided in the aforementioned embodiments belong to the same concept. The specific way in which each module and unit performs operations has been described in detail in the method embodiments, and will not be repeated here.

[0082] Figure 8 The diagram illustrates the structure of an embodiment of the electronic device of this application, and also shows the structure of a computer system suitable for implementing the electronic device of this application. The specific embodiments of this application do not limit the specific implementation of the electronic device.

[0083] Please see Figure 8 As shown, the electronic device includes: a controller; and a memory for storing one or more programs, which, when executed by the controller, perform the numerical simulation method for condensation phase change described above.

[0084] Please continue reading. Figure 8 As shown, the computer system 700 of this electronic device includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 702 or programs loaded from storage portion 708 into Random Access Memory (RAM) 703. The RAM 703 also stores various programs and data required for system operation. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0085] The following components are connected to I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 710 as needed so that computer programs read from it can be installed into storage section 708 as needed.

[0086] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs various functions defined in the system of this application.

[0087] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the numerical simulation method for condensation phase change as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not incorporated into the electronic device.

[0088] Another aspect of this application provides a computer program product or computer program comprising at least one executable instruction that, when executed on a condensation phase change numerical simulation device / electronic device, causes the condensation phase change numerical simulation device / electronic device to perform the condensation phase change numerical simulation method as described below: A basic numerical framework based on the Lee model is constructed; wherein, the Lee model includes a phase change mass source term and a phase change energy source term; Obtain the gas-liquid equilibrium phase diagram data of the non-ideal binary mixture, and construct an explicit phase equilibrium correlation between the component content of each component and the saturation temperature. Based on the differences in volatility of each component, a differentiated saturation temperature is matched for each component; During the iterative process of numerical simulation of condensation phase change, the real-time component content of the computational grid cells is obtained in each iteration time step; The real-time saturation temperature of the corresponding component is determined based on the phase equilibrium explicit correlation and the real-time component content, and the phase change mass source term and phase change energy source term of the corresponding component are corrected based on the real-time saturation temperature.

[0089] In one alternative embodiment, the non-ideal binary mixture comprises a first component and a second component, wherein the volatility of the first component is less than that of the second component; the executable instructions may also be used to cause the condensation phase change numerical simulation device / electronic device to perform the following operations: The gas-liquid equilibrium phase diagram data are fitted using polynomial fitting, and explicit phase equilibrium correlations are constructed for the first component and the second component, respectively; wherein the independent variable of the explicit phase equilibrium correlation is the gas phase mole fraction or the liquid phase mole fraction, and the dependent variable is the saturation temperature. During the fitting process, the order of the polynomial fitting corresponding to the first component is less than the order of the polynomial fitting corresponding to the second component.

[0090] The first component corresponds to a polynomial fitting order of 3, and the second component corresponds to a polynomial fitting order of 6.

[0091] In an alternative approach, the executable instructions can also be used to cause the condensation phase change numerical simulation device / electronic device to perform the following operations: The method of matching differentiated saturation temperatures to each component based on their volatility differences includes: To match the dew point temperature of the non-ideal binary mixture to the first component; To match the bubble point temperature of the non-ideal binary mixture to the second component.

[0092] In an alternative approach, the executable instructions can also be used to cause the condensation phase change numerical simulation device / electronic device to perform the following operations: In the predetermined component gradient region or gas-liquid interface region of the non-ideal binary mixture, the dew point temperature and the bubble point temperature are smoothed; wherein... The preset component gradient region includes an azeotropic composition region, and the smoothing process includes at least one of smooth interpolation, weighted average, and piecewise function processing.

[0093] In an alternative approach, the executable instructions can also be used to cause the condensation phase change numerical simulation device / electronic device to perform the following operations: The iterative process of numerical simulation of condensation phase change also includes: The mole fraction of instantaneous gas phase components in each computational grid cell is extracted using a custom function; Based on the phase equilibrium explicit correlation and the mole fraction of the instantaneous gas phase components, update the real-time saturation temperature of each component at the current time step. Based on the updated real-time saturation temperature and the Lee model, the corresponding instantaneous phase transition source terms are determined and transiently coupled; wherein, the instantaneous phase transition source terms include instantaneous phase transition mass source terms and instantaneous phase transition energy source terms.

[0094] In the numerical simulation method for condensation phase change provided in this application, an accurate simulation of the condensation process of a non-ideal binary mixture is achieved by constructing a basic numerical framework based on the Lee model and introducing an explicit phase equilibrium correlation driven by gas-liquid equilibrium phase diagram data. First, gas-liquid equilibrium phase diagram data of the non-ideal binary mixture is obtained, and an explicit phase equilibrium correlation between component content and saturation temperature is constructed, solving the problem of traditional complex thermodynamic iterative calculations. Then, based on the volatility differences of each component in the non-ideal binary mixture, saturation temperatures are differentially matched for different components, allowing low-volatility components and high-volatility components to participate in phase change calculations separately. During the iterative process of numerical simulation, the component content within the computational grid cells is extracted in real time, and the real-time saturation temperature of each component at the current moment is dynamically determined using the constructed explicit phase equilibrium correlation. The phase change mass source term and phase change energy source term in the Lee model are then corrected based on the determined real-time saturation temperature. The technical solution of this application, based on the deep coupling of phase diagram data-driven and improved Lee models, constructs a closed loop from basic framework building, explicit correlation construction of phase equilibrium, differentiated temperature matching to transient source term correction. This enables the phase change driving force to adaptively adjust with the transient changes in component concentration, thereby effectively solving the problem of phase change source term calculation deviation caused by using fixed saturation temperature or ideal solution. It avoids the loss of temperature glide effect and distortion of selective condensation behavior when simulating non-ideal systems, and improves the calculation accuracy, convergence stability and applicability of numerical simulation of condensation phase change in the condensation process of non-ideal binary mixtures, thus improving the accuracy and stability of the condensation process.

[0095] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0097] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0098] According to one aspect of the embodiments of this application, a computer system is also provided, including a Central Processing Unit (CPU), which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from storage into random access memory (RAM), such as performing the methods described above. Various programs and data required for system operation are also stored in the RAM. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0099] The following components are connected to the I / O interface: input components including keyboards, mice, etc.; output components including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage components including hard drives; and communication components including network interface cards such as LAN (Local Area Network) cards and modems. The communication components perform communication processing via networks such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical discs, magneto-optical discs, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage components as required.

[0100] The above description is merely a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be determined by the scope of protection claimed in the claims.

[0101] In practice, the collection and processing of data in this application should strictly comply with the requirements of relevant national laws and regulations, obtain the informed consent or separate consent of the data subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the data subject.

Claims

1. A numerical simulation method for condensation phase change, characterized in that, Applied to non-ideal binary mixtures, the method includes: A basic numerical framework based on the Lee model is constructed; wherein, the Lee model includes a phase change mass source term and a phase change energy source term; Obtain the gas-liquid equilibrium phase diagram data of the non-ideal binary mixture, and construct an explicit phase equilibrium correlation between the component content of each component and the saturation temperature. Based on the differences in volatility of each component, a differentiated saturation temperature is matched for each component; During the iterative process of numerical simulation of condensation phase change, the real-time component content of the computational grid cells is obtained in each iteration time step; The real-time saturation temperature of the corresponding component is determined based on the phase equilibrium explicit correlation and the real-time component content, and the phase change mass source term and phase change energy source term of the corresponding component are corrected based on the real-time saturation temperature.

2. The simulation method according to claim 1, characterized in that, The non-ideal binary mixture includes a first component and a second component, wherein the volatility of the first component is less than that of the second component; Construct explicit phase equilibrium correlations between the component content of each component and the saturation temperature, including: The gas-liquid equilibrium phase diagram data are fitted using polynomial fitting, and explicit phase equilibrium correlations for the first component and the second component are constructed respectively; wherein, the independent variable of the explicit phase equilibrium correlation is the gas phase mole fraction or the liquid phase mole fraction, and the dependent variable is the saturation temperature; During the fitting process, the order of the polynomial fitting corresponding to the first component is less than the order of the polynomial fitting corresponding to the second component.

3. The simulation method according to claim 2, characterized in that, The first component corresponds to a polynomial fitting order of 3, and the second component corresponds to a polynomial fitting order of 6.

4. The simulation method according to claim 2, characterized in that, The method of matching differentiated saturation temperatures to each component based on their volatility differences includes: To match the dew point temperature of the non-ideal binary mixture to the first component; To match the bubble point temperature of the non-ideal binary mixture to the second component.

5. The simulation method according to claim 4, characterized in that, In the predetermined component gradient region or gas-liquid interface region of the non-ideal binary mixture, the dew point temperature and the bubble point temperature are smoothed; wherein... The preset component gradient region includes an azeotropic composition region, and the smoothing process includes at least one of smooth interpolation, weighted average, and piecewise function processing.

6. The simulation method according to claim 1, characterized in that, The iterative process of numerical simulation of condensation phase change also includes: The mole fraction of instantaneous gas phase components in each computational grid cell is extracted using a custom function; Based on the phase equilibrium explicit correlation and the mole fraction of the instantaneous gas phase components, update the real-time saturation temperature of each component at the current time step. Based on the updated real-time saturation temperature and the Lee model, the corresponding instantaneous phase transition source terms are determined and transiently coupled; wherein, the instantaneous phase transition source terms include instantaneous phase transition mass source terms and instantaneous phase transition energy source terms.

7. The simulation method according to any one of claims 1 to 6, characterized in that, The non-ideal binary mixture is water-ethanol or water-ethylene glycol.

8. A numerical simulation device for condensation phase change, characterized in that, Applied to non-ideal binary mixtures, the device includes: The module is used to construct a basic numerical framework based on the Lee model; wherein the Lee model includes a phase change mass source term and a phase change energy source term; the gas-liquid equilibrium phase diagram data of the non-ideal binary mixture is obtained, and an explicit phase equilibrium correlation between the component content of each component and the saturation temperature is constructed. The matching module is used to match different saturation temperatures for each component based on the differences in volatility. The acquisition module is used to acquire the real-time component content of the computational grid cells in each iteration time step during the iterative process of numerical simulation of condensation phase change. The correction module is used to determine the real-time saturation temperature of the corresponding component based on the phase equilibrium explicit correlation and the real-time component content, and to correct the phase change mass source term and phase change energy source term of the corresponding component based on the real-time saturation temperature.

9. An electronic device, characterized in that, include: Controller; A memory for storing one or more programs, which, when executed by a controller, cause the controller to implement the numerical simulation method for condensation phase change as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a condensation phase change numerical simulation device / electronic device, causes the condensation phase change numerical simulation device / electronic device to perform the steps of the condensation phase change numerical simulation method as described in any one of claims 1 to 7.

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