Simulation convergence judgment method, electronic equipment and computer readable storage medium

Abstract

The invention relates to the field of simulation, and discloses a simulation convergence judgment method, electronic equipment and a computer readable storage medium. The method comprises the following steps: constructing a gas flow simulation equation in a target space based on the target space, wherein the target space comprises at least one return air inlet; simulating gas flow in the target space by utilizing iteration of the simulation equation; after each time of iteration of the simulation equation, at least one of the current space average temperature of the target space, the first average value of the target space, the current air opening average temperature of all the air return openings and each second average value corresponding to each air return opening is obtained; and in response to the condition that the preset condition is met, determining that the result of the simulation equation meets convergence, and stopping iteration of the simulation equation. In this way, the accuracy of simulation convergence judgment can be improved.

Description

Technical Field

[0001] The present application relates to the field of simulation, and in particular to a simulation convergence determination method, an electronic device and a computer-readable storage medium. Background Art

[0002] In the field of building airflow simulation, the residual in the equation solving process is usually used to determine whether the result has converged and whether the calculation process has reached a stable state. However, for different scenarios, the residual of the equation can be very low in some scenarios, but it is difficult for the residual of the equation to reach a very low standard in some scenarios. If the residual value of the equation corresponding to the convergence standard is increased, it is possible that the equation may be mistakenly judged to have reached convergence before it is stable, and the equation iteration process is terminated prematurely, resulting in calculation errors. Summary of the invention

[0003] The main purpose of this application is to provide a simulation convergence determination method, an electronic device and a computer-readable storage medium, which can improve the accuracy of simulation convergence determination.

[0004] In order to solve the above technical problems, the first technical solution adopted by the present application is: to provide a simulation convergence determination method. The method includes constructing a gas flow simulation equation in a target space based on a target space, wherein the target space includes at least one return air outlet; simulating the gas flow in the target space by iterating the simulation equation; obtaining at least one of the following after each iteration of the simulation equation: a current spatial average temperature of the target space, a first average value of the target space, a current average air outlet temperature of all return air outlets, and each second average value corresponding to each return air outlet; wherein the first average value is an average value of the current spatial average temperature and a historical spatial average temperature of a preset number of iterations, and each second average value is an average value of the current average air outlet temperature of each return air outlet and a historical average air outlet temperature of a corresponding preset number of iterations; in response to satisfying a preset condition, determining that a result of the simulation equation satisfies convergence, and stopping the iteration of the simulation equation; wherein the preset condition includes at least one of the following: a fluctuation degree of the spatial average temperature within a current and preset number of iterations is less than a first preset value, a fluctuation degree of the average air outlet temperature of the return air outlets that is higher than a first preset proportion within a current and preset number of iterations is less than a second preset value, a fluctuation degree of the first average value within a current and preset number of iterations is less than a third preset value, and a fluctuation degree of the second average value of the return air outlets that is higher than a second preset proportion within a current and preset number of iterations is less than a fourth preset value.

[0005] To solve the above technical problem, the second technical solution adopted by the present application is to provide an electronic device, which includes a memory and a processor, wherein the memory is used to store program data, and the program data can be executed by the processor to implement the method described in the first technical solution.

[0006] To solve the above technical problems, the third technical solution adopted by the present application is to provide a computer-readable storage medium. The computer-readable storage medium stores program data and can be executed by a processor to implement the method described in the first technical solution.

[0007] The beneficial effects of the present application include at least: by acquiring the current temperature data of the current iteration process of the target space and the return air outlet and the historical temperature data of the preset iteration number, it is determined whether at least one of the following conditions is met: the fluctuation degree of the average temperature of the target space within the current and preset iteration number is less than the first preset value, the fluctuation degree of the average temperature of the return air outlet that is higher than the first preset ratio within the current and preset iteration number is less than the second preset value, the fluctuation degree of the first average value within the current and preset iteration number is less than the third preset value, and the fluctuation degree of the second average value of the return air outlet that is higher than the second preset ratio within the current and preset iteration number is less than the fourth preset value. If it is met, convergence is determined. By judging the fluctuation degree of the temperature data to determine whether the simulation result converges, the accuracy of the convergence judgment is improved, and the convergence timing can be accurately determined, so that when the residual of the equation is large when the simulation equation converges, the number of simulations and the simulation time can be reduced, and the simulation efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which: Figure 1 It is a schematic diagram of simulation results of prior art one; Figure 2 It is a schematic diagram of simulation results of prior art one; Figure 3 It is a flowchart of the first embodiment of the simulation convergence determination method of the present application; Figure 4 It is a flow chart of the second embodiment of the simulation convergence determination method of the present application; Figure 5 It is a flowchart of a specific embodiment of the simulation convergence determination method of the present application; Figure 6 It is a schematic diagram of the convergence of simulation results; Figure 7 It is a schematic diagram of the convergence of simulation results; Figure 8 It is a structural schematic diagram of an embodiment of the electronic device of the present application; Fig. 9It is a structural diagram of an embodiment of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0010] The terms "first", "second", etc. in this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0011] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0012] In the field of indoor and outdoor airflow simulation, in some cases, the residual of the equation can be used to determine whether the result has converged. Figure 1 , Figure 1 It is a schematic diagram of the simulation results of the prior art. The horizontal axis is time, and the vertical axis is the residual of the equation. The residual numerical results of each parameter are shown in the figure. The parameters may include temperature, velocity components in the three directions of x, y, and z, turbulent kinetic energy dissipation rate, turbulent kinetic energy, pressure, etc. It can be seen that at 200 seconds, each residual result has dropped to a level less than 0.01, and even multiple residuals have reached below 0.0001. In some cases, the numerical value of the residual of the equation is still large when the result converges, refer to Figure 2 , Figure 2 It is a schematic diagram of the simulation result of the prior art. If it is based on the conventional residual judgment standard, it cannot meet the residual judgment standard, and the simulation result cannot be judged as converged, and the simulation equation will continue to iterate. If the residual judgment standard is raised, it may cause the convergence to be judged in advance, and the equation iteration is terminated when the result of the simulation equation has not yet converged. Therefore, the present application proposes a simulation convergence judgment method, which can be specifically referred to the following embodiments.

[0013] Reference Figure 3 , Figure 3 This is a flow chart of the first embodiment of the simulation convergence determination method of the present application, which includes but is not limited to the following steps.

[0014] S11: constructing a gas flow simulation equation in the target space based on the target space, where the target space includes at least one return air outlet.

[0015] S12: Simulate the gas flow in the target space by iterating the simulation equation.

[0016] First, according to the structure of the target space, the corresponding simulation equation is constructed to simulate the gas flow in the target space. The target space includes at least one return air outlet. The main function of the return air outlet is to absorb the gas in the target space and send it back to the airflow processing equipment for filtering and adjustment again. The target space may also include other air outlets, such as air outlets, exhaust outlets, fresh air outlets and the like. The air outlet is the air outlet through which the airflow processing equipment transports the airflow into the target space. The exhaust outlet is the air outlet that discharges the airflow in the target space to the outside of the target space. The fresh air outlet is the air outlet that transports the airflow outside the target space that has not been processed by the airflow processing equipment to the target space.

[0017] S13: After each iteration of the simulation equation, at least one of the following is obtained: the current space average temperature of the target space, the first average value of the target space, the current air outlet average temperature of all return air outlets, and each second average value corresponding to each return air outlet.

[0018] Optionally, the current space average temperature of the target space may be obtained.

[0019] Optionally, a first average value of the target space may be obtained. The first average value is the average value of the current space average temperature and the historical space average temperature of a preset number of iterations. The first average value is obtained by adding the current space average temperature obtained by the current iteration and the historical space average temperature obtained by the previous iteration and then averaging them. The preset number of iterations is at least one iteration number before the current number of iterations. For example, assuming that the current number of iterations is 100, the preset number of iterations includes three iteration numbers, and the preset number of iterations may be 97, 98, and 99.

[0020] Optionally, the current average air outlet temperature of all return air outlets may be obtained.

[0021] Optionally, each second average value corresponding to each return air outlet may be obtained. Each second average value is an average value of the current air outlet average temperature of each return air outlet and the historical air outlet average temperature of the corresponding preset number of iterations.

[0022] Optionally, the current space average temperature of the target space and the first average temperature of the target space may be acquired.

[0023] Optionally, the current average air outlet temperature of all return air outlets and each second average value corresponding to each return air outlet may be obtained.

[0024] Optionally, the current space average temperature of the target space, the first average value of the target space, the current air outlet average temperature of all return air outlets, and each second average value corresponding to each return air outlet may be obtained.

[0025] Other possible acquisition situations will not be described here.

[0026] Since the preset number of iterations is at least one number of iterations before the current number of iterations, in one embodiment, the number intervals of all the number of iterations in the preset number of iterations are the same. For example, assuming that the current number of iterations is 100, the intervals of all the number of iterations in the preset number of iterations is 1, and the preset number of iterations includes three numbers of iterations, then the preset number of iterations may be 99, 97, and 95. Alternatively, the preset number of iterations may be 98, 96, and 94.

[0027] In one embodiment, the intervals of all the iteration numbers in the preset iteration number are zero, that is, all the iteration numbers in the preset iteration number are continuous iteration numbers.

[0028] Further, in one embodiment, the maximum number of iterations in the preset number of iterations is one less than the current number of iterations. If it is continuous with all the numbers of iterations in the preset number of iterations, it is also continuous with the current number of iterations. For example, if the current number of iterations is N, and the preset number of iterations includes three numbers of iterations, then the preset numbers of iterations are N-3, N-2, and N-1. N-1 is one less than N, and the new continuous numbers of iterations formed by the preset number of iterations and the current number of iterations are N-3 to N.

[0029] S14: In response to satisfying a preset condition, determining that a result of the simulation equation satisfies convergence, and stopping iteration of the simulation equation.

[0030] Among them, the preset conditions include at least one of the following: the fluctuation degree of the average temperature of the space within the current and preset number of iterations is less than the first preset value, the fluctuation degree of the average air outlet temperature of the return air outlet that is higher than the first preset proportion within the current and preset number of iterations is less than the second preset value, the fluctuation degree of the first average value within the current and preset number of iterations is less than the third preset value, and the fluctuation degree of the second average value of the return air outlet that is higher than the second preset proportion within the current and preset number of iterations is less than the fourth preset value.

[0031] When the acquired data includes the current spatial average temperature of the target space, the preset condition may include that the fluctuation degree of the spatial average temperature within the current and preset iteration times is less than a first preset value. This is to determine whether the simulation result converges by monitoring whether the change of the average temperature of the entire target space is less than a predetermined value.

[0032] When the acquired data includes a first average value of the target space, the preset condition may include that the fluctuation degree of the average temperature of the return air outlets that is higher than the first preset ratio within the current and preset iteration times is less than a second preset value. This is to determine whether the simulation result converges by monitoring whether the change of the average temperature of all return air outlets in the target space is less than a predetermined value.

[0033] When the acquired data includes the current average air outlet temperature of all return air outlets, the preset condition may include that the fluctuation degree of the first average value within the current and preset iteration times is less than the third preset value. This is to determine whether the simulation result converges by monitoring whether the change of the average value of the average temperature of the entire target space is less than the predetermined value.

[0034] When the acquired data includes each second average value corresponding to each return air outlet, the preset condition may include that the fluctuation degree of the second average value of the return air outlets higher than the second preset proportion within the current and preset iteration times is less than a fourth preset value. This is to determine whether the simulation result converges by monitoring whether the change in the average value of the average temperature of all return air outlets in the target space is less than a predetermined value.

[0035] In one embodiment, when making a convergence judgment, the convergence conditions include that the fluctuation degree of the average temperature of the space within the current and preset number of iterations is less than a first preset value and that the fluctuation degree of the first average value within the current and preset number of iterations is less than a third preset value. The convergence is judged by the change in the average temperature of the entire field and all return air outlets in the target space. The double judgment improves the accuracy of the convergence judgment.

[0036] In one embodiment, when determining convergence, the convergence conditions include that the fluctuation degree of the first average value within the current and preset number of iterations is less than the third preset value and the fluctuation degree of the second average value of the return air outlet that is higher than the second preset proportion within the current and preset number of iterations is less than the fourth preset value. The convergence is determined by the change in the average value of the average temperature of the entire field and all return air outlets in the target space. The double determination improves the accuracy of the convergence determination.

[0037] In one embodiment, when determining convergence, the convergence conditions include the above four conditions, and whether the result is converged is determined by both the change in the average temperature and the change in the average value of the average temperature, so that the convergence determination is more accurate.

[0038] The first preset value, the second preset value, the third preset value, the fourth preset value, the first preset ratio and the second preset ratio can be set according to the specific circumstances of the simulation experiment, and their values ​​can be the same or different, and are not limited here.

[0039] In one embodiment, the degree of fluctuation may include at least one of the following: a difference between a maximum value and a minimum value, a standard deviation, and a variance.

[0040] For example, if the fluctuation degree is the difference between the maximum value and the minimum value, then the fluctuation degree of the average temperature of the space within the current and preset iterations is less than the first preset value, that is, within the current and preset iterations, the difference between the maximum value of the average temperature of the space and the minimum value of the average temperature of the space is less than the first preset value. Then the fluctuation degree of the average temperature of the return air outlets that is higher than the first preset proportion within the current and preset iterations is less than the second preset value, that is, within the current and preset iterations, the number of return air outlets whose difference between the maximum value of the average temperature of the air outlet and the minimum value of the average temperature of the air outlet is less than the second preset value accounts for more than the first preset proportion of the total data of the return air outlets.

[0041] In the first embodiment, by acquiring the current temperature data of the current iteration process of the target space and the return air outlet and the historical temperature data of the preset iteration number, it is determined whether at least one of the following conditions is met: the fluctuation degree of the average temperature of the target space within the current and preset iteration number is less than the first preset value, the fluctuation degree of the average temperature of the return air outlet that is higher than the first preset ratio within the current and preset iteration number is less than the second preset value, the fluctuation degree of the first average value within the current and preset iteration number is less than the third preset value, and the fluctuation degree of the second average value of the return air outlet that is higher than the second preset ratio within the current and preset iteration number is less than the fourth preset value. If it is met, convergence is determined. By judging the fluctuation degree of the temperature data to determine whether the simulation result converges, the accuracy of the convergence judgment is improved, and the convergence timing can be accurately determined, so that when the residual of the equation is large when the simulation equation converges, the number of simulations and the simulation time can be reduced, and the simulation efficiency is improved.

[0042] Reference Figure 4 , Figure 4 This is a flow chart of the second embodiment of the simulation convergence determination method of the present application, which includes but is not limited to the following steps.

[0043] S21: Obtain the equation residual corresponding to the current iteration process of the simulation equation.

[0044] S22: In response to the equation residual being greater than a fifth preset value, obtaining at least one of the following: the current space average temperature of the target space, the first average value, the current air outlet average temperature of all return air outlets, and each second average value corresponding to each return air outlet.

[0045] Before obtaining at least one of the current space average temperature of the target space, the first average value, the current air outlet average temperature of all return air outlets, and each second average value corresponding to each return air outlet, the equation residual corresponding to the current iteration process of the simulation equation is first obtained.

[0046] In the usual equation iteration simulation process, the equation residual in the equation iteration process is used to determine whether the simulation result has converged. The equation residual is the value of the same parameter after the current number of iterations minus the value after the previous number of iterations. Therefore, before using temperature data for convergence judgment, the equation residual can be used to determine whether the result has converged.

[0047] A fifth preset value is set to determine whether the result converges through the equation residual. If it is less than or equal to the fifth preset value, it means that the result converges and the equation can stop iterating.

[0048] If it is greater than the fifth preset value, it means that the result cannot be determined to be converged by the equation residual. For example, for a simulation where the residual is still large when converged, it is impossible to determine the convergence by the equation residual, so the temperature data in the target space is further obtained to determine whether the simulation result is converged by the temperature data.

[0049] In one embodiment, after obtaining the equation residual of the equation, the next equation iteration is performed in response to the equation residual being greater than the fifth preset value. If after one iteration, the equation residual is greater than the fifth preset value, the next equation iteration can be performed directly. This is because it takes a certain amount of time to obtain the temperature data in the target space and to make a convergence judgment based on the temperature data. If it is judged that it does not converge, the next iteration is continued. If it is judged that it converges, the iteration is stopped. If it is judged that it converges, the subsequent iterations are also convergent, so the impact on the final result is not significant. If it is judged that it does not converge, the iteration process is performed in advance, saving simulation time. This step is mainly to save simulation time and improve simulation efficiency.

[0050] In one embodiment, before obtaining the equation residual of the equation, including within a preset initial number of iterations, no convergence determination is performed. The initial number of iterations is at least one number of iterations starting from the first iteration. Since the equation will not converge immediately, convergence determination may not be performed in the initial number of iterations, and convergence determination is performed after exceeding a certain number of iterations. This step can save computing resources, reduce simulation time, and improve simulation efficiency.

[0051] The convergence determination includes a convergence determination based on the residual of the equation and / or a convergence determination based on the temperature data in the target space. The convergence determination based on the temperature data in the target space may include a convergence determination based on at least one of the following data: the current space average temperature of the target space, the first average value of the target space, the current air outlet average temperature of all return air outlets, and each second average value corresponding to each return air outlet. The corresponding convergence determination conditions may refer to the description in the above embodiment.

[0052] The following is a specific example to illustrate the simulation convergence determination method of the present application in more detail. Figure 5 , Figure 5 It is a flowchart of a specific embodiment of the simulation convergence determination method of the present application.

[0053] For HVAC airflow simulations in buildings, convergence is determined in the following manner.

[0054] Step 1: Generate lists for storing statistics in advance, listList1, list1, listList2, and list2.

[0055] listList1, the length is equal to the number of return air outlets, each unit is also a list, the length is nWindow.

[0056] list1, the length is equal to nWindow, and the data type of each unit is scalar type.

[0057] listList2, its length is equal to the number of return air vents, and each unit is also a list with a length of nWindow.

[0058] list2, the length is equal to nWindow, and the data type of each unit is scalar type.

[0059] Step 2: Iterate the equation, and do not make convergence judgment for the initial nSkip iterations.

[0060] Step 3: Calculate the residuals of each equation in each iteration step during the CFD simulation process. If the convergence criteria are not met, proceed to step 4, otherwise, the calculation stops if the convergence criteria are met.

[0061] Step 4: After each iteration, the average temperature TReturn of each return air outlet is counted in real time. TReturn of each air outlet is inserted into the end of the corresponding cell list of listList1. If the list is full, delete the head element of the list to keep its length as nWindow.

[0062] Step 5: After each iteration, the average temperature TField of the entire field is counted in real time. TField is inserted to the end of list1. If the length of the list exceeds nWindow, the head element of the list is deleted to keep its length to nWindow.

[0063] Step 6: After each iteration step, the average temperature of each return air outlet within nWindow iteration steps is counted in real time. The average value TReturnMean of each air outlet is inserted into the end of the corresponding unit list of listList2. If the list is full, delete the head element of the list to keep its length as nWindow.

[0064] Step 7: After each iteration step, the average temperature of the entire field within nWindow iteration steps is counted in real time as TFieldMean, and TFieldMean is inserted to the end of list2. If the list length exceeds nWindow, delete the head element of the list to keep its length at nWindow.

[0065] The order of calculating and counting the above data is only for illustration and does not limit it.

[0066] Step 8: Count the difference between the maximum and minimum values ​​of each cell in listList1, and then count the proportion of interpolation values ​​less than the predetermined value A. If the proportion is higher than the predetermined proportion X%, the condition is met, indicating that TReturn meets the condition.

[0067] Step 9: Count the difference between the maximum and minimum values ​​in list1 and determine whether it is less than the preset value B. If so, the condition is met, indicating that TField meets the condition.

[0068] Step 10: Count the difference between the maximum and minimum values ​​of each cell in listList2, and then count the proportion of interpolation values ​​less than the predetermined value C. If the proportion is higher than the predetermined proportion Y%, the condition is met, indicating that TReturnMean meets the condition.

[0069] Step 11: Count the difference between the maximum and minimum values ​​in list2 and determine whether it is less than the predetermined value D. If so, the condition is met, indicating that TFieldMean meets the condition.

[0070] Step 12: If the conditions in step 8 and step 9 are satisfied, and / or the conditions in step 10 and step 11 are satisfied, then the convergence is judged and the calculation is stopped; otherwise, the next iteration is continued and the following steps in step 3 are repeated. The judgment of step 8 and step 9 is to judge whether the convergence is based on the change of the average temperature value, and the judgment of step 10 and step 11 is to judge whether the convergence is based on the change of the average value of the average temperature value. Each judgment step can be separated or judged at the same time. The figure shows that the judgment is based on the change of the average temperature value and the change of the average value of the average temperature value at the same time.

[0071] The residuals do not converge, but the statistical values ​​change slightly, satisfying convergence ( Figure 6 , Figure 6 This is a schematic diagram of the convergence of simulation results. Figure 6 What is shown is the convergence result of temperature simulation of each tuyere, the horizontal axis is time, the vertical axis is Kelvin temperature, and the temperature value is stable after convergence). The corresponding conditions are met in the eighth step and the ninth step.

[0072] Some statistics change greatly, but their mean changes are small within nWindow iterations, meeting the convergence criteria ( Figure 7 , Figure 7 It is a simulation result convergence diagram, which shows the simulation convergence result of an air outlet. The horizontal axis is time and the vertical axis is Kelvin temperature. The temperature value is in an oscillating state and changes greatly, but the temperature mean value changes little within the iteration step). The corresponding tenth step meets the conditions and the eleventh step meets the conditions.

[0073] The statistical process of the eighth to tenth steps can be performed simultaneously or in steps, such as calculating the statistical results of the eighth and / or ninth steps first, and then calculating the statistical results of the tenth and / or eleventh steps. Alternatively, only one of the steps can be statistically performed.

[0074] For example, only the statistics and judgment of the eighth step can be performed to determine whether it satisfies convergence.

[0075] For example, only the statistics and judgment of the ninth step can be performed to determine whether it satisfies convergence.

[0076] For example, only the statistics and judgment of the tenth step can be performed to determine whether it meets the convergence requirement.

[0077] For example, only the statistics and judgment of the eleventh step can be performed to determine whether it satisfies convergence.

[0078] like Figure 8 As shown, Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application.

[0079] The electronic device includes a processor 110 and a memory 120 .

[0080] The processor 110 controls the operation of the electronic device, and the processor 110 may also be referred to as a CPU (Central Processing Unit). The processor 110 may be an integrated circuit chip having the ability to process a signal sequence. The processor 110 may also be a general-purpose processor, a digital signal sequence processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0081] The memory 120 stores instructions and program data required for the processor 110 to operate.

[0082] The processor 110 is used to execute instructions to implement the method provided by any embodiment and possible combination of the simulation convergence determination method described above in the present application.

[0083] like Fig. 9 As shown, Fig. 9 This is a schematic diagram of the structure of an embodiment of a computer-readable storage medium of the present application.

[0084] An embodiment of a readable storage medium of the present application includes a memory 210, and the memory 210 stores program data. When the program data is executed, the method provided by any embodiment and possible combination of the simulation convergence determination method of the present application is implemented.

[0085] The memory 210 may include a medium that can store program instructions, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or may be a server that stores the program instructions. The server may send the stored program instructions to other devices for execution, or may execute the stored program instructions itself.

[0086] In summary, by acquiring the current temperature data of the current iteration process of the target space and the return air outlet and the historical temperature data of the preset iteration number, it is determined whether it satisfies at least one of the following conditions: the fluctuation degree of the average temperature of the target space within the current and preset iteration number is less than the first preset value, the fluctuation degree of the average temperature of the return air outlet that is higher than the first preset proportion within the current and preset iteration number is less than the second preset value, the fluctuation degree of the first average value within the current and preset iteration number is less than the third preset value, and the fluctuation degree of the second average value of the return air outlet that is higher than the second preset proportion within the current and preset iteration number is less than the fourth preset value. If it is satisfied, convergence is determined. By judging the fluctuation degree of the temperature data to determine whether the simulation result converges, the accuracy of the convergence judgment is improved, and the convergence timing can be accurately determined, so that when the residual of the equation is large when the simulation equation converges, the number of simulations and the simulation time can be reduced, and the simulation efficiency is improved.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only illustrative, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0089] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0090] If the integrated units in the above other embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program codes.

[0091] The above descriptions are merely embodiments of the present application and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A simulation convergence determination method, characterized in that: The method comprises: Constructing a gas flow simulation equation in the target space based on the target space, wherein the target space includes at least one return air outlet; Simulating the gas flow in the target space by iterating the simulation equation; After each iteration of the simulation equation, at least one of the following is obtained: the current space average temperature of the target space, the first average value of the target space, the current air outlet average temperature of all the return air outlets, and each second average value corresponding to each return air outlet; wherein the first average value is the average value of the current space average temperature and the historical space average temperature of the preset number of iterations, and each second average value is the average value of the current air outlet average temperature of each return air outlet and the historical air outlet average temperature of the corresponding preset number of iterations; In response to satisfying a preset condition, determining that the result of the simulation equation meets convergence, and stopping iteration of the simulation equation; Among them, the preset conditions include at least one of the following: the fluctuation degree of the average temperature of the space within the current and preset number of iterations is less than a first preset value, the fluctuation degree of the average air outlet temperature of the return air outlet that is higher than a first preset proportion within the current and preset number of iterations is less than a second preset value, the fluctuation degree of the first average value within the current and preset number of iterations is less than a third preset value, and the fluctuation degree of the second average value of the return air outlet that is higher than a second preset proportion within the current and preset number of iterations is less than a fourth preset value.

2. The method according to claim 1, characterized in that Before obtaining at least one of the current space average temperature of the target space, the first average temperature of the target space, the current air outlet average temperature of all the return air outlets, and each second average temperature corresponding to each of the return air outlets, the method includes: Obtaining the equation residual corresponding to the current iteration process of the simulation equation; In response to the equation residual being greater than a fifth preset value, at least one of the following is obtained: the current space average temperature of the target space, the first average value, the current air outlet average temperature of all the return air outlets, and each of the second average values ​​corresponding to each of the return air outlets.

3. The method according to claim 2, characterized in that After obtaining the residual of the equation, it includes: In response to the equation residual being greater than the fifth preset value, performing the next equation iteration.

4. The method according to claim 2, characterized in that: Before obtaining the equation residual, the method includes: No convergence determination is performed within the preset initial number of iterations.

5. The method according to claim 1, characterized in that The number intervals of all the iteration numbers in the preset number of iterations are the same.

6. The method according to claim 5, characterized in that The interval between all the iteration times in the preset iteration times is zero.

7. The method according to claim 6, characterized in that The maximum number of iterations in the preset number of iterations is one less than the current number of iterations.

8. The method according to claim 1, characterized in that The degree of fluctuation includes at least one of the following: a difference between a maximum value and a minimum value, a standard deviation, and a variance.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store program data, and the program data can be executed by the processor to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: Program data is stored and can be executed by a processor to implement the method according to any one of claims 1 to 8.

Images