Parallel Computing Method, Device, Medium and Product for Flame Surface Data of Combustion Model

Abstract

The present disclosure relates to the technical field of computational fluid dynamics, and in particular, provides a parallel computing method, device, medium, and product for flame surface data of a combustion model. The parallel computing method for flame surface data of the combustion model includes: constructing a set of laminar flame cases and a shared array; based on the extinction mixture fraction upper and lower limit arrays, parallelly determining the extinction states of the laminar flame cases. In the case where the extinction state is unextinguished, parallelly calculating the laminar flame cases to obtain laminar flame data and updating the shared array; after the parallel calculation is completed, updating the extinction mixture fraction upper and lower limit arrays based on the laminar flame propagation speed array; and converting the laminar flame data into turbulent flame data through turbulent probability density function integration. The present disclosure transforms the serial calculation process with a sequential calculation order into a parallel calculation process of multi-process synchronization through a parallel strategy of updating the shared array in real time and determining whether new cases need to be calculated through the latest shared array.

Description

Technical Field

[0001] The present disclosure relates to the technical field of computational fluid dynamics, and particularly to a method, device, medium, and product for parallel computing of flame surface data of a combustion model. Background Art

[0002] In the combustion simulation calculation of an aero-engine, a flame surface type combustion model is usually used to describe the turbulent combustion process. The flame surface type combustion model describes the shape and position of the flame through a scalar field (i.e., a function of the flame surface position), and calculates the propagation speed and direction of the flame in the flow field according to the data provided by the turbulent flame database.

[0003] In the related art, the process of constructing a turbulent flame database includes calculating the laminar flame case data at different mixture fractions, and then converting the laminar data into turbulent flame data based on the integral method of the turbulent probability density function, thereby forming a turbulent flame database.

[0004] However, when calculating the laminar flame cases, it is necessary to determine whether each case is in a stable ignition state, and the state determination depends on the results of the previous case. This dependency relationship between the case results leads to the serial sequential calculation method being used when calculating the laminar flame cases, significantly reducing the calculation efficiency when constructing the flame surface database. Summary of the Invention

[0005] In view of this, the exemplary embodiments of the present disclosure provide a method, device, medium, and product for parallel computing of flame surface data of a combustion model to solve the problems existing in the related art.

[0006] One aspect of the exemplary embodiments of the present disclosure provides a method for parallel computing of flame surface data of a combustion model, the method comprising:

[0007] Constructing a set of laminar flame cases and a shared array, the set of laminar flame cases including laminar flame cases corresponding to different mixture fractions respectively, and the shared array including an array of upper and lower limits of the extinction mixture fraction and an array of laminar flame propagation speeds;

[0008] Based on the array of upper and lower limits of the extinction mixture fraction, parallelly determining the extinction states of the laminar flame cases, and in the case where the extinction state is unextinguished, parallelly calculating the laminar flame cases to obtain laminar flame data corresponding to different mixture fractions respectively, and updating the shared array;

[0009] After the parallel computing is completed, updating the array of upper and lower limits of the extinction mixture fraction based on the array of laminar flame propagation speeds;

[0010] Converting the laminar flame data into turbulent flame data through the integral of the turbulent probability density function.

[0011] Another aspect of the exemplary embodiments of the present disclosure provides a computer device, including a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the method of the exemplary embodiments of the present disclosure.

[0012] Another aspect of the exemplary embodiments of the present disclosure provides a computer-readable storage medium, on which a computer program / instructions are stored, and when the computer program / instructions are executed by a processor, the method of the exemplary embodiments of the present disclosure is implemented.

[0013] Another aspect of the exemplary embodiments of the present disclosure provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the method of the exemplary embodiments of the present disclosure is implemented.

[0014] As will be described in detail below, a parallel calculation method for flame surface data of a combustion model according to an embodiment of the present disclosure constructs a set of laminar flame cases and a shared array by constructing a set of laminar flame cases corresponding to different mixture fractions respectively, and the shared array includes an array of upper and lower limits of extinction mixture fractions and an array of laminar flame propagation speeds; based on the array of upper and lower limits of extinction mixture fractions, the extinction states of each laminar flame case are judged in parallel, and when the extinction state is unextinguished, the laminar flame cases are calculated in parallel to obtain laminar flame data corresponding to different mixture fractions respectively, and the shared array is updated; after the parallel calculation is completed, the array of upper and lower limits of extinction mixture fractions is updated based on the array of laminar flame propagation speeds; through the integration of the turbulent probability density function, the laminar flame data is converted into turbulent flame data. Therefore, in the process of calculating the laminar flame data, the parallel calculation method for flame surface data of the combustion model provided by the present disclosure synchronizes the calculation results of the cases in real time in the form of a shared array, and judges whether new cases need to be calculated through the real-time updated data. This parallel strategy transforms the serial calculation process with a sequential calculation order into a parallel calculation process of multi-process synchronization calculation. In addition, the main process effectively identifies the critical mixture fraction of extinction and determines the final upper limit of extinction, so as to greatly improve the calculation efficiency of the flame surface database and significantly shorten the calculation time under the premise of ensuring that the extinction upper and lower limit criteria remain unchanged. Description of the Drawings

[0015] By describing the embodiments of the present disclosure in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present disclosure will become more obvious. The drawings are used to provide a further understanding of the embodiments of the present disclosure, and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation to the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1Schematic flowchart of the parallel computing method for flame surface data of the combustion model provided by an exemplary embodiment of the present disclosure;

[0017] Figure 2 Schematic flowchart of the process for constructing a set of laminar flame cases and a shared array provided by an exemplary embodiment of the present disclosure;

[0018] Figure 3 Schematic flowchart of the parallel computing of laminar flame cases provided by an exemplary embodiment of the present disclosure;

[0019] Figure 4 Schematic flowchart of the main process for determining the upper and lower limits of flameout provided by an exemplary embodiment of the present disclosure;

[0020] Figure 5 Schematic flowchart of the process for constructing turbulent flame data provided by an exemplary embodiment of the present disclosure;

[0021] Figure 6 Schematic block diagram of the functional modules of the parallel computing device for flame surface data of the combustion model provided by an exemplary embodiment of the present disclosure;

[0022] Figure 7 Schematic block diagram of the electronic device provided by an exemplary embodiment of the present disclosure;

[0023] Figure 8 Schematic diagram of the computer program product provided by an exemplary embodiment of the present disclosure. Detailed implementation manners

[0024] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Instead, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0025] It should be understood that the various steps recited in the method embodiments of the present disclosure can be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0026] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0027] It should be noted that the modifications of "one" and "a plurality of" mentioned in this disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0028] The names of the messages or information exchanged between multiple devices in the embodiments of this disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0029] It can be understood that before using the technical solutions disclosed in the embodiments of this disclosure, the types, usage scopes, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] For example, when responding to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, an application program, a server, or a storage medium that performs the operations of the technical solutions of this disclosure according to the prompt message.

[0031] As an optional but non-limiting implementation manner, the manner of sending a prompt message to the user in response to receiving an active request from the user can be, for example, in the form of a pop-up window. The prompt message can be presented in text in the pop-up window. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It can be understood that the above process of notifying and obtaining the user's authorization is only illustrative and does not limit the implementation manners of this disclosure. Other manners that meet relevant laws and regulations can also be applied to the implementation manners of this disclosure.

[0032] Before introducing the embodiments of this disclosure, the following interpretations are first made for the relevant terms involved in the embodiments of this disclosure:

[0033] Flamelet Database: A flamelet database is a library that contains a large amount of pre-computed laminar flame data. This data typically includes: flame speed, flame shape, the concentration and temperature distribution of various chemical substances in the flame, and reaction path analysis. When conducting flow combustion simulations of an aero-engine combustor, the flamelet database provides rich data resources, enabling the combustion model to use the data in the database to more accurately predict the flame behavior under specific flight conditions and combustor designs.

[0034] In aero-engine combustion simulation calculations, flamelet-based combustion models are typically used to describe the turbulent combustion process. Flamelet-based combustion models describe the shape and position of the flame through a scalar field (i.e., a function of the flamelet position), and calculate the propagation speed and direction of the flame in the flow field based on the data provided by the turbulent flame database.

[0035] In the related art, the process of constructing a turbulent flame database includes calculating the laminar flame case data at different mixture fractions, and then, based on the integral method of the turbulent probability density function, converting the laminar data into turbulent flame data, thereby forming a turbulent flame database.

[0036] However, when calculating laminar flame cases, it is necessary to determine whether each case is in a stable ignition state, and the state determination depends on the results of the previous case. This dependency relationship between case results leads to the calculation of laminar flame cases only being able to adopt a serial sequential calculation method, significantly reducing the calculation efficiency when constructing the flamelet database.

[0037] Therefore, to solve the above problems, the exemplary embodiments of the present disclosure provide a parallel calculation method for flamelet data of a combustion model. During the calculation process of laminar flame data, a parallel strategy of real-time synchronizing the case calculation results in the form of a shared array and using them to determine whether a new case needs to be calculated is adopted, transforming the serial calculation process with a sequential calculation order into a parallel calculation process of multi-process synchronization. On the premise of ensuring that the extinction upper and lower limit criteria remain unchanged, the calculation efficiency of the flamelet database is greatly improved, and the calculation duration is significantly shortened.

[0038] Exemplarily, Figure 1 is a schematic flow diagram of the parallel calculation method for flamelet data of a combustion model provided by an exemplary embodiment of the present disclosure, as Figure 1 shown, and specifically may include the following steps:

[0039] Step S110: Construct a set of laminar flame cases and a shared array.

[0040] Exemplarily, a set of laminar flame cases with different mixture fractions can be constructed according to the operating conditions. Among them, the operating conditions may include: ambient pressure, inlet fuel temperature, inlet air temperature, and the specified chemical reaction mechanism. The operating conditions are the basic input conditions for laminar flame simulation. Ambient pressure affects the rate and direction of chemical reactions during combustion; inlet fuel temperature and inlet air temperature affect fuel evaporation and initial reaction kinetics; the chemical reaction mechanism indicates that a specific chemical reaction mechanism is used in combustion simulation to accurately simulate the reaction between fuel and oxidizer. The mixture fraction is used to describe the mixing degree of fuel and oxidizer.

[0041] In addition, two shared arrays of the upper and lower limits of the extinction mixture fraction and the laminar flame propagation speed are also constructed. The array of the upper and lower limits of the extinction mixture fraction refers to the range of mixture ratios within which the flame can stably exist under a specific environment and chemical reaction mechanism; the array of laminar flame propagation speeds records the propagation speeds of the flame at different mixture fractions and is an important indicator for evaluating flame stability and kinetic characteristics.

[0042] Exemplarily, step S110 may include:

[0043] Obtain the operating conditions, calculation parameters, and the theoretical mixture fraction of the combustion reactants;

[0044] Construct different mixture fraction calculation arrays based on the theoretical mixture fraction;

[0045] Construct a set of laminar flame cases based on the operating conditions, calculation parameters, and different mixture fraction calculation arrays.

[0046] Exemplarily, Figure 2 The following is a schematic flow chart for constructing a set of laminar flame cases and shared arrays provided by an exemplary embodiment of the present disclosure. As Figure 2 shown, step S110 may include the following sub-steps:

[0047] Step S111: Obtain the operating conditions.

[0048] The operating conditions may include the ambient pressure of the calculation condition, inlet fuel temperature, inlet air temperature, names and mass percentages of each component of the fuel vapor, names and mass percentages of each component of the air, one-dimensional flame calculation domain length, chemical reaction mechanism file, thermodynamic file, and transport coefficient file.

[0049] Step S112: Set the calculation parameters of the calculation case.

[0050] The calculation parameters may include the initial number of grids, the maximum number of grids for iterative refinement, calculation residuals, and grid refinement setting parameters.

[0051] Initial number of grids: It is the number of grids used at the beginning of numerical simulation calculation.

[0052] Maximum number of meshes for iterative encryption: Defines the maximum number of meshes that can be reached during the mesh encryption process. As the number of iterations increases, if the error still does not reach the predetermined target, the mesh will continue to be refined until the maximum number of meshes is reached.

[0053] Calculating the residual: Reflects the deviation between the actual calculated value and the theoretical value, and is an important basis for judging whether the calculation converges.

[0054] Mesh encryption setting parameters: Control how the mesh is refined or coarsened according to the error estimate and calculation requirements. Mesh encryption can automatically increase the mesh density in areas that require higher resolution, such as areas with large temperature gradients, rapid velocity changes, or intense chemical reactions. An appropriate mesh encryption strategy can significantly improve the calculation efficiency and the accuracy of the results, while avoiding the huge computational burden brought by uniformly refining the mesh throughout the computational domain.

[0055] Step S113: Create a folder for storing laminar flame data.

[0056] To systematically manage the generated data, a dedicated folder can be created to store the calculation results and related data.

[0057] Step S114: Calculate the theoretical mixture fraction (Stoichiometric Mixture Fraction, FST) of the reactants.

[0058] The theoretical mixture fraction at which the reactants react exactly completely can be obtained based on the names of the components of the fuel vapor and their mass fractions, and the names of the components of the air and their mass fractions. Among them, the fuel vapor refers to the gaseous components formed by the evaporation of the fuel in the combustion chamber.

[0059] Specifically, the calculation of the theoretical mixture fraction depends on the equilibrium equation of the chemical reaction. By considering the molar reaction ratio of each component in the combustion reaction, it is ensured that each hydrocarbon molecule in the fuel can react with sufficient oxygen molecules to achieve complete combustion without residue.

[0060] Step S115: Construct arrays for calculating different mixture fractions.

[0061] Exemplarily, the interval of the mixture fraction from 0 to 1 is divided into 500 parts to obtain an initial array starting from 0 and ending at 1, with a step size of 0.002 (i.e., 1 / 500), and this initial array has 501 elements.

[0062] Then, according to the value of FST, the initial array is divided into two parts. The first sub-array contains all elements less than FST, and the second sub-array contains all elements greater than or equal to FST. The first sub-array is sorted in reverse order and placed at the end of the second sub-array to form a new calculation array. The new calculation array is used as the calculation array for different mixture fractions.

[0063] Based on this, the calculation array for different mixture fractions starts from FST, gradually increases to a mixture fraction of 1.0, then jumps to the largest element less than FST, and continues to decrease to 0. Through this arrangement, the calculation array can focus more on the changes around the theoretical mixture fraction, thereby more accurately analyzing the sensitivity and dynamic changes of the combustion process.

[0064] In addition, the position of the jump element in the calculation array for different mixture fractions needs to be recorded, denoted as ii_cut. The jump element is the element that jumps from the second sub-array to the first sub-array. The jump element marks the key turning point of parameter changes in the numerical simulation.

[0065] Exemplarily, the initial array is represented as [0, 0.002, 0.004, …, 1]. Assuming FST is 0.012, the first sub-array is represented as [0, 0.002, 0.004, …, 0.010], the second sub-array is represented as [0.012, 0.014, …, 1], the calculation array for different mixture fractions is represented as [0.012, 0.014, …, 1, 0.010, 0.008, …, 0], the jump element is 0.010, and ii_cut is the position of 0.010 in the calculation array for different mixture fractions.

[0066] Based on this, by constructing the calculation array for different mixture fractions, it can be ensured that the combustion characteristics under different mixture fractions are explored more carefully and systematically in the subsequent numerical simulation process.

[0067] Step S116: Construct two shared arrays for the lower and upper limits of the extinction mixture fraction and the laminar flame propagation speed.

[0068] The array for the lower and upper limits of the extinction mixture fraction has two elements, representing the lower limit and the upper limit of the extinction mixture fraction respectively. The extinction mixture fraction refers to the critical condition where the flame cannot continue to burn at a specific fuel-air mixture ratio. The first element of the array, i.e., the lower limit of the extinction mixture fraction, is initially set to 0, indicating the starting point of flame extinction at an extremely lean (or overly fuel-lean) mixture ratio. The second element, i.e., the upper limit of the extinction mixture fraction, is initially set to 1, corresponding to the end point where the flame also extinguishes at an overly rich (or overly fuel-rich) mixture ratio. These two values demarcate the range of mixture fractions within which the flame can continue to burn.

[0069] The size of the laminar flame speed array is the same as the different mixture fraction calculation arrays constructed in the aforementioned step S115, and is used to record the laminar flame speed under the corresponding mixture fraction conditions. Each element in the laminar flame speed array corresponds to a specific mixture fraction and records the flame speed under this condition.

[0070] Based on this, through these two shared arrays, combustion simulations can efficiently exchange and update key parameters between multiple processors or computing nodes, making data synchronization and resource utilization in the parallel computing process more efficient, and greatly improving the computing efficiency.

[0071] Step S120: Based on the extinction mixture fraction upper and lower limit arrays, parallelly judge the extinction states of each laminar flame case. When the extinction state is unextinguished, parallelly calculate the laminar flame cases to obtain the laminar flame data corresponding to different mixture fractions, and update the shared arrays.

[0072] During the parallel computing process, use the shared arrays to synchronize the calculation results of the cases in real time, and decide whether to calculate new cases based on the shared arrays.

[0073] Specifically, based on the information in the above steps S111, S112, and S115, parallelly synchronously call the one-dimensional laminar flame calculation software Cantera to parallelly calculate the laminar flame data for the mixture fractions (a total of 501 values) equally divided from 0 to 1 into 500 parts. During the parallel computing process, when a certain process completes the calculation of a laminar flame case for a mixture fraction, the upper and lower limits of the extinction mixture fraction in the shared arrays are updated in real time, as well as the laminar flame speed corresponding to this mixture fraction.

[0074] This real-time update mechanism ensures that all parallel computing processes can access the latest calculation data, so as to make a decision on whether to continue the calculation at the beginning of the calculation. That is, before calculating the case for a certain mixture fraction, it is necessary to first check whether this mixture fraction has been determined to be in the extinction state. If this mixture fraction falls within the known extinction interval, the calculation of this mixture fraction is skipped, thus avoiding unnecessary consumption of computing resources. This not only reduces the overall calculation time, but also improves the processing efficiency, making the parallel computing more efficient and targeted.

[0075] Exemplarily, step S120 may include:

[0076] Obtain the number of parallel computing cores, and evenly divide the laminar flame cases based on the number of parallel computing cores;

[0077] Based on the extinction mixture fraction upper and lower limit arrays, parallelly judge whether the mixture fractions corresponding to each laminar flame case are within the range of the extinction mixture fraction upper and lower limits;

[0078] When the mixture fraction is within the upper and lower limits of the extinction mixture fraction, it is determined that the extinction state is unextinguished;

[0079] When the mixture fraction is outside the upper and lower limits of the extinction mixture fraction, it is determined that the extinction state is extinguished.

[0080] Exemplarily, Figure 3 FIG. is a schematic flow chart of parallel calculation of a laminar flame example provided by an exemplary embodiment of the present disclosure. As Figure 3 shown, step S120 may include the following sub-steps:

[0081] Step S121: Construct an example information array for the parallel laminar flame example.

[0082] Exemplarily, according to the operating conditions in step S111 and the calculation parameters in step S112, combined with the different mixture fraction calculation arrays constructed in step S115, a corresponding laminar flame example can be constructed for each mixture fraction calculation value.

[0083] First, 501 different mixture fraction values are integrated into the corresponding example settings one by one. Except for the mixture fraction values, the operating conditions and calculation parameters of each example are kept consistent. This way of constructing the input information array not only ensures the standardization of the example settings, but also simplifies the calculation process and subsequent data processing.

[0084] Then, the example setting information of these 501 mixture fractions is organized into an example information array. The example information array contains 501 elements, and each element is the computing power setting information corresponding to each mixture fraction. The example information array will be used as the input information for parallel calculation of 501 laminar flame examples, and each example runs independently of other examples.

[0085] Step S122: Allocate the laminar flame example.

[0086] Exemplarily, obtain the number of parallel computing cores n, and synchronously call n instances of the one-dimensional laminar flame calculation software Cantera to process 501 laminar flame examples in parallel.

[0087] In parallel computing, in order to effectively allocate computing tasks, these 501 examples are evenly divided into n different processes. During the division process, it is ensured as much as possible that the number of examples allocated to each process is the same or nearly the same, so as to achieve balanced computing load.

[0088] Based on this, through parallel computing processing and balanced allocation, not only can it be ensured that all parallel processes can complete their respective computing tasks in approximately the same time, avoiding the problem that some processes are idle or overloaded too early, but also the overall computing time can be significantly reduced and the utilization efficiency of computing resources can be improved.

[0089] Step S123: Preprocessing of laminar flame cases.

[0090] During the execution of each laminar flame case, it is first necessary to determine whether the mixture fraction in the case is less than the lower limit or greater than the upper limit of the extinction mixture fraction. If the condition is met (i.e., the mixture fraction is not within the combustible range), the case is skipped and the next laminar flame case calculation is directly carried out. If the condition is not met (i.e., the mixture fraction is within the combustible range), the one-dimensional grid number n_grid is set to the initial grid number, and the loop calculation of gradually refining the grid is started.

[0091] Based on this, through such preprocessing and condition determination, it can be ensured that only the cases with practical calculation significance are invested with computing resources, thereby improving the overall calculation efficiency and effectiveness. In addition, appropriate grid settings and optimizations can also ensure the accuracy and reliability of the calculation results.

[0092] Exemplarily, step S120 may further include:

[0093] When the extinction state is unextinguished, the laminar flame case is iteratively encrypted and solved, and it is judged whether the laminar flame case is successfully solved according to the preset judgment conditions;

[0094] In the case of failed solution, the upper and lower limit arrays of the extinction mixture fraction are updated based on the mixture fraction corresponding to the laminar flame case;

[0095] In the case of successful solution, the laminar flame data corresponding to the laminar flame case is obtained, and the shared array is updated based on the laminar flame data.

[0096] Step S124: Dynamically grid-iterative calculation of laminar flame cases.

[0097] In step S124, the laminar cases are iteratively calculated by successively refining the grid, and the shared data is dynamically updated to judge in real time whether the mixture fraction has been confirmed to exceed the limit.

[0098] Since other calculation processes may update the upper and lower limits of the extinction mixture fraction at any time, it is necessary to re-judge whether the current mixture fraction is still within the effective range of the extinction mixture fraction, that is, whether it is less than the upper limit and greater than the lower limit, at the beginning of each iteration. If the mixture fraction is not within the combustible range, the case is skipped to avoid performing invalid or unnecessary calculations.

[0099] If the mixture fraction is within the flammable range, the operating conditions in step S111 and the calculation parameters in step S112 are imported into the Cantera program, the number of grids is set to n_grid, and the solution function of the Cantera program is called to solve the one-dimensional laminar flame example. The preset judgment condition is: if the number of grids has reached the maximum number of grids and no convergence result has been obtained, the solution fails; if a convergence result is obtained before the number of grids reaches the maximum number of grids, the solution is successful.

[0100] If the solution fails, that is, no convergence result is obtained at the current number of grids, the number of grids is set to n_grid = n_grid + 1. When the number of grids does not exceed the maximum number of grids for iterative refinement, return to the beginning of step S124 to start the calculation again. If the solution still fails when the number of grids has reached the maximum number of grids, the current mixture fraction will be regarded as the extinguished state, and the relationship between the mixture fraction of the current example and the theoretical mixture fraction will be further judged. If the mixture fraction of the current example is greater than the theoretical mixture fraction, the upper limit of the extinguished mixture fraction is updated to the mixture fraction of the current example. If the mixture fraction of the current example is less than the theoretical mixture fraction, the lower limit of the extinguished mixture fraction is updated to the mixture fraction of the current example, and at the same time, this laminar flame example is exited.

[0101] Exemplarily, in the case of a successful solution, it may include:

[0102] Judging the extinguished state of the laminar flame example based on the laminar flame data and the mixture fraction;

[0103] When the extinguished state is non-extinguished, updating the laminar flame propagation speed array based on the laminar flame data;

[0104] When the extinguished state is extinguished, updating the laminar flame propagation speed array based on the laminar flame data and updating the upper and lower limits array of the extinguished mixture fraction based on the mixture fraction.

[0105] If the solution is successful, record the laminar flame propagation speed of this mixture fraction and the corresponding element position of this laminar flame propagation speed in the laminar flame propagation speed array. However, a successful solution does not directly indicate that this mixture fraction is in a stable ignition state. If the flame propagation speed of this mixture fraction is less than half of the laminar flame propagation speed of the adjacent mixture fraction, and the gap between this mixture fraction and the theoretical mixture fraction is greater than half of the theoretical mixture fraction, then the example of this mixture fraction is also judged as the extinguished state, and the upper and lower limits of the extinguished mixture fraction are updated. Among them, the flame propagation speed of the mixture fraction being less than half of the laminar flame propagation speed of the adjacent mixture fraction indicates that the flame speed drops too fast. The gap between the mixture fraction value and the theoretical mixture fraction being greater than half of the theoretical mixture fraction value indicates that this mixture fraction value has deviated far from the theoretical mixture fraction.

[0106] Conversely, the example of the mixture fraction is determined to be in an unextinguished state, and the corresponding laminar flame propagation speed is recorded in the laminar flame propagation speed array according to the laminar flame data.

[0107] Similarly, the upper and lower limits of the extinguished mixture fraction can be updated according to the relationship between the mixture fraction of the current example and the theoretical mixture fraction. If the mixture fraction of the current example is greater than the theoretical mixture fraction, the upper limit of the extinguished mixture fraction is updated to the mixture fraction of the current example. If the mixture fraction of the current example is less than the theoretical mixture fraction, the lower limit of the extinguished mixture fraction is updated to the mixture fraction of the current example. And the corresponding laminar flame propagation speed is recorded in the laminar flame propagation speed array according to the laminar flame data, and at the same time, this laminar flame example is skipped.

[0108] In addition, if the mixture fraction is exactly at the ii_cut position marked in the different mixture fraction calculation arrays in step S115, then the adjacent mixture fraction of this mixture fraction should be the first element in the different mixture fraction calculation arrays. In other cases, the adjacent mixture fraction is the mixture fraction at the previous position of this mixture fraction. If this mixture fraction does not meet the conditions of too rapid decrease in flame speed and the mixture fraction being far from the theoretical mixture fraction, this state can be considered as a stable ignition state.

[0109] Exemplarily, assume that the different mixture fraction calculation arrays are represented as [0.012, 0.014, …, 1, 0.010, 0.008, …, 0], the jump element is 0.010, and ii_cut is the position of 0.010 in the different mixture fraction calculation arrays. If the current mixture fraction is 0.010, then the current mixture fraction is at the marked ii_cut position, and the adjacent mixture fraction of the current mixture fraction is 0.012. If the current mixture fraction is 0.008, then the current mixture fraction is not at the marked ii_cut position, and the adjacent mixture fraction of the current mixture fraction is 0.010.

[0110] When it is confirmed that the current mixture fraction is in a stable ignition state or the flame propagation data of the adjacent mixture fraction has not been obtained, a laminar flame example data file named after the current mixture fraction will be output. The laminar flame example data file can include key data such as one-dimensional spatial position coordinates, mass fractions of each component, temperature, and density.

[0111] Step S130: After the parallel calculation is completed, update the upper and lower limit arrays of the extinguished mixture fraction based on the laminar flame propagation speed array.

[0112] In parallel computing with multiple threads, due to the synchronous update of the extinction upper and lower limits, there may be a situation where some laminar flame cases of adjacent mixture fractions have not been completed. In this case, it is impossible to accurately determine whether the flame propagation speed of a certain mixture fraction has decreased significantly, and thus it is also impossible to determine whether the mixture fraction is in a stable ignition state. To solve this problem, after all parallel computations are completed, the main process needs to perform a unified calculation of the extinction upper and lower limits. And according to the finally determined extinction upper limit, the laminar flame data outside the extinction upper and lower limits is cleared.

[0113] Exemplarily, Figure 4 The following is a schematic flowchart of the main process for determining the extinction upper and lower limits provided by an exemplary embodiment of the present disclosure. As Figure 4 shown, step S130 may include the following sub-steps:

[0114] Step S131: Use the main process to determine the final extinction upper limit value.

[0115] After all parallel cases are calculated, the laminar flame propagation speed array has been filled with all data. At this time, the main process can traverse each element in the array of different mixture fractions from front to back in sequence. For those elements with mixture fraction values greater than the theoretical mixture fraction, the main process further determines whether the corresponding laminar flame speed data exists.

[0116] If the corresponding laminar flame speed data exists, check whether the flame speed drops too fast and whether the mixture fraction deviates from the theoretical mixture fraction. If the above conditions are met, determine this mixture fraction as the extinction upper limit and terminate the traversal loop.

[0117] If the corresponding laminar flame speed data does not exist, it indicates that the solution of this mixture fraction fails. Determine the previous value adjacent to this mixture fraction as the extinction upper limit and terminate the traversal loop.

[0118] Step S132: Use the main process to determine the final extinction lower limit value.

[0119] The main process can traverse each element in the array of different mixture fractions from back to front in sequence. For those elements with mixture fraction values less than the theoretical mixture fraction, the main process further determines whether the corresponding laminar flame speed data exists.

[0120] If the corresponding laminar flame speed data exists, check whether the flame speed drops too fast and whether the mixture fraction deviates from the theoretical mixture fraction. If the above conditions are met, determine this mixture fraction as the extinction lower limit and terminate the traversal loop.

[0121] If the corresponding laminar flame speed data does not exist, it indicates that the solution of this mixture fraction fails. Determine the previous value adjacent to this mixture fraction as the extinction lower limit and terminate the traversal loop.

[0122] Step S133: Clear the laminar flame data outside the flameout upper and lower limits.

[0123] Delete the laminar flame case data files with a mixture fraction less than the lower limit of the flameout mixture fraction or greater than the upper limit of the flameout mixture fraction, and complete the calculation of the laminar flame data.

[0124] Step S140: Convert the laminar flame data into turbulent flame data by integrating the turbulent probability density function.

[0125] Exemplarily, the beta probability density distribution function can be utilized, combined with the corresponding relationships of the mixture fraction, normalized reaction progress variable, and the mass fractions, temperature, and density of each component obtained through the calculation of the laminar flame data, to integrate the mixture fraction and the normalized reaction progress variable in the range of 0 to 1 respectively to obtain the turbulent flame data.

[0126] Exemplarily, Figure 5 is a schematic flow chart for constructing turbulent flame data provided by an exemplary embodiment of the present disclosure. As Figure 5 shown, step S140 may include the following sub-steps:

[0127] Step S141: Obtain the laminar flame data within the combustible range and the laminar flame data outside the combustible range based on the flameout mixture fraction upper and lower limit array.

[0128] Read all the laminar flame case data files to obtain the corresponding relationships between different mixture fractions, different spatial positions, and the mass fractions, temperature, and density of each component. Among them, one mixture fraction corresponds to multiple different spatial positions; one spatial position corresponds to one mass fraction, temperature, density, and chemical reaction progress variable of a component. According to the flameout mixture fraction upper and lower limit array, the laminar flame data is divided into the laminar flame data within the combustible range and the laminar flame data outside the combustible range.

[0129] Step S142: Construct the chemical reaction progress variable and the normalized progress variable based on the laminar flame data within the combustible range, and obtain the corresponding relationship of the mixture fraction within the combustible range.

[0130] Exemplarily, the corresponding relationships between the mass fractions, temperature, density, mixture fraction, and normalized progress variable can be established by constructing the chemical reaction progress variable and the normalized progress variable.

[0131] First, define the sum of the mass fractions of the three components CO, CO2, and H2O as the chemical reaction progress variable. In the laminar flame data file with the same mixture fraction Z, different spatial position coordinates correspond to different chemical reaction progress variables.

[0132] Then, find the maximum value of the chemical reaction progress variable in the same mixture fraction file, and define the normalized progress variable C1 as the ratio of the chemical reaction progress variable at this spatial position to the maximum chemical reaction progress variable.

[0133] Furthermore, the discrete relationships Yi(Z, C1), T(Z, C1), and rho(Z, C1) corresponding to the mass fraction Yi, temperature T, density rho of each component, mixture fraction Z, and normalized progress variable C1 within the flammability limit can be obtained respectively.

[0134] Step S143: Based on the laminar flame data outside the flammability range and the physical property calculation rules of the ideal gas mixture, obtain the corresponding relationship of the mixture fraction outside the flammability range.

[0135] Exemplarily, according to the physical property calculation rules of the ideal gas mixture, combined with the mixture fraction, fuel inlet temperature, and air inlet temperature, the corresponding relationships between the mass fraction, temperature, density of each component, mixture fraction, and normalized progress variable in the intervals from 0 to the lower limit of the extinction mixture fraction and from the upper limit of the extinction mixture fraction to 1 can be obtained.

[0136] Based on this, improving the corresponding relationships between the mass fraction, temperature, density of each component outside the flammability limit, mixture fraction, and normalized progress variable not only fills the data gap in the analysis of flame combustion characteristics but also provides a more comprehensive perspective for the entire combustion process, thus more accurately simulating and predicting the behavior of the flame under extreme conditions, and further improving the accuracy and practicality of the combustion model.

[0137] Step S144: Based on the corresponding relationship of the mixture fraction within the flammability range and the corresponding relationship of the mixture fraction outside the flammability range, convert the laminar flame data into turbulent flame data through the integral of the turbulent probability density function.

[0138] Based on the corresponding relationships between the mass fraction, temperature, density of each component within the flammability limit obtained in step S142 and outside the flammability limit obtained in S143, mixture fraction, and normalized progress variable, the mass fraction, temperature, and density of each mixture fraction under different reaction progress conditions are obtained through the integral of the turbulent probability density function, thereby converting the laminar flame data into turbulent flame data.

[0139] Exemplarily, the integral of the turbulent probability density function can be expressed as:

[0140]

[0141] Among them, C1 represents the normalized progress variable; represents the mean value of the progress variable; represents the variance of the progress variable; Z represents the mixture fraction; represents the mean value of the mixture fraction; It represents the variance of the mixture fraction; beta(mean, variance) represents the beta probability density distribution function; Yi represents the mass fraction corresponding to component i.

[0142] Based on the integral of the turbulent probability density function, the relationship formula for the mass fraction Yi of component i corresponding to the turbulent flame data can be obtained as follows: Temperature relationship formula: Density relationship formula:

[0143] Step S145: Construct a turbulent flamelet database according to the turbulent flame data.

[0144] First, through the probability density function integration method in step S144, discrete data such as the component mass fraction, temperature, and density corresponding to each mixture fraction are obtained. Each set of data represents the behavior and characteristics of the flamelet under specific turbulent intensity and mixture ratio conditions.

[0145] Furthermore, these discrete data are systematically written into the turbulent flamelet database for use by researchers and engineers in the design and optimization of combustion equipment, thereby improving combustion efficiency and ensuring operational safety.

[0146] In one or more technical solutions provided by the exemplary embodiments of the present disclosure, during the calculation process of laminar flame data, the calculation results of the cases are synchronized in real time in the form of a shared array, and it is determined whether a new case needs to be calculated through the real-time updated data. This parallel strategy transforms the serial calculation process with a sequential calculation order into a parallel calculation process of multi-process synchronization calculation. In addition, the main process effectively identifies the critical mixture fraction of flameout and determines the final upper limit of flameout.

[0147] Therefore, the parallel calculation method of the flamelet data of the combustion model provided by the exemplary embodiments of the present disclosure significantly improves the calculation efficiency of the flamelet database and significantly shortens the calculation duration on the premise of ensuring that the flameout upper and lower limit criteria remain unchanged.

[0148] The above mainly introduces the solutions provided by the exemplary embodiments of the present disclosure. It can be understood that, in order to implement the above functions, the electronic device includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0149] Exemplary embodiments of the present disclosure may divide functional units of an electronic device according to the above method examples. For example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The above integrated module may be implemented in the form of hardware or in the form of a software functional module. It should be noted that the division of modules in the exemplary embodiments of the present disclosure is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0150] In the case of dividing each functional module corresponding to each function, an exemplary embodiment of the present disclosure provides a parallel computing device for flame surface data of a combustion model. The parallel computing device for flame surface data of the combustion model may be a server or a chip applied to a server. Figure 6 It is a schematic block diagram of functional modules of a parallel computing device for flame surface data of a combustion model provided by an exemplary embodiment of the present disclosure. As Figure 6 shown, the parallel computing device 600 for flame surface data of the combustion model includes:

[0151] A data acquisition module 610, configured to construct a laminar flame case set and a shared array. The laminar flame case set includes laminar flame cases corresponding to different mixture fractions, and the shared array includes an extinction mixture fraction upper and lower limit array and a laminar flame propagation speed array.

[0152] A data processing module 620, configured to parallelly determine the extinction state of each laminar flame case based on the extinction mixture fraction upper and lower limit array. In the case where the extinction state is unextinguished, parallelly calculate the laminar flame cases to obtain laminar flame data corresponding to different mixture fractions, and update the shared array;

[0153] The data processing module 620 is further configured to update the extinction mixture fraction upper and lower limit array based on the laminar flame propagation speed array after the parallel calculation ends;

[0154] The data processing module 620 is further configured to convert the laminar flame data into turbulent flame data through turbulent probability density function integration.

[0155] In another embodiment provided by the present disclosure, the data processing module 620 is further configured to obtain working condition parameters, calculation parameters, and the theoretical mixture fraction of combustion reactants; construct different mixture fraction calculation arrays based on the theoretical mixture fraction; and construct a laminar flame case set based on the working condition parameters, calculation parameters, and different mixture fraction calculation arrays.

[0156] In yet another embodiment provided by the present disclosure, the data processing module 620 is further configured to obtain the number of parallel computing cores, and evenly divide the laminar flame case based on the number of parallel computing cores; based on the extinction mixture fraction upper and lower limit array, parallelly determine whether the mixture fraction corresponding to each laminar flame case is within the range of the extinction mixture fraction upper and lower limits; in the case where the mixture fraction is within the range of the extinction mixture fraction upper and lower limits, determine that the extinction state is unextinguished; in the case where the mixture fraction is outside the range of the extinction mixture fraction upper and lower limits, determine that the extinction state is extinguished.

[0157] In yet another embodiment provided by the present disclosure, the data processing module 620 is further configured to, in the case where the extinction state is unextinguished, perform iterative encrypted solution on the laminar flame case, and determine whether the laminar flame case is successfully solved according to a preset judgment condition; in the case of failed solution, update the extinction mixture fraction upper and lower limit array based on the mixture fraction corresponding to the laminar flame case; in the case of successful solution, obtain the laminar flame data corresponding to the laminar flame case, and update the shared array based on the laminar flame data.

[0158] In yet another embodiment provided by the present disclosure, the data processing module 620 is further configured to determine the extinction state of the laminar flame case based on the laminar flame data and the mixture fraction; in the case where the extinction state is unextinguished, update the laminar flame propagation speed array based on the laminar flame data; in the case where the extinction state is extinguished, update the laminar flame propagation speed array based on the laminar flame data, and update the extinction mixture fraction upper and lower limit array based on the mixture fraction.

[0159] In yet another embodiment provided by the present disclosure, the data processing module 620 is further configured to, based on the extinction mixture fraction upper and lower limit array, obtain the laminar flame data within the combustible range and the laminar flame data outside the combustible range; construct a chemical reaction progress variable and a normalized progress variable based on the laminar flame data within the combustible range, and obtain the corresponding relationship of the mixture fraction within the combustible range; based on the laminar flame data outside the combustible range and the physical property calculation rule of the ideal gas mixture, obtain the corresponding relationship of the mixture fraction outside the combustible range; based on the corresponding relationship of the mixture fraction within the combustible range and the corresponding relationship of the mixture fraction outside the combustible range, through the integral of the turbulent probability density function, convert the laminar flame data into turbulent flame data.

[0160] In yet another embodiment provided by the present disclosure, the data processing module 620 is further configured to the integral of the turbulent probability density function can be expressed as:

[0161]

[0162] wherein, C1 represents the normalized progress variable; Represents the mean of the progress variable; Represents the variance of the progress variable; Z represents the mixture fraction; Represents the mean of the mixture fraction; Represents the variance of the mixture fraction; beta(mean, variance) represents the beta probability density distribution function; Yi represents the mass fraction corresponding to component i;

[0163] Based on the integration of the turbulent probability density function, the turbulent flame data corresponding to each mixture fraction is obtained; the turbulent flame data includes component mass fraction, temperature, and density.

[0164] An exemplary embodiment of the present disclosure further provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, it is used to cause the electronic device to execute the method according to the embodiment of the present disclosure.

[0165] An exemplary embodiment of the present disclosure further provides a non-transitory computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor of a computer, it is used to cause the computer to execute the method according to the embodiment of the present disclosure.

[0166] Figure 7 FIG. is a block diagram of an electronic device provided as an example of the present disclosure. Now, a block diagram of an electronic device 700 that can be used as a server or a client of the present disclosure will be described. It is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0167] As Figure 7As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0168] Multiple components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. The input unit 706 can be any type of device capable of inputting information into the electronic device 700. The input unit 706 can receive input digital or character information and generate key signal inputs related to the user settings and / or function controls of the electronic device. The output unit 707 can be any type of device capable of presenting information and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 708 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a BluetoothTM device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0169] The computing unit 701 can be various general-purpose and / or dedicated processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 executes the various methods and processes described above. Each of the various methods described above can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 700 via the ROM 702 and / or the communication unit 709.

[0170] Figure 8Schematic diagram of a computer program product provided for an exemplary embodiment of the present disclosure. An exemplary embodiment of the present disclosure also provides a computer program product 800, including a computer program 801, wherein the computer program 801, when executed by a processor of a computer, is configured to cause the computer to execute the method according to the embodiments of the present disclosure.

[0171] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0172] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0173] As used in the present disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., a disk, an optical disc, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0174] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0175] The systems and techniques described here can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described here), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0176] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other.

[0177] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present disclosure are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device, or other programmable devices. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrating one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid state drive (SSD).

[0178] Although the present disclosure has been described in connection with specific features and their embodiments, it is obvious that various modifications and combinations can be made without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely exemplary illustrations of the present disclosure defined by the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present disclosure. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalent technologies, the present disclosure also intends to include these changes and modifications therein.

Claims

1. A parallel computing method for flame surface data of a combustion model, characterized in that, The method includes: Constructing a laminar flame case set and a shared array, where the laminar flame case set includes laminar flame cases corresponding to different mixture fractions, and the shared array includes an extinction mixture fraction upper and lower limit array and a laminar flame propagation speed array; Based on the extinction mixture fraction upper and lower limit array, parallelly judging the extinction state of each laminar flame case. When the extinction state is unextinguished, parallelly calculating the laminar flame cases to obtain laminar flame data corresponding to different mixture fractions, and updating the shared array; After the parallel calculation ends, updating the extinction mixture fraction upper and lower limit array based on the laminar flame propagation speed array; Converting the laminar flame data into turbulent flame data through integration of the turbulent probability density function.

2. The method according to claim 1, wherein The constructing of the laminar flame case set includes: Obtaining the working condition, calculation parameters, and the theoretical mixture fraction of the combustion reactants; Constructing different mixture fraction calculation arrays based on the theoretical mixture fraction; Constructing a laminar flame case set based on the working condition, calculation parameters, and different mixture fraction calculation arrays.

3. The method according to claim 1, wherein The parallelly judging the extinction state of each laminar flame case based on the extinction mixture fraction upper and lower limit array includes: Obtaining the number of parallel computing cores and equally dividing the laminar flame cases based on the number of parallel computing cores; Based on the extinction mixture fraction upper and lower limit array, parallelly judging whether the mixture fraction corresponding to each laminar flame case is within the range of the extinction mixture fraction upper and lower limits; When the mixture fraction is within the range of the extinction mixture fraction upper and lower limits, determining the extinction state as unextinguished; When the mixture fraction is outside the range of the extinction mixture fraction upper and lower limits, determining the extinction state as extinguished.

4. The method according to claim 3, wherein The parallelly calculating the laminar flame cases when the extinction state is unextinguished to obtain laminar flame data corresponding to different mixture fractions and updating the shared array includes: When the extinction state is unextinguished, performing iterative encryption solution on the laminar flame cases and judging whether the laminar flame cases are successfully solved according to a preset judgment condition; When the solution fails, updating the extinction mixture fraction upper and lower limit array based on the mixture fraction corresponding to the laminar flame case; When the solution is successful, obtaining the laminar flame data corresponding to the laminar flame case and updating the shared array based on the laminar flame data.

5. The method according to claim 4, wherein The updating of the shared array based on the laminar flame data includes: Judging the extinction state of the laminar flame case based on the laminar flame data and the mixture fraction; When the extinction state is unextinguished, updating the laminar flame propagation speed array based on the laminar flame data; When the extinction state is extinguished, updating the laminar flame propagation speed array based on the laminar flame data and updating the extinction mixture fraction upper and lower limit array based on the mixture fraction.

6. The method according to claim 1, wherein The converting of the laminar flame data into turbulent flame data through integration of the turbulent probability density function includes: Based on the extinction mixture fraction upper and lower limit array, obtaining the laminar flame data within the combustible range and the laminar flame data outside the combustible range; Construct a reaction progress variable and a normalized progress variable based on the laminar flame data within the flammable range, and obtain the corresponding relationship of the mixture fraction within the flammable range; Based on the laminar flame data outside the flammable range and the physical property calculation rules of the ideal gas mixture, obtain the corresponding relationship of the mixture fraction outside the flammable range; Based on the corresponding relationship of the mixture fraction within the flammable range and the corresponding relationship of the mixture fraction outside the flammable range, convert the laminar flame data into turbulent flame data through the integration of the turbulent probability density function.

7. The method according to claim 6, characterized in that, The integration of the turbulent probability density function is expressed as: Among them, C1 represents the normalized progress variable; represents the mean value of the progress variable; represents the variance of the progress variable; Z represents the mixture fraction; represents the mean value of the mixture fraction; represents the variance of the mixture fraction; beta(mean, variance) represents the beta probability density distribution function; Yi represents the mass fraction corresponding to component i; Based on the integration of the turbulent probability density function, obtain the turbulent flame data corresponding to each mixture fraction; the turbulent flame data includes the component mass fraction, temperature, and density.

8. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the method described in claim 1.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that, When the computer program / instructions are executed by the processor, the method described in claim 1 is implemented.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, the method described in claim 1 is implemented.

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