Parallel computing method and device for flame surface data of combustion model, medium and product

Abstract

The invention relates to the technical field of computational fluid mechanics, and particularly provides a flame surface data parallel computing method and device of a combustion model, a medium and a product. The flame surface data parallel computing method of the combustion model comprises the following steps: constructing a laminar flame example set and a shared array; the flameout state of each laminar flame example is judged in parallel based on the flameout mixed score upper and lower limit array, under the condition that the flameout state is non-flameout, the laminar flame examples are calculated in parallel, laminar flame data are obtained, and the shared array is updated; after parallel computing is finished, updating a flameout mixed fraction upper and lower limit array based on a laminar flame propagation velocity array; and converting the laminar flame data into turbulent flame data through turbulent probability density function integration. According to the method and the device, through a parallel strategy of updating the shared array in real time and judging whether the new calculation example needs to be calculated or not through the newest shared array, a serial calculation process with a sequential calculation sequence is transformed into a parallel calculation process of multi-process synchronous calculation.

Description

Technical Field

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

[0002] In aircraft engine combustion simulation, the flame surface combustion model is usually used to describe the turbulent combustion process. The flame surface 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 based on the data provided by the turbulent flame database.

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

[0004] However, when calculating laminar flame examples, it is necessary to determine whether each example is in a stable ignition state, and the state determination depends on the result of the previous example. This dependency between the results of the examples results in that the calculation of laminar flame examples can only be done in a serial manner, which significantly reduces the calculation efficiency when building the flame surface database. Summary of the invention

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

[0006] In one aspect of an exemplary embodiment of the present disclosure, a flame surface data parallel calculation method of a combustion model is provided, the method comprising:

[0007] Constructing a laminar flame example set and a shared array, wherein the laminar flame example set includes laminar flame examples corresponding to different mixture fractions, and the shared array includes an array of upper and lower limits of the flameout mixture fraction and an array of laminar flame propagation speed;

[0008] Based on the flameout mixture fraction upper and lower limit arrays, the flameout state of each laminar flame example is judged in parallel. When the flameout state is not flameout, the laminar flame example is calculated in parallel to obtain laminar flame data corresponding to different mixture fractions, and the shared array is updated.

[0009] After the parallel calculation is completed, the flameout mixture fraction upper and lower limit arrays are updated based on the laminar flame propagation speed array;

[0010] The laminar flame data is converted into turbulent flame data by integrating 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 in the memory, wherein the processor executes the computer program to implement the method described in the exemplary embodiments of the present disclosure.

[0012] According to another aspect of the exemplary embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0013] According to another aspect of the exemplary embodiments of the present disclosure, a computer program product is provided, including a computer program / instruction. When the computer program / instruction is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0014] As described in detail below, according to a flame surface data parallel calculation method of a combustion model of the embodiment of the present disclosure, by constructing a laminar flame example set and a shared array, the laminar flame example set includes laminar flame examples corresponding to different mixture fractions, and the shared array includes an extinction mixture fraction upper and lower limit array and a laminar flame propagation velocity array; based on the extinction mixture fraction upper and lower limit arrays, the extinction state of each laminar flame example is judged in parallel, and when the extinction state is not extinguished, the laminar flame example is calculated in parallel to obtain laminar flame data corresponding to different mixture fractions, and the shared array is updated; after the parallel calculation is completed, the extinction mixture fraction upper and lower limit arrays are updated based on the laminar flame propagation velocity array; the laminar flame data is converted into turbulent flame data by integrating the turbulent probability density function. Therefore, in the flame surface data parallel calculation method of the combustion model provided by the present disclosure, in the laminar flame data calculation process, the example calculation results are synchronized in real time in the form of a shared array, and the new example is judged whether it needs to be calculated by 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 synchronous calculation. In addition, the critical mixture fraction of flameout is effectively identified through the main process, and the final upper limit of flameout is determined, thereby greatly improving the calculation efficiency of the flame surface database and significantly shortening the calculation time while ensuring that the upper and lower limit criteria of flameout remain unchanged. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying 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 of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1A schematic diagram of a flow chart of a method for parallel calculation of flame surface data of a combustion model provided by an exemplary embodiment of the present disclosure;

[0017] Figure 2 A schematic diagram of a process for constructing a laminar flame example set and a shared array provided as an example in the present disclosure;

[0018] Figure 3 A schematic diagram of a parallel calculation process of a laminar flame example provided by the present disclosure;

[0019] Figure 4 A schematic diagram of a flow chart for determining the upper and lower limits of flameout in a main process provided as an example in the present disclosure;

[0020] Figure 5 A schematic diagram of a process for constructing turbulent flame data provided as an example in the present disclosure;

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

[0022] Figure 7 A structural block diagram of an electronic device provided as an example in the present disclosure;

[0023] Figure 8 A schematic diagram of a computer program product provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0025] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders 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 respect.

[0026] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "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 "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated 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 the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present 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, in response 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 to be performed 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, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0031] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0032] Before introducing the embodiments of the present disclosure, the following definitions are given for the terms involved in the embodiments of the present disclosure:

[0033] Flame surface database: The flame surface database is a library containing a large amount of pre-calculated laminar flame data. These data usually include: flame speed, flame shape, concentration and temperature distribution of various chemical substances in the flame, and reaction path analysis. When performing flow combustion simulation in the combustion chamber of an aircraft engine, the flame surface database provides a rich data resource, allowing the combustion model to use the data in the database to more accurately predict the flame behavior under specific flight conditions and combustion chamber designs.

[0034] In aircraft engine combustion simulation, the flame surface combustion model is usually used to describe the turbulent combustion process. The flame surface 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 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 laminar flame example data under different mixing fractions, and then converting the laminar data into turbulent flame data based on the integration method of the turbulent probability density function, thereby forming a turbulent flame database.

[0036] However, when calculating laminar flame examples, it is necessary to determine whether each example is in a stable ignition state, and the state determination depends on the result of the previous example. This dependency between the results of the examples results in that the calculation of laminar flame examples can only be done in a serial manner, which significantly reduces the calculation efficiency when building the flame surface database.

[0037] Therefore, in order to solve the above problems, an exemplary embodiment of the present invention provides a parallel calculation method for flame surface data of a combustion model. In the laminar flame data calculation process, a parallel strategy is adopted to synchronize the calculation results of the example in real time in the form of a shared array and to judge whether a new example needs to be calculated. The serial calculation process with a sequential calculation order is transformed into a parallel calculation process with multi-process synchronous calculation. Under the premise of ensuring that the upper and lower limits of the flameout criterion remain unchanged, the calculation efficiency of the flame surface database is greatly improved and the calculation time is significantly shortened.

[0038] For example, Figure 1 A schematic diagram of a flow chart of a flame surface data parallel calculation method for a combustion model provided by the present disclosure, as shown in FIG. Figure 1 As shown, the following steps may be specifically included:

[0039] Step S110: constructing a laminar flame case set and a shared array.

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

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

[0042] Exemplarily, step S110 may include:

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

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

[0045] A laminar flame example set is constructed based on operating conditions, calculation parameters, and calculation arrays with different mixture fractions.

[0046] For example, Figure 2 The schematic diagram of the process of constructing a laminar flame example set and a shared array provided by the present disclosure is as follows: Figure 2 As shown, step S110 may include the following sub-steps:

[0047] Step S111: Obtain operating conditions.

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

[0049] Step S112: setting calculation parameters of the calculation example.

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

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

[0052] Maximum number of meshes for iterative refinement: defines the maximum number of meshes that can be reached during mesh refinement. 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] Calculation residual: reflects the deviation between the actual calculated value and the theoretical value, and is an important basis for judging whether the calculation has converged.

[0054] Mesh Refinement Settings: Controls how to refine or coarsen the mesh based on error estimates and computational requirements. Mesh refinement can automatically increase mesh density in areas where higher resolution is needed, such as areas with large temperature gradients, rapid velocity changes, or intense chemical reactions. An appropriate mesh refinement strategy can significantly improve computational efficiency and result accuracy while avoiding the huge computational burden of uniformly refining the mesh across the entire computational domain.

[0055] Step S113: creating a laminar flame data storage folder.

[0056] In order to systematically manage the generated data, special folders can be created to store calculation results and related data.

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

[0058] The theoretical mixing fraction at which the reactants react completely can be obtained based on the names of the components of the fuel vapor and the mass proportions of the components, and the names of the components of the air and the mass proportions of the components. The fuel vapor refers to the gaseous component volatilized by the fuel in the combustion chamber.

[0059] Specifically, the calculation of the theoretical mixture fraction relies on the equilibrium equation of the chemical reaction, which ensures that each hydrocarbon molecule in the fuel can react with enough oxygen molecules to achieve complete combustion without residue by considering the molar reaction ratio of each component in the combustion reaction.

[0060] Step S115: construct different mixture fraction calculation arrays.

[0061] Exemplarily, the interval of the mixing fraction from 0 to 1 is divided into 500 parts, and an initial array starting from 0 and ending at 1 with a step length of 0.002 (ie, 1 / 500) is obtained, and the 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 arrays of different mixture fractions start from FST, gradually increase to a mixture fraction of 1.0, then jump to the largest element less than FST, and continue 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, it is also necessary to record the position of the jump element in the calculation array of different mixture fractions, which is recorded 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 change in the numerical simulation.

[0065] Exemplarily, the initial array is represented as [0, 0.002, 0.004, …, 1]. Assuming that 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], and the calculation arrays of different mixing fractions 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 calculation arrays of different mixing fractions.

[0066] Based on this, by constructing calculation arrays 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 of the upper and lower limits of the flameout mixture fraction and the laminar flame propagation speed.

[0068] The flameout mixture fraction upper and lower limits array has two elements, representing the lower and upper limits of the flameout mixture fraction. The flameout mixture fraction refers to the critical condition under which the flame cannot continue to burn at a specific fuel and air mixture ratio. The first element of the array, the flameout mixture fraction lower limit, is initially set to 0, indicating the starting point of flame extinction at an extremely lean (or too lean) mixture ratio. The second element, the flameout mixture fraction upper limit, is initially set to 1, corresponding to the end point where the flame will also go out at an overly thick (or overly rich) mixture ratio. These two values ​​calibrate the range of mixture fractions in which the flame can continue to burn.

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

[0070] Based on this, through these two shared arrays, the combustion simulation 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, greatly improving computing efficiency.

[0071] Step S120: Based on the flameout mixture fraction upper and lower limit arrays, the flameout status of each laminar flame example is judged in parallel. When the flameout status is not flameout, the laminar flame example is calculated in parallel to obtain laminar flame data corresponding to different mixture fractions, and the shared array is updated.

[0072] During parallel computing, the shared array is used to synchronize the case calculation results in real time, and the decision on whether to calculate a new case is made based on the shared array.

[0073] Specifically, based on the information in the above steps S111, S112 and S115, the one-dimensional laminar flame calculation software cantera is called in parallel and synchronously to parallel calculate the laminar flame data for the mixture fractions (501 values ​​in total) divided into 500 parts from 0 to 1. In the parallel calculation process, when a process completes the calculation of a laminar flame example with a mixture fraction, the upper and lower limits of the flameout mixture fraction of the shared array and the laminar flame propagation speed corresponding to the mixture fraction are updated in real time.

[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 a certain mixed fraction, it is necessary to check whether the mixed fraction has been determined to be in a flameout state. If the mixed fraction falls within the known flameout interval, the calculation of the mixed fraction is skipped, thereby avoiding unnecessary consumption of computing resources. This not only reduces the overall calculation time, but also improves processing efficiency, making parallel computing more efficient and targeted.

[0075] Exemplarily, step S120 may include:

[0076] Get the number of parallel computing cores and average the stratified flow flame examples based on the number of parallel computing cores;

[0077] Based on the flameout mixture fraction upper and lower limit arrays, it is determined in parallel whether the mixture fraction corresponding to each laminar flame example is within the flameout mixture fraction upper and lower limit range;

[0078] When the mixture fraction is within the range of the flameout mixture fraction upper and lower limits, determining the flameout state as not flameout;

[0079] When the mixture fraction is outside the flameout mixture fraction upper and lower limit ranges, the flameout state is determined to be flameout.

[0080] For example, Figure 3 The following is a schematic diagram of a parallel calculation process of a laminar flame example provided by the present disclosure, as shown in FIG. Figure 3 As shown, step S120 may include the following sub-steps:

[0081] Step S121: constructing a calculation example information array of a parallel laminar flame calculation 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 ​​were integrated into the corresponding case settings one by one. Except for the mixture fraction value, the working conditions and calculation parameters of each case were kept consistent. This way of constructing the input information array not only ensured the standardization of the case settings, but also simplified 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, which contains 501 elements, each of which is the calculation power setting information corresponding to each mixture fraction. The example information array will be used as input information for parallel computing of 501 laminar flame examples, and each example will run independently of other examples.

[0085] Step S122: Allocate laminar flame calculation examples.

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

[0087] In parallel computing, in order to effectively distribute computing tasks, these 501 examples are equally divided into n different processes. During the division process, the number of examples assigned to each process is kept the same or nearly the same as far as possible, so as to achieve balanced computing load.

[0088] Based on this, through parallel computing processing and balanced distribution, not only can we ensure that all parallel processes can complete their respective computing tasks in roughly the same time, avoiding the problem of some processes being idle or overloaded too early, but we can also significantly reduce the overall computing time and improve the efficiency of computing resource utilization.

[0089] Step S123: laminar flame example preprocessing.

[0090] In the execution process of each laminar flame example, it is first necessary to determine whether the mixture fraction in the example is less than the lower limit of the flameout mixture fraction or greater than the upper limit. If the condition is met (i.e., the mixture fraction is not within the flammable range), skip the example and directly proceed to the next laminar flame example. If the condition is not met (i.e., the mixture fraction is within the flammable range), set the one-dimensional grid number n_grid as the initial grid number, and start the loop calculation with the grid gradually encrypted.

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

[0092] Exemplarily, step S120 may further include:

[0093] When the flameout state is not flameout, the laminar flame example is solved by iterative encryption, and whether the laminar flame example is solved successfully is determined according to the preset judgment conditions;

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

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

[0096] Step S124: dynamic grid iterative calculation of laminar flame example.

[0097] In step S124, the laminar flow example is calculated by iteratively calculating the grids one by one, and the shared data is dynamically updated to determine in real time whether the mixture fraction has been confirmed to be out of limit.

[0098] Since other calculation processes may update the upper and lower limits of the flameout mixture fraction at any time, it is necessary to re-determine whether the current mixture fraction is still within the valid range of the flameout mixture fraction at the beginning of each iteration, that is, whether it is less than the upper limit and greater than the lower limit. If the mixture fraction is not within the flammable range, skip the calculation to avoid 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 solver 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 the convergence result has not been obtained, the solution fails; if the number of grids obtains the convergence result before reaching the maximum number of grids, the solution is successful.

[0100] If the solution fails, that is, no convergence result is obtained under 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 encryption, return to the beginning of step S124 and restart the calculation. If the number of grids has reached the maximum number of grids and the solution still fails, the current mixture fraction will be regarded as a flameout state, and the relationship between the mixture fraction of the current example and the theoretical mixture fraction will be further determined. If the mixture fraction of the current example is greater than the theoretical mixture fraction, the upper limit of the flameout 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 flameout mixture fraction is updated to the mixture fraction of the current example, and the laminar flame example is exited at the same time.

[0101] For example, when the solution is successful, it may include:

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

[0103] When the flameout state is not flameout, the laminar flame propagation speed array is updated based on the laminar flame data;

[0104] When the flameout state is flameout, the laminar flame propagation velocity array is updated based on the laminar flame data, and the flameout mixture fraction upper and lower limit arrays are updated based on the mixture fraction.

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

[0106] Otherwise, 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 flameout 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 flameout 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 flameout mixture fraction is updated to the mixture fraction of the current example. The corresponding laminar flame propagation velocity is recorded in the laminar flame propagation velocity array according to the laminar flame data, and the laminar flame example is exited at the same time.

[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 the 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 the mixture fraction. If the mixture fraction does not meet the conditions that the flame speed decreases too fast and the mixture fraction is far from the theoretical mixture fraction, the state can be considered as a stable ignition state.

[0109] Exemplarily, assume that different mixed 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 different mixed fraction calculation arrays. If the current mixed fraction is 0.010, the current mixed fraction is at the marked ii_cut position, and the adjacent mixed fraction of the current mixed fraction is 0.012. If the current mixed fraction is 0.008, the current mixed fraction is not at the marked ii_cut position, and the adjacent mixed fraction of the current mixed fraction is 0.010.

[0110] When the current mixture fraction is confirmed to be 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, the flameout mixture fraction upper and lower limit arrays are updated based on the laminar flame propagation velocity array.

[0112] In multi-threaded parallel calculations, due to the synchronous update of the flameout upper and lower limits, some laminar flame calculations of adjacent mixture fractions may not be completed. In this case, it is impossible to accurately determine whether the flame propagation speed of a certain mixture fraction is significantly reduced, and therefore it is impossible to determine whether the mixture fraction is in a stable ignition state. To solve this problem, after all parallel calculations are completed, the main process needs to uniformly calculate the flameout upper and lower limits. And according to the final flameout upper and lower limits, clear the laminar flame data outside the flameout upper and lower limits.

[0113] For example, Figure 4 The following is a flow chart of determining the upper and lower limits of flameout in the main process provided by the present disclosure as an example. Figure 4 As shown, step S130 may include the following sub-steps:

[0114] Step S131: Determine the final flameout upper limit value using the main process.

[0115] After all parallel examples are calculated, the laminar flame propagation speed array has been filled with all data. At this time, the main process can be used to traverse each element in the array from front to back for different mixture fractions. For those elements whose mixture fraction values ​​are 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, the mixture fraction is determined as the flameout upper limit and the traversal loop is terminated.

[0117] If the corresponding laminar flame speed data does not exist, it indicates that the mixture fraction solution fails, the previous value of the mixture fraction is determined as the flameout upper limit, and the traversal loop is terminated.

[0118] Step S132: Determine the final flameout lower limit value using the main process.

[0119] The main process can be used to traverse each element in the calculation array from back to front with different mixture fractions. For those elements whose mixture fraction values ​​are 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, the mixture fraction is determined as the lower limit of flameout and the traversal loop is terminated.

[0121] If the corresponding laminar flame speed data does not exist, it indicates that the mixture fraction solution fails, the previous value of the mixture fraction is determined as the flameout lower limit, and the traversal loop is terminated.

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

[0123] Delete the laminar flame calculation data files whose mixture fraction is less than the lower limit of the flameout mixture fraction or greater than the upper limit of the flameout mixture fraction to complete the laminar flame data calculation.

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

[0125] For example, the beta probability density distribution function can be used, combined with the mixture fraction, normalized reaction progress variable, and the corresponding relationship between the mass fraction of each component, temperature, and density calculated through laminar flame data, and the mixture fraction and the normalized reaction progress variable are respectively integrated in the range of 0 to 1 to obtain turbulent flame data.

[0126] For example, Figure 5 A schematic diagram of a process for constructing turbulent flame data provided by an exemplary embodiment of the present disclosure is as follows: Figure 5 As shown, step S140 may include the following sub-steps:

[0127] Step S141: Based on the flameout mixture fraction upper and lower limit arrays, laminar flame data within the flammable range and laminar flame data outside the flammable range are obtained.

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

[0129] Step S142: constructing chemical reaction progress variables and normalized progress variables based on the laminar flame data within the flammable range, and obtaining a corresponding relationship of the mixture fraction within the flammable range.

[0130] For example, the correspondence between the component mass fraction, temperature, density and the mixture fraction and the normalized progress variable can be established by constructing the chemical reaction progress variable and the normalized progress variable.

[0131] First, the sum of the mass fractions of CO, CO2 and H2O is defined 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, the maximum chemical reaction progress variable value is found in the same mixture fraction file, and the normalized progress variable C1 is defined as the ratio of the chemical reaction progress variable at the spatial position to the maximum chemical reaction progress variable.

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

[0134] Step S143: Based on the laminar flame data outside the flammable range and the physical property calculation rule of the ideal gas mixture, the corresponding relationship of the mixture fraction outside the flammable range is obtained.

[0135] Exemplarily, the physical property calculation law of an ideal gas mixture can be used to combine the mixture fraction, the gas inlet temperature and the air inlet temperature to obtain the corresponding relationship between the mass fraction of each component, the temperature, the density and the mixture fraction, and the normalized progress variable in the mixture fraction range from 0 to the lower limit of the flameout mixture fraction and the upper limit of the flameout mixture fraction to 1.

[0136] Based on this, the correspondence between the mass fraction of each component outside the flammable limit, temperature, density and mixture fraction, and normalized progress variable is improved, which 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, thereby more accurately simulating and predicting the behavior of the flame under extreme conditions, thereby improving the accuracy and practicality of the combustion model.

[0137] Step S144: Based on the corresponding relationship between the mixture fractions within the flammable range and the corresponding relationship between the mixture fractions outside the flammable range, the laminar flame data is converted into turbulent flame data by integrating the turbulent probability density function.

[0138] Based on the correspondence between the mass fraction, temperature, density and mixture fraction of the components within the flammable limit obtained in step S142 and outside the flammable limit obtained in step S143, and the normalized progress variable, the mass fraction, temperature and density of each mixture fraction under different reaction progress conditions are obtained by integrating the turbulent probability density function, thereby converting the laminar flame data into turbulent flame data.

[0139] For example, the integral of the turbulence probability density function can be expressed as:

[0140]

[0141] Among them, C1 represents the normalized progress variable; represents the mean of the progress variable; represents the variance of the progress variable; Z represents the mixed fraction; represents the mixture fraction mean; 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 between the mass fraction Yi of component i in the turbulent flame data can be obtained as follows: Temperature relationship: Density relationship:

[0143] Step S145: constructing a turbulent flame surface database according to the turbulent flame data.

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

[0145] Furthermore, these discrete data are systematically input into the turbulent flame surface database for use by researchers and engineers in designing and optimizing combustion equipment, thereby improving combustion efficiency and ensuring operational safety.

[0146] One or more technical solutions provided in the exemplary embodiments of the present disclosure synchronize the calculation results of the examples in real time in the form of a shared array during the calculation process of laminar flame data, and determine whether a new example needs to be calculated by using the real-time updated data. This parallel strategy transforms the serial calculation process with a sequential calculation order into a parallel calculation process with multi-process synchronous calculation. In addition, the critical mixture fraction of flameout is effectively identified through the main process to determine the final flameout upper limit.

[0147] Therefore, the flame surface data parallel calculation method of the combustion model provided in the exemplary embodiment of the present disclosure greatly improves the calculation efficiency of the flame surface database and significantly shortens the calculation time while ensuring that the upper and lower limit criteria of flameout remain unchanged.

[0148] The above mainly introduces the solutions provided by the exemplary embodiments of the present disclosure. It is understandable that in order to implement the above functions, the electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with 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 function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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] The exemplary embodiments of the present disclosure can divide the functional units of the electronic device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the exemplary embodiments of the present disclosure is schematic and is only a logical function division. There may be other division methods in actual implementation.

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

[0151] The data acquisition module 610 is used to construct a laminar flame example set and a shared array, wherein the laminar flame example set includes laminar flame examples corresponding to different mixture fractions, and the shared array includes an array of upper and lower limits of the flameout mixture fraction and an array of laminar flame propagation speed.

[0152] The data processing module 620 is used to determine the flameout state of each laminar flame example in parallel based on the flameout mixture fraction upper and lower limit arrays, and when the flameout state is not flameout, calculate the laminar flame example in parallel 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 flameout mixture fraction upper and lower limit arrays based on the laminar flame propagation velocity array after the parallel calculation is completed;

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

[0155] In another embodiment provided by the present disclosure, the data processing module 620 is also used to obtain operating conditions, calculation parameters and theoretical mixture fractions of combustion reactants; construct different mixture fraction calculation arrays based on the theoretical mixture fraction; and construct a laminar flame example set based on the operating conditions, calculation parameters and different mixture fraction calculation arrays.

[0156] In another embodiment provided by the present disclosure, the data processing module 620 is further used to obtain the number of parallel computing cores, and equally divide the laminar flame example based on the number of parallel computing cores; based on the flameout mixing fraction upper and lower limit arrays, parallelly judge whether the mixing fraction corresponding to each laminar flame example is within the flameout mixing fraction upper and lower limit range; when the mixing fraction is within the flameout mixing fraction upper and lower limit range, determine that the flameout state is not flameout; when the mixing fraction is outside the flameout mixing fraction upper and lower limit range, determine that the flameout state is flameout.

[0157] In another embodiment provided by the present disclosure, the data processing module 620 is further used to iteratively encrypt and solve the laminar flame example when the flameout state is not flameout, and judge whether the laminar flame example is solved successfully according to a preset judgment condition; if the solution fails, update the flameout mixture fraction upper and lower limit arrays based on the mixture fraction corresponding to the laminar flame example; if the solution is successful, obtain the laminar flame data corresponding to the laminar flame example, and update the shared array based on the laminar flame data.

[0158] In another embodiment provided by the present disclosure, the data processing module 620 is further used to determine the flameout state of the laminar flame example based on the laminar flame data and the mixture fraction; when the flameout state is not flameout, the laminar flame propagation speed array is updated based on the laminar flame data; when the flameout state is flameout, the laminar flame propagation speed array is updated based on the laminar flame data, and the flameout mixture fraction upper and lower limit arrays are updated based on the mixture fraction.

[0159] In another embodiment provided by the present disclosure, the data processing module 620 is further used to obtain laminar flame data within the flammable range and laminar flame data outside the flammable range based on the upper and lower limit arrays of the extinction mixture fraction; construct chemical reaction progress variables and normalized progress variables based on the laminar flame data within the flammable range to obtain the corresponding relationship of the mixture fraction within the flammable range; obtain the corresponding relationship of the mixture fraction outside the flammable range based on the laminar flame data outside the flammable range and the physical property calculation law of the ideal gas mixture; 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, the laminar flame data is converted into turbulent flame data by integrating the turbulent probability density function.

[0160] In another embodiment provided by the present disclosure, the data processing module 620 is also used for the turbulence probability density function integral which can be expressed as:

[0161]

[0162] Among them, C1 represents the normalized progress variable; represents the mean of the progress variable; represents the variance of the progress variable; Z represents the mixed fraction; represents the mixture fraction mean; 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 integral of the turbulence probability density function, turbulent flame data corresponding to each mixing fraction is obtained; the turbulent flame data includes component mass fraction, temperature and density.

[0164] The exemplary embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to cause the electronic device to perform the method according to the embodiment of the present disclosure when executed by the at least one processor.

[0165] The exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method according to the embodiments of the present disclosure.

[0166] Figure 7 The block diagram of the electronic device provided as an example of the present disclosure, the block diagram of the electronic device 700 that can be used as the server or client of the present disclosure will now be described, which 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 laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0167] like 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] A plurality of 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 may be any type of device capable of inputting information to the electronic device 700, and the input unit 706 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 707 may be any type of device capable of presenting information, and may 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 may include but is not limited to a disk, an optical disk. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may 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 Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0169] The computing unit 701 may be a variety of general and / or special 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, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above. Each of the methods described above may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 700 via the ROM 702 and / or the communication unit 709.

[0170] Figure 8This is a schematic diagram of an exemplary computer program product provided by the present disclosure. The exemplary embodiment of the present disclosure also provides a computer program product 800, including a computer program 801, wherein the computer program 801 is used to enable the computer to perform a method according to an embodiment of the present disclosure when executed by a processor of the computer.

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

[0172] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0173] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, 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 interaction with a user, the systems and techniques described herein 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 herein may be implemented in a computing system that includes back-end 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 front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0176] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to 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 by 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 instruction is loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is executed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device or other programmable device. The computer program or instruction may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instruction may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server, data center, etc. that integrates one or more available media. The available medium may be a magnetic medium, for example, a floppy disk, a hard disk, a tape; it may also be an optical medium, for example, a digital video disc (DVD); it may also be a semiconductor medium, for example, a solid state drive (SSD).

[0178] Although the present disclosure has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto 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 as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations 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 equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A flame surface data parallel calculation method for a combustion model, characterized in that: The method comprises: Constructing a laminar flame example set and a shared array, wherein the laminar flame example set includes laminar flame examples corresponding to different mixture fractions, and the shared array includes an array of upper and lower limits of the flameout mixture fraction and an array of laminar flame propagation speed; Based on the flameout mixture fraction upper and lower limit arrays, the flameout state of each laminar flame example is judged in parallel. When the flameout state is not flameout, the laminar flame example is calculated in parallel to obtain laminar flame data corresponding to different mixture fractions, and the shared array is updated. After the parallel calculation is completed, the flameout mixture fraction upper and lower limit arrays are updated based on the laminar flame propagation speed array; The laminar flame data is converted into turbulent flame data by integrating the turbulent probability density function.

2. The method according to claim 1, characterized in that The construction of the laminar flame example set includes: Obtain operating conditions, calculation parameters and theoretical mixture fractions of combustion reactants; Constructing different mixture fraction calculation arrays based on the theoretical mixture fraction; Based on the operating conditions, calculation parameters and calculation arrays of different mixture fractions, a laminar flame example set is constructed.

3. The method according to claim 1, characterized in that The method of determining the flameout state of each laminar flame example in parallel based on the flameout mixture fraction upper and lower limit arrays includes: Obtaining the number of parallel computing cores, and equally dividing the laminar flame calculation example based on the number of parallel computing cores; Based on the flameout mixture fraction upper and lower limit arrays, it is determined in parallel whether the mixture fraction corresponding to each laminar flame example is within the flameout mixture fraction upper and lower limit ranges; When the mixture fraction is within the range of the upper and lower limits of the flameout mixture fraction, determining that the flameout state is not flameout; When the mixture fraction is outside the upper and lower limits of the flameout mixture fraction, it is determined that the flameout state is flameout.

4. The method according to claim 3, characterized in that When the flameout state is not flameout, parallel calculation of laminar flame examples is performed to obtain laminar flame data corresponding to different mixture fractions, and update the shared array, including: When the flameout state is not flameout, performing iterative encryption to solve the laminar flame example, and judging whether the laminar flame example is solved successfully according to a preset judgment condition; In case of a solution failure, updating the flameout mixture fraction upper and lower limit arrays based on the mixture fraction corresponding to the laminar flame example; When the solution is successful, laminar flame data corresponding to the laminar flame calculation example is obtained, and the shared array is updated based on the laminar flame data.

5. The method according to claim 4, characterized in that The updating of the shared array based on the laminar flame data comprises: Determining the flameout state of the laminar flame example based on the laminar flame data and the mixture fraction; When the flameout state is not flameout, updating the laminar flame propagation speed array based on the laminar flame data; When the flameout state is flameout, the laminar flame propagation speed array is updated based on the laminar flame data, and the flameout mixture fraction upper and lower limit arrays are updated based on the mixture fraction.

6. The method according to claim 1, characterized in that The step of converting the laminar flame data into turbulent flame data by integrating the turbulent probability density function comprises: Based on the flameout mixture fraction upper and lower limit arrays, laminar flame data within the flammable range and laminar flame data outside the flammable range are obtained; Constructing chemical reaction progress variables and normalized progress variables based on the laminar flame data within the flammable range to obtain a corresponding relationship between the mixture fraction within the flammable range; Based on the laminar flame data outside the flammable range and the physical property calculation law of the ideal gas mixture, a corresponding relationship of the mixture fraction outside the flammable range is obtained; Based on the corresponding relationship between the mixture fractions within the flammable range and the corresponding relationship between the mixture fractions outside the flammable range, the laminar flame data is converted into turbulent flame data by integrating the turbulent probability density function.

7. The method according to claim 6, characterized in that The integral of the turbulence probability density function can be expressed as: Among them, C1 represents the normalized progress variable; represents the mean of the progress variable; represents the variance of the progress variable; Z represents the mixed fraction; represents the mixture fraction mean; 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 integral of the turbulence probability density function, turbulent flame data corresponding to each mixing fraction is obtained; the turbulent flame data includes component mass fraction, temperature and density.

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

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