CN116882306B - Multi-dimensional searching method, device, equipment and medium based on flame surface model - Google Patents
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Abstract
The application discloses a multi-dimensional searching method, a device, equipment and a medium based on a flame surface model, which are applied to the technical field of combustion technology and comprise the following steps: acquiring a turbulent flame surface database; establishing a target tree according to the principle of a tree table searching method according to the mixing fraction, the mixing fraction fluctuation quantity and the scalar dissipation rate of the turbulent flame surface database; acquiring a flow field mixing fraction, flow field mixing fraction fluctuation quantity and flow field scalar dissipation ratio of a current grid point of a flow field, and establishing a target point; and carrying out interpolation calculation according to the target point and the target tree to obtain the target temperature, the target component mass fraction and the target density corresponding to the target point. According to the application, only the flow field is repeated in the iteration solving part, the turbulent flow physical quantity, the mixing fraction and the fluctuation quantity are solved, the distribution condition of the temperature, the component mass fraction and the density under the given boundary condition is presupposed, the calculated quantity is greatly saved, and when the flame surface model is combined with the tree table searching algorithm with lower time complexity, the calculation efficiency can be greatly improved.
Description
Technical Field
The application relates to the technical field of combustion, in particular to a multi-dimensional searching method, device, equipment and medium based on a flame surface model.
Background
In engineering, combustion generally occurs in turbulent flow and there is a strong interaction with the turbulence. However, up to now, research on the interaction mechanism of turbulence and combustion has not theoretically led to scientific and clear knowledge. The probability density transportation model, the eddy dissipation concept model and the like in the turbulent combustion model commonly used in the existing engineering need to repeatedly solve the energy equation and the component transportation equation, so that the repeated solving steps are too many, and the calculation efficiency is low.
Therefore, how to improve the computational efficiency of turbulent combustion models is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the present application aims to provide a multidimensional searching method, apparatus, device and medium based on a flame surface model, which solve the problem of low calculation efficiency of the flame surface model in the prior art.
In order to solve the technical problems, the application provides a multidimensional searching method based on a flame surface model, which comprises the following steps:
acquiring a turbulent flame face database consisting of temperature, component mass fractions and density distribution given a mixing fraction, mixing fraction fluctuation amount and scalar dissipation ratio;
establishing a target tree by using a tree table lookup method according to the mixing fraction, the mixing fraction fluctuation quantity and the scalar dissipation ratio in the turbulent flame surface database;
acquiring a flow field mixing fraction, flow field mixing fraction fluctuation quantity and flow field scalar dissipation ratio of a current grid point of a flow field, and establishing a target point;
performing data interpolation calculation according to the target point and the target tree to obtain a target temperature, a target component mass fraction and a target density corresponding to the target point;
wherein, before the flow field mixing fraction, the flow field mixing fraction fluctuation amount and the flow field scalar dissipation ratio of the current grid point of the flow field are obtained, the method further comprises:
solving a flame surface equation set to obtain the turbulent flame surface database;
solving the flow field to obtain a speed parameter and a pressure distribution parameter;
solving a turbulence model to obtain turbulence physical quantity;
solving a mixed fraction transportation equation and a mixed fraction fluctuation amount transportation equation to obtain the mixed fraction and the mixed fraction fluctuation amount;
solving the scalar dissipation ratio of the flow field according to the turbulent physical quantity and the mixed fractional pulsation quantity.
Optionally, the creating a target tree according to the mixing score, the mixing score fluctuation amount and the scalar dissipation ratio in the turbulent flame front database by using a tree table lookup method includes:
selecting a preset dimension for sorting from the mixed fraction, the mixed fraction fluctuation amount and the scalar dissipation ratio;
sorting the mixing fraction or the mixing fraction fluctuation amount or the scalar dissipation ratio according to the preset dimension to obtain sorting data, and splitting the sorting data according to the median of the preset dimension to obtain split data;
and determining father nodes and child nodes of the current level of the target tree according to the data after the subdivision.
Optionally, after performing data interpolation calculation according to the target point and the target tree to obtain a target temperature, a target component mass fraction and a target density corresponding to the target point, the method further includes:
determining the speed, pressure, turbulence physical quantity, mixing fraction and mixing fraction fluctuation quantity corresponding to each point according to the target density;
judging whether the speed, pressure, turbulence physical quantity, mixing fraction and mixing fraction fluctuation quantity of each point have convergence or not;
if yes, stopping calculation;
if not, continuing to carry out iterative solution.
Optionally, the creating a target tree according to the mixing score, the mixing score fluctuation amount and the scalar dissipation factor in the turbulent flame front database by using a tree table lookup method includes:
and utilizing a KD tree according to the mixing fraction, the mixing fraction fluctuation amount and the scalar dissipation ratio to establish the target tree.
Optionally, the calculating the data interpolation according to the target point and the target tree includes:
and carrying out data interpolation calculation by using a tri-linear interpolation method according to the target point and the target tree.
The application also provides a multidimensional searching device based on the flame surface model, which comprises:
the basic data acquisition module is used for acquiring a turbulent flame surface database formed by temperature, component mass fraction and density distribution under the given mixing fraction, mixing fraction fluctuation quantity and scalar dissipation ratio;
the target tree establishing module is used for establishing a target tree by utilizing a tree table searching method according to the mixing fraction, the mixing fraction fluctuation quantity and the scalar dissipation ratio in the turbulent flame face database;
the target point determining module is used for obtaining the flow field mixing fraction, the flow field mixing fraction fluctuation quantity and the flow field scalar dissipation ratio of the current grid point of the flow field and establishing a target point;
the target temperature, target component mass fraction and target density determining module is used for carrying out data interpolation calculation according to the target point and the target tree to obtain target temperature, target component mass fraction and target density corresponding to the target point;
wherein, still include:
the flame surface equation set solving module is used for solving the flame surface equation set to obtain the turbulent flame surface database;
the flow solving module is used for solving the flow field to obtain a speed parameter and a pressure distribution parameter;
the turbulence model solving module is used for solving a turbulence model to obtain turbulence physical quantity;
the mixed fraction transportation equation and mixed fraction fluctuation amount transportation equation request module is used for solving a mixed fraction transportation equation and a mixed fraction fluctuation amount transportation equation to obtain the mixed fraction and the mixed fraction fluctuation amount;
and a scalar dissipation ratio solving module for solving the scalar dissipation ratio of the flow field according to the turbulent physical quantity and the mixed fraction fluctuation quantity.
Optionally, the target tree building module includes:
the dimension determining unit is used for selecting a preset dimension for sorting from the mixing fraction, the mixing fraction fluctuation amount and the scalar dissipation ratio;
the subdivision unit is used for sorting the mixed fraction or the mixed fraction fluctuation amount or the scalar dissipation ratio according to the preset dimension to obtain sorting data, and subdividing the sorting data according to the median of the data in the preset dimension to obtain subdivided data;
and the construction unit is used for determining father nodes and child nodes of the current level of the target tree according to the data after the subdivision.
The application also provides a multi-dimensional searching device based on the flame face model, which comprises:
a memory for storing a computer program;
and the processor is used for executing the computer program to realize the steps of the multi-dimensional searching method based on the flame surface model.
The application also provides a medium, wherein the medium is stored with a computer program, and the computer program realizes the steps of the multi-dimensional searching method based on the flame surface model when being executed by a processor.
It can be seen that the application is realized by acquiring a turbulent flame surface database; establishing a target tree according to the principle of a tree table searching method according to the mixing fraction, the mixing fraction fluctuation quantity and the scalar dissipation rate of the turbulent flame surface database; acquiring a flow field mixing fraction, flow field mixing fraction fluctuation quantity and flow field scalar dissipation ratio of a current grid point of a flow field, and establishing a target point; and carrying out interpolation calculation according to the target point and the target tree to obtain the target temperature, the target component mass fraction and the target density corresponding to the target point. The combustion model selected by the application only repeatedly solves the flow field, the turbulence physical quantity, the mixing fraction and the fluctuation quantity thereof in the iteration solving part, namely, the distribution condition of temperature, component mass fraction, density and the like under the given boundary condition is presupposed, thus greatly saving the calculated quantity and improving the calculation efficiency.
On the basis, the application also provides a multi-dimensional searching device, equipment and medium based on the flame surface model, and the multi-dimensional searching device, equipment and medium have the beneficial effects similar to the multi-dimensional searching method based on the flame surface model.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a multi-dimensional search method based on a flame face model according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a multi-dimensional searching method based on a flame face model according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a multi-dimensional searching device based on a flame surface model according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a multidimensional searching device based on a flame surface model according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, fig. 1 is a flowchart of a multi-dimensional searching method based on a flame surface model according to an embodiment of the present application. The method may include:
s100, acquiring a turbulent flame surface database formed by temperature, component mass fraction and density distribution under the given mixing fraction, mixing fraction fluctuation amount and scalar dissipation ratio.
This embodiment is not limited to a specific turbulent flame surface database. For example, the turbulent flame surface database in this embodiment may be a solution to the interactive flame surface model; or the flame face database in this embodiment may be a solution to a non-interactive flame face model. The mixing fraction in this embodiment is a physical quantity of the fuel mass fraction in the reaction control body. The mixing fraction pulsation amount in this embodiment reflects the pulsation of the transient mass fraction over time due to turbulence. The scalar dissipation ratio in this embodiment is a physical quantity that quantifies the effect of the flow field on the flame structure.
S101, establishing a target tree by using a tree table searching method according to the mixing fraction, the mixing fraction fluctuation quantity and the scalar dissipation ratio in the turbulent flame surface database.
The embodiment is not limited to a specific tree table lookup method. For example, the tree table lookup method may be a KD-tree (multidimensional binary tree); or the tree table lookup method can also be a balanced lookup tree; or the tree table lookup method may also be red black tree. This embodiment may find the nearest neighbors of the acquisition target point based on the scalar dissipation ratio, the blending score, and the value of the blending score pulsation.
It can be understood that the KD-tree belongs to a tree-like structure, and if the left subtree of a node is not empty, the values of all nodes on the left subtree are smaller than the value of the node in the given dimension; meanwhile, if the right subtree of a node is not empty, the values of all nodes on the right subtree are larger than the value of the node in the given dimension. Each node in the binary tree comprises a child node and a parent node, and the root node is the only node without the parent node. The KD-tree has the advantage of efficient and fast querying of multidimensional data. Compared with other searching methods, if the data volume is represented by N, the storage volume of the KD tree in tree construction is proportional to N, and the calculated volume is proportional to KN log N. Once the tree building process is completed, the calculation amount is proportional to log n in the stage of searching nearest neighbor. In the interpolation calculation of the turbulent flame surface database, the turbulent flame surface database is calculated in advance, and the tree building process is only needed once in interpolation, so that the efficiency of the whole interpolation process depends on the searching efficiency more. Compared with other algorithms, the KD tree has a logarithmic relation between the calculation efficiency and the data quantity in the part, and the good calculation efficiency is embodied. Therefore, the method can be applied to interpolation calculation of the turbulent flame surface database, so that an interpolation interval can be found quickly and efficiently, and the calculation efficiency is improved.
S102, obtaining a flow field mixing fraction, flow field mixing fraction fluctuation quantity and flow field scalar dissipation ratio of a current grid point of the flow field, and establishing a target point.
The embodiment obtains the flow field mixing fraction, the flow field mixing fraction fluctuation quantity and the flow field scalar dissipation ratio of each grid point in the flow field, establishes a target point and prepares for the subsequent searching process.
And S103, carrying out data interpolation calculation according to the target point and the target tree to obtain the target temperature, the target component mass fraction and the target density corresponding to the target point.
The embodiment is not limited to a specific method for obtaining the target temperature, the target component mass fraction and the target density corresponding to the target point by data interpolation calculation. For example, trilinear interpolation may be used to obtain a target temperature, a target component mass fraction, and a target density for the target point; or a nearest neighbor interpolation method can be adopted to obtain the target temperature, the target component mass fraction and the target density corresponding to the target point; or the target temperature, the mass fraction of the target component and the target density corresponding to the target point can be obtained by adopting three-dimensional cubic spline interpolation.
Further, to increase the speed of creating the target tree, the creating the target tree according to the mixing score, the mixing score fluctuation amount and the scalar dissipation ratio in the turbulent flame surface database by using a tree table searching method may include:
selecting a preset dimension for sorting from the mixed fraction, the mixed fraction fluctuation amount and the scalar dissipation ratio;
sorting the mixed fraction or the mixed fraction fluctuation amount or the scalar dissipation ratio according to a preset dimension to obtain sorting data, and splitting the sorting data according to the median of the preset dimension to obtain split data;
and determining father nodes and child nodes of the current level of the target tree according to the data after the subdivision.
When the embodiment builds a tree, the data needs to be ordered and split to determine whether each data is stored in the left subtree or the right subtree of the parent node. The embodiment is not limited to a specific subdivision basis; for example, the subdivision basis may be the dimension in which the data variance is greatest in the dimensions (the mixed fraction dimension, the mixed fraction fluctuation dimension, and the scalar dissipation factor dimension); or may be the dimension of the largest span of data in each dimension, i.e., the difference between the maximum and minimum values. It will be appreciated that when the blending score is selected as the preset dimension, only the blending score is ordered; when the mixed fraction fluctuation amount is selected as a preset dimension, sequencing only the mixed fraction fluctuation amount; when scalar dissipation ratios are selected as the preset dimensions, only the scalar dissipation ratios are ordered.
Further, in order to improve the accuracy of the solution, after performing data interpolation calculation according to the target point and the target tree to obtain the target temperature, the target component mass fraction and the target density corresponding to the target point, the method may further include:
determining the speed, pressure, turbulence physical quantity, mixing fraction and mixing fraction fluctuation quantity corresponding to each point according to the target density;
judging whether the speed, pressure, turbulence physical quantity, mixing fraction and mixing fraction fluctuation quantity of each point have convergence or not;
if yes, stopping calculation;
if not, continuing to carry out iterative solution.
The embodiment can judge the convergence of the speed, pressure, turbulence physical quantity, mixing fraction and mixing fraction fluctuation quantity of each point, and the non-convergence indicates that no numerical solution conforming to the actual is found, so that iterative solution needs to be continued.
Further, in order to improve the searching efficiency, the creating the target tree according to the mixing fraction, the mixing fraction fluctuation amount and the scalar dissipation ratio in the turbulent flame surface database by using a tree table searching method may include:
and establishing a target tree by utilizing a KD tree according to the mixed fraction, the mixed fraction fluctuation quantity and the scalar dissipation ratio.
The tree table lookup in this embodiment is a multidimensional binary tree lookup (KD-tree). When using a multidimensional binary tree lookup, the ordering step may generally include: when the data span is used as a dividing standard, the data span of each dimension is calculated firstly when the tree is built, the dimension with the largest span is selected as a subdivision basis for sorting, after the sorting is finished for the first time, the dimension with the largest span of the rest data under each dimension is used as a basis for cutting along the dimension by the same method until all the dimensions are traversed, and one round of the process is finished. After one round is finished, the selection of the subdivision dimension is circulated according to the previous sequence.
The specific implementation steps comprise: first, a target tree is established: if the mixing fraction is calculated, the spans of the mixing fraction pulse amount and the scalar dissipation ratio are 1, 10 and 40 respectively, then the scalar dissipation ratio is selected as the dimension of the first subdivision. Then, the mixing fraction in the rest data is calculated, the spans of the fluctuation amounts of the mixing fraction (when the spans of the scalar dissipation ratios are not calculated) are respectively 1 and 9,then the mixing fraction pulsation amount is selected as the dimension of the second subdivision. And finally, taking the mixed fraction as the dimension of the third subdivision, and completing the calculation of the round. And repeating the operation, and sequentially performing each round of subdivision until the end. The formula of the whole process is as follows:and->Wherein k represents the total dimension of the data, i represents the current dividing times, mod is a remainder function, N is a dimension mark selected by the next subdivision,representing the next subdivision dimension, j representing the current subdivision dimension, dis (j) representing the span of the data in the j dimension. Sorting all data points according to the dimension selected in the step 1, searching the median of the selected divided dimension, taking the group of data with the median as a cutting point, namely a father node P, wherein the value of the rest data in the dimension is smaller than the left child node Q of the median and is placed in the P, and if the value is larger than the median, the rest data is placed in the right child node R of the P, the rest data is used for marking the rest data>The value representing the i dimension of the Q node, then there are child nodes for each parent node that satisfy: />. Repeating the steps on the sub-nodes Q and R until all data splitting is completed and no sub-nodes exist.
Second, find nearest neighbor node: after the tree is built, the nearest point to the target point can be found on the KD tree, namely nearest neighbor searching is performed. Given the distance function D, the data set B (k dimension) of the tree to be built, the point P (k dimension) to be solved, the nearest neighbor Q point is defined as follows:where NP (P) represents the nearest neighbor of the P-point.
The detailed steps of the search are as follows:
1: the selected distance calculation method is commonly as follows: manhattan distance, euclidean distance, chebyshev distance, minmin distance. The patent selects the Min distance to calculate, and the calculation formula of the distance between two points i and j in k dimension is as follows:。
2: starting from a root node, taking a preset dimension of the layer of split sub-node data selected during tree construction as a standard, and if the value of a point to be queried in the dimension is smaller than the value of a current node in the dimension, continuing to find a left sub-node of the current node; if the value of the point to be queried in the dimension is larger than the value of the current node in the dimension, the right sub-node of the current node is continuously found. If the value of the point to be queried in the dimension is equal to the value of the current node in the dimension, returning to the position of the current node, and jumping to the step 4.
3: and (5) recursion circulation. And (3) repeating the operation of the step (2) by taking the node found in the step (2) as a basis until the leaf node is found.
4: it is checked whether the found node is the nearest neighbor. Namely, the child node on the opposite side and the distance between the child node and the target point are calculated, and if the distance between the child node and the target point is the smallest, the child node is the nearest neighbor. Otherwise, repeating the searching process of the step 2 in the data set represented by the child node on the opposite side until the nearest neighbor is found.
Further, in order to increase the solving speed of the flame surface model, before the flow field mixing fraction, the flow field mixing fraction fluctuation amount and the flow field scalar dissipation ratio of the current grid point of the flow field are obtained, the method further comprises the following steps:
solving a flame surface equation set to obtain the turbulent flame surface database;
solving the flow field to obtain a speed parameter and a pressure distribution parameter;
solving a turbulence model to obtain turbulence physical quantity;
solving a mixed fraction transportation equation and a mixed fraction fluctuation amount transportation equation to obtain the mixed fraction and the mixed fraction fluctuation amount;
solving the scalar dissipation ratio of the flow field according to the turbulent physical quantity and the mixed fractional pulsation quantity. The combustion model selected by the embodiment separates the flame surface equation solving step and the flow field solving step, so that the solving of the flow field, the turbulence physical quantity, the mixing fraction and the mixing fraction fluctuation quantity is only repeated in the iteration solving part, and the solving speed can be improved.
Further, in order to increase the speed of calculating the temperature, the mass fraction of the components and the density, the data interpolation calculation is performed according to the target point and the target tree, including:
and carrying out data interpolation calculation by using a tri-linear interpolation method according to the target point and the target tree.
This embodiment takes into account the accuracy and complexity of the interpolation algorithm, so a tri-linear interpolation is chosen. The steps of this embodiment may include a first step of: calculating a scalar dissipation ratio for the point; and a second step of: taking the scalar dissipation rate of the point, the mixed fraction and the value of the mixed fraction fluctuation quantity as target points; and a third step of: and searching a nearest neighbor algorithm by using the KD tree, finding a point nearest to the target point, determining an interval to which an interpolation algorithm is applied, and calculating by using the interpolation algorithm to obtain the temperature at the target point, and the numerical value of the component mass fraction and the density.
The embodiment of the application provides a turbulent flame surface database of component mass fraction and density distribution by acquiring the temperature under the given mixing fraction, mixing fraction fluctuation amount and scalar dissipation ratio; establishing a target tree by using a tree table searching method according to the mixing fraction, the mixing fraction fluctuation quantity and the scalar dissipation rate of the turbulent flame surface database; acquiring a mixing fraction, mixing fraction fluctuation quantity and scalar dissipation ratio of a current grid point of a flow field, and establishing a target point; and obtaining the target temperature, the target component mass fraction and the target density corresponding to the target point by interpolation calculation according to the data of the target point. Compared with other combustion models which need to repeatedly solve the energy equation and the component transportation equation at present, the combustion model selected by the application only repeatedly solves the flow field, the turbulence physical quantity, the mixing fraction and the mixing fraction pulse quantity in the iteration solving part, and the energy equation and the component transportation equation do not need to be solved in an iteration way, namely, the distribution condition of temperature, component mass fraction, density and the like under the given boundary condition is presupposed, so that the calculated quantity is greatly saved, the calculation efficiency is improved, and the calculation efficiency is higher due to the combination with the tree table searching algorithm. And the data are firstly ordered, so that the speed of binary tree establishment is improved; moreover, by using the KD tree, the high-efficiency and rapid query of the multidimensional data is realized; and the solution of the turbulent flame surface database is completed before the flow field is solved, so that the calculation efficiency is improved. And the efficiency of the interpolation process depends more on the efficiency of the search. Compared with other algorithms, the KD tree has a logarithmic relation between the calculation efficiency and the data quantity in the part, and the good calculation efficiency is embodied.
In order to facilitate understanding of the present application, referring to fig. 2 specifically, fig. 2 is a flowchart illustrating a multi-dimensional searching method based on flame surface according to an embodiment of the present application, which may specifically include:
and S200, solving a flame surface equation to obtain a flame surface database.
S201, solving a flow field to obtain a speed parameter and a pressure distribution parameter.
S202, solving a turbulence model to obtain turbulence physical quantity;
s203, solving a mixed fraction transportation equation and a mixed fraction fluctuation amount transportation equation to obtain a mixed fraction and a mixed fraction fluctuation amount.
S204, calculating the scalar dissipation ratio of the flow field according to the turbulence physical quantity and the mixing fraction fluctuation quantity.
S205, establishing a target KD tree by utilizing a KD tree searching method according to the mixed fraction, the mixed fraction fluctuation quantity and the scalar dissipation ratio.
S206, obtaining a mixing fraction of a current grid point of the flow field, mixing fraction fluctuation quantity and scalar dissipation rate, and establishing a target point;
s207, obtaining the temperature, the component mass fraction and the density of the target point by utilizing target tree interpolation calculation according to the data of the target point;
s208, judging whether the speed, pressure, turbulence physical quantity, mixing fraction and mixing fraction fluctuation quantity of each point have convergence.
And S209, if yes, stopping calculation, and obtaining a target temperature, a target component mass fraction and a target density corresponding to the target point according to the scalar dissipation ratio of each point, the mixing fraction and the mixing fraction fluctuation quantity.
And S210, if not, continuing to carry out iterative solution.
The following describes a multi-dimensional searching device based on a flame surface model according to an embodiment of the present application, and the multi-dimensional searching device based on a flame surface model described below and the multi-dimensional searching method based on a flame surface model described above may be referred to correspondingly.
Referring to fig. 3 specifically, fig. 3 is a schematic structural diagram of a multidimensional searching device based on a flame surface model according to an embodiment of the present application, which may include:
a base data acquisition module 100 for acquiring a turbulent flame front database consisting of temperature, component mass fractions and density distribution given a mixing fraction, mixing fraction fluctuation amount and scalar dissipation ratio;
a target tree creation module 200, configured to create a target tree according to the mixing score, the mixing score fluctuation amount, and the scalar dissipation ratio in the turbulent flame front database by using a tree table lookup method;
the target point determining module 300 is used for obtaining the flow field mixing fraction, the flow field mixing fraction fluctuation quantity and the flow field scalar dissipation ratio of the current grid point of the flow field and establishing a target point;
the target temperature, target component mass fraction and target density determining module 400 is configured to perform data interpolation calculation according to the target point and the target tree to obtain a target temperature, target component mass fraction and target density corresponding to the target point;
wherein, still include:
the flame surface equation set solving module is used for solving the flame surface equation set to obtain the turbulent flame surface database;
the flow field solving module is used for solving the flow field to obtain a speed parameter and a pressure distribution parameter;
the turbulence physical quantity solving module is used for solving a turbulence model to obtain turbulence physical quantity;
the mixed fraction and mixed fraction fluctuation amount solving module is used for solving a mixed fraction transportation equation and a mixed fraction fluctuation amount transportation equation to obtain the mixed fraction and the mixed fraction fluctuation amount;
and a scalar dissipation ratio solving module for solving the scalar dissipation ratio of the flow field according to the turbulent physical quantity and the mixed fraction fluctuation quantity.
Further, based on the above embodiment, the above target tree creation module 200 may include:
the dimension determining unit is used for selecting a preset dimension for sorting from the mixing fraction, the mixing fraction fluctuation amount and the scalar dissipation ratio;
the subdivision unit is used for sorting the mixed fraction or the mixed fraction fluctuation amount or the scalar dissipation ratio according to the preset dimension to obtain sorting data, and subdividing the sorting data according to the median of the data in the preset dimension to obtain subdivided data;
and the construction unit is used for determining father nodes and child nodes of the current level of the target tree according to the data after the subdivision.
Further, based on any of the above embodiments, the multi-dimensional search device based on a flame face model may further include:
the parameter determining module is used for determining the speed, pressure, turbulence physical quantity, mixing fraction and mixing fraction fluctuation quantity corresponding to each point according to the target density;
the judging module is used for judging whether the speed, the pressure, the turbulence physical quantity, the mixing fraction and the mixing fraction fluctuation quantity of each point have convergence or not;
the stopping searching module is used for stopping searching when the speed, the pressure, the turbulence physical quantity, the mixing fraction and the mixing fraction fluctuation quantity of each point have convergence;
and the continuous solving module is used for carrying out iterative solving when the speed, the pressure, the turbulent physical quantity, the mixing fraction and the fluctuation quantity of each point do not have convergence.
Further, based on any of the above embodiments, the above target tree creation module 200 may include:
and the target tree building unit is used for building the target tree by utilizing a KD tree according to the mixing score, the mixing score fluctuation quantity and the scalar dissipation ratio.
Further, based on any of the above embodiments, the above target temperature, target component mass fraction, and target density determination module 400 may include:
and the interpolation calculation unit is used for carrying out data interpolation calculation by utilizing a tri-linear interpolation method according to the target point and the target tree.
The multidimensional searching device based on the flame surface model provided by the embodiment of the application comprises: a base data acquisition module 100 for acquiring a turbulent flame front database consisting of temperature, component mass fractions and density distribution given a mixture fraction, mixture fraction fluctuation amount and scalar dissipation ratio; a target tree creation module 200, configured to create a target tree according to the mixing score, the mixing score fluctuation amount, and the scalar dissipation ratio in the turbulent flame front database by using a tree table lookup method; the target point determining module 300 is used for obtaining the flow field mixing fraction, the flow field mixing fraction fluctuation quantity and the flow field scalar dissipation ratio of the current grid point of the flow field and establishing a target point; and the target temperature, target component mass fraction and target density determining module 400 is configured to perform data interpolation calculation according to the target point and the target tree, so as to obtain a target temperature, target component mass fraction and target density corresponding to the target point. Compared with other combustion models which need to repeatedly solve the energy equation and the component transportation equation at present, the combustion model selected by the application only repeatedly solves the flow field in the iteration solving part, and solves the turbulence physical quantity, the mixing fraction and the mixing fraction pulse quantity without iteratively solving the flame surface equation set, namely, presupposing the distribution condition of temperature, component mass fraction, density and the like under the given boundary condition, thereby greatly saving the calculated quantity and improving the calculation efficiency, and the calculation efficiency is higher due to the combination with the tree table searching algorithm. And the data are firstly ordered, so that the speed of binary tree establishment is improved; in addition, the value of each point is ensured to be converged, so that the actual situation is more met; moreover, by using the KD tree, the high-efficiency and rapid query of the multidimensional data is realized; and the solution of the turbulent flame surface library is completed before the flow field is solved, so that the calculation efficiency is improved. And the efficiency of the interpolation process is more dependent on the searching efficiency, compared with other algorithms, the calculation efficiency of the KD tree in the part and the data quantity are in a logarithmic relation, and the good calculation efficiency is reflected.
The following describes a multi-dimensional searching device based on a flame surface model according to an embodiment of the present application, and the multi-dimensional searching device based on a flame surface model described below and the multi-dimensional searching method based on a flame surface model described above may be referred to correspondingly.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a multi-dimensional searching device based on a flame surface model according to an embodiment of the present application, which may include:
a memory 10 for storing a computer program;
a processor 20 for executing a computer program to implement the steps of the multi-dimensional search method based on a flame face model described above.
The memory 10, the processor 20, and the communication interface 31 all communicate with each other via a communication bus 32.
In the embodiment of the present application, the memory 10 is used for storing one or more programs, the programs may include program codes, the program codes include computer operation instructions, and in the embodiment of the present application, the memory 10 may store programs for implementing the following functions:
acquiring a turbulent flame face database consisting of temperature, component mass fractions and density distribution given a mixing fraction, mixing fraction fluctuation amount and scalar dissipation ratio;
establishing a target tree by using a tree table searching method according to the mixing fraction, the mixing fraction fluctuation quantity and the scalar dissipation ratio in the turbulent flame surface database;
acquiring a flow field mixing fraction, flow field mixing fraction fluctuation quantity and flow field scalar dissipation ratio of a current grid point of a flow field, and establishing a target point;
and carrying out data interpolation calculation according to the target point and the target tree to obtain the target temperature, the target component mass fraction and the target density corresponding to the target point.
In one possible implementation, the memory 10 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, and at least one application program required for functions, etc.; the storage data area may store data created during use.
In addition, memory 10 may include read only memory and random access memory and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, where the operating instructions may include various operating instructions for performing various operations. The operating system may include various system programs for implementing various basic tasks as well as handling hardware-based tasks.
The processor 20 may be a central processing unit (Central Processing Unit, CPU), an asic, a dsp, a fpga or other programmable logic device, and the processor 20 may be a microprocessor or any conventional processor. The processor 20 may call a program stored in the memory 10.
The communication interface 31 may be an interface of a communication module for connecting with other devices or systems.
Of course, it should be noted that the structure shown in fig. 4 does not limit the multi-dimensional searching apparatus based on the flame surface model in the embodiment of the present application, and in practical application, the multi-dimensional searching apparatus based on the flame surface model may include more or fewer components than those shown in fig. 4, or may combine some components.
The media provided by the embodiments of the present application are described below, and the media described below and the multi-dimensional searching method based on the flame surface model described above may be referred to correspondingly.
The application also provides a medium, wherein the medium is stored with a computer program, and the computer program realizes the steps of the multi-dimensional searching method based on the flame surface model when being executed by a processor.
The medium in this embodiment is a computer-readable storage medium, which may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Finally, it is further noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The multi-dimensional searching method, device, equipment and medium based on the flame face model provided by the application are described in detail, and specific examples are applied to illustrate the principle and the implementation mode of the application, and the description of the examples is only used for helping to understand the method and the core idea of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (9)
1. A multi-dimensional search method based on a flame face model, comprising:
acquiring a turbulent flame face database consisting of temperature, component mass fractions and density distribution given a mixing fraction, mixing fraction fluctuation amount and scalar dissipation ratio;
establishing a target tree by using a tree table lookup method according to the mixing fraction, the mixing fraction fluctuation quantity and the scalar dissipation ratio in the turbulent flame surface database;
acquiring a flow field mixing fraction, flow field mixing fraction fluctuation quantity and flow field scalar dissipation ratio of a current grid point of a flow field, and establishing a target point;
performing data interpolation calculation according to the target point and the target tree to obtain a target temperature, a target component mass fraction and a target density corresponding to the target point;
wherein, before the flow field mixing fraction, the flow field mixing fraction fluctuation amount and the flow field scalar dissipation ratio of the current grid point of the flow field are obtained, the method further comprises:
solving a flame surface equation set to obtain the turbulent flame surface database;
solving the flow field to obtain a speed parameter and a pressure distribution parameter;
solving a turbulence model to obtain turbulence physical quantity;
solving a mixed fraction transportation equation and a mixed fraction fluctuation amount transportation equation to obtain the mixed fraction and the mixed fraction fluctuation amount;
solving the scalar dissipation ratio of the flow field according to the turbulent physical quantity and the mixed fractional pulsation quantity.
2. The flame face model-based multidimensional lookup method of claim 1, wherein the establishing a target tree from the mixture score, the mixture score pulsation amount, and the scalar dissipation ratio in the turbulent flame face database using a tree table lookup method comprises:
selecting a preset dimension for sorting from the mixed fraction, the mixed fraction fluctuation amount and the scalar dissipation ratio;
sorting the mixing fraction or the mixing fraction fluctuation amount or the scalar dissipation ratio according to the preset dimension to obtain sorting data, and splitting the sorting data according to the median of the data in the preset dimension to obtain split data;
and determining father nodes and child nodes of the current level of the target tree according to the data after the subdivision.
3. The method for multi-dimensional searching based on a flame surface model according to claim 1, wherein after performing data interpolation calculation according to the target point and the target tree to obtain a target temperature, a target component mass fraction and a target density corresponding to the target point, the method further comprises:
determining the speed, pressure, turbulence physical quantity, mixing fraction and mixing fraction fluctuation quantity corresponding to each point according to the target density;
judging whether the speed, pressure, turbulence physical quantity, mixing fraction and mixing fraction fluctuation quantity of each point have convergence or not;
if yes, stopping calculation;
if not, continuing to carry out iterative solution.
4. A multi-dimensional lookup method based on a flame face model as claimed in any one of claims 1 to 3 in which the establishing a target tree from the mixing score, the mixing score pulsation amount and the scalar dissipation ratio in the turbulent flame face database utilises a tree table lookup method comprising:
and utilizing a KD tree according to the mixing fraction, the mixing fraction fluctuation amount and the scalar dissipation ratio to establish the target tree.
5. The method of claim 1, wherein the performing data interpolation calculation according to the target point and the target tree comprises:
and carrying out data interpolation calculation by using a tri-linear interpolation method according to the target point and the target tree.
6. A multi-dimensional lookup device based on a flame face model, comprising:
the basic data acquisition module is used for acquiring a turbulent flame surface database formed by temperature, component mass fraction and density distribution under the given mixing fraction, mixing fraction fluctuation quantity and scalar dissipation ratio;
the target tree establishing module is used for establishing a target tree by utilizing a tree table searching method according to the mixing fraction, the mixing fraction fluctuation quantity and the scalar dissipation ratio in the turbulent flame face database;
the target point determining module is used for obtaining the flow field mixing fraction, the flow field mixing fraction fluctuation quantity and the flow field scalar dissipation ratio of the current grid point of the flow field and establishing a target point;
the target temperature, target component mass fraction and target density determining module is used for carrying out data interpolation calculation according to the target point and the target tree to obtain target temperature, target component mass fraction and target density corresponding to the target point;
wherein, still include:
the flame surface equation set solving module is used for solving the flame surface equation set to obtain the turbulent flame surface database;
the flow field solving module is used for solving the flow field to obtain a speed parameter and a pressure distribution parameter;
the turbulence physical quantity solving module is used for solving a turbulence model to obtain turbulence physical quantity;
the mixed fraction and mixed fraction fluctuation amount solving module is used for solving a mixed fraction transportation equation and a mixed fraction fluctuation amount transportation equation to obtain the mixed fraction and the mixed fraction fluctuation amount;
and a scalar dissipation ratio solving module for solving the scalar dissipation ratio of the flow field according to the turbulent physical quantity and the mixed fraction fluctuation quantity.
7. The flame face model-based multidimensional finding device of claim 6, wherein the target tree creation module comprises:
the dimension determining unit is used for selecting a preset dimension for sorting from the mixing fraction, the mixing fraction fluctuation amount and the scalar dissipation ratio;
the subdivision unit is used for sorting the mixed fraction or the mixed fraction fluctuation amount or the scalar dissipation ratio according to the preset dimension to obtain sorting data, and subdividing the sorting data according to the median of the data in the preset dimension to obtain subdivided data;
and the construction unit is used for determining father nodes and child nodes of the current level of the target tree according to the data after the subdivision.
8. A multi-dimensional lookup apparatus based on a flame face model, comprising:
a memory for storing a computer program;
a processor for executing the steps of the computer program implementing a multi-dimensional flame face model-based lookup method as claimed in any one of claims 1 to 5.
9. A medium having stored thereon a computer program which, when executed by a processor, implements the steps of a flame face model based multidimensional finding method as claimed in any one of claims 1 to 5.
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