CN114692525B - Combustion simulation dimension reduction and speed acceleration method and device and steady state calculation method - Google Patents
Combustion simulation dimension reduction and speed acceleration method and device and steady state calculation method Download PDFInfo
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Abstract
The invention provides a combustion simulation dimension reduction acceleration method and device and a steady-state calculation method, which are used for constructing a progress variable variance and solving the progress variable variance, inquiring position information and weight information of the progress variable variance in a column vector, combining the position information and the weight information of an average mixing fraction, a mixing fraction variance and an average progress variable, looking up a table to obtain chemical thermodynamic state parameters, updating a fluid domain density field and finishing calculation of a turbulent combustion model. The method reduces the number of transport equations within the allowable range of precision errors, and realizes faster simulation calculation of the turbulent combustion process.
Description
Technical Field
The invention belongs to the technical field of simulation of flow combustion of an aircraft engine combustion chamber, and particularly relates to a combustion simulation dimension reduction and speed acceleration method based on a flame surface generated manifold (FGM) model.
Background
The combustion chamber is one of the core components of an aircraft engine, and the design of the combustion chamber directly influences the overall performance of the engine. In the development of an aircraft engine, a combustion chamber is a combustion chemical reaction system containing multiple components, the generated turbulent combustion process is extremely complex and may contain thousands of intermediate reactions, and the calculation amount required for directly solving detailed chemical reaction mechanisms and complete component transport equations is generally difficult to bear, so that the research on a turbulent combustion model is challenging.
The main common problems in the prior turbulent combustion model engineering are as follows: finite velocity class models and flame plane class models. Solving a multi-component transport equation by using a finite rate model, wherein the calculated amount is increased along with the increase of the component amount; the flame surface model introduces a small amount of control variables, establishes the corresponding relation between the control variables and the chemical thermodynamic state so as to obtain a flame surface database, solves the transport equation of the small amount of control variables, and then looks up the table in the flame surface database so as to obtain all chemical thermodynamic state parameters. A flame surface generation manifold (FGM) model is one type of flame surface type model. Among FGM models, there are three-way models, four-way models, and the like. Based on average mixed fraction
Mixed fraction variance
Average progress variable
The mode of the three-variable transport equation set only considers
The average value of (2) is relatively low in precision without considering the fluctuation influence of the progress variable. Based on
The mode of the four-variable transport equation set is considered at the same time
The calculation result is more accurate due to the average value and the fluctuation, but the calculation speed is obviously reduced due to the fact that one transport equation is solved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a combustion simulation dimension reduction and speed acceleration method based on a flame surface generated manifold (FGM) model, which reduces the number of transport equations within the allowable range of precision errors, realizes the speed acceleration of a combustion calculation process and has higher engineering application value.
The present invention achieves the above-described object by the following means.
A combustion simulation dimension reduction and acceleration method is used for a flame surface database type combustion model, and variables of the flame surface database type combustion model at least comprise average mixing fraction
Mixed fraction variance
Average progress variable
And variance of progress variables
Four variables of (a); the method comprises the following steps: sequentially solving and calculating the flame surface database type combustion model on each grid unit of a combustion chamber fluid domain according to time steps; wherein the solution calculation for any time step comprises:
respectively solving the value of each variable of each grid unit;
based on a flame surface database, acquiring a column vector of each variable;
for any variable, determining position information and weight information of the variable in each grid unit in the flame surface database in the corresponding variable dimension direction according to the value of the variable of each grid unit and the column vector of the variable;
and determining chemical thermodynamic related state parameters corresponding to the current time step based on the flame surface database according to the position information and the weight information corresponding to each variable, and updating the fluid density of the current time step.
Further, when the variable is the average mixing fraction
Mixed fractional variance
Or average progress variable
Solving the value of any variable of each grid cell, including: and solving a transport equation of the variable based on the flame surface database to obtain the value of the variable of each grid unit.
Further, when the variable is a mixture fraction variance
Of each grid cell
Is a column vector of
Wherein the content of the first and second substances,
value of the mixed fraction of the local grid cells
A value of (d);
is a column vector read from a flame surface database, the elements in the column vector are arranged from small to large, and the value of each element is [0,1 ]]Within the range.
Further, when the variable is a progress variable variance
Then, based on the flame surface database, the variance of the progress variable is obtained
The column vector of (1), comprising:
according to grid cells
Value sum column vector f C (n) obtaining the location information (i) temp ，i temp + 1) and weight information (P) i ，P i+1 ) Combined with a two-dimensional matrix
Structure of the device
The n-th element in the column vector of
Traverse of the loop
At each position in the dimension, obtaining
A column vector of (a);
wherein: f. of C (n) is a two-dimensional matrix f C (m, n) in
The nth number is taken from the dimension
And taking the column vectors formed from the 1 st to the Mth numbers in dimension.
Further, the variance of the progress variable of each grid cell is solved
The values of (a) include:
constructing a variance of the solution progress variables
The algebraic expression of (a):
wherein: theta is a proportionality coefficient of the estimated value, and theta belongs to [0,1 ]]；
In the local grid
Is one half of the sum of the maximum and minimum values of the column vector of (1);
estimates are predicted for the variance of the progress variables, and
wherein: t represents the t-th time step, t +1 represents the t + 1-th time step, μ t For turbulent viscosity, σ t Is the Schmidt number, is the fluid density, k is the turbulence energy, ε is the dissipation ratio of the turbulence energy, R f As a coefficient, at is the time step,
is the gradient operator.
Further, when the variable is a progress variable variance
Then, based on the flame surface database, the average progress variable is obtained
Comprises:
according to the variance of the progress variable
Value of (d) and column vector thereof, obtaining
Position information (n, n + 1) and weight information (F) n ，F n+1 ) Combined with a two-dimensional matrix f C (m, n) structure
M-th element of the column vector of (1) is f C (m,n)*F n +f C (m,n+1)*F n+1 Go through in a loop
At each position in the dimension, obtaining
A column vector of (a);
wherein: f. of C (m, n) are represented in
The m number is taken from the dimension
When the nth number is taken in dimension, corresponding
The magnitude of the value.
Further, for any variable, determining position information and weight information of the variable in each grid cell in the direction of the corresponding variable dimension in the flame surface database according to the value of the variable and the column vector of the variable of each grid cell, including:
according to the value of the variable of each grid unit, inquiring and obtaining position information and weight information of the variable in the corresponding variable dimension direction in a flame surface database by utilizing a dichotomy;
the position information is that the value X of the variable is positioned between the I element and the I +1 element in the column vector corresponding to the variable; the weight information is that a pair of real numbers (α, β) exists, and the value of the variable X = α X I +β*X I+1 And α + β =1, α, β are both located [0,1]Within the range.
Further, according to the position information and the weight information corresponding to each variable, determining chemical thermodynamic related state parameters corresponding to the current time step based on a flame surface database, wherein the chemical thermodynamic related state parameters comprise: temperature, component mass fraction, specific heat capacity, thermal conductivity, density, molecular viscosity of the mixed gas.
A combustion simulation dimension reduction and speed increase device comprises:
the progress variable variance obtaining module is used for constructing an algebraic expression for solving the progress variable variance and obtaining the progress variable variance;
position weight information acquisition moduleBlock for obtaining average mixed fraction
Mixed fraction variance
Average progress variable
And variance of progress variable
Location information and weight information of;
and the updating module is used for updating the chemical thermodynamic property field and updating the fluid domain density field based on the acquired position information and the weight information.
A steady state computation method, the method comprising:
and performing steady state calculation based on a pseudo-time method by adopting the combustion simulation dimension reduction and speed acceleration method, and performing the steady state calculation based on the pseudo-time method by adopting a four-way model method after the calculation result is stably converged.
The beneficial effects of the invention are as follows:
(1) The invention constructs the variance of the progress variable
Is an algebraic formula of
Adopting algebraic solution to calculate the average mixed fraction in the four-equation FGM model
Mixed fraction variance
Average progress variable
Variance of progress variable
The transport equation set for the four variables is reduced to one relating to
The transport equation set with three variables realizes faster simulation calculation of the turbulent combustion process within the precision error allowable range, ensures the accuracy and efficiency of the calculation result, has higher engineering applicability, and provides a new method for the rapid simulation of the turbulent combustion process in the engine combustion chamber.
(2) The invention varies according to the progress variable
Column vector, finding
Maximum and minimum values of column vectors
Prediction of the variance of the progress variables for each grid
And performing truncation to ensure that the truncation is within a reasonable prediction range.
Drawings
FIG. 1 is a flow chart of a combustion simulation dimension reduction and speed increase method according to the invention;
FIG. 2 is a flow chart of an embodiment of S1 of the present invention;
FIG. 3 is a flowchart of an embodiment of S2 of the present invention;
FIG. 4 is a flow chart of the flame surface database lookup according to the present invention;
FIG. 5 is a flowchart illustrating an embodiment of S3 according to the present invention;
FIG. 6 is a flowchart illustrating an embodiment of S4 according to the present invention;
FIG. 7 (a) is a temperature profile of a gas phase combustion four transport equation;
FIG. 7 (b) is a temperature profile of a gas phase combustion process of the present invention;
FIG. 7 (c) is a gas phase combustion four transport equation Y-CO 2 A distribution map;
FIG. 7 (d) is a Y-CO of the gas phase combustion process of the present invention 2 A distribution map;
FIG. 7 (e) is a gas phase combustion four transport equation Y _ H 2 O distribution diagram;
FIG. 7 (f) is a graph of Y _ H for gas phase combustion in accordance with the method of the present invention 2 O distribution diagram;
FIG. 7 (g) is a gas phase combustion four transport equation
A distribution diagram;
FIG. 7 (h) is a diagram of gas phase combustion in accordance with the method of the present invention
A distribution diagram;
FIG. 8 (a) is a temperature distribution diagram of a gas-liquid two-phase combustion four-transport equation;
FIG. 8 (b) is a temperature distribution diagram of the gas-liquid two-phase combustion method of the present invention;
FIG. 8 (c) is a gas-liquid two-phase combustion four-transport equation Y _ CO 2 A distribution diagram;
FIG. 8 (d) is a Y-CO of the gas-liquid two-phase combustion method of the present invention 2 A distribution map;
FIG. 8 (e) is a gas-liquid two-phase combustion four-transport equation Y _ H 2 O distribution diagram;
FIG. 8 (f) is Y _ H of the gas-liquid two-phase combustion method of the present invention 2 O distribution diagram;
FIG. 8 (g) is a four-transport equation for gas-liquid two-phase combustion
A distribution diagram;
FIG. 8 (h) is a diagram of gas-liquid two-phase combustion in the method of the present invention
And (5) distribution diagram.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
As shown in fig. 1, the invention relates to a combustion simulation dimension reduction and speed increase method based on a flame surface generated manifold (FGM) model, which specifically comprises the following steps:
S1，
constructing a four-variable flame surface database,
solution of transport equation variables, and
pre-estimation of the value.
Before the flow field calculation, the construction of a flame surface database and the solution of transport equation variables need to be completed. Here, to reduce the dimension and speed up, only the solution is obtained
Transport equation of three variables, for the variance of the progress variable
The pre-estimation is performed by constructing an algebraic expression based on ripple generation and dissipation effects. With particular reference to fig. 2:
s11, construction
A four-variable turbulent flame surface database.
The method for constructing the flame surface database in the invention is the prior art and obtains
Position information and weight information of four variables in a turbulent flame surface database so as to determine
Corresponding state parameters; in particular, the last two column additions to the turbulent flame front database state parameter table list the progress variable variance
And average progress variable
For subsequent construction of column vectors of these two quantities. Wherein the state parameter table comprises temperature, component mass fraction, specific heat capacity, thermal conductivity, density and molecular viscosity of the mixed gas.
S12, solving
Carrying out solution calculation on the flame surface model on each grid unit in the fluid domain of the combustion chamber to obtain the transport equation of each grid unit
Numerical values.
To pair
The transport equations of the three variables are numerically solved, and the equation form is in the prior art. In particular, in calculating the average progress variable
During the transport equation, the calculated
Gradient field of
The data is stored in the memory and stored in the memory,in preparation for subsequent construction
Is used when algebraic expressions of (c) are used.
S13, traversing each fluid domain grid unit, and constructing a progress variable variance pre-estimated value based on pulse generation and dissipation effects
An algebraic expression of (c).
Considering that the generation of the process variable variance is mainly due to the generation and dissipation effects of the scalar variance caused by turbulence pulsation, a process variable variance estimate is first constructed
Subsequently, the estimated value is weighted and corrected to obtain the final progress variable variance
Is used as the algebraic expression of (1).
The rate of formation of the variance of the progress variable is
A dissipation ratio of
(expressions for Generation and dissipation ratios are prior art), where μ t For turbulent viscosity, σ t For the Schmidt number, usually 0.7 is taken, ρ is the fluid density, k is the turbulence energy, ε is the dissipation ratio of the turbulence energy, and the coefficient R f It is generally taken to be 0.5,
is the gradient operator.
Neglecting convection and diffusion effects, constructing an expression
Where Δ t is the time step.
Neglecting the influence of the change of the gas density in a very small time step to obtain a schedule variable variance estimated value
Where t represents the t-th time step and t +1 represents the t + 1-th time step. The calculation of the speed, the density and the pressure of the step t +1 and the calculation of a turbulence model are completed in the calculation, so the right side of the equation is a known quantity, and the variance of the progress variables of the initial field
Is set to 0. As can be seen from the pre-estimated value expression, when
When the value is too large, the temperature of the alloy is lowered,
the value will decrease due to the increase in dissipation ratio and will not be
If the value is too large, the value is increased continuously; when in use
Values that are too small, result in dissipation ratios close to 0,
the value will increase due to the generation rate and will not be at
If the value is too small, the reduction is continued, whereby the rationality of the expression configuration can be reflected.
S2, obtaining
And
column vector of, query
And
location information and weight information of.
For each grid cell
And
value of obtaining
And
is determined by the dichotomy method on the basis of the column vector of
And
the location and weight information of where the value is located in its column vector. With particular reference to fig. 3:
s21, obtaining
Column vector union query
Location information and weight information of.
For convenience of description, the flame surface database constructed by k control variables is referred to as a k-dimensional flame surface database. The column vector of the control variable is used for inquiring the position information and the weight of the control variable in the corresponding dimension direction in the flame surface databaseAnd (4) repeating the information. Each element in the column vector is arranged in order from small to large. The grid cells having been obtained by solving transport equations before traversing each grid cell of the fluid domain
Value, by reading the flame surface database
Column vector of (on each grid)
The column vectors of (a) are all the same). Calculated from each grid while traversing each grid cell of the fluid domain
Value, which is queried using dichotomy
Position information and weight information in the column vector. Wherein the content of the first and second substances,
the position information of the value refers to a pair of integer values (i, i + 1) such that
Is located between the ith element and the (i + 1) th element in the column vector, such that
The weight information of the value refers to a pair of real values (A) i ，A i+1 ) So that
And A is i +A i+1 ＝1，0<＝A i ，A i+1 <=1, as shown in fig. 4.
S22, structure
Column vector union query
Location information and weight information of.
First, a column vector is read from the flame-plane database before traversing each grid cell
The value of each element of the vector is between 0 and 1, and is different from each other, and the value of the first element is 0, and the value of the last element is 1. Constructing each grid cell of the fluid domain as it is traversed
Is a column vector of
Wherein
The value is the mixed fraction of the grid cell
The value of (d); then searching by dichotomy
Position information (j, j + 1) and weight information (B) of values in column vectors thereof j ，B j+1 ) Specific methods and
the value lookup method is the same.
S3, structure
The column vector of,
Algebraic expression of values.
Structure of the device
Column vectors of (a) are successively queried
And
location information of values and weight information.
Wherein the content of the first and second substances,
the algebraic expression of the values is constructed as
Wherein
In the local grid
Is one half of the sum of the maximum and minimum values of the column vector,
constructed on the basis of pulse generation and dissipation effects
And the estimated value is theta, and the proportional coefficient of the estimated value is theta. The method can be used for calibrating parameter settings of different examples by adjusting the size of the theta value, the larger the theta value is, the larger the estimated value weight considering generation and dissipation effects is, and the smaller the theta value is, the closer the theta value is to the parameter settings of different examples
Intermediate levels of values that may produce a fluctuating magnitude. With particular reference to fig. 5:
s31, while traversing the fluid domain mesh, according to
And
value query information is respectively constructed through a flame surface database f (i, j, p, q)
And
of the two-dimensional matrix f C (m, n) and
wherein i represents in a two-dimensional matrix
Number of positions in dimension, j representing in a two-dimensional matrix
Position number in dimension, m represents in two-dimensional matrix
Position number in dimension, n represents in two-dimensional matrix
Position sequence number in dimension; the flame surface database function f (i, j, m, n) is represented in
The number i is taken from the dimension
Dimensionally, take the jth number of
The m number is taken from the dimension
The nth number corresponds to a certain chemical thermodynamic state quantity (such as temperature f) T And component mass fraction f Y Specific heat capacity f Cp Thermal conductivity f λ Or in S11 to indicate the last two columns listed
Value f C 、
Value of
) The size of (d); f. of C (m, n) are represented in
The m number is taken from the dimension
When the nth number is taken in dimension, corresponding
The magnitude of the value;
is shown in
The m number is taken from the dimension
When the nth number is taken in dimension, corresponding to
The magnitude of the value. Using calculation formulas
Sum formula
Can obtain f C (m, n) and
two-dimensional matrices, i +1, A i 、A i+1 、j、j+1、B j 、B j+1 Calculated in S21 and S22 respectively
Value position information weight information and
value position information, weight information.
S32, according to
Value f C (m, n) and
structure of the device
The column vector of (2).
Solved by known transport equations
Value of, and f C (m, n) and
the two-dimensional matrix is a matrix with M rows and N columns. Cycle through
Each position in dimension, i.e. N, is from 1 to N. According to a grid
Value sum column vector f C (n), the dichotomy search obtains the location information (i) temp ，i temp + 1) and weight information (P) i ，P i+1 ) Wherein the column vector f C (n) is a two-dimensional matrix f C (m, n) in
The nth number is taken from the dimension
And taking the column vectors formed from the 1 st to the Mth numbers in dimension. Structure of the device
The size of the nth element in the column vector is
When the traversal loop of N from 1 to N is finished
And constructing a column vector.
S33, estimating variance of progress variables
And (6) performing truncation.
Obtained according to S32
Column vector, further finding
Maximum and minimum values of column vectors
Estimating the variance of the progress variable of each grid obtained in S13
And performing truncation to ensure that the truncation is within a reasonable prediction range.
S34, structure
An algebraic expression of (c).
Variance of progress variable
Get
Can be used for characterization
The values may produce intermediate levels of fluctuation magnitude, where,
combining the estimated value of the variance of the progress variable obtained after the truncation of S13 and S33
Will be provided with
The value algebra expression is constructed as
Where θ is the scaling factor of the estimated value and θ is ∈ [0,1 ]]According to different calculation example configurations and calculation working conditions, the method is described in [0,1]Arbitrarily taking a numerical value, changing the value of theta, and calibrating the parameter settings of different examples; the larger the estimated weight considering generation and dissipation effects, the closer the value of theta is to 1; the closer to
The closer to 0 the value of theta is, the intermediate level of the value that may produce a fluctuation in magnitude.
S35, inquiring
Value is at
Position information and weight information in the column vector.
Constructed by S34
Value obtained by S32
The column vector of (2) is searched out by using a dichotomy
Value position information (n, n + 1) and weight information (F) n ，F n+1 )。
S36, according to
Value, position information (n, n + 1) obtained at S35, and weight information (F) n ，F n+1 ) And S31, obtaining a two-dimensional matrix f C (m, n) structure
The column vector of (2).
Cycle through
Each position in the dimension, i.e., M, is from 1 to M. Structure of the device
Has the size of the mth element in the column vector of f C (m,n)* n + C (m,n+1)*F n+1 . When the traversal loop of M from 1 to M is finished, the process is finished
Construction of column vectors。
S37, inquiring
Value is at
Position information and weight information in the column vector.
Solved by transport equations
Value obtained in S36
The column vector of (2) is searched out by using a dichotomy
Value position information (m, m + 1) and weight information (E) m ，E m+1 )。
And S4, updating the chemical thermodynamic physical field by looking up a table, updating the fluid domain density field, and completing the calculation process of the turbulent combustion model.
By obtaining
And inquiring a flame surface database to obtain chemical and thermodynamic relevant state parameters and updating the fluid density according to the position information and the weight information of the four variables, so as to realize physical update of the combustion process and further complete the calculation process of the turbulent combustion model. With particular reference to fig. 6:
s41, root of
Four quantities of position information and weight information are looked up to update the chemical thermodynamic field. Based on the position information and the weight information of the four variables in the corresponding column vectors, a turbulent flame surface database f (k) is inquired 1 ,k 2 ,k 3 ,k 4 ) Using a calculation formula
Obtaining all chemical and thermodynamic relevant state parameter values of the flow field, wherein
Represents any chemical thermodynamic state parameter in the flame surface database, such as temperature, component mass fraction, specific heat capacity, thermal conductivity, density and molecular viscosity of mixed gas.
And S42, updating the fluid domain density field.
Calculating to obtain density field data rho according to the temperature updated in S41 and the reference pressure value set by the calculation example by using the gas state equation Calculating out (ii) a By calculation of the relaxation (p) from the density field data of the previous step n+1 ＝α*ρ Computing +(1-α)ρ n In the first calculation, p 1 ＝ρ Computing (ii) a And updating the fluid domain density field to complete the calculation process of the turbulent combustion model, wherein alpha is a relaxation factor, and n is the nth time step, namely the previous step.
The invention constructs the variance of the progress variable
Replace pairs with function of
The solution of the transport equation realizes the dimension reduction and speed acceleration of the flame surface generation manifold (FGM) model applied to the turbulent flow combustion simulation calculation process of the combustion chamber of the practical aeroengine in engineering, ensures the accuracy and efficiency of the calculation result and has higher engineering applicability. The specific effects obtained are as follows:
(1) By making a pair
Compared with the result of solving the four-transport equation, the dimension reduction and speed acceleration method constructed by the algebraic calculation formula has the advantages that the field distribution trends of the temperature field, the component field and the like are consistent, the field mean error is small, and the calculation precision level of solving the four-transport equation can be achieved. For example, in each caseBy using a base based on
Four-variable transport equation solving method and four-variable transport equation solving method based on
Calculation of three-variable transport equation combined with algebraic expression
The solving method of (1) displays the simulation calculation result of the step flow gas phase combustion example with the grid number of 5190, and compared with the solving result of a four-variable transport equation, the flow field temperature, the component and the progress variable variance of the calculation result of the dimension reduction and speed acceleration method
The distribution trends of the isovariates are consistent, and the error of the average value is small. As shown in fig. 7 (a), (b), by comparing the four transport equation with the temperature profile of the method of the present invention, the error of the average value of the resulting fluid field at the temperature T is 0.14%; as shown in FIGS. 7 (c), (d), Y _ CO by comparing the four transport equation with the method of the present invention 2 Distribution diagram to obtain component Y _ CO 2 The error of the fluid domain result average of (a) is 0.32%; as shown in FIGS. 7 (e), (f), Y _ H by comparing the four transport equation with the method of the present invention 2 Distribution of O to obtain component Y _ H 2 The error of the fluid domain result average of O is 0.34%; by comparing the four transport equation with the method of the present invention, as shown in FIGS. 7 (g), (h)
Distribution diagram to obtain the variance of progress variables
The error of the mean value of the fluid domain results of (1) is 0.29%. In the calculation result of the gas-liquid two-phase combustion calculation example of the model combustion chamber with the grid number of 461530, the distribution trends of variables such as flow field temperature and components are consistent, and the error of the average value is small. As shown in FIGS. 8 (a) and (b),by comparing the four-transport equation with the temperature distribution diagram of the method, the error of the average value of the obtained fluid domain result of the temperature T is 0.71%; as shown in FIGS. 7 (c), (d), Y _ CO by comparing the four transport equation with the method of the present invention 2 Distribution diagram to obtain component Y _ CO 2 The error of the fluid domain result average of (a) is 0.61%; as shown in FIGS. 7 (e), (f), Y _ H by comparing the four transport equation with the method of the present invention 2 Distribution of O to obtain component Y _ H 2 Error of fluid domain result average of O is 1.5%; by comparing the four transport equation with the method of the present invention, as shown in FIGS. 7 (g), (h)
Distribution diagram to obtain the variance of progress variables
The error of the mean value of the fluid domain results of (2.8%).
(2) Compared with a four-variable transport solving method, the method provided by the invention can obviously shorten the calculation time of the combustion model. In turbulent combustion simulation of an engine combustion chamber, the solution of a combustion model usually accounts for a large proportion in the whole turbulent combustion simulation process and is only inferior to the solution of an N-S equation, so that the solution time of the combustion model is shortened, and the method is one of important ways for improving the calculation efficiency of combustion simulation. By comparing the simulation test results, it can be found that the FGM model is based on
Compared with the four-variable transport equation solving result, the calculation time for solving the combustion model in the step flow gas-phase combustion example is shortened by 24.7%, and the calculation time for solving the combustion model in the model combustion chamber gas-liquid two-phase combustion example is shortened by 22.6%.
(3) The method of the invention can be used not only alone, but also in combination
The four-way model is switched and used: in the invention, when the steady state calculation based on the pseudo-time method is carried out, the calculation starts to use the method of the invention, and the calculation result is switched into a four-equation model for steady state calculation after being stably converged, so that the four-equation calculation result in the steady state can be obtained, the iterative convergence speed of the four-equation solution can be accelerated, and the method has great application value in engineering practice.
(4) The methods of the present invention may also be used in other applications including, but not limited to
Flame surface type combustion model with four control variables (such as
A variable flame plane progress variable FPV model,
non-adiabatic FGM model of total enthalpy H five variables). By removing
Solving transport equation, and calculating by algebraic expression
The method can obtain the similar calculation precision comparison effect and acceleration comparison effect before and after the FGM combustion model is improved.
A combustion simulation dimension reduction and acceleration device comprises:
the progress variable variance obtaining module is used for constructing an algebraic expression for solving the progress variable variance and obtaining the progress variable variance;
a position weight information obtaining module for obtaining the average mixed scoreNumber of
Mixed fraction variance
Average progress variable
And variance of progress variables
Location information and weight information of;
and the updating module is used for updating the chemical thermodynamic property field and updating the fluid domain density field based on the acquired position information and the weight information.
Based on the same inventive concept as a combustion simulation dimension reduction speed-up method, the present application also provides an electronic device comprising one or more processors and one or more memories, wherein the memories store computer readable codes, and the computer readable codes, when executed by the one or more processors, perform the implementation of the combustion simulation dimension reduction speed-up method of the present invention. Wherein, the memory may include a nonvolatile storage medium and an internal memory; the non-volatile storage medium may store an operating system and computer readable code. The computer readable code includes program instructions that, when executed, cause a processor to perform any of the combustion simulation dimension reduction and speed increase methods. The processor is used for providing calculation and control capability and supporting the operation of the whole electronic equipment. The memory provides an environment for execution of computer readable code in a non-volatile storage medium, which when executed by the processor, causes the processor to perform any of the combustion simulation dimension reduction speed-up methods.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The computer-readable storage medium may be an internal storage unit of the electronic device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.
Claims (10)
1. The combustion simulation dimension reduction and acceleration method is characterized by being used for a flame surface database type combustion model, and the variables of the flame surface database type combustion model at least comprise average mixing fraction
Mixed fractional variance
Average progress variable
And variance of progress variables
Four variables of (1); the method comprises the following steps: performing database-like combustion model on the flame surface on each grid cell of the fluid domain of the combustion chamberSolving and calculating in sequence according to the time steps; wherein the solution calculation for any time step comprises:
separately solving for the average mixture score of each grid cell
Mixed fraction variance
Average progress variable
Variance of progress variable
The value of each variable;
based on
Establishing a flame surface database, and establishing a flame surface database,
solving for transport equation variables, and
pre-estimating of values, obtaining
Column vector of, query
And
traversing the fluid domain mesh according to the position information and the weight information
And
the query information of (2) is expressed by a flame surface database function f (i, j, m, n) and corresponds to a certain chemical thermodynamic state quantity when the ith number is taken in dimension, the jth number is taken in dimension, the mth number is taken in dimension and the nth number is taken in dimension, and i is expressed in a two-dimensional matrix
Number of positions in dimension, j representing in a two-dimensional matrix
Position number in dimension, m represents in two-dimensional matrix
Position number in dimension, n represents in two-dimensional matrix
Position numbers in dimension, respectively constructed
And
of the two-dimensional matrix f C (m, n) and
structure of the device
Column vector of (1), estimation of variance of progress variable
Cutting off; structure of the device
Algebraic expressions of, queries
Value sum
Position information and weight information in column vector according to progress variable variance
Value of (d) and column vector thereof, obtaining
Position information (n, n + 1) and weight information (F) n ，F n+1 ) Combined with a two-dimensional matrix f C (m, n) structure
Column vector of, query
Value is at
Acquiring the column vector of each variable by using the position information and the weight information in the column vector;
for any variable, determining position information and weight information of the variable in each grid unit in the flame surface database in the corresponding variable dimension direction according to the value of the variable of each grid unit and the column vector of the variable;
and determining chemical thermodynamic related state parameters corresponding to the current time step based on the flame surface database according to the position information and the weight information corresponding to each variable, and updating the fluid density of the current time step.
2. According to claim 1The dimension reduction and acceleration method for the combustion simulation is characterized in that when the variable is the average mixing fraction
Mixed fraction variance
Or average progress variable
Solving the value of any variable of each grid cell, including: and solving a transport equation of the variable based on the flame surface database to obtain the value of the variable of each grid unit.
3. The combustion simulation dimension reduction and speed increase method according to claim 2, characterized in that when the variable is a mixed fraction variance
Of each grid cell
Is a column vector of
Wherein the content of the first and second substances,
value as the mixed fraction of the local grid cell
A value of (d);
is a column vector read from a flame surface database, and elements in the column vector are arranged from small to largeColumn, and the value of each element is located at [0,1]Within the range.
4. The combustion simulation dimension reduction and speed acceleration method according to claim 2, characterized in that when the variable is a progress variable variance
Then, based on the flame surface database, the variance of the progress variable is obtained
The column vector of (1), comprising:
according to grid cells
Value sum column vector f C (n) obtaining the location information (i) temp ，i temp + 1) and weight information (P) i ，P i+1 ) Combined with a two-dimensional matrix
Structure of the device
The n-th element in the column vector of
Cycle through
At each position in the dimension, obtaining
A column vector of (a);
wherein: f. of C (n) is a two-dimensional matrix f C (m, n) in
The nth number is taken from the dimension
Taking the column vectors formed from the 1 st to the Mth numbers in dimension respectively;
f C (m, n) are represented in
The m number is taken from the dimension
When the nth number is taken in dimension, corresponding
The size of the value(s) is (are),
is shown in
The m number is taken from the dimension
When the nth number is taken in dimension, corresponding to
The magnitude of the value.
5. The combustion simulation dimension reduction and acceleration method according to claim 4, characterized in that the progress variable variance of each grid cell is solved
The values of (a) include:
constructing solution progress variable variance
The algebraic expression of (A):
wherein: theta is a proportionality coefficient of the estimated value, and theta belongs to [0,1 ]]；
In the local grid
Is one half of the sum of the maximum and minimum values of the column vector of (1);
estimates are predicted for the variance of the progress variables, and
wherein: t denotes the t-th time step, t +1 denotes the t + 1-th time step,. Mu. t For turbulent viscosity, σ t Is the Schmidt number, ρ is the fluid density, k is the turbulence energy, ε is the dissipation ratio of the turbulence energy, R f As a coefficient, at is the time step,
is the gradient operator.
6. The combustion simulation dimension reduction and speed acceleration method according to claim 5, characterized in that when the variable is a progress variable variance
Then, based on the flame surface database, the average progress variable is obtained
A column vector of：
According to the variance of the progress variable
Value of (d) and column vector thereof, obtaining
Position information (n, n + 1) and weight information (F) n ，F n+1 ) Combined with a two-dimensional matrix f C (m, n) structure
The m-th element in the column vector of (1) is f C (m,n)*F n +f C (m,n+1)*F n+1 Go through in a loop
At each position in dimension, obtaining
A column vector of (a);
wherein: f. of C (m, n) are represented in
The m number is taken from the dimension
When the nth number is taken in dimension, corresponding to
The magnitude of the value.
7. The combustion simulation dimension reduction and speed acceleration method according to any one of claims 1 to 6, wherein for any variable, determining position information and weight information of the variable in each grid cell in a corresponding variable dimension direction in a flame surface database according to the value of the variable and a column vector of the variable of each grid cell comprises:
according to the value of the variable of each grid unit, inquiring and obtaining position information and weight information of the variable in the corresponding variable dimension direction in a flame surface database by utilizing a dichotomy;
the position information is that the value X of the variable is positioned between the I element and the I +1 element in the column vector corresponding to the variable; the weight information is that a pair of real numbers (α, β) exists, and the value of the variable X = α X I +β*X I+1 And α + β =1, α, β are both located [0,1]Within the range.
8. The combustion simulation dimension reduction and acceleration method according to claim 1, characterized in that the determination of the chemical thermodynamic related state parameters corresponding to the current time step based on the flame surface database according to the position information and the weight information corresponding to each variable comprises: temperature, component mass fraction, specific heat capacity, thermal conductivity, density, molecular viscosity of the mixed gas.
9. An apparatus for implementing the combustion simulation dimension reduction and acceleration method according to any one of claims 1 to 8, comprising:
the progress variable variance obtaining module is used for constructing an algebraic expression for solving the progress variable variance and obtaining the progress variable variance;
a position weight information acquisition module for obtaining an average mixed score
Mixed fraction variance
Average progress variable
And variance of progress variable
Location information and weight information of;
and the updating module is used for updating the chemical thermodynamic property field and updating the fluid domain density field based on the acquired position information and the weight information.
10. A steady state computing method, the method comprising:
the combustion simulation dimension reduction and acceleration method according to any one of claims 1 to 8 is adopted to perform steady state calculation based on a pseudo-time method, and after the calculation result is stably converged, the four-way model method is adopted to perform the steady state calculation based on the pseudo-time.
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