CN115618498B - Prediction method, device, equipment and medium for cross-basin flow field of aircraft - Google Patents
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Abstract
The application discloses a prediction method, a prediction device, prediction equipment and a prediction medium for a cross-basin flow field of an aircraft, which relate to the technical field of cross-basin flow field simulation of an extremely high Mach number aircraft, and comprise the following steps: determining a physical space grid corresponding to the target aircraft, and setting an initial incoming flow condition; respectively generating an initial speed space grid and determining the initial values of the distribution functions of grid units in a physical space grid based on the initial incoming flow conditions; taking the initial value of the distribution function as a first initial iteration parameter, and performing multiple iterations based on the physical space grid and the initial speed space grid until the preset iteration steps are met to obtain the current distribution functions of the target speed space grid and the grid units; and taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until convergence to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of grid units in the physical space grid. Accurate prediction of cross-basin flow fields is achieved.
Description
Technical Field
The invention relates to the technical field of cross-basin flow field simulation of an extremely high Mach number aircraft, in particular to a prediction method, a prediction device, prediction equipment and a prediction medium of a cross-basin flow field of the aircraft.
Background
During reentry of a manned spacecraft and other high supersonic aircrafts, the surrounding atmospheric density and pressure are low, and the assumption of continuity is not established. To accurately simulate the aerodynamic thermal characteristics of an aircraft in this process, a method of the DSMC (Direct simulation Monte Carlo) or boltzmann model equation type is required. A unified aerokinetics method is a Boltzmann model equation type method. The method is based on product decomposition of a Boltzmann model equation, the molecular motion and collision process are coupled in the process of solving the flux of the interface distribution function, the propulsion time step length of the method is not restricted by the average collision time among molecules, and the physical space grid is not restricted by the free path of the molecules, so that the method is a multi-scale method. The method is suitable for accurately predicting the reentry aerodynamic thermal characteristics of the hypersonic flight vehicle.
However, during reentry of the hypersonic flight vehicle, the flight speed is high, and the Mach number can reach 20-30. The air around the aircraft changes from extremely high speed to the vicinity of the wall surface close to 0 within an extremely short distance (generally, a slip effect exists, the speed on the wall surface is not 0), the temperature changes from about 200K of the incoming flow to tens of thousands K in a shock wave-like structure to hundreds of K on the wall surface (isothermal wall condition), and a great temperature and speed gradient exists in the flow field. The method is reflected on a mesoscopic distribution function, namely the form change of the distribution function is severe, great difficulty is brought to unified gas dynamics simulation based on solving the distribution function in physical space and speed space distribution, and the phenomena that calculation is diverged midway and reasonable and accurate results cannot be obtained often occur.
In summary, how to improve the robustness of the unified gas dynamics method in simulating the extremely high mach number cross-basin flow so as to realize the accurate prediction of the cross-basin flow field of the aircraft is a problem to be solved at present.
Disclosure of Invention
In view of this, the present invention provides a method, an apparatus, a device, and a medium for predicting a cross-basin flow field of an aircraft, which can improve the robustness of a uniform gas dynamics method in simulating a very high mach number cross-basin flow, so as to realize accurate prediction of the cross-basin flow field of the aircraft. The specific scheme is as follows:
in a first aspect, the application discloses a prediction method for a cross-basin flow field of an aircraft, comprising:
determining a physical space grid corresponding to the target aircraft, and setting an initial incoming flow condition;
generating an initial velocity space grid based on the initial incoming flow condition, and determining a distribution function initial value of grid cells in the physical space grid based on the initial incoming flow condition;
taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met to obtain a target speed space grid and a current distribution function of grid units in the physical space grid;
and taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid.
Optionally, the setting an initial incoming flow condition includes:
setting an initial incoming flow Mach number, an initial incoming flow pressure, an initial incoming flow temperature and an initial incoming flow velocity vector;
correspondingly, the generating an initial velocity space grid based on the initial incoming flow condition includes:
an initial velocity spatial grid is generated based on the initial incoming flow velocity vector.
Optionally, the determining an initial value of a distribution function of a grid cell in the physical space grid based on the initial incoming flow condition includes:
and determining a balanced state distribution function, and taking the initial incoming flow condition as an input parameter of the balanced state distribution function to obtain an initial value of the distribution function of the grid cells in the physical space grid.
Optionally, the taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid until a preset number of iteration steps is met to obtain current distribution functions of the target speed space grid and grid units in the physical space grid, includes:
determining the equilibrium state distribution function as an incoming flow boundary distribution function, and taking an initial value of the incoming flow boundary function and an initial value of the distribution function as a first initial iteration parameter; wherein, in the first iteration step, the initial value of the incoming flow boundary distribution function is the same as the initial value of the distribution function;
and performing multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met to obtain a target speed space grid, a current distribution function of grid units in the physical space grid and a target incoming flow boundary distribution function.
Optionally, the step of taking the value of the current distribution function as a second initial iteration parameter, and performing multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid includes:
and under the condition that the target speed space grid and the target incoming flow boundary distribution function are kept unchanged, taking the current distribution function as a second initial iteration parameter, and performing multiple iterations based on the physical space grid and the target speed space grid until a residual value is smaller than a preset threshold value so as to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid.
Optionally, the determining a physical space grid corresponding to the target aircraft includes:
constructing an aircraft surface grid according to the shape and size of the target aircraft, and determining the outer boundary range of the target aircraft according to preset incoming flow conditions;
generating a physical space grid for the target aircraft based on the aircraft surface grid and the outer boundary range.
Optionally, the method for predicting the cross-watershed flow field of the aircraft further includes:
and determining the incoming flow velocity vector of each iteration step according to a preset incoming flow velocity vector in the preset incoming flow condition, an initial incoming flow velocity in the initial incoming flow condition and the preset iteration step number, so as to determine the velocity space grid of each iteration step based on the incoming flow velocity vector.
In a second aspect, the present application discloses a prediction apparatus for a cross-basin flow field of an aircraft, including:
the physical space grid determining module is used for determining a physical space grid corresponding to the target aircraft;
the incoming flow condition setting module is used for setting initial incoming flow conditions;
a velocity space grid determination module for generating an initial velocity space grid based on the initial incoming flow condition;
an initial value determining module, configured to determine an initial value of a distribution function of a grid cell in the physical space grid based on the initial incoming flow condition;
the first iteration module is used for taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met so as to obtain a target speed space grid and a current distribution function of grid units in the physical space grid;
and the second iteration module is used for taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met so as to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid.
In a third aspect, the present application discloses an electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the method for predicting an aircraft cross-basin flow field disclosed in the foregoing.
In a fourth aspect, the present application discloses a computer readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the method for predicting an aircraft cross-basin flow field as disclosed above.
Therefore, the physical space grid corresponding to the target aircraft is determined, and the initial incoming flow condition is set; generating an initial velocity space grid based on the initial incoming flow condition, and determining a distribution function initial value of grid cells in the physical space grid based on the initial incoming flow condition; taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met to obtain a target speed space grid and a current distribution function of grid units in the physical space grid; and taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid. Therefore, the physical space grid corresponding to the target aircraft and the initial speed space grid generated according to the set initial inflow conditions are determined, and the initial values of the distribution functions of the grid units in the physical space grid are set based on the initial inflow conditions; then, taking the initial value of the distribution function as a first initial iteration parameter, and performing multiple iterations based on the physical space grid and the initial speed space grid to obtain the current distribution function of the target speed space grid and the grid unit after the iterations; and then, continuously taking the value of the current distribution function as a second initial iteration parameter, and performing multiple iterations based on the physical space grid and the target speed space grid to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of the grid unit. Therefore, the method and the device adopt the distribution function of the velocity space grid and the incoming flow condition, and develop multiple iterations under the preset condition to reach the target state, so that the iteration process is optimized, the numerical instability caused by directly adopting the unified gas dynamics is reduced, the robustness of the unified gas dynamics method in simulating the cross-basin flow of the aircraft is improved, and the accurate prediction of the cross-basin flow field of the aircraft is realized.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a method for predicting a cross-basin flow field of an aircraft according to the present disclosure;
FIG. 2 is a flowchart of a specific method for predicting a cross-basin flow field of an aircraft according to the present disclosure;
FIG. 3 is a schematic illustration of a particular X38-like profile aircraft as disclosed herein;
FIG. 4 is a diagram of a particular physical space grid disclosed herein;
FIG. 5 is a schematic diagram of the velocity space range and incoming flow distribution function of the 1 st iteration disclosed in the present application;
FIG. 6 is a schematic diagram of a velocity space range, a grid and an incoming flow distribution function of a 1000 th iteration step disclosed in the present application;
FIG. 7 is a graph illustrating velocity space ranges, grids, and incoming flow distribution functions for a 2000 th iteration of the present disclosure;
FIG. 8 is a schematic view of a converged symmetry plane and object plane streamlines disclosed herein;
FIG. 9 is a schematic representation of a three-dimensional distribution function iso-surface near a stagnation point as disclosed herein;
fig. 10 is a schematic structural view of a prediction device of a cross-river basin flow field of an aircraft according to the present disclosure;
fig. 11 is a block diagram of an electronic device disclosed in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, in the reentry process of a hypersonic aircraft, the flight speed is very high, and the Mach number can reach 20-30. The air around the aircraft changes from extremely high speed to the vicinity of the wall surface close to 0 within an extremely short distance (generally, a slip effect exists, the speed on the wall surface is not 0), the temperature changes from about 200K of the incoming flow to tens of thousands K in a shock wave-like structure to hundreds of K on the wall surface (isothermal wall condition), and a great temperature and speed gradient exists in the flow field. The method is reflected on a mesoscopic distribution function, namely the change of the form of the distribution function is severe, great difficulty is brought to the unified gas dynamics simulation based on solving the distribution function in physical space and speed space distribution, and the phenomena that calculation is diverged midway and a reasonable and accurate result cannot be obtained often occur. Therefore, the embodiment of the application discloses a method, a device, equipment and a medium for predicting a cross-basin flow field of an aircraft, which can improve the robustness of a uniform gas dynamic method in the process of simulating the extremely high Mach number cross-basin flow so as to realize the accurate prediction of the cross-basin flow field of the aircraft.
Referring to fig. 1, an embodiment of the present application discloses a prediction method for a cross-basin flow field of an aircraft, where the method includes:
step S11: and determining a physical space grid corresponding to the target aircraft, and setting an initial incoming flow condition.
In this embodiment, first determining a physical space grid corresponding to the target aircraft, and setting an initial incoming flow condition, where the determining the physical space grid corresponding to the target aircraft includes: constructing an aircraft surface grid according to the shape and the size of the target aircraft, and determining the outer boundary range of the target aircraft according to a preset incoming flow condition; generating a physical space grid for the target aircraft based on the aircraft surface grid and the outer boundary range. That is, the aircraft surface mesh is constructed for the shape and size of the target aircraft, and then the outer boundary range of the target aircraft physical space mesh is determined for the given preset incoming flow condition. A physical space grid for the target aircraft is generated based on the aircraft surface grid and the outer boundary range.
The predetermined inflow conditions generally include a predetermined inflow pressure
Presetting incoming flow velocity vector
Preset incoming flow temperature
Preset incoming stream Mach number
. And, preset incoming flow velocity vector
Presetting the incoming flow temperature
And preset incoming stream Mach number
There is the following relationship between:
in the formula (I), the compound is shown in the specification,
as an incoming velocity vector
The die of (a) is used,
r is a specific gas constant.
Step S12: and generating an initial speed space grid based on the initial incoming flow condition, and determining the initial value of the distribution function of the grid cells in the physical space grid based on the initial incoming flow condition.
In this embodiment, after the initial incoming flow condition is set, the initial velocity space grid is generated based on the initial incoming flow condition, and the initial values of the distribution functions of the grid cells in the physical space grid are determined based on the initial incoming flow condition
Step S13: and taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met to obtain the current distribution functions of the grid units in the target speed space grid and the physical space grid.
In this embodiment, the initial value of the distribution function is used as a first initial iteration parameter, multiple iterations are performed based on the physical space grid and the initial velocity space grid, and a first stage (N steps in total) of iterative solution is started, that is, the preset number of iteration steps in the first stage is N. In this embodiment, N may take about 2000 steps. The larger N, the more stable the process of iterative simulation of the unified gas dynamics approach, but with a concomitant increase in time consumption. In specific practice, a compromise between simulation iteration stability and calculation cost can be selected according to actual conditions.
Further, the method further comprises: and determining the incoming flow velocity vector of each iteration step according to a preset incoming flow velocity vector in the preset incoming flow condition, an initial incoming flow velocity in the initial incoming flow condition and the preset iteration step number, so as to determine a velocity space grid of each iteration step based on the incoming flow velocity vector. That is to say thatIn the iteration process of the first stage, the incoming flow velocity vector of each iteration step is determined according to a preset incoming flow velocity vector in a preset incoming flow condition, an initial incoming flow velocity in an initial incoming flow condition and a preset iteration step number, and the incoming flow velocity vector of the nth iteration step is recorded as V n Then, it is
The incoming velocity vector V representing the nth iteration step n Modulo (m), the expression is as follows:
in the formula, V n An incoming flow velocity vector representing n iterative steps,
representing a predetermined incoming flow velocity vector, V 1 And the initial incoming flow speed in the initial incoming flow condition is represented, and N is a preset iteration step number.
The velocity space grid for each iteration step can be determined based on the incoming flow velocity vector, and then the velocity space grid for the nth iteration step can be determined from the incoming flow velocity vectors for the n iteration steps. It should be noted that the velocity space grid is three-dimensional, and the three directions are u, v, and w, respectively, (n: (v) (w))
) The grid range of the velocity space grid in the iterative step is set to
。
And obtaining the current distribution functions of the target speed space grid of the target state and the grid units in the physical space grid after the preset iteration steps are met. The target state is also the state satisfying the preset incoming flow condition, and the grid range of the target velocity space grid is
。
Step S14: and taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid.
In this embodiment, the value of the current distribution function is used as a second initial iteration parameter, multiple iterations are performed based on the physical space grid and the target velocity space grid, and a second-stage iterative solution is performed. And in the second stage, iteration solution is carried out for a plurality of steps, after the convergence condition is met, the simulation process is finished, and the cross-basin flow field variable of the extremely high Mach number aircraft and the target distribution function of the grid unit in the physical space grid are obtained.
Therefore, the physical space grid corresponding to the target aircraft is determined, and the initial incoming flow condition is set; generating an initial velocity space grid based on the initial incoming flow condition, and determining a distribution function initial value of grid cells in the physical space grid based on the initial incoming flow condition; taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met to obtain a target speed space grid and a current distribution function of grid units in the physical space grid; and taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid. Therefore, the physical space grid corresponding to the target aircraft and the initial speed space grid generated according to the set initial inflow conditions are determined, and the initial values of the distribution functions of the grid units in the physical space grid are set based on the initial inflow conditions; then, taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid to obtain the current distribution functions of the iterated target speed space grid and grid units; and then, continuously taking the value of the current distribution function as a second initial iteration parameter, and performing multiple iterations based on the physical space grid and the target speed space grid to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of the grid unit. Therefore, the method and the device adopt the distribution function of the velocity space grid and the incoming flow condition, and develop multiple iterations under the preset condition to reach the target state, so that the iteration process is optimized, the numerical instability caused by directly adopting the unified gas dynamics is reduced, the robustness of the unified gas dynamics method in simulating the cross-basin flow of the aircraft is improved, and the accurate prediction of the cross-basin flow field of the aircraft is realized.
Referring to fig. 2, the embodiment of the present application discloses a specific prediction method for a cross-basin flow field of an aircraft, and compared with the previous embodiment, the present embodiment further describes and optimizes the technical solution. The method specifically comprises the following steps:
step S21: and determining a physical space grid corresponding to the target aircraft, and setting an initial incoming flow Mach number, an initial incoming flow pressure, an initial incoming flow temperature and an initial incoming flow velocity vector.
In this embodiment, when the initial incoming flow condition is set, the iterative initial incoming flow mach number is set to 2, and the initial incoming flow pressure is set to
Initial incoming flow temperature
Initial incoming flow velocity vector V 1 ，
. It should be noted that the initial mach number of the iterative incoming flow is set to 2 in this embodiment, so as to ensure that the phenomena that the interpolation is unreasonable and the temperature is negative in the process of solving the interface flux, which results in the simulation being unable to be performed, do not occur in the conventional gas dynamic method at the initial stage of the iteration. And providing a reasonable distribution function initial value for subsequent iterative solution. Therefore, the iteration initial incoming flow mach number is recommended to be 2, and the higher the value is, the instability of the iteration calculation can be caused, and the final calculation is divergent and cannot obtain a reasonable result.
Step S22: and generating an initial velocity space grid based on the initial incoming flow velocity vector, determining a balanced distribution function, and taking the initial incoming flow condition as an input parameter of the balanced distribution function to obtain an initial value of a distribution function of grid cells in the physical space grid.
In this embodiment, a uniform distribution cartesian initial velocity spatial grid required for the simulation of the unified gas dynamics method is generated based on the initial incoming flow velocity vector, and the grid range of the initial velocity spatial grid is set as
Wherein
For the first iteration the incoming flow velocity vector V 1 The die of (1). Further, when initial iteration is set, the initial value of the distribution function in each grid cell of the physical space grid is the equilibrium distribution function corresponding to the initial incoming flow condition, that is, after the equilibrium distribution function is determined, the initial incoming flow condition is used as an input parameter of the equilibrium distribution function, so as to obtain the initial value of the distribution function of the grid cell in the physical space grid.
Wherein the equilibrium distribution function g 1 The expression of (c) is:
whereinU 1 、V 1 、W 1 Respectively, the incoming flow velocity vector V 1 The components in the x, y, z directions.
Step S23: determining the equilibrium state distribution function as an incoming flow boundary distribution function, and taking the initial value of the incoming flow boundary function and the initial value of the distribution function as a first initial iteration parameter; in the first iteration step, the initial value of the incoming flow boundary distribution function is the same as the initial value of the distribution function.
In this embodiment, the incoming flow boundary distribution function of the 1 st iteration step also takes the balanced distribution function g 1 . The initial value of the incoming flow boundary function and the initial value of the distribution function are used as the first initial iteration parameter, that is, in the first iteration step, the initial value of the incoming flow boundary distribution function is the same as the initial value of the distribution function.
Step S24: and performing multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met to obtain a target speed space grid, a current distribution function of grid units in the physical space grid and a target incoming flow boundary distribution function.
In this embodiment, during the iteration process, the nth one (
) The initial values of the speed space grid, the incoming flow boundary distribution function and the distribution function of the grid unit in the physical space grid in the iteration step are respectively set according to the following methods:
for the velocity space grid, the number of grid points in three directions (u, v and w) of the uniformly distributed three-dimensional velocity space grid is kept unchanged, and the grid range is set to be
Wherein
For the nth iteration step incoming flow velocity vector V n The die of (1).
Distribution function g for incoming flow boundary n The expression is:
whereinU n 、V n 、W n Respectively, the incoming flow velocity vector V n The components in the x, y, z directions.
For the initial values of the distribution function of the grid cells in the physical space grid, the velocity space grid point number of the (n-1) th iteration step is assumed to be
Is shown in the formula, wherein r n-1 、s n-1 And t n-1 Are all positive integers which are not less than zero,
，
，
. The velocity space grid points of the nth iteration are numbered
Is represented by the formula (I) in which r n 、s n And t n Are all positive integers which are not less than zero,
，
，
。
、
and
the grids are respectively in three directions (u, v and w) of a three-dimensional velocity space grid with uniform distributionThe total number of points. To be provided with
The number of the n-1 iteration step is shown as
The function values are distributed over the velocity space grid points. The number of the nth iteration step is
Velocity space grid point of (1) distribution function initial value
Is taken as
Since the total number of velocity space grid points for the nth iteration step is consistent with the total number of velocity space grid points for the (n-1) th iteration step,
and with
The two groups are in one-to-one correspondence, so that complicated operations such as interpolation are not needed.
Step S25: and under the condition that the target speed space grid and the target incoming flow boundary distribution function are kept unchanged, taking the current distribution function as a second initial iteration parameter, and performing multiple iterations based on the physical space grid and the target speed space grid until a residual value is smaller than a preset threshold value so as to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid.
In this embodiment, the target velocity spatial grid and the target incoming flow boundary distribution function are kept unchanged, and the current distribution function obtained in the first stage iteration process is used
As a second initial iteration parameter, wherein
The velocity space grid points used for the last iteration step of the first stage are numbered. And expanding a plurality of steps of iteration based on the physical space grid and the target speed space grid until the residual value is smaller than a preset threshold value, and ending the simulation process to obtain the cross-basin flow field variable of the extremely high Mach number aircraft and the target distribution function of the grid unit in the physical space grid. It should be noted that the number of steps for the second stage iterative solution is related to a preset residual threshold, and the smaller the threshold is, the more steps are needed for the iteration, and conversely, the fewer steps are needed for the iteration.
Therefore, the physical space grid corresponding to the target aircraft is determined, the initial incoming flow mach number, the initial incoming flow pressure, the initial incoming flow temperature and the initial incoming flow velocity vector are set, and the initial velocity space grid is generated based on the initial incoming flow velocity vector. During initial iteration, the initial value of a distribution function in each grid unit of the physical space grid is a balanced distribution function corresponding to an initial incoming flow condition, the incoming flow boundary distribution function of the 1 st iteration step in the first stage is also a balanced distribution function, namely, the initial value of the incoming flow boundary distribution function is the same as the initial value of the distribution function, then the initial value of the incoming flow boundary function and the initial value of the distribution function are used as first initial iteration parameters, multiple iterations are performed based on the physical space grid and the initial speed space grid to obtain a target speed space grid, the current distribution function of the grid units in the physical space grid and a target incoming flow boundary distribution function, in the iteration process of the second stage, the target speed space grid and the target incoming flow boundary distribution function are kept unchanged, the current distribution function is used as a second initial iteration parameter, a plurality of iterations are performed based on the physical space grid and the target speed space grid, and a cross-flow field variable of the target aircraft and the target distribution function of the grid units in the physical space grid are obtained until a residual value is smaller than a preset threshold value. Therefore, by adopting a method of gradually changing speed space grids and a free incoming flow equilibrium state distribution function, numerical instability caused by directly adopting unified gas dynamics is reduced, robustness of the unified gas dynamics method in simulating the cross-basin flow of the extremely high Mach number is improved, and a reasonable and reliable cross-basin flow field of the extremely high Mach number aircraft is obtained.
The technical solution of the present application will be described below by taking an X38-like profile aircraft shown in fig. 3 as an example.
Assume a predetermined incoming flow mach number of 25, an angle of attack of 20 degrees, and a sideslip angle of 0 degrees. The technical effect of the invention is illustrated by taking the height of 100km as an example. At the height, the pressure of incoming flow
Temperature of incoming stream
Kelvin.
And generating a physical space grid corresponding to the aircraft by using Gridgen, wherein the number of grid units is 33 thousands, and a specific grid schematic diagram is shown in FIG. 4.
The number of points in three directions of the velocity space grid is 121, i.e.
. The mach number of the incoming flow is 25, and if the simulated initial incoming flow boundary distribution function directly gives the equilibrium distribution in a target state, iterative solution is extremely easy to disperse, and a reasonable flow field result cannot be obtained.
According to the method of the invention, the incoming stream Mach number in the initial incoming stream boundary is set to 2.0, and the incoming stream velocity vector
The amount of the water-soluble organic fertilizer in the meter per second,
. The 1 st iteration step speed space grid range is
I.e., -1400, 1400.
Setting the initial value of the distribution function in each unit of the physical space and the distribution function in the incoming flow boundary of the 1 st iteration step as g 1 ：
The first stage of the iteration presets the number of iteration steps to be 2000 steps. The velocity space grid range and the incoming flow equilibrium distribution function of the 1 st iteration step are shown in fig. 5.
In the iterative process, the speed space range, the grid and the incoming flow distribution function of the 1000 th iteration step are shown in fig. 6, and correspondingly, the speed space range, the grid and the incoming flow distribution function of the 2000 th iteration step are shown in fig. 7. As shown in fig. 5 to 7, the numerical values on the circle with the number 1 in the figures are all 7.67E-18, the numerical values on the circle with the number 2 in the figures are all 1.00E-16, the numerical values on the circle with the number 3 in the figures are all 7.65E-16, and the numerical values on the circle with the number 4 in the figures are all 2.36E-15. For convenience of display, the velocity in the w direction in the distribution function is taken as 0, and in reality, in a real simulation, the incoming flow distribution function is a three-dimensional spherical distribution shape.
After the first stage iteration is completed, the velocity space grid is kept, and the distribution of the incoming flow equilibrium state is not changed any more. And (5) continuing the step 830 of iterative solution, reducing the residual error by 3 orders of magnitude, and regarding the residual error as calculation convergence to obtain the flow field variable and distribution function distribution of the X38-like aircraft under the conditions of 100km and the incoming flow Mach number of 25. Figure 8 shows the symmetry plane and object plane streamlines. FIG. 9 shows the iso-surface of the three-dimensional distribution function near the stagnation point, which is very different from the shape of the incoming flow in the equilibrium state, indicating that the flow is in a highly unbalanced state, which cannot be predicted by the conventional NS solver.
Referring to fig. 10, an embodiment of the present application discloses a prediction apparatus for a cross-basin flow field of an aircraft, where the apparatus includes:
a physical space grid determining module 11, configured to determine a physical space grid corresponding to the target aircraft;
an incoming flow condition setting module 12, configured to set an initial incoming flow condition;
a velocity space grid determining module 13, configured to generate an initial velocity space grid based on the initial incoming flow condition;
an initial value determining module 14, configured to determine an initial value of a distribution function of a grid cell in the physical space grid based on the initial incoming flow condition;
a first iteration module 15, configured to use the initial value of the distribution function as a first initial iteration parameter, and perform multiple iterations based on the physical space grid and the initial velocity space grid until a preset number of iteration steps is met, so as to obtain a current distribution function of grid cells in a target velocity space grid and the physical space grid;
and a second iteration module 16, configured to take the value of the current distribution function as a second initial iteration parameter, and perform multiple iterations based on the physical space grid and the target velocity space grid until a preset convergence condition is satisfied, so as to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid.
Therefore, the physical space grid corresponding to the target aircraft is determined, and the initial incoming flow condition is set; generating an initial velocity space grid based on the initial incoming flow condition, and determining a distribution function initial value of grid cells in the physical space grid based on the initial incoming flow condition; taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met to obtain a target speed space grid and a current distribution function of grid units in the physical space grid; and taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid. Therefore, the physical space grid corresponding to the target aircraft and the initial speed space grid generated according to the set initial incoming flow condition are determined, and the initial value of the distribution function of the grid unit in the physical space grid is set based on the initial incoming flow condition; then, taking the initial value of the distribution function as a first initial iteration parameter, and performing multiple iterations based on the physical space grid and the initial speed space grid to obtain the current distribution function of the target speed space grid and the grid unit after the iterations; and then, continuously taking the value of the current distribution function as a second initial iteration parameter, and expanding for a plurality of iterations based on the physical space grid and the target speed space grid to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of the grid unit. Therefore, the method and the device adopt the distribution function of the velocity space grid and the incoming flow condition, and develop multiple iterations under the preset condition to reach the target state, so that the iteration process is optimized, the numerical instability caused by directly adopting the unified gas dynamics is reduced, the robustness of the unified gas dynamics method in simulating the cross-basin flow of the aircraft is improved, and the accurate prediction of the cross-basin flow field of the aircraft is realized.
Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The method specifically comprises the following steps: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is used for storing a computer program, and the computer program is loaded and executed by the processor 21 to implement relevant steps in the prediction method of the aircraft cross-river basin flow field executed by the electronic device disclosed in any of the foregoing embodiments.
In this embodiment, the power supply 23 is configured to provide a working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and a communication protocol followed by the communication interface is any communication protocol applicable to the technical solution of the present application, and is not specifically limited herein; the input/output interface 25 is configured to acquire external input data or output data to the outside, and a specific interface type thereof may be selected according to specific application requirements, which is not specifically limited herein.
The processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 21 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in a wake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 21 may further include an AI (Artificial Intelligence) processor for processing a calculation operation related to machine learning.
In addition, the storage 22 is used as a carrier for storing resources, and may be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., the resources stored thereon include an operating system 221, a computer program 222, data 223, etc., and the storage may be a transient storage or a permanent storage.
The operating system 221 is used for managing and controlling each hardware device on the electronic device 20 and the computer program 222, so as to implement the operation and processing of the mass data 223 in the memory 22 by the processor 21, which may be Windows, unix, linux, or the like. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the method for predicting the cross-river basin flow field of an aircraft, which is performed by the electronic device 20 disclosed in any of the foregoing embodiments. The data 223 may include data received by the electronic device and transmitted from an external device, or may include data collected by the input/output interface 25 itself.
Further, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored, and when the computer program is loaded and executed by a processor, the method steps performed in the prediction process of the cross-river basin flow field of the aircraft disclosed in any of the foregoing embodiments are implemented.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.
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 components and steps have been described above generally in terms of their functionality in order to clearly illustrate this 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 technical 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.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The prediction method, the prediction device, the prediction equipment and the prediction medium of the cross-basin flow field of the aircraft provided by the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (7)
1. A prediction method for an aircraft cross-basin flow field is characterized by comprising the following steps:
determining a physical space grid corresponding to the target aircraft, and setting an initial incoming flow condition;
generating an initial velocity space grid based on the initial incoming flow condition, and determining a distribution function initial value of grid cells in the physical space grid based on the initial incoming flow condition;
taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met to obtain a target speed space grid and a current distribution function of grid units in the physical space grid;
taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid;
wherein the determining an initial value of a distribution function of grid cells in the physical space grid based on the initial incoming flow condition comprises:
determining a balanced state distribution function, and taking the initial incoming flow condition as an input parameter of the balanced state distribution function to obtain an initial value of the distribution function of a grid unit in a physical space grid;
the step of taking the initial value of the distribution function as a first initial iteration parameter, and performing multiple iterations based on the physical space grid and the initial velocity space grid until a preset iteration step number is met to obtain the current distribution functions of the target velocity space grid and the grid units in the physical space grid includes:
determining the equilibrium state distribution function as an incoming flow boundary distribution function, and taking an initial value of the incoming flow boundary distribution function and an initial value of the distribution function as a first initial iteration parameter; wherein, in a first iteration step, the initial value of the incoming flow boundary distribution function is the same as the initial value of the distribution function;
performing multiple iterations based on the physical space grid and the initial velocity space grid until a preset iteration step number is met to obtain a target velocity space grid, a current distribution function of grid cells in the physical space grid and a target incoming flow boundary distribution function;
the step of taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid includes:
under the condition that the target speed space grid and the target incoming flow boundary distribution function are kept unchanged, the current distribution function is used as a second initial iteration parameter, and multiple iterations are carried out on the basis of the physical space grid and the target speed space grid until a residual value is smaller than a preset threshold value, so that a cross-basin flow field variable of the target aircraft and a target distribution function of grid units in the physical space grid are obtained.
2. The method for predicting the cross-basin flow field of an aircraft according to claim 1, wherein the setting of the initial inflow conditions comprises:
setting an initial incoming flow Mach number, an initial incoming flow pressure, an initial incoming flow temperature and an initial incoming flow velocity vector;
correspondingly, the generating an initial velocity space grid based on the initial incoming flow condition includes:
an initial velocity spatial grid is generated based on the initial incoming flow velocity vector.
3. The method for predicting the cross-basin flow field of the aircraft according to any one of claims 1 or 2, wherein the determining the physical space grid corresponding to the target aircraft comprises:
constructing an aircraft surface grid according to the shape and size of the target aircraft, and determining the outer boundary range of the target aircraft according to preset incoming flow conditions;
generating a physical space grid for the target aircraft based on the aircraft surface grid and the outer boundary range.
4. The method of predicting an aircraft cross-basin flow field of claim 3, further comprising:
and determining the incoming flow velocity vector of each iteration step according to a preset incoming flow velocity vector in the preset incoming flow condition, an initial incoming flow velocity in the initial incoming flow condition and the preset iteration step number, so as to determine the velocity space grid of each iteration step based on the incoming flow velocity vector.
5. An aircraft cross-basin flow field prediction device, comprising:
the physical space grid determining module is used for determining a physical space grid corresponding to the target aircraft;
the incoming flow condition setting module is used for setting initial incoming flow conditions;
a velocity space grid determination module for generating an initial velocity space grid based on the initial incoming flow condition;
an initial value determining module, configured to determine an initial value of a distribution function of a grid cell in the physical space grid based on the initial incoming flow condition;
the first iteration module is used for taking the initial value of the distribution function as a first initial iteration parameter, and expanding multiple iterations based on the physical space grid and the initial speed space grid until a preset iteration step number is met so as to obtain a target speed space grid and a current distribution function of grid units in the physical space grid;
the second iteration module is used for taking the value of the current distribution function as a second initial iteration parameter, and expanding multiple iterations based on the physical space grid and the target speed space grid until a preset convergence condition is met so as to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid;
the initial value determining module is specifically configured to:
determining a balanced state distribution function, and taking the initial incoming flow condition as an input parameter of the balanced state distribution function to obtain an initial value of the distribution function of a grid unit in a physical space grid;
the first iteration module is specifically configured to:
determining the equilibrium state distribution function as an incoming flow boundary distribution function, and taking the initial value of the incoming flow boundary distribution function and the initial value of the distribution function as a first initial iteration parameter; wherein, in the first iteration step, the initial value of the incoming flow boundary distribution function is the same as the initial value of the distribution function; performing multiple iterations based on the physical space grid and the initial velocity space grid until a preset iteration step number is met to obtain a target velocity space grid, a current distribution function of grid cells in the physical space grid and a target incoming flow boundary distribution function;
the second iteration module is specifically configured to:
and under the condition that the target speed space grid and the target incoming flow boundary distribution function are kept unchanged, taking the current distribution function as a second initial iteration parameter, and performing multiple iterations based on the physical space grid and the target speed space grid until a residual value is smaller than a preset threshold value so as to obtain a cross-basin flow field variable of the target aircraft and a target distribution function of a grid unit in the physical space grid.
6. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the method of predicting an aircraft cross-basin flow field of any one of claims 1 to 4.
7. A computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the method of predicting an aircraft cross-basin flow field of any one of claims 1 to 4.
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