A method for eliminating interference components of a fine flow field structure of a tilt-rotor propeller

Abstract

The application provides a method for eliminating interference components of a tilt-rotor propeller fine flow field structure, comprising the following steps: S1, obtaining calculation data: obtaining a tilt-rotor propeller fine flow field time sequence through a structured CFD solver, and the result mainly comprises discrete conservation variables of a space flow field in a certain time step; S2, establishing a new grid: based on a typical unsteady flow characteristic region of interest, a new structured grid corresponding to the region and equivalent to the space scale of a flow field calculation grid is established; S3, searching for a mapping relationship; S4, flow field interpolation; S5, POD mode calculation; and S6, flow field reconstruction. The application utilizes the orthogonal feature extraction characteristics of POD, combines a structured grid node corresponding relationship fast searching method, achieves the effect of efficiently and accurately extracting main features of a structured propeller fine flow field, and effectively realizes the purpose of eliminating interference component modes based on the extracted mode features and the mode reconstruction function.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flow field analysis, and particularly relates to a method for eliminating interference components of fine flow field structure of tilt-rotor propeller. BACKGROUND

[0002] Fine flow field simulation of tilt-rotor propeller requires that the calculation method has good engineering application prospect and sufficient turbulence analysis capability. The RANS / LES hybrid method under the structure grid framework has good application potential in the prediction of tilt-rotor propeller complex flow field, which takes into account the RANS and LES methods.

[0003] However, at present, in the analysis of complex flow field of propeller, there is a lack of effective interference component elimination means, so that the functional flow field characteristic analysis of complex working conditions of propeller is difficult to achieve target accuracy.

[0004] The flow field main feature extraction and reconstruction method based on proper orthogonal decomposition (POD) provides certain technical support for the elimination of interference components of fine flow field of propeller, but further combination of efficient grid processing and flow field interpolation methods is still needed to realize more accurate and efficient elimination of interference components. SUMMARY

[0005] In view of the above problems, the present application provides a method for eliminating interference components of fine flow field structure of tilt-rotor propeller, which provides an effective interference component elimination means for tilt-rotor propeller complex flow field analysis, so that the functional flow field characteristic analysis of complex working conditions of propeller is more targeted.

[0006] To solve the above problems, the technical scheme adopted by the present application is as follows:

[0007] A method for eliminating interference components of fine flow field structure of tilt-rotor propeller, the specific implementation steps are as follows:

[0008] 1. Calculation data acquisition: obtain the tilt-rotor propeller fine flow field time series through the structured CFD solver, and the results mainly include the spatial flow field discrete conservation variables at a certain time step. The fine numerical flow field prediction of the present application mainly relies on the high-precision and low-dissipation secondary development open source code CFL3D, and the flow field grid and output dimensionless variable are in plot3d format. The cfl3d.xyz is a multi-block structured grid file, and the spatial coordinates x, y and z are stored according to the i, j and k order arrangement relationship; the.bin file is a flow field information storage file, mainly storing 5 dimensionless conservation variables of the flow field, which are arranged in turn according to different time.

[0009] 2. New grid establishment: Based on the typical unsteady flow characteristics region of interest, a new structured grid corresponding to the region and with a spatial scale comparable to the flow field calculation grid is established. Since the size of the flow domain is usually large in the calculation of the flow field outside the propeller, and the POD algorithm requires all flow field snapshots to be stored in a matrix, the flow field characteristics can be stored in a new grid with smaller scale and less grid amount according to the analyzed flow field characteristic scale during actual calculation, so as to reduce the number of grid points n as much as possible.

[0010] 3. Mapping relationship search: The node number corresponding relationship of the two sets of grid nodes is obtained by using the node corresponding relationship search program of the flow field grid and the new structured grid. Each node of the new grid is sequentially taken as a node to be interpolated (the program is implemented in parallel by using MPICH2, and multiple nodes to be interpolated can be processed at the same time), and all original grid blocks are searched one by one. First, according to the maximum and minimum x, y and z coordinates of the old grid block, it is preliminarily judged whether the node to be interpolated is in the original grid block, if yes, the original grid block is divided into two parts in i, j and k directions, and the judgment of whether the node to be interpolated is in the grid partition is repeated, if not, the relationship between the next original grid block and the node to be interpolated is calculated; if yes, the next step of recursion is performed, until all i, j and k values are 2, that is, the number of grids in three directions is 1, and the vector discrimination method is used to judge whether the node to be interpolated is in the original grid. After mapping all the new grid nodes to be interpolated to the original grid, the mapping relationship between the two is written for the subsequent interpolation program.

[0011] 4. Flow field interpolation: The flow field conservation variables of the new structured grid at different times are obtained by using the grid node corresponding relationship by using the flow field interpolation program. The second set of programs of the interpolation platform mainly uses the inverse distance weighted method to interpolate the new grid nodes by using the eight nodes of the old grid. According to the text file of the mapping relationship between the new and old grid nodes written before, the space interpolation algorithm is used to assign the flow field information of the new grid nodes.

[0012] 5. POD mode calculation: Based on the new flow field sequence obtained by interpolation, the POD program is used to decompose the main modal characteristics of a single variable. The POD program written for the plot3d format file is used to calculate different POD modes and modal coefficients, wherein the mode storage format is plot3d format, and the modal coefficient is text.

[0013] 6. Flow field reconstruction: according to the modal space characteristics and the modal coefficient curve, the POD reconstruction program is used to remove the modal with small energy proportion and reconstruct the remaining modal, and the flow field characteristic sequence after the interference component is eliminated is obtained. If a certain number of flow field snapshots are selected for POD decomposition, the modal with small energy contribution proportion is determined according to the cumulative energy curve, and the modal is removed and the remaining modal is reconstructed. For example, 1000 flow field snapshots (the parameter is dimensionless density) are selected for POD decomposition, because the first 100 modes have contributed more than 80% to the flow field energy, so the modes after 100 modes are removed, and the flow field of 1-100 modes is reconstructed.

[0014] The beneficial effects of the present application are:

[0015] POD provides an effective extraction measure for the main spatial characteristics of complex flow fields. The present application uses the orthogonal feature extraction characteristics of POD, combined with the fast search method of the corresponding relationship of the structural grid nodes, to achieve the effect of efficient and accurate extraction of the main characteristics of the structured propeller fine flow field, and based on the extracted modal characteristics and modal reconstruction function, the purpose of eliminating the interference component modal is effectively achieved. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The grid and flow field information file of the present application is shown in the figure;

[0017] Figure 2 The mapping relationship output program logic diagram of the present application is shown in the figure;

[0018] Figure 3 The vector discrimination method of the present application is shown in the figure;

[0019] Figure 4 The different (upper) POD modal and (lower) modal coefficient file of the present application is shown in the figure;

[0020] Figure 5 The cumulative energy curve of the present application is shown in the figure;

[0021] Figure 6 The dimensionless density space distribution (upper) original (lower) reconstruction diagram of the blade element section at time 1 of the present application is shown in the figure;

[0022] Figure 7 The dimensionless density space distribution (upper) original (lower) reconstruction diagram of the blade element section at time 2 of the present application is shown in the figure;

[0023] Figure 8 The dimensionless density space distribution (upper) original (lower) reconstruction diagram of the blade element section at time 3 of the present application is shown in the figure;

[0024] Figure 9Non-dimensional density spatial distribution of time 4 blade element section of the present application (top) original (bottom) reconstruction map;

[0025] Figure 10 Non-dimensional density variation curve chart of the present application at the position of 0.5 chord length from the trailing edge monitoring point;

[0026] Figure 11 Non-dimensional density power spectral density PSD chart of the present application at the position of 0.5 chord length from the trailing edge monitoring point;

[0027] Figure 12 Mode 1 spatial distribution map of the present application;

[0028] Figure 13 Mode 3 spatial distribution map of the present application;

[0029] Figure 14 Mode 10 spatial distribution map of the present application;

[0030] Figure 15 Mode 100 spatial distribution map of the present application;

[0031] Figure 16 Mode 200 spatial distribution map of the present application;

[0032] Figure 17 Mode 500 spatial distribution map of the present application;

[0033] Figure 18 Mode coefficient comparison chart of different modes of the present application. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. In the following description, a large number of specific details are set forth in order to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the scope of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0035] REFERENCE Figures 1-18 A method for eliminating interference components of the fine flow field structure of a tilt-rotor propeller, comprising a first step of calculating data acquisition and a second step of POD mode calculation.

[0036] 1. Data acquisition

[0037] The fine numerical flow field prediction of the present application mainly relies on the secondary development open source code CFL3D with high precision and low dissipation, and the flow field grid and output non-dimensional variable of the present application are in plot3d format, such as Figure 1cfl3d.xyz is a multi-block structured grid file, and the parameters of spatial coordinates x, y, z are stored according to the order of i, j, k; the.bin file is a flow field information storage file, and mainly stores five non-dimensionalized conservation variables of the flow field, which are arranged in sequence according to different time (12 time points in the figure, namely 1, 2, 3, …, representing the flow field information of the first, second, third, …, time step).

[0038] In the calculation of the outflow field of the propeller, the size of the flow field is usually several tens of times the diameter of the propeller disc, and the POD algorithm involved in the present application needs to store all the flow field snapshots (flow field information at different times) in an n x m matrix with the number of spatial grid points n as the row and the number of time snapshots m as the column. Generally, m is of the order of 10 2 ~ 10 3 , and n is of the order of 10 6 ~ 10 8 . Therefore, in actual calculation, the flow field characteristics can be stored in a new grid with smaller size and less grid quantity according to the characteristic scale of the analyzed flow field, so as to reduce n as much as possible.

[0039] The flow field parameter assignment for the new grid makes it possible to use the efficient interpolation platform established by the present application for the plot3d hexahedral structured grid to perform local interpolation on the original calculated flow field. The interpolation platform mainly includes two sets of programs. The main purpose of the first set of programs is to scan the mapping relationship between the nodes of the new and old grids; and the main purpose of the second set of programs is to use the spatial interpolation algorithm to assign the flow field information of the new grid nodes according to the text file of the mapping relationship between the nodes of the new and old grids written by the first set of programs. The second set of programs of the interpolation platform of the present application mainly uses the inverse distance weighting method to perform interpolation on the nodes of the new grid using the eight nodes of the old grid. The logic of the first set of programs is more complex than that of the second set of programs.

[0040] The logical relationship of the first set of programs is shown in Figure 2 . The nodes of the new grid are sequentially taken as the nodes to be interpolated (the program is implemented in parallel by MPICH2, and can process multiple nodes to be interpolated at the same time), and all the original grid blocks are searched one by one. First, whether the node to be interpolated is in the original grid block is preliminarily judged according to the maximum and minimum x, y, z coordinates of the original grid block. If it is in the original grid block, the original grid block is divided into eight parts according to i, j, k the coordinates of the node to be interpolated. At this time, the original grid block is divided into eight parts, and the method of determining whether the node to be interpolated is in the eight grid partitions is repeated. If it is not in the grid partitions, the relationship between the next original grid block and the node to be interpolated is calculated; if it is in the grid partitions, the next recursion is performed until all i, j, kIf the value is 2, meaning the number of grid cells in all three directions is 1, then the relationship between the node to be interpolated and the grid cell is determined. This invention uses a vector discrimination method to determine whether the node to be interpolated is within the original grid cell. Figure 3 The grid and interpolation points shown. M Let's take an example to illustrate.

[0041] Figure 3 As shown A~H These are the 8 fixed points of the original hexahedral mesh. M , N The points are two points inside and two points outside the hexahedron. Let the normal vectors of the six faces of the hexahedron point into the hexahedron, such as... ABCD The surface, whose normal vector is Because the three faces of the hexahedron have passed through A Point, while the other three faces pass through H Point, passing through A The normal vectors of the three faces of a point and A Point to Space M or N If the dot product of all vectors is greater than 0, then begin judging the path taken by the vectors. H The normal vectors of the three faces of a point and H Point to Space M or N If the dot product of any vector is greater than 0, then the point in space is definitely within the hexahedron. If the dot product of any vector is less than 0, then... BDHF normal vector of the surface and If the dot product is less than 0, then the point in space is outside the hexahedron.

[0042] After mapping all the new mesh nodes to be interpolated to the original mesh, the mapping relationship between the two can be written out for the second set of programs to read and use.

[0043] 2. POD Modal Calculation

[0044] The second step of this invention involves performing intrinsic eigenvalue decomposition of the POD using flow field snapshots at different times. A patented POD program, written for plot3d format files, is used to calculate different POD modes and modal coefficients. The modal storage format is plot3d, and the modal coefficients are displayed as text. Figure 4 As shown.

[0045] This invention illustrates the flow characteristics around the blade element at the propeller tip when the propeller tip Mach number reaches 0.73 during overclocking. By comparing the spatial morphology of the flow field before and after removing features that contribute little to the flow field, and the flow field parameter monitoring curves at a certain monitoring point, the functionality of the program for feature removal is demonstrated. The purpose of this invention is to provide targeted flow field data for other physical fields, such as noise and optical transmission.

[0046] This example uses 1000 flow field snapshots (with dimensionless density as the parameter) for POD decomposition, resulting in 1000 decomposed modes. The cumulative energy curve of this POD decomposition is shown below. Figure 5 As shown in the figure, the horizontal axis represents the modes, and the vertical axis represents the modal energy. The meaning is that the first number of modes contributes what percentage of the total energy of the flow field. Taking mode 100 in the figure as an example, the scale of 100 indicates that the contribution value of the first 100 modes to the flow field energy is approximately 0.812.

[0047] Since the first 100 modes contribute more than 80% of the flow field energy, this example removes modes after the 100th mode and reconstructs the flow field of modes 1-100. The reconstruction principle is as follows:

[0048]

[0049] In the formula, v i Characterizable i A snapshot of the flow field at time a j Characterizing the first j The modal coefficients of each mode, P j Characterizing the first j One modality.

[0050] Figures 6-9 The spatial dimensionless density distribution of blade elements at the blade tip is compared at four different time points. As shown in the figure, after removing modes 101 to 1000, the spatial density distribution of the flow field remains almost unchanged. Figure 10 and Figure 11 The figures show the dimensionless density and the power spectral density (PSD) of the monitoring point at 0.5 times the chord length from the trailing edge after removing modes 101 to 1000. As can be seen from the figures, there are some fluctuations in the time domain curves, while the frequency domain curves clearly show that the reconstructed flow field characteristics have been almost smoothed out in the high-frequency region, indicating that the noise components of the data have been well removed.

[0051] At this point, the entire procedural workflow is complete, and the high-order small perturbation features of the flow field have been eliminated. Furthermore, this method can also be used to eliminate specific features, providing the most direct means for targeted research on flow field characteristics.

[0052] Figures 10-15 The spatial characteristic distributions of the 1st, 3rd, 10th, 100th, 200th and 500th modes are given respectively. As the mode order increases, the spatial characteristic scale it represents gradually decreases, the frequency increases, the energy decreases, and the influence on the flow field also decreases.

[0053] Figure 18 Given Figures 10-15The modal coefficient of the corresponding mode, according to the curve in the figure, the higher the modal order, the modal coefficient gradually develops to the trend of low amplitude and high frequency, and removing certain high-order components (noise components) is theoretically more convenient for flow field analysis.

[0054] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for eliminating interference components of a fine flow field structure of a tilt-rotor propeller, characterized in that, The method comprises the following steps: S1, obtaining a tilt-rotor propeller fine flow field time series through a structured CFD solver, the result mainly including time step spatial flow field discrete conservation variables; S2, establishing a new structured grid corresponding to a typical unsteady flow characteristic region and matching the spatial scale of the flow field calculation grid; S3, obtaining the numbering correspondence of the nodes of the two grids by using a node correspondence searching program of the flow field grid and the new structured grid; S4, obtaining the flow field conservation variables of the new structured grid at different times by using a flow field interpolation program and the node correspondence; S5, performing single variable principal mode characteristic decomposition on the new flow field sequence obtained by interpolation by using a POD program; S6, removing the modes with small energy proportion and reconstructing the remaining modes to obtain the flow field characteristic sequence after interference elimination according to the mode space characteristics and the mode coefficient curve by using a POD reconstruction program.

2. The method of claim 1, wherein, The prediction of the fine flow field mainly relies on the secondary development open source code CFL3D with high precision and low dissipation, and the flow field grid and the output dimensionless variable are in the plot3d format.

3. The method of claim 1, wherein, In S3, each node of the new structured grid is sequentially taken as a node to be interpolated, all original grid blocks are searched one by one, all new grid nodes to be interpolated are mapped to the original grid, and the mapping relationship of the two is written for the subsequent interpolation program.

4. The method of claim 3, wherein the fine flow field structure of the tilt-rotor propeller is a three-dimensional structure. According to the text file of the written new and old grid node mapping relationship, the new grid node flow field information is valued by using a spatial interpolation algorithm.

5. The method of claim 1, wherein, In S6, a flow field snapshot is selected for POD decomposition, the modes with small energy contribution proportion to the flow field are removed and the remaining modes are reconstructed according to the cumulative energy curve.

6. The method of claim 1, wherein, In S5, the POD program written for the plot3d format file is used to calculate different POD modes and mode coefficients, wherein the mode storage format is the plot3d format and the mode coefficient is the text.

Images