Interference component elimination method for fine flow field structure of tilt rotor propeller

Abstract

The invention provides an interference component elimination method for a fine flow field structure of a tilt rotor propeller, and the method comprises the following steps: S1, calculation data acquisition: obtaining a fine flow field time sequence of the tilt rotor propeller through a structured CFD solver, and the result mainly comprises a space flow field discrete conservation variable of a certain time step; s2, new grid establishment: based on the concerned typical unsteady flow characteristic region, establishing a new structured grid which corresponds to the region and is equivalent to the spatial scale of the flow field calculation grid; s3, searching a mapping relation; s4, performing flow field interpolation; s5, carrying out POD modal calculation; and S6, reconstructing the flow field. According to the method, the orthogonal feature extraction characteristics of POD are utilized, and a structural grid node corresponding relation rapid searching method is combined, so that the efficient and accurate extraction effect of the main features of the fine flow field of the structured propeller is achieved, and the purpose of eliminating the interference component modal is effectively achieved based on the extracted modal features and the modal 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: A method for eliminating interference components of fine flow field structure of tilt-rotor propeller, the specific implementation steps are as follows: 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 of 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 sequence according to different time1.

[0007] 2. New Grid Establishment: Based on the typical unsteady flow characteristic region of interest, a new structured grid is established that corresponds to the region and has a spatial scale comparable to the flow field calculation grid. Since the flow domain size is typically large in propeller external flow field calculations, and the POD algorithm requires storing all flow field snapshots in a matrix, in actual calculations, the flow field characteristics can be stored in a new grid with a smaller scale and fewer grid points, based on the scale of the analyzed flow field features, so that the number of grid points n is reduced to as little as possible by 1.

[0008] 3. Mapping Relationship Search: A node correspondence search program for the flow field mesh and the new structure mesh is used to obtain the node number correspondence between the two sets of meshes. Each node of the new mesh is sequentially used as the node to be interpolated (the program uses MPICH2 for parallel processing, allowing multiple nodes to be interpolated simultaneously), and all original mesh blocks are searched one by one. First, based on the maximum and minimum x, y, and z coordinates of the old mesh block, it is initially determined whether the point to be interpolated is within the original mesh block. If it is, the original mesh block is divided into two parts along the i, j, and k directions, and this process is repeated. If the point is not within any of these parts, the relationship between the next original mesh block and the node to be interpolated is calculated. If the point is within the mesh partition, the next recursive step is performed until all values ​​of i, j, and k are 2, meaning the number of meshes in all three directions is 1. A vector discrimination method is then used to determine whether the node to be interpolated is within the original mesh. After mapping all the new mesh nodes to be interpolated to the original mesh, the mapping relationship is written out for use by the subsequent interpolation program.

[0009] 4. Flow Field Interpolation: A flow field interpolation program is used to obtain the conserved variables of the flow field at different times in the new structured grid by utilizing the correspondence between grid nodes. The second program of the interpolation platform of this invention mainly adopts the inverse distance weighting method, using the 8 nodes of the old grid to interpolate the nodes of the new grid. Based on the previously written text file of the mapping relationship between the old and new grid nodes, the spatial interpolation algorithm is used to assign the flow field information of the new grid nodes.

[0010] 5. POD Modal Calculation: Based on the new flow field sequence obtained through interpolation, the POD program is used to decompose the main modal characteristics of a single variable. The POD program, written for plot3d format files, calculates different POD modes and modal coefficients. The modal storage format is plot3d, and the modal coefficients are stored as text files (text1).

[0011] 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, so as to obtain the flow field characteristic sequence after the interference component is eliminated. If a certain number of flow field snapshots are selected for POD decomposition, the modal with small energy contribution proportion is removed according to the cumulative energy curve, and the remaining modal is reconstructed. For example, 1000 flow field snapshots (the parameter is the dimensionless density) are selected for POD decomposition, since the first 100 modes have contributed more than 80% to the energy of the flow field, the modes after the 100th mode are removed, and the flow field of the 1-100th mode is reconstructed.

[0012] The beneficial effects of the present application are: POD provides an effective extraction measure for the main spatial characteristics of a complex flow field. 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 effectively realize the purpose of eliminating the interference component mode based on the extracted modal characteristics and modal reconstruction function. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The grid and flow field information file of the present application is shown in the figure; Figure 2 The mapping relationship output program logic diagram of the present application is shown in the figure; Figure 3 The vector discrimination method of the present application is shown in the figure; Figure 4 The different (upper) POD mode and (lower) modal coefficient file of the present application is shown in the figure; Figure 5 The cumulative energy curve of the present application is shown in the figure; Figure 6 The dimensionless density spatial distribution (upper) original (lower) reconstruction diagram of the blade element section at time 1 of the present application is shown in the figure; Figure 7 The dimensionless density spatial distribution (upper) original (lower) reconstruction diagram of the blade element section at time 2 of the present application is shown in the figure; Figure 8 The dimensionless density spatial distribution (upper) original (lower) reconstruction diagram of the blade element section at time 3 of the present application is shown in the figure; Figure 9 The dimensionless density spatial distribution (upper) original (lower) reconstruction diagram of the blade element section at time 4 of the present application is shown in the figure; Figure 10 The dimensionless density change curve diagram of the monitoring point at the position of 0.5 chord length from the trailing edge of the present application is shown in the figure; Figure 11Non-dimensional density power spectrum density (PSD) plot for monitoring point at 0.5 chord length position from trailing edge of the invention; Figure 12 Mode 1 spatial distribution plot for the invention; Figure 13 Mode 3 spatial distribution plot for the invention; Figure 14 Mode 10 spatial distribution plot for the invention; Figure 15 Mode 100 spatial distribution plot for the invention; Figure 16 Mode 200 spatial distribution plot for the invention; Figure 17 Mode 500 spatial distribution plot for the invention; Figure 18 Mode coefficient comparison plot for different modes of the invention. DETAILED DESCRIPTION

[0014] In order to make the above objectives, features and advantages of the invention more obvious and easy to understand, the specific embodiments of the invention are 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 invention. However, the invention 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 spirit of the invention, so the invention is not limited to the specific implementation disclosed below.

[0015] REFERENCE Figures 1-18 A method for eliminating interference components of a 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.

[0016] 1. Calculation of data acquisition The fine numerical flow field prediction of the invention mainly relies on the secondary development of the open source code CFL3D with high precision and low dissipation, and the flow field grid and output non-dimensional variable of the invention is in plot3d format, as shown in Figure 1 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 five non-dimensionalized conservation variables of the flow field, which are arranged in sequence according to different time (12 time points in the figure, i.e. 1, 2, 3, … represent the flow field information of the 1st, 2nd, 3rd, … time step).

[0017] Since the size of flow field in the calculation of propeller outflow is usually tens of times of the diameter of the propeller disc, and the POD algorithm of the present application needs to store all the flow field snapshots (flow field information at different time) 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 10 2 ~10 3 orders of magnitude, and n is 10 6 ~10 8 orders of magnitude. 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.

[0018] The assignment of flow field parameters to the new grid enables the use of 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. 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 new grid nodes using the 8 nodes of the old grid. The logic of the first set of programs is more complex than that of the second set of programs.

[0019] The logical relationship of the first set of programs is shown in Figure 2 . With each node of the new grid as the to-be-interpolated node (the program is implemented in parallel using MPICH2, and multiple to-be-interpolated nodes can be processed at the same time), all original grid blocks are searched one by one. First, the maximum and minimum x, y, z coordinates of the old grid block are used to preliminarily determine whether the to-be-interpolated point is within the original grid block. If it is within the original grid block, the original grid block is divided into 8 parts according to the i, j, k coordinates of the to-be-interpolated point. Then, the method of determining whether the to-be-interpolated point is within the original grid block is repeated to determine whether the to-be-interpolated point is within the 8 grid partitions. If it is not within any of the grid partitions, the relationship between the next original grid block and the to-be-interpolated node is calculated. If it is within a grid partition, the next recursion is performed until the value of i, j, k is 2, i.e., the number of grids in three directions is 1, and then the relationship between the to-be-interpolated node and the grid is determined. The present application uses the vector discrimination method to determine the relationship between the to-be-interpolated node and the original grid. The grid and the to-be-interpolated point Figure 3 are used as an example for illustration. M

[0020] Figure 3 The 8 fixed points of the original hexahedral grid A~H , 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.

[0021] 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.

[0022] 2. POD Modal Calculation 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.

[0023] This paper uses the flow characteristics around the blade element at the blade tip of a propeller when it reaches a Mach number of 0.73 during overclocking as an example. 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 illustrated. The purpose of this invention is to provide targeted flow field data for other physical fields, such as noise and optical transmission.

[0024] 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.

[0025] 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:

[0026] In the formula, v i Characterizable i A snapshot of the flow field at a given moment, a j Characterizing the first j The modal coefficients of each mode, P j Characterizing the first j One modality.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 18 Given Figures 10-15 According to the curves in the figure, the higher the modal order, the more the modal coefficients tend to develop towards lower amplitude and higher frequency. Removing certain higher-order components (noise components) is theoretically more conducive to the analysis of the flow field.

[0031] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

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 the method further comprises: 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.

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