Pressure reconstruction method and system based on error correction and adaptive grid, and storage medium

Abstract

The invention discloses a pressure reconstruction method and system based on error correction and an adaptive grid, and a storage medium. The system comprises a PIV experiment test subsystem, a processing module (comprising a PIV post-processing unit and a pressure reconstruction unit) and the like. The method comprises the following steps: firstly, controlling a laser emitting sheet to irradiate tracer particles in a water tank through a synchronizer, and simultaneously triggering a high-speed camera to capture a particle image sequence at a microsecond-to-millisecond-level time interval; the PIV post-processing unit performs cross-correlation analysis on the image to generate a gridding velocity field; a pressure reconstruction unit solves a pressure Poisson equation based on the velocity field to obtain a preliminary pressure field, then iteratively corrects an error through an error correction mechanism and gradient correlation optimization, and triggers self-adaptive mesh refinement / coarsening according to a pressure / velocity gradient amplitude; and finally, outputting a high-precision pressure field on the optimized grid. The method can be applied to flow field tests and hydrodynamic performance analysis in ship and ocean engineering, especially pressure field reconstruction in a complex flow field, and has a wide application prospect.

Description

Technical Field

[0001] This invention belongs to the field of fluid dynamics testing technology, specifically relating to a pressure reconstruction method, system, and storage medium based on error correction and adaptive grid. Background Technology

[0002] In the field of shipbuilding and ocean engineering, accurate measurement and in-depth analysis of flow fields are crucial for ship design optimization, performance evaluation of marine structures, and research on fluid dynamics phenomena. Traditional flow field testing methods, such as using pressure sensor arrays, can directly measure pressure, but their invasive nature interferes with the flow field, and the limited sensor placement makes it difficult to obtain high-resolution, global pressure distribution information. Particle image velocimetry (PIV), as an advanced non-invasive optical measurement technique, can accurately capture the instantaneous velocity distribution of the flow field and has been widely used in various fluid dynamics studies. However, PIV technology primarily provides velocity field information, and directly obtaining accurate pressure field information from PIV data remains a technical challenge. In recent years, methods for reconstructing pressure fields based on velocity field data have received widespread attention. These methods typically rely on solving the pressure Poisson equation. However, existing techniques often face problems of insufficient computational accuracy and numerical instability when applying the Poisson equation to solve pressure fields, especially when dealing with complex flow fields with strong shear, high turbulence, or when the velocity field data obtained from PIV is noisy, incomplete, or has insufficient resolution. Furthermore, traditional methods have limited computational accuracy in regions with high pressure or velocity gradients in the flow field (such as the boundary layer and wake region), making it difficult to meet the demand for high-precision pressure data for refined analysis of complex flow fields.

[0003] Therefore, there is an urgent need for a method and system that can fully leverage the advantages of PIV technology to obtain high-precision velocity fields, while optimizing pressure reconstruction algorithms, introducing error correction mechanisms and adaptive calculation strategies, to improve the computational accuracy, stability, and adaptability to complex flow fields in pressure field reconstruction. This invention aims to solve the above-mentioned technical problems and provide a more reliable and efficient pressure field reconstruction scheme. Summary of the Invention

[0004] The purpose of this invention is to provide a pressure reconstruction method, system, and storage medium based on error correction and adaptive grids, which is applicable to complex flow field analysis and hydrodynamic performance evaluation in shipbuilding and marine engineering.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A pressure reconstruction method based on error correction and adaptive mesh, characterized by the following steps:

[0007] Step 1: Obtain the particle image sequence of the flow field through the PIV test system, and obtain the gridded velocity field information through PIV post-processing;

[0008] Step 2: Based on the obtained velocity field information and combined with fluid property parameters, obtain the preliminary pressure field by solving the pressure Poisson equation;

[0009] Step 3: Introduce an error correction mechanism and correlation optimization to iteratively correct the initial pressure field; the error correction mechanism introduces a local error correction coefficient α(x) i ,y j This allows local errors to be corrected in real time; the correlation optimization is achieved by minimizing the error function E between the velocity field gradient and the pressure field gradient.

[0010] Step 4: Adaptively adjust the mesh size based on the changes in the pressure and velocity gradients of the flow field to optimize the spatial distribution of the pressure field;

[0011] Step 5: Re-interpolate the velocity field and pressure field on the optimized adaptive mesh, and repeat steps 2-3 until the error function E reaches the set threshold or the maximum number of iterations is reached, and output the final pressure field distribution.

[0012] Furthermore, the post-processing in step 1 includes:

[0013] Enhancement and noise reduction preprocessing are performed on particle images;

[0014] The continuous frame images are divided into query windows, and the cross-correlation algorithm is used to calculate the average displacement vector of the particle swarm within the time interval.

[0015] False vectors are removed through validity checks, and missing data is interpolated to complete the data, generating a gridded two-dimensional velocity field component u(x). i ,y j ), v(x) i ,y j ), or the three-dimensional velocity field component u(x) i ,y j ,z k ), v(x) i ,y j ,z k ), w(x i ,y j ,z k ).

[0016] Furthermore, in step 2, when the fluid is a compressible viscous fluid, the pressure Poisson equation is:

[0017]

[0018] in Let p be the Laplace operator for the pressure field, ρ be the fluid density, and u be the velocity vector. denoted as the dot product of the gradient of the fluid velocity field and the velocity field itself, and μ as the dynamic viscosity of the fluid. The Laplace operator for velocity field divergence characterizes the effects of viscosity and compressibility on pressure;

[0019] When the fluid is an incompressible fluid, i.e. Poisson's equation for pressure:

[0020]

[0021] The pressure Poisson equation is discretized on a computational grid, and by combining it with the corresponding boundary conditions, a system of linear algebraic equations is solved to obtain the preliminary pressure field distribution p. initial (x i ,y j ).

[0022] Furthermore, in step 3, the correction coefficient α(x) i ,y j ):

[0023] α(x i ,y j )=k1·B(x i ,y j )·tanh(k2·|ω(x i ,y j )|)+k3

[0024] Wherein, B(x) i ,y j ) is the boundary type that evaluates the influence of the boundary on the point, ω(x) i ,y j ) is a grid point (x) i ,y j The local vorticity at point () is represented by k1, k2, and k3, which are adjustable parameters that need to be optimized according to the specific application scenario. The tanh function maps the vorticity value to the interval [0,1], making the value α larger in the high vorticity region.

[0025] Error function E:

[0026]

[0027] Where u(x) i ,y j ) and v(x i ,y j ) represent the horizontal and vertical velocity components in the flow field, respectively; and At grid point (x)i ,y j At point ), the rate of change of the pressure field p along the x and y directions; the pressure difference p(x) i+1 ,y j ) to p(x i-1 ,y j The difference spans a distance of 2Δx, so dividing this difference by 2Δx gives the point (x). i ,y j Approximate values ​​of the pressure gradient in the x and y directions at point ( );

[0028] Correlation optimization updates the stress iteratively, with the iteration terminating when the error function E is less than a threshold or the maximum number of iterations is reached.

[0029]

[0030] Here, η is the learning rate, which needs to be adjusted according to the specific circumstances. For a single grid point (x) E i ,y j The sensitivity of the pressure p to changes.

[0031] Furthermore, the aforementioned and They are respectively:

[0032]

[0033] The The formula:

[0034]

[0035] Furthermore, step 4, adaptively adjusting the mesh size, includes: mesh refinement criteria:

[0036] or

[0037] Among them, threshold is selected based on experience, and an initial threshold is set according to empirical values; when the conditions are met, the grid of the local area will be marked and refined.

[0038] The velocity field and optimized pressure field of the coarse mesh are calculated, and the pressure field is recalculated on the refined mesh.

[0039] A system for implementing a pressure reconstruction method based on error correction and adaptive grids includes: a PIV experimental testing subsystem comprising a test water tank, a high-frequency laser, a synchronizer, and a high-speed camera for acquiring particle image sequences;

[0040] Processing module: Runs on CPU / GPU processor, including: PIV post-processing unit, which performs velocity field calculation; and pressure reconstruction unit, which integrates error correction mechanism and adaptive mesh optimization algorithm;

[0041] Memory: Stores program instructions and experimental data;

[0042] Display: Visualizes the velocity and pressure field distributions.

[0043] Furthermore, the test water tank contains uniformly distributed tracer particles. The tracer particles in the flow field area of ​​the test water tank are irradiated by a high-frequency laser, and a high-speed camera is used to capture a sequence of particle images with time intervals. The high-speed camera is connected to the processing module through a synchronizer, which is used to control the high-speed camera and the high-frequency laser to perform synchronous acquisition.

[0044] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of a stress reconstruction method based on error correction and adaptive grid.

[0045] A computer program product includes a computer program / instructions that, when executed by a processor, implement steps of a stress reconstruction method based on error correction and adaptive grid.

[0046] The beneficial effects of this invention are as follows:

[0047] This invention is versatile and applicable to a wide range of hydrodynamic performance testing and analysis in shipbuilding and marine engineering. By introducing an error correction mechanism, a correlation optimization algorithm, and adaptive mesh technology, this application dynamically adjusts the mesh density according to changes in the flow field gradient, ensuring computational accuracy in high-gradient regions and providing reliable technical support for refined pressure field analysis under complex flow conditions. Attached Figure Description

[0048] Figure 1 This is an overall schematic diagram of the present invention;

[0049] Figure 2 This is a flowchart of the system described in this invention;

[0050] Figure 3 This is a rendering of the adaptive mesh technology described in this invention;

[0051] Figure 4 This is a schematic diagram of the pressure reconstruction method described in this invention. Detailed Implementation

[0052] The present invention will now be further described with reference to the accompanying drawings.

[0053] To make the objectives, technical solutions, and advantages of this invention clearer and more complete, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0054] See attached document Figure 1 This invention discloses a pressure reconstruction system based on error correction and adaptive mesh. Its operation begins with a PIV experimental testing system: First, in a test tank 5 equipped with tracer particles, a high-frequency laser 1 emits a sheet laser under the precise control of a synchronizer 2 to illuminate the tracer particles in the flow field region. Simultaneously, a high-speed camera 3, coordinated by the synchronizer 2, captures image sequences of these illuminated particles at preset time intervals. Subsequently, the acquired particle image sequences are transmitted to a processing module 4 running on a CPU / GPU processor. This module includes a PIV post-processing unit and a pressure reconstruction unit. First, the PIV post-processing unit processes the images to accurately calculate the velocity field information of the flow field. Then, this velocity field information, as a key input, is sent to the pressure reconstruction unit, also running on a CPU / GPU processor. Using the core error correction mechanism and adaptive mesh technology of this invention, the velocity field is deeply analyzed and calculated, ultimately reconstructing a high-precision pressure field distribution. Throughout the operation, the CPU / GPU processor provides core computing support, the memory is responsible for temporarily storing and permanently saving relevant program instructions and experimental data, and the display is used for real-time or subsequent visualization of the processing results, including intermediate velocity fields and the final pressure field.

[0055] See attached document Figure 2 This paper illustrates the overall workflow of the system and the logical block diagram of the pressure reconstruction method described in this invention. The PIV experimental testing system first acquires particle image sequences through physical experiments, and then obtains velocity field information after PIV post-processing. This velocity field information is subsequently input into the pressure reconstruction method, undergoing steps such as solving the Poisson equation, error correction, correlation optimization, and adaptive mesh optimization, ultimately outputting accurate pressure field information. The hardware system provides computational and storage support for the entire process.

[0056] The following will combine Figure 2 , Figure 3 and Figure 4 The following details the specific steps of the pressure reconstruction method based on error correction and adaptive mesh described in this invention:

[0057] Step S1: Obtain the velocity field information obtained through PIV post-processing.

[0058] In this step, a PIV experiment is first conducted. An appropriate amount of tracer particles are evenly distributed into the test fluid within the test tank 5. Under the precise control of the synchronizer 2, a high-frequency laser 1 emits a laser sheet of a specific frequency and thickness, illuminating a pre-defined two-dimensional or three-dimensional flow field region within the test tank 5. Triggered by the synchronization signal from the synchronizer 2, a high-speed camera 3 continuously captures two or more frames of laser-illuminated tracer particle images within this region at extremely short time intervals Δt (e.g., on the order of microseconds to milliseconds), forming a time-correlated sequence of particle images.

[0059] Subsequently, the acquired particle image sequence is input into processing module 4 for PIV post-processing, which specifically includes the following steps:

[0060] (a) Particle image enhancement and noise reduction: The original particle image is preprocessed, such as by using grayscale stretching and histogram equalization to enhance the contrast between particles and background, and by using Gaussian filtering and median filtering to remove image noise in order to improve the accuracy of particle recognition.

[0061] (b) Cross-correlation analysis: Two or more consecutive frames of particle images are spatially divided into several interrogation windows of the same size or adaptively adjusted size. For each sub-region of the particle image within the interrogation window, a cross-correlation algorithm accelerated by Fast Fourier Transform (FFT) or other advanced cross-correlation algorithms (such as least squares matching, optical flow, etc.) is used to calculate the average displacement vector of the particle swarm within the time interval Δt.

[0062] (c) Post-processing correction: The original velocity vector field obtained through cross-correlation analysis is validated, for example, by setting a velocity amplitude range and performing neighborhood median checks, to identify and remove erroneous or physically unreasonable outliers. For the removed outliers or regions with missing data, appropriate interpolation algorithms (such as bilinear interpolation, radial basis function interpolation, etc.) are used to fill in the data, thereby obtaining a smooth and physically reasonable two-dimensional velocity field component u(x) distributed on a preset grid (usually a uniform Cartesian grid). i ,y j (e.g., horizontal velocity) and v(x) i ,y j (e.g., vertical velocity), or three-dimensional velocity field component u(x) i ,y j ,z k ), v(x) i ,y j ,z k ), w(x i ,y j ,z kThe final output of this step is gridded velocity field data, which serves as input for subsequent pressure field reconstruction.

[0063] Step S2: Perform a preliminary solution to the Poisson equation based on the velocity field information.

[0064] In this step, processing module 4 receives the velocity field information u(x) output in step S1. i ,y j ) and v(x i ,y j (Taking two dimensions as an example), and preset fluid properties, mainly including fluid density ρ and dynamic viscosity μ. The pressure field is initially solved using the pressure Poisson equation.

[0065] For compressible viscous fluids, the general form of the pressure Poisson equation is:

[0066]

[0067] in Let p be the Laplace operator for the pressure field, ρ be the fluid density, and u be the velocity vector. Let μ be the dot product of the gradient of the fluid velocity field and the velocity field itself, and μ be the dynamic viscosity of the fluid. The Laplace operator for velocity field divergence characterizes the effects of viscosity and compressibility on pressure.

[0068] In many engineering applications, especially for the flow of liquids such as water, the fluid can be assumed to be incompressible. Under these conditions, the above pressure Poisson equation can be simplified to:

[0069]

[0070] The right-hand side of this equation depends only on known velocity field data. Processing module 4 uses numerical methods (e.g., finite difference method based on central difference, finite element method, or finite volume method) to discretize the Poisson equation on a computational grid, and combines appropriate boundary conditions (such as Dirichlet boundary, Neumann boundary, or mixed boundary conditions, which can be set according to the actual physical problem) to solve the system of linear algebraic equations, thereby obtaining the preliminary pressure field distribution p. initial (x i ,y j This preliminary pressure field may have certain calculation errors and instabilities in specific regions (especially complex flow regions or regions with high data noise) due to factors such as PIV measurement errors and numerical discretization errors.

[0071] Step S3: Introduce an error correction mechanism to reduce calculation errors and perform correlation optimization by locally correcting the pressure field.

[0072] To improve the accuracy and stability of pressure field reconstruction, this step introduces an error correction mechanism and a correlation optimization strategy. First, according to claim 5 of this invention, a local error correction coefficient α(x) is introduced. i ,y j This correction factor α(x) is used to correct local errors in real time when dealing with complex flow fields. i ,y j The calculation formula for ) is as follows:

[0073] α(x i ,y j )=k1·B(xi,yj)·tanh(k2·|ω(xi,yj)|)+k3 (3)

[0074] Where B(xi,yj) is an evaluation function used to quantify the influence of the boundary type (such as solid wall, inlet, outlet, etc.) at the grid point (xi,yj) on the local flow field characteristics at that point; ω(xi,yj) is the local vorticity at the grid point (xi,yj) (for a two-dimensional flow field, ... The vorticity can be obtained by numerical differentiation of the velocity field obtained from S1. |ω(xi,yj)| represents the absolute value of vorticity. k1, k2, and k3 are adjustable parameters, and their values ​​need to be set empirically or determined by optimization algorithms based on the specific application scenario and flow field characteristics to achieve the best correction effect. The tanh function (hyperbolic tangent function) maps the vorticity value (after scaling by k2) to a bounded interval (e.g., [-1,1] or [0,1], depending on k2 and the vorticity sign treatment). In the high vorticity region (usually corresponding to the region with complex flow and potentially large errors), α(x i ,y j The value of ) is relatively large, thus applying a more significant correction.

[0075] Secondly, according to claims 6 and 7 of this invention, correlation optimization is performed by minimizing a defined error function E to iteratively update the pressure field. The error function E defines the difference between the velocity field gradient (obtained by PIV measurement) and the pressure field gradient (obtained by the currently calculated pressure field), and its expression is:

[0076]

[0077] pressure gradient term and Calculations are performed using numerical difference schemes, such as the central difference scheme:

[0078]

[0079] Where Δx and Δy are the grid spacing in the x and y directions, respectively.

[0080] The pressure is updated using gradient descent or a similar optimization algorithm, and the update formula is:

[0081]

[0082] Where, p old (x i ,y j p represents the pressure value of the current iteration step. new (x i ,y j ) represents the updated pressure value, and η represents the learning rate (a positive decimal) which controls the update step size for each iteration. It is the partial derivative of the error function E with respect to the pressure p at the grid point (xi,yj), and its specific calculation formula is shown in (7).

[0083] This iterative process continues until the value of the error function E converges to a preset value below a certain threshold, or until a preset maximum number of iterations is reached. Through this step, the pressure field p, after error correction and correlation optimization, is obtained. optimized (x i ,y j ).

[0084] Step S4: Use adaptive mesh refinement technology to refine the mesh based on the coarse-scale mesh and optimize the spatial distribution of the pressure field.

[0085] To further improve computational accuracy in high-gradient regions while avoiding the significant computational overhead of global mesh refinement, this step employs adaptive meshing technology. The criteria for adaptive mesh refinement are based on the magnitudes of the pressure and / or velocity gradients in the flow field. Specifically, the criteria can be expressed as:

[0086] like or (And the corresponding y-direction gradient and other velocity component gradients, if applicable), then mark the mesh cell or its neighboring region as needing refinement.

[0087] threshold p and threshold u It is a pre-set threshold that can be selected based on experience, such as a certain percentage (e.g., 10%) of the maximum pressure gradient or maximum velocity gradient in the current flow field.

[0088] The pressure reconstruction module first calculates the pressure and velocity gradients at each point on the current mesh (initially the mesh used in S2). Then, based on the aforementioned criteria, it identifies regions where the gradient values ​​exceed a threshold. These regions are typically critical areas with drastic flow changes and rapid changes in physical quantities (e.g., boundary layer, shear layer, vortex core region, etc.). For regions marked as needing refinement, a suitable mesh refinement algorithm (such as quadtree / octree partitioning, cell subdivision, etc.) is used to increase the mesh density, while in regions with smaller and gentler gradients, the original mesh density is maintained or even the mesh can be coarsened.

[0089] Figure 3 The diagram illustrates the effect of adaptive mesh refinement, showing how a uniform initial mesh (left) evolves into an adaptive mesh (right) with a denser mesh in the central region and a sparser mesh in the surrounding region, based on flow field characteristics (e.g., a high gradient in the central region). The output of this step is an optimized, non-uniform adaptive mesh structure.

[0090] Step S5: Recalculate and integrate the pressure data to obtain the final pressure field distribution.

[0091] After generating the optimized adaptive mesh in step S4, the pressure field needs to be recalculated on the new mesh to fully utilize the accuracy improvement brought by the mesh refinement.

[0092] First, it is necessary to convert the relevant physical quantities on the coarse mesh (or the mesh from the previous iteration) into (e.g., the original velocity field components u and v obtained from S1, and the optimized pressure field p obtained from S3, which may serve as the initial value for the next round of calculations). optimized (x i ,y j The data is mapped to new, refined mesh nodes using interpolation methods. Commonly used interpolation methods include bilinear interpolation (for two-dimensional quadrilateral meshes), bicubic interpolation, or higher-order conformal interpolation methods to ensure interpolation accuracy and the conservation of physical quantities.

[0093] After obtaining the interpolated velocity field on the new adaptive mesh, the pressure reconstruction module will re-execute the pressure field calculation. This typically means repeating step S2 (i.e., solving the pressure Poisson equation on the new adaptive mesh). More preferably, the iterative optimization process of steps S2 and S3 can be completely repeated on the new adaptive mesh until the convergence condition is met. Since the mesh is effectively refined in the high gradient region, the calculation at this point can more accurately capture the flow details in these regions, thereby significantly improving the final pressure field p. final (x i, y j (The overall accuracy and stability of)

[0094] In particular, in some preferred embodiments of the present invention, a computer device is also provided, including a memory and a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the pressure reconstruction method based on error correction and adaptive grid described in any of the above embodiments.

[0095] In some other preferred embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program / instruction is stored, wherein when the computer program is executed by a processor, it implements the steps of the pressure reconstruction method based on error correction and adaptive grid described in any of the above embodiments.

[0096] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the pressure reconstruction method embodiments based on error correction and adaptive mesh described above, which will not be repeated here.

[0097] Computer-readable storage media encompass a variety of types, including persistent and non-persistent, portable and fixed. These media store information using different technologies, and the content can be machine instructions, data structures, program modules, or other types of data. Some typical examples of computer storage media include: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), various types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory and other storage technologies, optical storage media such as CD-ROM and digital video disc (DVD), magnetic storage devices such as magnetic tape and disks, and other non-transferable media used to store information accessible to computing devices. It is important to note that the computer-readable media described herein do not include temporary storage media, such as modulated data signals and carrier waves.

[0098] Those skilled in the art will further recognize that the operation of the module can be achieved using existing technical protocols or programs, without relying on new computer programs themselves. The units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0099] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0100] The pressure reconstruction method and system based on error correction and adaptive grid described in this invention can effectively overcome the problems of insufficient accuracy and computational instability of traditional pressure field reconstruction methods when dealing with complex flow fields. It provides a more accurate and reliable technical means for flow field testing and hydrodynamic performance analysis in fields such as shipbuilding and marine engineering, and shows broad application prospects, especially in the fine analysis of pressure fields under complex flow conditions.

[0101] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A pressure reconstruction method based on error correction and adaptive mesh, characterized in that: Includes the following steps: Step 1: Obtain the particle image sequence of the flow field through the PIV test system, and obtain the gridded velocity field information through PIV post-processing; Step 2: Based on the obtained velocity field information and combined with fluid property parameters, obtain the preliminary pressure field by solving the pressure Poisson equation; Step 3: Introduce an error correction mechanism and correlation optimization to iteratively correct the initial pressure field; the error correction mechanism introduces a local error correction coefficient α(x) i ,y j This allows local errors to be corrected in real time; the correlation optimization is achieved by minimizing the error function E between the velocity field gradient and the pressure field gradient. Step 4: Adaptively adjust the mesh size based on the changes in the pressure and velocity gradients of the flow field to optimize the spatial distribution of the pressure field; Step 5: Re-interpolate the velocity field and pressure field on the optimized adaptive mesh, and repeat steps 2-3 until the error function E reaches the set threshold or the maximum number of iterations is reached, and output the final pressure field distribution.

2. The pressure reconstruction method based on error correction and adaptive mesh according to claim 1, characterized in that: The post-processing in step 1 includes: Enhancement and noise reduction preprocessing are performed on particle images; The continuous frame images are divided into query windows, and the cross-correlation algorithm is used to calculate the average displacement vector of the particle swarm within the time interval. False vectors are removed through validity checks, and missing data is interpolated to complete the data, generating a gridded two-dimensional velocity field component u(x). i ,y j ), v(x) i ,y j ), or the three-dimensional velocity field component u(x) i ,y j ,z k ), v(x) i ,y j ,z k ), w(x i ,y j ,z k ).

3. The pressure reconstruction method based on error correction and adaptive mesh according to claim 2, characterized in that: In step 2, when the fluid is a compressible viscous fluid, the pressure Poisson equation applies: in, Let p be the Laplace operator for the pressure field, ρ be the fluid density, and u be the velocity vector. denoted as the dot product of the gradient of the fluid velocity field and the velocity field itself, and μ as the dynamic viscosity of the fluid. The Laplace operator for velocity field divergence characterizes the effects of viscosity and compressibility on pressure; When the fluid is an incompressible fluid, i.e. Poisson's equation for pressure: The pressure Poisson equation is discretized on a computational grid, and by combining it with the corresponding boundary conditions, a system of linear algebraic equations is solved to obtain the preliminary pressure field distribution p. initial (x i ,y j ).

4. The pressure reconstruction method based on error correction and adaptive mesh according to claim 3, characterized in that: The correction coefficient α(x) i ,y j ): α(x i ,y j )=k1·B(x i ,y j )·tanh(k2·|ω(x i ,y j )|)+k3 Wherein, B(x) i ,y j ) is the boundary type that evaluates the influence of the boundary on the point, ω(x) i ,y j ) is a grid point (x) i ,y j The local vorticity at point () is represented by k1, k2, and k3, which are adjustable parameters that need to be optimized according to the specific application scenario. The tanh function maps the vorticity value to the interval [0,1], making the value α larger in the high vorticity region. Error function E: Where u(x) i ,y j ) and v(x i ,y j ) represent the horizontal and vertical velocity components in the flow field, respectively; and At grid point (x) i ,y j At point ), the rate of change of the pressure field p along the x and y directions; the pressure difference p(x) i+1 ,y j ) to p(x i-1 ,y j The difference spans a distance of 2Δx, so dividing this difference by 2Δx gives the point (x). i ,y j Approximate values ​​of the pressure gradient in the x and y directions at point ( ); Correlation optimization updates the stress iteratively, with the iteration terminating when the error function E is less than a threshold or the maximum number of iterations is reached. Here, η is the learning rate, which needs to be adjusted according to the specific circumstances. For a single grid point (x) E i ,y j The sensitivity of the pressure p to changes.

5. The pressure reconstruction method based on error correction and adaptive mesh according to claim 4, characterized in that: The and They are respectively: The The formula:

6. The pressure reconstruction method based on error correction and adaptive mesh according to claim 4, characterized in that: Step 4, adaptively adjusting the mesh size, includes: Mesh refinement criteria: or Among them, threshold is selected based on experience, and an initial threshold is set according to empirical values; when the conditions are met, the grid of the local area will be marked and refined. The velocity field and optimized pressure field of the coarse mesh are calculated, and the pressure field is recalculated on the refined mesh.

7. A pressure reconfiguration system for implementing the method of any one of claims 1-6, characterized in that: include: PIV experimental testing subsystem: includes experimental water tank (5), high-frequency laser (1), synchronizer (2) and high-speed camera (3) for acquiring particle image sequences; Processing module (4): running on CPU / GPU processor, including: PIV post-processing unit, which performs velocity field calculation; pressure reconstruction unit, which integrates error correction mechanism and adaptive mesh optimization algorithm; Memory: Stores program instructions and experimental data; Display: Visualizes the velocity and pressure field distributions.

8. A pressure reconstruction system based on error correction and adaptive grid according to claim 7, characterized in that: The test tank (5) contains tracer particles that are evenly distributed. The tracer particles in the flow field area of ​​the test tank (5) are irradiated by a high-frequency laser (1), and a high-speed camera (3) is used to capture a sequence of particle images with time intervals. The high-speed camera (3) is connected to the processing module (4) through a synchronizer (2). The synchronizer (2) is used to control the high-speed camera (3) and the high-frequency laser (1) to perform synchronous acquisition.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 6.

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