Compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization

Abstract

The invention relates to the technical field of gas compressors, in particular to a gas compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization, and the method comprises the steps: constructing gas compressor grids at different regions; the target outlet pressure and the target outlet mass flow rate of the gas compressor close to the stall working condition are obtained according to the steady-state gas compressor performance curve; defining Riemann-Invariant boundary conditions according to the target outlet pressure and the target outlet mass flow rate when the gas compressor is close to the stall working condition and the pressure when the mass flow rate is zero; and the stall working condition and the surge working condition of the gas compressor are predicted on the basis of Riemann-Invariant boundary conditions. According to the method, the transient behavior of internal flow of the gas compressor is accurately simulated by combining a CFD numerical simulation technology with Riemann-Invariant boundary conditions, and the prediction precision and calculation stability of stall and surge phenomena are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of compressors, and in particular to a compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization. Background Art

[0002] In the design and operation of key power equipment such as aircraft engines and large industrial compressors, surge is a serious and complex problem. It not only causes a sharp decline in equipment performance, but also may cause mechanical damage and even lead to safety accidents. Traditionally, the prediction of surge relies on experimental tests and empirical formulas, but these methods are not only costly and time-consuming, but also difficult to fully capture the transient flow characteristics and boundary layer effects in the internal flow channel during the surge process.

[0003] With the rapid development of computational fluid dynamics (CFD) technology, the use of numerical simulation to predict and analyze complex flow phenomena inside fluid machinery has become a common method. However, the direct use of conventional CFD methods to accurately predict compressor surge still faces huge challenges. Surge involves highly nonlinear flow instability processes, including complex phenomena such as flow separation, vortex shedding, and pressure fluctuations. These phenomena show rapid changes in time and space, which requires extremely high calculation accuracy and stability during the simulation process. In addition, CFD is often used to calculate the compressor from large flow conditions to peak efficiency conditions to near-stall conditions, and the occurrence of unstable conditions such as stall and surge is often closely related to slight changes in the compressor boundary conditions, which further increases the difficulty of simulation. In experimental calculations, when calculating unstable conditions, the compressor calculation under this condition is extremely easy to diverge.

[0004] Therefore, using CFD technology to accurately calculate compressor surge is not only a major breakthrough in traditional prediction methods, but also an urgent need to improve the design level of power equipment and ensure operational safety. By optimizing the Riemann-Invariant boundary conditions in the CFD model, the transient behavior of the flow inside the compressor can be simulated more accurately, revealing the internal mechanism of the surge phenomenon, and providing a scientific basis for the prediction and control of surge. This method not only reduces experimental costs and shortens the R&D cycle, but also can evaluate and optimize compressor performance under a wider range of operating conditions, which is of great significance to the overall performance and reliability of compressors and aircraft engines.

[0005] At present, the study of compressor surge phenomenon mainly relies on experimental methods. Although the experimental method can directly observe the actual changes in the flow inside the compressor when surge occurs, the surge test itself has significant limitations. Surge is a highly transient and complex flow instability phenomenon, and its occurrence process is often very rapid, which makes it extremely difficult to accurately capture and record all key measurement quantities within a limited time.

[0006] During the experiment, due to the suddenness of surge, it is often difficult for researchers to synchronously obtain all necessary flow parameters, such as pressure, temperature, flow velocity, and vortex structure, especially in the early and peak stages of surge. This limited measurement not only limits our comprehensive and in-depth understanding of the surge phenomenon, but also affects the accuracy and reliability of subsequent prediction models.

[0007] Therefore, although the experimental method is still one of the important means of studying the surge phenomenon, exploring more efficient, comprehensive and cost-controlled prediction methods, such as combining CFD numerical simulation technology, has become an important trend in this field. By optimizing the simulation algorithm and boundary conditions, the ability of the CFD model to capture the transient behavior of surge is improved, providing more powerful support for the prediction and control of surge. Summary of the invention

[0008] In order to solve the deficiencies in the above-mentioned background technology, the present invention mainly solves the instability problems such as stall and surge of the compressor under complex flow conditions. The present invention provides a compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization. The method simulates the changes in flow boundary conditions by deeply studying the physical mechanism of complex flow inside the compressor, especially using Riemann-Invariant theory as the mathematical basis of numerical simulation, so as to accurately capture the continuous evolution process of the compressor working state from stability to stall and then to surge. By optimizing the representation of these boundary conditions in the numerical model, we can significantly improve the accuracy and robustness of the prediction model, and provide strong technical support for the design optimization, fault diagnosis and real-time control of the compressor. This innovative method is expected to accurately predict the performance of the compressor under unstable conditions such as stall and surge and the mechanism of the occurrence of unstable conditions, and then provide suggestions for the low-cost and efficient optimization of compressor design, thereby greatly reducing the operating risks caused by compressor stall and surge, and improving the overall performance and reliability of the equipment.

[0009] The first object of the present invention is to provide a compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization, comprising: Construct compressor grids in different areas; Adaptively refine the compressor mesh to obtain a refined mesh; According to the refined grid, the steady-state compressor performance curve is obtained by constant flow / back pressure method; According to the steady-state compressor performance curve, the target outlet pressure and the target outlet mass flow rate of the compressor close to the stall condition are obtained; The Riemann-Invariant boundary condition is defined according to the target outlet pressure and target outlet mass flow rate of the compressor near the stall condition, and the pressure at zero mass flow rate; Compressor stall and compressor surge conditions are predicted based on Riemann-Invariant boundary conditions.

[0010] Preferably, the compressor grid is divided into three different fluid calculation domains: an inlet large cavity, a blade portion grid and an outlet large cavity.

[0011] Preferably, obtaining the refined grid includes: The constructed compressor mesh is subjected to an independence test and is adaptively refined based on an error threshold to obtain a refined mesh.

[0012] Preferably, after predicting the compressor stall condition and the compressor surge condition, the method further includes: According to the predicted compressor stall condition and compressor surge condition, the predicted compressor stall condition and compressor surge condition are compared with the test data respectively. When the first error between the stall condition and the corresponding test data, or the second error between the surge condition and the corresponding test data is greater than 20%, the target outlet pressure and the target outlet mass flow rate of the compressor close to the stall condition are used as the benchmark, and the Riemann-Invariant boundary condition is redefined by decreasing the benchmark value by 5% successively until the compressor stall condition and the compressor surge condition when the first error and the second error are less than 20% are obtained, as well as the corresponding final Riemann-Invariant boundary condition; According to the final Riemann-Invariant boundary conditions, the stall and surge of other compressors are predicted.

[0013] Preferably, the first error between the stall condition and the corresponding test data refers to the error between the predicted stall group propagation speed and the stall group propagation speed during the test; The second error between the surge condition and the corresponding test data refers to the error between the predicted surge frequency and the surge frequency during the test.

[0014] Preferably, when defining the Riemann-Invariant boundary condition, it includes: The characteristic curve is defined by the target outlet pressure and target outlet mass flow rate when the compressor is close to stall condition, and the pressure at zero mass flow rate. Determine the Riemann-Invariant boundary conditions based on the characteristic curve.

[0015] The second object of the present invention is to provide a compressor stall and surge prediction system based on Riemann-Invariant boundary condition optimization, comprising: A grid construction module is used to construct compressor grids in different areas; the compressor grids are adaptively refined to obtain refined grids; The condition definition module is used to obtain the steady-state compressor performance curve by constant flow / back pressure according to the refined grid; obtain the target outlet pressure and target outlet mass flow rate of the compressor close to the stall condition according to the steady-state compressor performance curve; define the Riemann-Invariant boundary condition according to the target outlet pressure and target outlet mass flow rate of the compressor close to the stall condition, and the pressure at zero mass flow; The prediction module predicts compressor stall and compressor surge conditions based on Riemann-Invariant boundary conditions.

[0016] The third object of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization are implemented.

[0017] A fourth object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization are implemented.

[0018] The present invention has at least the following beneficial effects: The present invention provides a compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization. The method significantly improves the prediction accuracy and robustness of compressor stall and surge by optimizing the Riemann-Invariant boundary condition, and provides strong technical support for compressor design optimization, fault diagnosis and real-time control. This method overcomes the accuracy and stability challenges of traditional CFD methods in simulating complex flow instability phenomena. By establishing a direct connection between mass flow and outlet pressure, the setting of boundary conditions under different working conditions is simplified, and the flexibility and accuracy of calculation are greatly improved. In addition, the present invention adopts a hybrid grid mode and a large cavity design to further improve the accuracy and calculation efficiency of the simulation. The hybrid grid mode combines the advantages of structured and unstructured grids, so that the calculation speed is guaranteed while taking into account the grid quality. Especially in areas with complex geometric shapes, the flexibility of the unstructured grid significantly improves the ability to capture flow details. The large cavity design ensures that more realistic boundary condition settings can be achieved during the simulation process, so that the reverse backflow of the inlet flow and the pressure regulation of the outlet are more in line with the physical details of the actual surge process. This series of innovations not only reduces experimental costs and R&D cycles, but also enables the evaluation and optimization of compressor performance under a wider range of operating conditions, thereby significantly reducing the operational risks caused by stall and surge, and improving the overall performance and reliability of the equipment. Through the combination of these technologies, the present invention can effectively predict the surge characteristics of aircraft engines or other large industrial compressors and provide numerical calculation methods for their optimization and design. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization provided by the present invention.

[0020] Figure 2 This is the distribution of different calculation domains during the numerical calculation of the compressor of the present invention; among them, 1-inlet large cavity, 2-blade calculation area, 3-outlet large cavity, 31-outlet boundary.

[0021] Figure 3 is the blade unit mesh.

[0022] Figure 4 is a compressor characteristic curve.

[0023] Figure 5 To numerically calculate the pressure pulsation and stall group propagation velocity in the compressor channel.

[0024] Figure 6 is the pressure pulsation and stall group propagation speed in the compressor channel during the test.

[0025] Figure 7This is a graph showing the flow rate changing with time during surge obtained through numerical calculation.

[0026] Figure 8 This is the compressor pressure signal pulsation diagram during the test.

[0027] Fig. 9 This is the flow field at 95% leaf height. DETAILED DESCRIPTION

[0028] In order to illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following is a detailed description in conjunction with embodiments.

[0029] The purpose of the present invention is to solve the instability problems such as stall and surge of the compressor under complex flow conditions. Traditional experimental tests and empirical formula methods have the limitations of high cost, long cycle and difficulty in capturing the details of the internal flow field. The present invention accurately simulates the transient behavior of the internal flow of the compressor through CFD numerical simulation technology combined with Riemann-Invariant boundary conditions, and significantly improves the prediction accuracy and calculation stability of stall and surge phenomena. Innovations include the introduction of Riemann-Invariant boundary condition optimization, hybrid grid mode, modular grid design and large cavity design. These technologies enable the present invention to not only simplify the process of setting boundary conditions, but also more accurately capture the details of the flow field, effectively reduce experimental costs and R&D cycles, and improve the overall performance and reliability of the compressor. The numerical simulation results are highly consistent with the experimental data, which proves the effectiveness of the method provided by the present invention in predicting compressor stall and surge, and provides strong technical support for compressor design optimization, fault diagnosis and operation safety.

[0030] To achieve the above purpose, see Figure 1 As shown, the present invention provides a compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization, comprising: S1. Construct compressor grids in different areas; The compressor grid is divided into three different fluid calculation domains: an inlet large cavity, a blade part grid and an outlet large cavity.

[0031] In order to accurately simulate the complex flow inside the compressor, it is critical to construct grids with different characteristics for different regions. Figure 2 As shown, the compressor grid can be divided into three different fluid calculation domains: the inlet large cavity 1, the blade part grid 2 and the outlet large cavity 3.

[0032] The main purpose of the large inlet cavity 1 is to ensure that the compressor inlet flow can be reversed during the oscillation of the surge airflow. At the same time, in the present invention, the boundary of the large cavity sets the inlet boundary condition of standard atmospheric pressure. The method sets the boundary condition to simulate the real environment and is more in line with the physical details of the actual surge process. The size of the large inlet cavity is usually more than ten times the equivalent diameter of the rotor to fully develop the inlet flow. In order to capture the details of the inlet flow and the rotor docking, in addition, in order to simulate the total pressure loss and flow separation and other phenomena, a special grid processing method needs to be set in the inlet section to accurately capture the flow details.

[0033] The blade part is the core area of ​​the compressor grid, and it is necessary to accurately capture the flow details such as the boundary layer, vortex, flow separation, etc. around the blade. Therefore, we usually use body-fitting grids on the blade surface and encrypt the grids in the boundary layer area. In order to improve the calculation accuracy, the present invention uses a grid with an O-type or H-type topology structure. In addition, for the tip clearance area, special attention should be paid to the encryption of the grid to accurately simulate the tip leakage flow.

[0034] The large outlet cavity is mainly used to satisfy the Riemann invariant boundary adjustment calculation so that the surge effect can be achieved in the compressor channel area. Its size is determined according to the specific calculation requirements, but generally should be consistent with the actual physical size to ensure the consistency between the numerical calculation and the experiment. In order to avoid the influence of the outlet boundary on the calculation results, the setting of the outlet boundary conditions also requires special attention. The present invention proposes to set a calculation grid shaped like a "tail vertebra" at the outlet boundary 31 to facilitate the calculation of surge conditions.

[0035] The selection of the grid generation method has a great influence on the grid quality. In the present invention, a hybrid grid mode is used in the grid division, and the grid is composed of a part of structured grid and a part of unstructured grid. This method can ensure the efficiency and speed of calculation while taking into account the grid quality. For areas with relatively simple geometric shapes, such as the import and export large cavities, structured grids can be used. For areas with complex geometric shapes such as the grid part, unstructured grids are more flexible.

[0036] The present invention innovatively introduces the concept of unit cell modular design in the field of grid division, deconstructing and reconstructing the large and complex computing architecture into three independent but closely cooperating unit cell modules. This innovative strategy greatly simplifies the complexity of the computing system, while making grid management efficient and flexible. Through this layout, when faced with failures, performance bottlenecks, or the need for fine-tuning for specific scientific research needs in a specific area of ​​the grid, the method described in the invention can quickly and accurately locate the unit cell where the problem is, and perform localized optimization and modification without touching the foundation of the entire computing grid, thereby avoiding the time cost and resource consumption caused by large-scale reconstruction. Among them, the blade unit cell grid such as Figure 3 shown.

[0037] In addition, the modular design of the unit structure has greatly promoted the reusability and scalability of the grid. Different projects can share verified unit modules, which accelerates the launch and iteration of new projects. At the same time, with the deepening of scientific research needs or the advancement of technical conditions, new unit modules can be seamlessly integrated into existing systems to achieve linear or nonlinear growth in computing power, providing technical support for coping with more complex and changeable engineering problems.

[0038] The quality of meshing is a key factor affecting the accuracy of calculation results. The meshing, smoothness, aspect ratio and distortion of meshing all have a direct impact on the calculation accuracy. For the sake of mesh quality, the present invention adopts some mesh optimization techniques, such as smoothing and fairing. In some cases, local encryption or encryption can be used to better capture the flow details. Constructing compressor meshes in different areas is a complex process, which requires comprehensive consideration of factors such as flow field characteristics, calculation requirements and mesh generation methods. By dividing the grid coverage, selecting the appropriate grid generation method and strictly controlling the grid quality, high-quality computational grids can be obtained, which provide a basis for subsequent numerical simulations.

[0039] S2, adaptively refining the compressor grid to obtain a refined grid; When obtaining the refined mesh, it includes: The constructed compressor mesh is subjected to an independence test and is adaptively refined based on an error threshold to obtain a refined mesh.

[0040] Traditional grid independence tests are usually performed by manually setting grids of different densities. The method described in the present invention will be applied in combination with adaptive grid refinement technology, which can automatically adjust the grid density according to the flow field characteristics, thereby reducing computing resource consumption while ensuring calculation accuracy. For example, the grid is automatically encrypted in key areas of the turbulence model (such as shear layers, vortices, etc.), while the grid density is kept low in non-critical areas. The theory of the grid adaptation method described in the present invention is as follows: The principle of adaptive grid refinement in the present invention is based on error estimation. Error estimation is based on the gradient, residual, and a posteriori error estimation of the solution. The a posteriori error estimation formula can be expressed as: (1) in, e i It is a region Ω i The error estimate on u h and u 2h Use the grid size h and2h The solution is calculated over this area. This formula estimates the error by comparing the solutions at different mesh resolutions.

[0041] In the process of irrelevance check and mesh refinement, the present invention performs refinement based on error threshold. The refinement criterion can be expressed as: E i > , then in Ω i Refine the mesh.

[0042] in, E i It is a region Ω i The error estimate ϵ on is the preset error threshold.

[0043] Take the calculation of the x-direction momentum equation using the NS equation discretized by the finite volume method as an example. (2) in, is the time step n Time grid point i The speed on Through the grid interface ij The mass flux, t is the time step, is the grid spacing. As the grid is refined, will decrease, thus increasing the resolution of the equation. In the adaptive mesh solving process, the above equations are recomputed based on the new mesh configuration at each time step or iteration.

[0044] S3. Obtain a steady-state compressor performance curve by constant flow / back pressure according to the refined grid; obtain a target outlet pressure and a target outlet mass flow rate of the compressor close to a stall condition according to the steady-state compressor performance curve; This step of the present invention is a process of calculating the steady-state characteristics and the boundary of the characteristic curve by means of constant flow / back pressure. This process involves the selection of calculation models and turbulence models, setting of initial and boundary conditions, numerical solution, result analysis and verification, and finally forms a steady-state compressor performance curve. The purpose of this step is to calculate the target outlet pressure and mass flow rate and other data under the near-stall condition of the compressor, in preparation for the next step of stall surge calculation.

[0045] S4, defining a Riemann-Invariant boundary condition based on a target outlet pressure and a target outlet mass flow rate of the compressor near a stall condition, and a pressure at zero mass flow rate; When defining Riemann-Invariant boundary conditions, include: The characteristic curve is defined by the target outlet pressure and target outlet mass flow rate when the compressor is close to stall condition, and the pressure at zero mass flow rate. Determine the Riemann-Invariant boundary conditions based on the characteristic curve.

[0046] It should be noted that when the compressor gradually approaches its stall state, the complexity of its internal flow field increases significantly, causing the change of pressure to mass flow to become small and difficult to capture. This sensitivity requires more sophisticated and accurate boundary condition settings during calculation to ensure the accuracy and reliability of the results.

[0047] Traditionally, in the area close to stall, the outlet mass flow rate tends to be used as the main boundary condition, because at this stage, small changes in mass flow rate can often more directly reflect the working state of the compressor. However, with the deepening of the compressor design process, the continuous optimization and adjustment of the geometric shape makes it difficult to accurately determine the calculation position in advance, which further increases the difficulty of selecting appropriate boundary conditions for the outlet.

[0048] In order to solve the above difficulties, the Riemann-Invariant boundary condition described in the present invention can effectively overcome this problem. This method not only simplifies the process of setting the boundary conditions, but also improves the flexibility and accuracy of the calculation. The Riemann-Invariant boundary condition establishes a direct connection between the mass flow rate and the outlet pressure, so that when processing the compressor characteristic curve, there is no need to choose between focusing on the pressure setting at the surge edge and focusing on the mass flow setting at the stall limit.

[0049] Specifically, the setting of the Riemann-Invariant boundary condition depends on three key parameters: the target outlet mass flow rate q m , Target outlet pressure p m and the pressure at zero mass flow p 0. These three parameters together define a parabolic characteristic curve, see Figure 4 As shown in the figure, the curve intuitively shows the dynamic relationship between mass flow and outlet pressure. In the actual calculation and simulation process, it is only necessary to set these three parameters according to the design requirements to automatically generate the corresponding Riemann-Invariant boundary conditions and apply them to the calculation of compressor stall and surge.

[0050] By introducing the Riemann-Invariant boundary condition, not only the calculation process is simplified, but also the accuracy and reliability of the calculation results are improved. The specific theory is as follows: The mass conservation and momentum conservation equations for one-dimensional unsteady flows form the basis of the theory. In the equations, variables such as density, velocity, pressure, time, and spatial coordinates are interrelated and together describe the changes in the flow state. However, when dealing with extreme flow conditions close to stall or surge, the direct solution of these equations becomes particularly complicated. Take isentropic flow as an example, ignoring the energy equation: The mass conservation equation: (3) Momentum conservation equation (isentropic flow is simplified to Euler equation): (4) in, ρ is the density, u is the flow rate, t It's time. x are spatial coordinates, p It's pressure.

[0051] When solving such equations using Riemann-Invariant boundary conditions, Riemann invariants are an important concept. For isentropic flows, two Riemann invariants R can be defined: + and R − , which correspond to the constants on the characteristic lines. For one-dimensional unsteady flow, these two invariants can be expressed as: (5) (6) in, a is the speed of sound, γ is the ratio of specific heats, for an ideal gas, γ = c p / c v ,in c p and c v are the specific heat capacities at constant pressure and constant volume respectively.

[0052] In the numerical calculation process, the application of Riemann-Invariant boundary conditions involves three key steps: parameter setting, characteristic curve generation, and boundary condition application. First, set the target outlet mass flow rate, target outlet pressure, and pressure at zero mass flow rate according to the design requirements. Then, generate a characteristic curve describing the relationship between these parameters through numerical methods. Finally, during the calculation process, find the characteristic curve according to the current flow state to determine the corresponding mass flow boundary conditions.

[0053] S5. Predict compressor stall and compressor surge conditions based on Riemann-Invariant boundary conditions.

[0054] After predicting compressor stall and compressor surge conditions, it also includes: According to the predicted compressor stall condition and compressor surge condition, the predicted compressor stall condition and compressor surge condition are compared with the test data respectively. When the first error between the stall condition and the corresponding test data, or the second error between the surge condition and the corresponding test data is greater than 20%, the target outlet pressure and the target outlet mass flow rate of the compressor close to the stall condition are used as the benchmark, and the Riemann-Invariant boundary condition is redefined by decreasing the benchmark value by 5% successively until the compressor stall condition and the compressor surge condition when the first error and the second error are less than 20% are obtained, as well as the corresponding final Riemann-Invariant boundary condition; According to the final Riemann-Invariant boundary conditions, the stall and surge of other compressors are predicted.

[0055] The first error between the stall condition and the corresponding test data refers to the error between the predicted stall group propagation speed and the stall group propagation speed during the test; The second error between the surge condition and the corresponding test data refers to the error between the predicted surge frequency and the surge frequency during the test.

[0056] Among them, the stall condition is solved based on the Riemann-Invariant boundary condition and compared with the experimental data: In order to verify the prediction results of numerical calculation, experimental data of a compressor under different operating conditions (including stall and surge conditions) were collected. These data come from reliable experimental equipment and strict testing procedures, ensuring the accuracy and reliability of the data. After data collection, necessary preprocessing work was carried out, including data cleaning, format conversion, and time-space alignment, to ensure that the experimental data and numerical prediction results are comparable.

[0057] Taking a compressor as an example, numerical calculation is carried out to solve the stall condition based on the Riemann-Invariant boundary condition. The stall group propagation velocity obtained by numerical solution is shown as Figure 5 The experimental data are shown as Figure 6 A detailed comparison is shown in the figure. First, the error of the stall group propagation speed between the two is calculated. The numerical analysis shows that the stall group propagation speed is about 62.7% of the speed. During the test, the stall group propagation speed is about 67.08% of the speed. The error between the two is 6.52%, which is basically consistent with the experimental value. Then, the comparison between the numerical prediction results and the experimental data is shown by visualization, which intuitively shows the similarity between the two. It can be seen from the experimental data and simulation data that the numerical model has a certain accuracy and reliability in predicting stall conditions, and plays a great role in improving the prediction accuracy.

[0058] Among them, the surge condition is solved based on the Riemann-Invariant boundary condition and compared with the experimental data: Comparing the surge frequency obtained based on the Riemann-Invariant boundary condition with the experimental data is a key step in verifying the effectiveness of the theoretical model. If the two are in good agreement, it means that the established mathematical model can accurately describe the surge phenomenon and effectively predict the frequency characteristics of the surge. Figure 7 The flow rate versus time relationship diagram during surge obtained by numerical calculation is shown in the figure. Figure 8 The figure is a pulsation diagram of the compressor pressure signal during the test. By comparison and analysis, the surge frequency predicted by the method of the present invention is 4.667, and the compressor frequency during the test is 4.88, with an error of 4.36%. Therefore, this method has a high accuracy in predicting the surge process.

[0059] It should be noted that when the target outlet flow rate and target outlet pressure under the near-stall condition are directly applied to the Riemann-Invariant boundary condition, the calculation will fail to enter the stall and surge condition or the stall group propagation speed and surge frequency after entering the stall and surge condition will not match the test data. At this time, the parameters of the Riemann-Invariant boundary condition need to be adjusted. The specific adjustment measures are: taking the target outlet flow rate and target outlet pressure under the near-stall condition as the benchmark, gradually reducing the benchmark value by 5% and recalculating. This solution is the empirical data of the present invention during application.

[0060] In the present invention, reasonable initial conditions are set based on the Riemann-Invariant boundary conditions to accurately simulate the flow behavior of the fluid inside the rotating machinery when surge occurs. Fig. 9 For example, the flow field at 95% blade height of the blade part was selected for analysis. The numerical simulation can capture the tiny structural changes in the flow field that are difficult to directly observe through experiments, such as the formation and propagation of shock waves, the generation and breakup of vortices, and the unsteady interactions in the flow field. The compressor surge mechanism can be effectively sensed, and the compressor can be optimized and improved.

[0061] The present invention provides a compressor stall and surge prediction system based on Riemann-Invariant boundary condition optimization, comprising: A grid construction module is used to construct compressor grids in different areas; the compressor grids are adaptively refined to obtain refined grids; The condition definition module is used to obtain the steady-state compressor performance curve by constant flow / back pressure according to the refined grid; obtain the target outlet pressure and target outlet mass flow rate of the compressor close to the stall condition according to the steady-state compressor performance curve; define the Riemann-Invariant boundary condition according to the target outlet pressure and target outlet mass flow rate of the compressor close to the stall condition, and the pressure at zero mass flow; The prediction module predicts compressor stall and compressor surge conditions based on Riemann-Invariant boundary conditions.

[0062] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization are implemented.

[0063] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization are implemented.

[0064] 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 principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization, characterized in that: include: Construct compressor grids in different areas; Adaptively refine the compressor mesh to obtain a refined mesh; According to the refined grid, the steady-state compressor performance curve is obtained by constant flow / back pressure method; According to the steady-state compressor performance curve, the target outlet pressure and the target outlet mass flow rate of the compressor close to the stall condition are obtained; The Riemann-Invariant boundary condition is defined according to the target outlet pressure and target outlet mass flow rate of the compressor near the stall condition, and the pressure at zero mass flow rate; Compressor stall and compressor surge conditions are predicted based on Riemann-Invariant boundary conditions.

2. The compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization according to claim 1, characterized in that: The compressor grid is divided into three different fluid calculation domains: an inlet large cavity, a blade part grid and an outlet large cavity.

3. The compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization according to claim 1, characterized in that: When obtaining the refined mesh, it includes: The constructed compressor mesh is subjected to an independence test and is adaptively refined based on an error threshold to obtain a refined mesh.

4. The compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization according to claim 1, characterized in that: After predicting compressor stall and compressor surge conditions, it also includes: According to the predicted compressor stall condition and compressor surge condition, the predicted compressor stall condition and compressor surge condition are compared with the test data respectively. When the first error between the stall condition and the corresponding test data, or the second error between the surge condition and the corresponding test data is greater than 20%, the target outlet pressure and the target outlet mass flow rate of the compressor close to the stall condition are used as the benchmark, and the Riemann-Invariant boundary condition is redefined by decreasing the benchmark value by 5% successively until the compressor stall condition and the compressor surge condition when the first error and the second error are less than 20% are obtained, as well as the corresponding final Riemann-Invariant boundary condition; According to the final Riemann-Invariant boundary conditions, the stall and surge of other compressors are predicted.

5. The compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization according to claim 4, characterized in that: The first error between the stall condition and the corresponding test data refers to the error between the predicted stall group propagation speed and the stall group propagation speed during the test; The second error between the surge condition and the corresponding test data refers to the error between the predicted surge frequency and the surge frequency during the test.

6. The compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization according to claim 1, characterized in that: When defining Riemann-Invariant boundary conditions, include: The characteristic curve is defined by the target outlet pressure and target outlet mass flow rate when the compressor is close to stall condition, and the pressure at zero mass flow rate. Determine the Riemann-Invariant boundary conditions based on the characteristic curve.

7. A compressor stall and surge prediction system based on Riemann-Invariant boundary condition optimization, characterized in that: include: Grid construction module, used to construct compressor grids in different areas; Adaptively refine the compressor mesh to obtain a refined mesh; The condition definition module is used to obtain the steady-state compressor performance curve by constant flow / back pressure according to the refined grid; and obtain the target outlet pressure and target outlet mass flow rate of the compressor close to the stall condition according to the steady-state compressor performance curve; The Riemann-Invariant boundary condition is defined according to the target outlet pressure and target outlet mass flow rate of the compressor near the stall condition, and the pressure at zero mass flow rate; The prediction module predicts compressor stall and compressor surge conditions based on Riemann-Invariant boundary conditions.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization according to any one of claims 1 to 6 are implemented.

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