Surge margin prediction method and device of gas compressor, computer equipment and medium

Abstract

The invention relates to a surge margin prediction method and device of a gas compressor, equipment and a storage medium. The method comprises the steps that key parameter point sets obtained through load step iteration of the gas compressor in all the operation stages are obtained, all the key parameter point sets are mapped to performance diagrams of temperature identification to obtain corresponding mapping performance diagrams, discrete points in the same temperature layer in all the mapping performance diagrams are fitted, and the corresponding mapping performance diagrams are obtained; and the thermal state performance curves of the gas compressor in all the operation stages are determined, the surge critical points of all the thermal state performance curves are obtained, the predicted surge margin of the gas compressor in all the operation stages is determined according to the surge critical points of all the thermal state performance curves and all the key parameter point sets, and load step iteration is achieved based on multi-physics field coupling of the gas compressor. By adopting the method, the surge margin of the gas compressor in different operation stages can be predicted based on the key parameter point set obtained by load step iteration realized by considering multi-physics field coupling in each operation stage of the gas compressor, so that the accuracy of a prediction result is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of compressors, and in particular to a compressor surge margin prediction method and device, computer equipment and a medium. BACKGROUND

[0002] Compressors are widely used in high-performance mechanical equipment such as aircraft engines, gas turbines, and industrial compressors. They are mainly used to increase the pressure of gas from low to high to meet the demand for gas pressure in subsequent work processes. Under high load conditions, the compressor is prone to surge, i.e., the gas flow undergoes severe periodic fluctuations, resulting in performance degradation or even equipment damage. Therefore, predicting the surge margin of the compressor is particularly important for reducing the failure rate of the compressor.

[0003] In related technologies, the surge margin of the compressor is mainly predicted based on the thermal field, structural field, or aerodynamic field generated during the operation of the compressor.

[0004] However, the predicted surge margin of the compressor in related technologies is not accurate. SUMMARY

[0005] Therefore, it is necessary to provide a compressor surge margin prediction method, device, computer equipment, and medium to solve the above technical problems.

[0006] In a first aspect, an embodiment of the present application provides a compressor surge margin prediction method, comprising:

[0007] Obtaining a set of key parameter points of the compressor at each operating stage obtained through load step iteration; wherein the load step iteration is realized based on multi-physical field coupling of the compressor;

[0008] Mapping each set of key parameter points to a performance map with temperature identification to obtain a corresponding mapping performance map;

[0009] Fitting the discrete points in each mapping performance map within the same temperature layer to determine the thermal performance curve of the compressor at each operating stage;

[0010] Obtaining the surge critical point of each thermal performance curve, and determining the predicted surge margin of the compressor at each operating stage according to the surge critical point of each thermal performance curve and the set of key parameter points.

[0011] In one embodiment, the multi-physical field includes a thermal field, a structural field, and an aerodynamic field; obtaining a set of key parameter points of the compressor at each operating stage obtained through load step iteration comprises:

[0012] For any operating stage, performing thermal field simulation according to the aerodynamic load obtained in the previous iteration step during the load step iteration process of the compressor at the operating stage to determine the temperature field of the current iteration step;

[0013] performing structural field simulation according to the temperature field of the current iteration step to determine a first blade tip clearance of the compressor at the current iteration step;

[0014] adjusting a fluid mechanics grid of the compressor according to the second blade tip clearance of the compressor at the previous iteration step, reestablishing a geometric boundary of the aerodynamic field simulation of the compressor, and obtaining a new fluid mechanics grid;

[0015] performing aerodynamic field simulation based on the new fluid mechanics grid and the operating condition parameters of the compressor at the current iteration step to obtain aerodynamic load of the current iteration step;

[0016] in a case where the current clearance variation quantity between the first blade tip clearance and the second blade tip clearance of the compressor at the previous iteration step reaches a preset convergence condition or a current iteration number reaches a preset iteration number threshold, obtaining the key parameter point set of the compressor at the operating stage load step iteration.

[0017] In one of the embodiments, the performing structural field simulation according to the temperature field of the current iteration step to determine a first blade tip clearance of the compressor at the current iteration step comprises:

[0018] solving structural mechanics equations of the compressor according to the temperature field of the current iteration step to obtain a displacement field of the compressor at the current iteration step;

[0019] extracting a blade tip radial displacement and a casing deformation quantity of the compressor from the displacement field of the current iteration step;

[0020] updating the second blade tip clearance according to the blade tip radial displacement and the casing deformation quantity to obtain the first blade tip clearance.

[0021] In one of the embodiments, the method further comprises:

[0022] in a case where the current clearance variation quantity does not reach the convergence condition or the current iteration number does not reach the iteration number threshold, taking the aerodynamic load of the current iteration step as an input of thermal field simulation in a next iteration step, repeating the above steps until the current clearance variation quantity reaches the convergence condition or the current iteration number reaches the iteration number threshold, and obtaining the key parameter point set of the compressor at the operating stage load step iteration.

[0023] In one of the embodiments, the performing aerodynamic field simulation based on the new fluid mechanics grid and the operating condition parameters of the compressor at the current iteration step to obtain aerodynamic load of the current iteration step comprises:

[0024] solving fluid motion control equations of the compressor according to the new fluid mechanics grid and the operating condition parameters of the compressor at the current iteration step to obtain the aerodynamic load of the compressor at the current iteration step.

[0025] In one of the embodiments, the surge critical point comprises a surge pressure ratio and a surge flow coefficient, the critical parameter point set comprises an iteration pressure ratio and an iteration flow coefficient; according to the surge critical point of each thermal performance curve and each critical parameter point set, a predicted surge margin of the compressor under each operating stage is determined, comprising:

[0026] For any operating stage, a pressure difference between the iteration pressure ratio and the surge pressure ratio corresponding to the compressor under the operating stage is obtained, to obtain a predicted pressure ratio margin of the compressor under the operating stage;

[0027] The iteration flow coefficient corresponding to the compressor under the operating stage is subtracted from the surge flow coefficient, and then divided by the surge flow coefficient, to obtain a predicted flow margin of the compressor under the operating stage.

[0028] In one of the embodiments, the method further comprises:

[0029] According to the critical parameter point set and the predicted surge margin of the compressor under each operating stage, a fitting process is performed, to obtain a surge margin surface of the compressor under each operating stage;

[0030] The surge margin surfaces under each operating stage are visually displayed.

[0031] In a second aspect, the embodiments of the present application further provide a surge margin prediction device of a compressor, the device comprising:

[0032] An acquisition module is configured to acquire critical parameter point sets obtained by load step iteration of the compressor under each operating stage; wherein the load step iteration is implemented based on multi-physical field coupling of the compressor;

[0033] A mapping module is configured to map each critical parameter point set to a temperature-identified performance map to obtain a corresponding mapped performance map;

[0034] A fitting module is configured to fit discrete points in the same temperature layer in each mapped performance map, to determine thermal performance curves of the compressor under each operating stage;

[0035] A determination module is configured to acquire surge critical points of each thermal performance curve, and determine a predicted surge margin of the compressor under each operating stage according to the surge critical points of each thermal performance curve and each critical parameter point set.

[0036] In a third aspect, the embodiments of the present application further provide a computer device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method of any one of the embodiments of the first aspect when executing the computer program.

[0037] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps of the method in any of the embodiments of the first aspect.

[0038] In a fifth aspect, the embodiments of the present application further provide a computer program product, and the computer program product includes a computer program. The computer program is executed by a processor to implement the steps of the method in any of the embodiments of the first aspect.

[0039] The compressor surge margin prediction method, device, equipment and storage medium provided by the embodiments of the present application include: obtaining a set of key parameter points of the compressor in each operating stage obtained through load step iteration, mapping each set of key parameter points to a performance map of a temperature identifier to obtain a corresponding mapping performance map, fitting discrete points in the same temperature layer in each mapping performance map to determine a thermal performance curve of the compressor in each operating stage, obtaining a surge critical point of each thermal performance curve, and determining a predicted surge margin of the compressor in each operating stage according to the surge critical point of each thermal performance curve and each set of key parameter points. The load step iteration is implemented based on the multi-physical field coupling of the compressor. The above method can predict the surge margin of the compressor in different operating stages based on the set of key parameter points of the compressor in each operating stage obtained through the load step iteration considering the multi-physical field coupling, thereby improving the accuracy of the prediction result. At the same time, the above method does not require human intervention, which not only reduces the human intervention error and greatly improves the accuracy of the prediction result, but also speeds up the prediction speed. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0041] Figure 1 A flowchart of a compressor surge margin prediction method in an embodiment;

[0042] Figure 2 A flowchart of a compressor surge margin prediction method in another embodiment;

[0043] Figure 3 A flowchart of a compressor surge margin prediction method in another embodiment;

[0044] Figure 4This is a flowchart illustrating the compressor surge margin prediction method in another embodiment;

[0045] Figure 5 This is a flowchart illustrating the compressor surge margin prediction method in another embodiment;

[0046] Figure 6 This is a structural block diagram of a compressor surge margin prediction device in one embodiment;

[0047] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] The compressor surge margin prediction method provided in this application can be applied to computer equipment. To improve the compressor surge margin, in practical applications, the compressor surge margin can be optimized from a hardware structure perspective, and the surge margin can be predicted after optimization. Subsequent control or optimization can then be based on the predicted surge margin. Specifically, eight eddy current displacement sensors (fixed by high-temperature insulating bases, heat resistant up to 800K, with a center frequency of 1MHz, a measurement range of 0-500μm, a resolution ≤5μm, and a bandwidth ≥1kHz) can be installed along the circumferential angle on the inner wall of the compressor casing. This ensures that when each blade tip passes through each eddy current displacement sensor, at least two eddy current displacement sensors can simultaneously sample the applied voltage. (The DC voltage value reflecting the change in the coil induced impedance in the eddy current displacement sensor), and then based on the relationship between the applied voltage and the blade tip clearance. ( Indicates the tip gap, and (All values ​​represent calibration constants) to calculate the actual blade tip clearance during compressor operation. Simultaneously, each pair of eddy current displacement sensors can be equipped with an infrared optical temperature measurement module (temperature range 300-1000K, resolution ≤0.1K, sampling rate ≥1kHz) to measure the actual temperature field of the compressor; when the infrared optical temperature measurement module is working, its non-contact infrared temperature measurement lens can be aimed at the blade tip and casing surface to measure the radiation temperature. The true surface temperature of the blade tip was obtained by combining emissivity correction. This refers to the actual temperature field during compressor operation. The infrared optical temperature measurement module and the eddy current displacement sensor can collect data synchronously.

[0050] Furthermore, the Kalman filter algorithm can be used to estimate the optimal tip clearance and optimal temperature field of the compressor based on the actual tip clearance and actual temperature field. Based on the optimal tip clearance and optimal temperature field, the actual tip clearance and actual temperature field of the compressor can be detected. If the actual tip clearance of the compressor deviates significantly from the optimal tip clearance, and / or the actual temperature field deviates significantly from the optimal temperature field, then an alarm can be triggered to allow the user to perform maintenance after determining that there is a fault in the compressor.

[0051] In practical applications, active clearance compensation design can be used to optimize compressor surge margin. The active clearance compensation design is described below: To correct tip clearance deviations caused by thermal expansion or sudden load changes during compressor operation, two types of active compensation subsystems can be integrated into the compressor casing: the adjustable casing section and the thermoelectric cooling channel. The adjustable casing section design can be understood as a segmented structure design, with equal intervals along the axial direction of the casing's inner wall. Each ring-shaped "compensation segment" has a width of [missing information]. To cover the entire length of the blade cascade, each compensation section is connected via a high-strength elastic interface, ensuring the overall rigidity of the casing while allowing for minor local axial displacement. Each section within the casing is equipped with 2-3 symmetrically arranged actuators to handle asymmetrical thermal loads and provide redundancy. The actuators include electric micro-apertures and piezoelectric ceramic stacks. Electric micro-apertures with a stroke of ±200μm, peak thrust ≥500N, resolution ≤1μm, and response time ≤20ms can be selected. Piezoelectric ceramic stacks, adjustable for faster response (<5ms) and higher precision (≤0.1μm) requirements, can be selected for local fine-tuning. A pneumatic-assisted locking mechanism can be designed at the output end of the electric micro-aperture, utilizing the internal pressure difference within the casing. (Typically 50-100kPa) Drives the locking hook to engage, automatically locking the current position in the event of power failure or loss of control. It works in conjunction with a travel sensor (magnetic scale or optical scale) to monitor displacement in real time. Closed-loop PID control ensures precise matching with the target compensation amount. Furthermore, the casing is made of high-temperature alloy steel or heat-resistant aluminum alloy, guaranteeing sufficient yield strength even at 800K. The thickness of the compensation section is the same as the original casing, ensuring that the heat conduction path and stiffness characteristics during the compensation process remain consistent with the original design.

[0052] Among them, the design structure of the thermoelectric cooling channel can realize the microchannel layout (parallel microchannels with a cross section of about 1 mm × 2 mm are opened on the outer side of the inner wall of each compensation section of the casing. The channels are arranged in the circumferential direction and connected in parallel with the main cooling circuit. The microchannel inlet is equipped with a micro digital valve, and the outlet is connected to the return water pipeline to realize segmented precise flow control), cooling medium and heat exchange (using deaerated deionized water or high temperature resistant cooling oil, the single channel flow rate is adjustable from 0-0.5 L / min, the micro-textured structure of the inner wall enhances turbulence, the heat transfer coefficient is ≥10 kW / m²·K, and it can continuously remove the heat load of ≥50W area under ΔT = 5K conditions) and minimize the interface thermal resistance (a high thermal conductivity interface material (thermal conductivity ≥150W / m·K) is laid between the channel wall and the casing metal, the overall thermal resistance is ≤0.05 K / W, ensuring a small temperature difference between the hot and cold sides and fast thermal response).

[0053] In one exemplary embodiment, such as Figure 1 As shown, a method for predicting the surge margin of a compressor is provided. Taking the application of this method to computer equipment as an example, the method includes the following steps:

[0054] S100. Obtain the set of key parameter points obtained by load step iteration under each operating stage of the compressor. The load step iteration is based on the multi-physics coupling of the compressor.

[0055] It should be noted that the compressor's entire lifecycle can be divided into three operating stages based on key parameters such as ambient temperature, engine speed, and fuel flow rate: the startup stage, the steady-state stage, and the sudden change stage. Since the dynamic physical processes of the compressor differ in each operating stage, the embodiments in this application predict the compressor's surge margin separately for each stage.

[0056] During the startup phase, the ambient temperature rises from 300K to 600K, and the speed can be increased from 0 to 50% of the rated value. During the steady-state phase, the ambient temperature rises from 600K to 800K, and the speed is maintained at a high load (90%-100% of the rated value). During the sudden change phase, the ambient temperature change is unclear and depends on the fuel change. The speed change depends on the steady-state basis, and a rapid response can be simulated by a ±10% step change in fuel flow.

[0057] In practical applications, when the compressor's outlet pressure suddenly increases or the inlet flow rate suddenly decreases, the airflow in the compressor may separate and vortex within the blade passage, leading to a momentary airflow reversal. This can cause a sudden increase in compressor vibration and noise, resulting in surge, and even damage to the compressor blade tips or casing. Therefore, it is necessary to avoid compressor surge during operation. The compressor surge margin is one of the indicators for measuring the "safety level of the compressor against surge," making its prediction crucial for preventing surge. During compressor operation, airflow is the main factor affecting the size of the compressor surge margin. Blade tip clearance influences the airflow magnitude. In practical applications, the thermal, structural, and aerodynamic fields generated by the compressor all affect the size of the blade tip clearance. Therefore, in this embodiment of the application, to improve prediction accuracy, the multiphysics field generated during compressor operation is considered in the load step iteration. Specifically, within each load step, the multiphysics field is coupled and iterated, allowing temperature, displacement, and aerodynamic loads to mutually correct each other, approximating the actual physical state. In this embodiment of the application, the aforementioned multiphysics field may include a temperature field, a structural field, and an aerodynamic field.

[0058] Specifically, the computer equipment can obtain the key parameter set obtained from the load step iteration of the compressor at each operating stage from the cloud, hard drive, disk, local storage, etc., or it can calculate the key parameter set obtained from the load step iteration of the compressor at each operating stage in real time. Optionally, the aforementioned key parameter set may include the iterative pressure ratio, iterative flow coefficient, and temperature field of the compressor at each step during the load step iteration process.

[0059] S200: Map each set of key parameter points to the performance graph with temperature label to obtain the corresponding mapped performance graph.

[0060] In one embodiment, the computer device can use a mapping tool to map the key parameter point set obtained by load step iteration of the compressor at each operating stage to the performance map with the same temperature label or the performance map with different temperature labels to obtain the corresponding mapped performance map.

[0061] In another embodiment, the computer device can also input the key parameter point set obtained by load step iteration of the compressor in each operating stage into a pre-trained algorithm model. The algorithm model maps each key parameter point set to a performance map with temperature markings and then outputs the corresponding mapped performance map.

[0062] It should be noted that the horizontal axis of the above performance mapping graph represents the flow coefficient, the vertical axis represents the pressure ratio, and the third-dimensional layer axis represents the temperature.

[0063] S300: Fit discrete points within the same temperature layer in each mapping performance graph to determine the thermal performance curves of the compressor at each operating stage.

[0064] Different temperatures correspond to different thermal performance curves. In this embodiment, an example is given where each operating stage corresponds to a mapped performance diagram. Specifically, for any mapped performance diagram, the computer device can use interpolation to perform interpolation processing based on discrete points within the same temperature layer in the mapped performance diagram to obtain the thermal performance curve of the compressor at the corresponding operating stage. Optionally, the above interpolation method can be linear interpolation, spline interpolation, etc.

[0065] In this embodiment, the computer device can use a fitting method to fit discrete points within the same temperature layer in the mapped performance graph to obtain the thermal performance curve of the compressor at the corresponding operating stage. Optionally, the fitting method may include polynomial fitting, linear fitting, exponential fitting, logarithmic fitting, or least squares fitting, etc. The thermal performance curve can represent the relationship between the compressor's pressure ratio and flow rate at the current temperature.

[0066] S400: Obtain the surge critical point of each hot performance curve, and determine the predicted surge margin of the compressor at each operating stage based on the surge critical point of each hot performance curve and the set of key parameter points.

[0067] The surge critical point of the thermal performance surface corresponding to any operating stage can represent the critical state point at which the compressor surges during that operating stage.

[0068] Specifically, the computer equipment can input each thermal performance curve into the critical point analysis model, and then the critical point analysis model analyzes and processes each thermal performance curve and outputs the surge critical point of each thermal performance curve in sequence.

[0069] Furthermore, for any given operating phase, the computer equipment can simulate the change in flow coefficient within a simplified one-dimensional compressor model based on the corresponding thermal performance curve for that phase, and monitor the change in pressure ratio with respect to the flow coefficient to identify the pressure drop point. In the iterative pressure ratio For iterative flow coefficients The derivative is 0 (i.e.) In the case of ( ), this point is determined as an extreme point, or, in the case of the iterative flow coefficient change exceeding a set threshold after iterative pressure ratio abrupt change. point ( This point can be identified as the critical point of pressure ratio change. At this point, the value corresponding to this point is identified as the surge critical point of the thermal performance curve corresponding to this operating stage.

[0070] Meanwhile, for any operating phase, the computer equipment can pre-train a prediction model, and then input the surge critical point of the thermal performance curve corresponding to that operating phase and the corresponding key parameter point set into the prediction model. The prediction model outputs the predicted surge margin of the compressor under that operating phase.

[0071] Optionally, the above prediction model can be implemented by at least one of the following: convolutional neural network model, fully connected neural network model, residual neural network model, recurrent recurrent neural network model, long short-term memory neural network model, etc.

[0072] The technical solution in this application embodiment obtains the key parameter point set obtained by load step iteration of the compressor at each operating stage, maps each key parameter point set to the performance map with temperature label to obtain the corresponding mapped performance map, fits discrete points within the same temperature layer in each mapped performance map to determine the thermal performance curve of the compressor at each operating stage, obtains the surge critical point of each thermal performance curve, and determines the predicted surge margin of the compressor at each operating stage based on the surge critical point of each thermal performance curve and each key parameter point set. The load step iteration is based on the multi-physics coupling of the compressor. This method can predict the surge margin of the compressor at different operating stages based on the key parameter point set obtained by load step iteration considering multi-physics coupling at each operating stage, which can improve the accuracy of the prediction results. Furthermore, this method does not require manual intervention, which not only reduces human error and greatly improves the accuracy of the prediction results, but also speeds up the prediction process.

[0073] The process of obtaining the key parameter point set obtained by load step iteration under each operating stage of the compressor is described below. In one embodiment, the above-mentioned multiphysics field includes a thermal field, a structural field, and an aerodynamic field; such as Figure 2 As shown, the steps in S100 above can be implemented in the following ways:

[0074] S110. For any operating stage, perform thermal field simulation based on the aerodynamic load obtained in the previous iteration step during the compressor's load step iteration process in the operating stage, and determine the temperature field of the current iteration step.

[0075] The aerodynamic load obtained in the previous iteration step can be understood as the aerodynamic load obtained from the aerodynamic field simulation in the previous load step. Specifically, for any operating phase, the computer equipment can display the aerodynamic load obtained in the previous iteration step during the load step iteration process of the compressor in the operating phase. The input is fed into the unsteady heat conduction equation, and the unsteady heat conduction equation is solved to realize the thermal field simulation, thus obtaining the temperature field of the current iteration step. (That is, the temperature distribution of the compressor rotor and casing). Indicates the number of iteration steps or load steps. Indicates the circumferential angle of the blade tip in the compressor. .

[0076] It should be noted that the above unsteady heat conduction equation can describe the unsteady heat transfer process of components such as the blade tip / casing in the compressor, and can be expressed by the following formula (1):

[0077] (1)

[0078] in, This indicates the mass density of the air compressor. This indicates the specific heat capacity at constant pressure of the compressor. This indicates the heat exchange of the compressor (i.e., the convective heat transfer on the blade tip surface in the compressor). This refers to the internal heat source of the compressor (i.e., the additional heat source generated by internal friction or fan operation). Represents the temperature gradient. This represents the divergence of heat conduction flux. Indicates time.

[0079] S120. Perform structural field simulation based on the temperature field of the current iteration step to determine the first blade tip clearance of the compressor in the current iteration step.

[0080] It should be noted that the compressor tip clearance can also be referred to as the compressor blade clearance. In practical applications, the temperature field of the current iteration step can be mapped to the structural field simulation to calculate the structural deformation caused by the thermal expansion of the compressor.

[0081] In practical applications, computer equipment can utilize a pre-defined structural field simulation strategy to perform structural field simulation based on the temperature field of the current iteration step, and calculate the first blade tip clearance of the compressor in the current iteration step.

[0082] In addition, the computer device can send the temperature field of the current iteration step to a third-party device, instructing the third-party device to perform structural field simulation based on the temperature field of the current iteration step to obtain the first tip clearance of the compressor in the current iteration step, and then send the first tip clearance of the compressor in the current iteration step to the computer device.

[0083] S130. Based on the second blade tip clearance of the compressor in the previous iteration step, adjust the hydrodynamic mesh of the compressor, reconstruct the geometric boundary of the aerodynamic field simulation of the compressor, and obtain a new hydrodynamic mesh.

[0084] Specifically, the compressor's hydrodynamic mesh (i.e., CFD mesh) must closely match the actual clearance after structural deformation; otherwise, the compressor's aerodynamic field simulation will be distorted. Correspondingly, if the change in clearance between the first blade tip clearance and the compressor's second blade tip clearance in the previous iteration step is greater than a preset threshold, or if the current iteration number is less than the iteration number threshold, the process can continue to adjust the compressor's hydrodynamic mesh based on the second blade tip clearance, reconstructing the geometric boundary of the compressor's aerodynamic field simulation, i.e., the geometric boundary of the airflow channel, to obtain a new hydrodynamic mesh.

[0085] The new fluid dynamics grid can reflect the actual flow channel geometry of components such as blade tips and casings after thermal deformation and displacement changes during the current operation phase, and includes boundary information of the airflow channel (such as blade profile, clearance size, wall roughness, etc.).

[0086] S140. Based on the new fluid dynamics grid and the compressor's operating parameters in the current iteration step, perform aerodynamic field simulation to obtain the aerodynamic load in the current iteration step.

[0087] In practical applications, computer equipment can perform aerodynamic field simulation based on a pre-defined aerodynamic field simulation strategy, using a new hydrodynamic grid and the compressor's operating parameters in the current iteration step, to obtain the aerodynamic load for the current iteration step. Optionally, the compressor's operating parameters in the current iteration step may include parameters such as the compressor's displacement field, temperature field, blade tip radial displacement, casing deformation, and rotational speed in the current iteration step.

[0088] In one embodiment, the step of obtaining the aerodynamic load of the current iteration step by performing aerodynamic field simulation based on the new fluid dynamics grid and the operating parameters of the compressor in the current iteration step in S140 may include: solving the fluid motion control equation of the compressor based on the new fluid dynamics grid and the operating parameters of the compressor in the current iteration step to obtain the aerodynamic load of the compressor in the current iteration step.

[0089] It should be noted that the fluid motion control equations of the compressor mentioned above can be called the incompressible Navier-Stokes equations, which include the continuity equation (i.e. the mass conservation equation) and the momentum equation.

[0090] Specifically, the computer equipment can use the new hydrodynamic grid and the compressor's operating parameters in the current iteration step to solve the continuity equation (i.e., the mass conservation equation) and the momentum equation, thus obtaining the compressor's performance in the current iteration step. Iteration speed, iteration pressure ratio Iterative flow coefficient And aerodynamic loads (or pressure distribution on the blade tip surface) .

[0091] In this embodiment of the application, the above momentum equation can be expressed as:

[0092] (2)

[0093] in, This indicates the airflow density of the compressor. Indicates airflow velocity. The gradient tensor representing the airflow velocity. Indicates the pressure of the airflow. This indicates the dynamic viscosity of the airflow.

[0094] Furthermore, the above mass conservation equation can be expressed as:

[0095] (3)

[0096] in, This represents the divergence of the airflow velocity vector.

[0097] S150. If the change in the current gap between the first blade tip gap and the second blade tip gap of the compressor in the previous iteration step reaches the preset convergence condition or the current iteration number reaches the preset iteration number threshold, the key parameter point set obtained by the compressor under load step iteration during the operation phase is acquired.

[0098] The aforementioned preset convergence conditions may include the current gap change being less than or equal to a preset threshold, and the current iteration count reaching the preset iteration count threshold can be understood as the current iteration count being greater than or equal to the iteration count threshold; the aforementioned preset threshold and iteration count threshold can both be user-defined or determined based on historical experience values.

[0099] In one embodiment, the multiphysics field further includes an aerodynamic field; after performing the steps in S120 above, the method may further include the following steps:

[0100] If the current gap change does not meet the convergence condition or the current iteration number does not reach the iteration number threshold, the aerodynamic load of the current iteration step is used as the input for the thermal field simulation in the next iteration step. The above steps are repeated until the current gap change reaches the convergence condition or the current iteration number reaches the iteration number threshold, thus obtaining the key parameter point set obtained by the load step iteration of the compressor during the operation phase.

[0101] It should be noted that the current gap change not reaching the convergence condition can be understood as the current gap change being greater than the preset threshold; the current iteration number not reaching the iteration number threshold can be understood as the current iteration number being less than the iteration number threshold.

[0102] Specifically, if the current clearance change does not meet the convergence condition or the current iteration number does not reach the iteration number threshold, the aerodynamic load of the current iteration step can be used as the input for the thermal field simulation in the next iteration step to repeat the steps in S110-S150 above until the clearance change between two adjacent iteration steps is less than or equal to the preset threshold or the current iteration number is greater than or equal to the iteration number threshold.

[0103] The technical solution in this application embodiment couples the thermal field, structural field and aerodynamic field of the compressor during the load step iteration process, making the load step iteration process more consistent with the actual operating scenario of the compressor, thereby improving the accuracy of the load step iteration.

[0104] The process of determining the first tip clearance of the compressor in the current iteration step through structural field simulation based on the temperature field of the current iteration step is described below. In one embodiment, as... Figure 3 As shown, the steps in S120 above may include:

[0105] S121. Based on the temperature field of the current iteration step, solve the structural mechanics equations of the compressor to obtain the displacement field of the compressor in the current iteration step.

[0106] In practical applications, the temperature field of the current iteration step can be substituted into the structural mechanics equations of the compressor, and the structural mechanics equations of the compressor can be solved to obtain the displacement field of the compressor in the current iteration step.

[0107] Specifically, the compressor material can generate a thermal strain field, and correspondingly, the computer equipment can adjust the temperature field based on the current iteration step k. Calculate the thermal strain generated by the compressor material in the current iteration step k. ,in, , These represent the three-dimensional spatial coordinates of the compressor. This represents the coefficient of linear thermal expansion of the compressor material. Indicates the first The load step compressor is in position Temperature field at that location (i.e., instantaneous temperature). Indicates the reference temperature (usually the cold assembly temperature).

[0108] Furthermore, the thermal strain generated by the compressor material in the current iteration step k can be... Substituting these equations into the structural mechanics equations of the compressor, we can solve for the compressor's performance at the current iteration step. The displacement field. The structural mechanics equations of the compressor can be expressed by the following formula (4):

[0109] (4)

[0110] in, This represents the stress tensor of the compressor. This represents the total strain tensor of the compressor. Indicates the divergence of stress. Volume density, representing stress Represents a unit tensor. This indicates the elastic properties of the compressor material.

[0111] S122. Extract the compressor tip radial displacement and casing deformation from the displacement field of the current iteration step.

[0112] Furthermore, the computer equipment can decompose the displacement field of the current iteration step to extract the compressor tip radial displacement and casing deformation from the displacement field of the current iteration step.

[0113] In this embodiment of the application, in the compressor structural model, the outermost ring of nodes at the blade tip is called the blade tip node set, and the ring of nodes on the inner wall of the casing immediately adjacent to the blade tip is called the casing node set. In practical applications, the displacement field of the current iteration step k... It can include the displacement vector of each blade tip node and the displacement vector of each casing node.

[0114] For each leaf tip node, the displacement vector of that leaf tip node can be used. Projecting in the radial direction yields... Furthermore, we can analyze the leaf tip node set corresponding to each leaf tip node. By averaging or taking the maximum value, the tip radial displacement of the blade in the current iteration step can be obtained. Simultaneously, for each casing node, the displacement vector of that casing node can be used. Projecting in the radial direction yields... Furthermore, it is possible to analyze the corresponding data for each casing node in the casing node set. The average or maximum value is used to obtain the casing deformation amount in the current iteration step. .

[0115] S123. Based on the radial displacement of the blade tip and the deformation of the casing, the second blade tip clearance is updated to obtain the first blade tip clearance.

[0116] It should be noted that, since the blade tip gap decreases when the blade tip expands and the blade tip gap increases when the casing expands, in order to balance the dynamic influence of the blade tip and casing on the blade tip gap, the second blade tip gap can be updated using the radial displacement of the blade tip and the deformation of the casing to obtain the true blade tip gap of the current iteration step, i.e., the first blade tip gap.

[0117] In practical applications, the computer equipment can update the second tip clearance according to a preset update rule, using the tip radial displacement and casing deformation, to obtain the first tip clearance. Alternatively, the computer equipment can perform arithmetic operations on the tip radial displacement, casing deformation, and second tip clearance to obtain the first tip clearance. Optionally, the aforementioned arithmetic operations may include at least one of addition, subtraction, multiplication, division, logarithmic operations, and exponential operations.

[0118] In this embodiment, the computer device can add the second tip clearance to the tip radial displacement and subtract it from the casing deformation to obtain the first tip clearance. For example, the first tip clearance in the current iteration step k... It can be equal to the previous iteration step. Add the tip radial displacement of the current iteration step k Then, subtract the casing deformation amount of the current iteration step k. ,Right now Where, if k equals 1, then It can represent the cold-state design gap.

[0119] The technical solution in this application embodiment has a relatively simple processing procedure and does not require the participation of complex algorithms, thereby reducing the update complexity of the second blade tip gap and accelerating the update speed of the second blade tip gap.

[0120] The process of determining the predicted surge margin of the compressor at each operating stage based on the surge critical points of each hot performance curve and the set of key parameter points is described below. In one embodiment, the surge critical points include the surge pressure ratio and the surge flow coefficient, and the set of key parameter points includes the iterative pressure ratio and the iterative flow coefficient; as shown below. Figure 4 As shown, the steps in S400 above may include:

[0121] S410. For any operating phase, the predicted pressure ratio margin of the compressor during the operating phase is obtained based on the pressure difference between the iterative pressure ratio and the surge pressure ratio corresponding to the compressor during the operating phase.

[0122] In the embodiments of this application, the predicted surge margin may include the predicted pressure ratio margin and the predicted flow margin. Specifically, for any operating phase, the computer device can subtract the corresponding iterative pressure ratio of the compressor in the operating phase from the corresponding surge pressure ratio to obtain the pressure difference, i.e., the predicted pressure ratio margin of the compressor in that operating phase.

[0123] For example, the predicted pressure ratio margin of the compressor at any operating stage. Where represents the iteration pressure ratio. This indicates the surge pressure ratio. It should be noted that the larger the predicted pressure ratio margin, the further the compressor is from surge, and the higher the compressor's safety margin.

[0124] S420. Subtract the surge flow coefficient from the iterative flow coefficient corresponding to the compressor during the operation phase, and then divide by the surge flow coefficient to obtain the predicted flow margin of the compressor during the operation phase.

[0125] Meanwhile, the computer equipment can subtract the iterative flow coefficient corresponding to the compressor during the operation phase from the surge flow coefficient, and then divide by the surge flow coefficient to obtain the predicted flow margin of the compressor during that operation phase.

[0126] For example, the predicted flow margin of the compressor at any operating stage ,in, Represents the iterative flow coefficient. This represents the surge flow coefficient. It's important to note that a larger predicted flow margin indicates that the compressor is further away from surge, and thus has a higher safety margin.

[0127] In this embodiment of the application, the execution order of the steps in S410 and S420 can be interchanged, and this embodiment of the application does not limit the execution order.

[0128] The technical solution in this application embodiment, for any operating stage, obtains the predicted pressure ratio margin of the compressor during the operating stage based on the pressure difference between the iterative pressure ratio and the surge pressure ratio corresponding to the compressor during the operating stage. Furthermore, it obtains the predicted flow margin of the compressor during the operating stage by subtracting the iterative flow coefficient corresponding to the compressor during the operating stage from the surge flow coefficient and then dividing by the surge flow coefficient. The above method is relatively simple and does not require complex algorithms, thereby reducing the complexity of calculating the predicted surge margin of the compressor and accelerating the calculation speed.

[0129] In some scenarios, it is necessary to visualize the predicted surge margin. The process of visualizing the predicted surge margin is described below. In one embodiment, after performing the steps in S400 above, as follows... Figure 5 As shown, the above method may further include the following steps:

[0130] S500: Based on the key parameter point set and predicted surge margin of the compressor under each operating stage, the surge margin surface of the compressor under each operating stage is obtained by fitting.

[0131] In the embodiments of this application, the key parameter point set of the compressor at each operating stage includes, in addition to, the iterative pressure ratio of the compressor at each operating stage. and iterative flow coefficient In addition, it may also include the temperature field obtained by load step iteration during the compressor operation phase (i.e., the temperature field of each load step). and the rotational speed of each load step .

[0132] Specifically, for any given operating phase, the computer equipment can use the temperature field and rotational speed of the compressor in each operating phase's key parameter point set, as well as the predicted pressure ratio margin or predicted flow margin in the predicted surge margin, as discrete sample points. A continuous surge margin surface is then constructed using a three-dimensional interpolation method, i.e., the compressor's surge margin surface for that operating phase. Optionally, the aforementioned three-dimensional interpolation method can be Kriging interpolation and / or radial basis function interpolation, etc.

[0133] S600 visualizes the surge margin surface under each operating stage.

[0134] Furthermore, the surge margin surface of the compressor at each operating stage can be output and displayed.

[0135] The technical solution in this application embodiment performs fitting processing based on the key parameter point set and predicted surge margin of the compressor at each operating stage to obtain the surge margin surface of the compressor at each operating stage, and then visualizes the surge margin surface of the compressor at each operating stage. The above method can visualize the surge margin surface of the compressor at each operating stage, so that users can intuitively understand the risks of the compressor at each operating stage and take preventive measures in advance to avoid the occurrence of risks, thereby improving the stability, reliability and safety of the compressor. At the same time, based on the surge margin surface, it is also convenient to call the control strategy of the compressor in the future.

[0136] In one embodiment, this application also provides a method for predicting the surge margin of a compressor, applied to computer equipment; the method includes the following steps:

[0137] (1) For any operating stage of the compressor, perform thermal field simulation based on the aerodynamic load obtained in the previous iteration step during the load step iteration process of the compressor in the operating stage, and determine the temperature field of the current iteration step;

[0138] (2) Based on the temperature field of the current iteration step, solve the structural mechanics equations of the compressor to obtain the displacement field of the compressor in the current iteration step;

[0139] (3) Extract the compressor tip radial displacement and casing deformation from the displacement field of the current iteration step;

[0140] (4) Based on the radial displacement of the blade tip and the deformation of the casing, update the second blade tip clearance of the compressor in the previous iteration step to obtain the first blade tip clearance of the compressor in the current iteration step;

[0141] (5) When the change in the current gap between the first blade tip gap and the second blade tip gap of the compressor in the previous iteration step reaches the preset convergence condition or the current iteration number reaches the preset iteration number threshold, the key parameter point set obtained by the load step iteration of the compressor during the operation phase is obtained; wherein, the load step iteration is based on the multi-physics coupling of the compressor.

[0142] (6) If the current gap change does not reach the convergence condition or the current iteration number does not reach the iteration number threshold, adjust the compressor's hydrodynamic mesh according to the second blade tip gap, reconstruct the geometric boundary of the compressor's aerodynamic field simulation, and obtain a new hydrodynamic mesh.

[0143] (7) Based on the new fluid dynamics grid and the compressor's operating parameters in the current iteration step, solve the fluid motion control equations of the compressor to obtain the aerodynamic load of the compressor in the current iteration step, and use the aerodynamic load of the current iteration step as the input for the thermal field simulation in the next iteration step, and repeat the above steps.

[0144] (8) Map each set of key parameter points to the performance graph with temperature label to obtain the corresponding mapped performance graph; the set of key parameter points includes the iterative pressure ratio and the iterative flow coefficient;

[0145] (9) Fit discrete points within the same temperature layer in each mapping performance diagram to determine the thermal performance curves of the compressor at each operating stage;

[0146] (10) Obtain the surge critical point of each hot performance curve, and obtain the predicted pressure ratio margin of the compressor during the operation phase based on the pressure difference between the iterative pressure ratio and the surge pressure ratio corresponding to the compressor during the operation phase.

[0147] (11) Subtract the iterative flow coefficient corresponding to the compressor during the operation phase from the surge flow coefficient, and then divide by the surge flow coefficient to obtain the predicted flow margin of the compressor during the operation phase.

[0148] (12) The compressor surge margin surface under each operating stage is obtained by fitting the key parameter point set of the compressor under each operating stage with the predicted pressure ratio margin or predicted flow margin.

[0149] (13) Visualize the surge margin surface under each operating stage.

[0150] The specific execution process of (1) to (13) above can be found in the description of the above embodiments. The implementation principle and technical effect are similar, and will not be repeated here.

[0151] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0152] Based on the same inventive concept, this application also provides a surge margin prediction device for implementing the compressor surge margin prediction method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more compressor surge margin prediction device embodiments provided below can be found in the limitations of the compressor surge margin prediction method described above, and will not be repeated here.

[0153] In one exemplary embodiment, such as Figure 6 As shown, a compressor surge margin prediction device is provided, comprising: an acquisition module 11, a mapping module 12, a fitting module 13, and a determination module 14, wherein:

[0154] The acquisition module 11 is used to acquire the set of key parameter points obtained by load step iteration under each operating stage of the compressor; wherein, the load step iteration is based on the multi-physics coupling of the compressor.

[0155] Mapping module 12 is used to map each key parameter point set to the performance map of the temperature label to obtain the corresponding mapped performance map;

[0156] Fitting module 13 is used to fit discrete points within the same temperature layer in each mapped performance graph to determine the thermal performance curves of the compressor at each operating stage.

[0157] The determination module 14 is used to obtain the surge critical point of each hot performance curve, and to determine the predicted surge margin of the compressor at each operating stage based on the surge critical point of each hot performance curve and the set of key parameter points.

[0158] The compressor surge margin prediction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0159] In one embodiment, the multiphysics field includes a thermal field, a structural field, and an aerodynamic field; the acquisition module 11 includes:

[0160] The thermal field simulation unit is used to perform thermal field simulation for any operating stage based on the aerodynamic load obtained in the previous iteration step during the load step iteration process of the compressor in the operating stage, and to determine the temperature field of the current iteration step.

[0161] The structural field simulation unit is used to perform structural field simulation based on the temperature field of the current iteration step, and to determine the first tip clearance of the compressor in the current iteration step.

[0162] The first determining unit is used to obtain the set of key parameter points obtained by the load step iteration of the compressor during the operation phase, when the change in the current gap between the first blade tip gap and the second blade tip gap of the compressor in the previous iteration step reaches the preset convergence condition or the current iteration number reaches the preset iteration number threshold.

[0163] The compressor surge margin prediction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0164] In one embodiment, the structural field simulation unit is specifically used for:

[0165] Based on the temperature field of the current iteration step, solve the structural mechanics equations of the compressor to obtain the displacement field of the compressor in the current iteration step;

[0166] Extract the compressor tip radial displacement and casing deformation from the displacement field of the current iteration step;

[0167] The second blade tip clearance is updated based on the blade tip radial displacement and casing deformation to obtain the first blade tip clearance.

[0168] The compressor surge margin prediction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0169] In one embodiment, the multiphysics field further includes an aerodynamic field; the acquisition module 11 further includes: a second determining unit and an aerodynamic field simulation unit, wherein:

[0170] The second determining unit is used to adjust the compressor's hydrodynamic mesh according to the second blade tip clearance when the current gap change has not reached the convergence condition or the current iteration number has not reached the iteration number threshold, and to reconstruct the geometric boundary of the compressor's aerodynamic field simulation to obtain a new hydrodynamic mesh.

[0171] The aerodynamic field simulation unit is used to perform aerodynamic field simulation based on the new hydrodynamic grid and the compressor's operating parameters in the current iteration step, obtain the aerodynamic load in the current iteration step, and use the aerodynamic load in the current iteration step as the input for the thermal field simulation in the next iteration step, repeating the above steps.

[0172] The compressor surge margin prediction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0173] In one embodiment, the aerodynamic field simulation unit is specifically used for:

[0174] Based on the new fluid dynamics grid and the compressor's operating parameters in the current iteration step, the fluid motion control equations of the compressor are solved to obtain the aerodynamic load of the compressor in the current iteration step.

[0175] The compressor surge margin prediction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0176] In one embodiment, the surge critical point includes the surge pressure ratio and the surge flow coefficient, and the key parameter point set includes the iterative pressure ratio and the iterative flow coefficient; the determination module 14 is specifically used for:

[0177] For any operating phase, the predicted pressure ratio margin of the compressor during the operating phase is obtained based on the pressure difference between the iterative pressure ratio and the surge pressure ratio corresponding to the compressor during the operating phase.

[0178] Subtract the surge flow coefficient from the iterative flow coefficient corresponding to the compressor during operation, and then divide by the surge flow coefficient to obtain the predicted flow margin of the compressor during operation.

[0179] The compressor surge margin prediction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0180] In one embodiment, the surge margin prediction device further includes: a margin surface acquisition module and a visualization module, wherein:

[0181] The margin surface acquisition module is used to fit the key parameter point set and predicted surge margin of the compressor under each operating stage to obtain the surge margin surface of the compressor under each operating stage.

[0182] The visualization module is used to visualize the surge margin surface at each operating stage.

[0183] The compressor surge margin prediction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0184] The modules in the surge margin prediction device described above can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0185] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a compressor surge margin prediction method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0186] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0187] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0188] The key parameter point set is obtained by load step iteration under each operating stage of the compressor; wherein, the load step iteration is based on the multi-physics coupling of the compressor.

[0189] The corresponding mapped performance map is obtained by mapping each set of key parameter points to the performance map with temperature labels.

[0190] By fitting discrete points within the same temperature layer in each mapping performance graph, the thermal performance curves of the compressor at each operating stage are determined;

[0191] The surge critical point of each thermal performance curve is obtained, and the predicted surge margin of the compressor at each operating stage is determined based on the surge critical point of each thermal performance curve and the set of key parameter points.

[0192] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0193] The key parameter point set is obtained by load step iteration under each operating stage of the compressor; wherein, the load step iteration is based on the multi-physics coupling of the compressor.

[0194] The corresponding mapped performance map is obtained by mapping each set of key parameter points to the performance map with temperature labels.

[0195] By fitting discrete points within the same temperature layer in each mapping performance graph, the thermal performance curves of the compressor at each operating stage are determined;

[0196] The surge critical point of each thermal performance curve is obtained, and the predicted surge margin of the compressor at each operating stage is determined based on the surge critical point of each thermal performance curve and the set of key parameter points.

[0197] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0198] The key parameter point set is obtained by load step iteration under each operating stage of the compressor; wherein, the load step iteration is based on the multi-physics coupling of the compressor.

[0199] The corresponding mapped performance map is obtained by mapping each set of key parameter points to the performance map with temperature labels.

[0200] By fitting discrete points within the same temperature layer in each mapping performance graph, the thermal performance curves of the compressor at each operating stage are determined;

[0201] The surge critical point of each thermal performance curve is obtained, and the predicted surge margin of the compressor at each operating stage is determined based on the surge critical point of each thermal performance curve and the set of key parameter points.

[0202] Those skilled in the art will understand that all or part of the processes in the methods of 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, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one of relational databases and non-relational databases. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0203] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0204] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for predicting the surge margin of a compressor, characterized in that, The method includes: The set of key parameter points is obtained by load step iteration under each operating stage of the compressor; wherein the load step iteration is based on the multi-physics coupling of the compressor. The corresponding mapped performance map is obtained by mapping each set of key parameter points to the performance map of temperature labeling. By fitting discrete points within the same temperature layer in each of the aforementioned performance mapping diagrams, the thermal performance curves of the compressor under each of the aforementioned operating stages are determined; The surge critical point of each of the aforementioned thermal performance curves is obtained, and the predicted surge margin of the compressor under each of the aforementioned operating stages is determined based on the surge critical point of each of the aforementioned thermal performance curves and the aforementioned key parameter point set.

2. The method according to claim 1, characterized in that, The multiphysics field includes a thermal field, a structural field, and an aerodynamic field; the acquisition of the key parameter point set obtained by load step iteration under each operating stage of the compressor includes: For any operating phase, thermal field simulation is performed based on the aerodynamic load obtained in the previous iteration step during the load step iteration process of the compressor in the operating phase to determine the temperature field of the current iteration step. Based on the temperature field of the current iteration step, structural field simulation is performed to determine the first tip clearance of the compressor in the current iteration step. Based on the second blade tip clearance of the compressor in the previous iteration step, the hydrodynamic mesh of the compressor is adjusted, the geometric boundary of the aerodynamic field simulation of the compressor is reconstructed, and a new hydrodynamic mesh is obtained. Based on the new hydrodynamic grid and the compressor's operating parameters in the current iteration step, aerodynamic field simulation is performed to obtain the aerodynamic load in the current iteration step; When the change in the current gap between the first blade tip gap and the second blade tip gap reaches a preset convergence condition or the current iteration number reaches a preset iteration number threshold, the key parameter point set obtained by the compressor under load step iteration during the operation phase is acquired.

3. The method according to claim 2, characterized in that, The step of performing structural field simulation based on the temperature field of the current iteration step to determine the first tip clearance of the compressor in the current iteration step includes: Based on the temperature field of the current iteration step, solve the structural mechanics equations of the compressor to obtain the displacement field of the compressor in the current iteration step; Extract the compressor tip radial displacement and casing deformation from the displacement field of the current iteration step; The second blade tip clearance is updated based on the blade tip radial displacement and the casing deformation to obtain the first blade tip clearance.

4. The method according to claim 2, characterized in that, The multiphysics field also includes an aerodynamic field; the method further includes: If the current gap change does not reach the convergence condition or the current iteration number does not reach the iteration number threshold, the aerodynamic load of the current iteration step is used as the input for the thermal field simulation in the next iteration step. The above steps are repeated until the current gap change reaches the convergence condition or the current iteration number reaches the iteration number threshold, thus obtaining the key parameter point set obtained by the load step iteration of the compressor during the operation phase.

5. The method according to claim 4, characterized in that, The aerodynamic field simulation based on the new hydrodynamic grid and the compressor's operating parameters in the current iteration step yields the aerodynamic loads for the current iteration step, including: Based on the new fluid dynamics grid and the compressor's operating parameters in the current iteration step, the fluid motion control equations of the compressor are solved to obtain the aerodynamic load of the compressor in the current iteration step.

6. The method according to any one of claims 1-5, characterized in that, The surge critical point includes the surge pressure ratio and the surge flow coefficient, and the key parameter point set includes the iterative pressure ratio and the iterative flow coefficient; determining the predicted surge margin of the compressor in each of the operating stages based on the surge critical point of each of the hot performance curves and the key parameter point set includes: For any operating phase, the predicted pressure ratio margin of the compressor during the operating phase is obtained based on the pressure difference between the iterative pressure ratio and the surge pressure ratio corresponding to the compressor during the operating phase. Subtract the surge flow coefficient from the iterative flow coefficient corresponding to the compressor during the operation phase, and then divide by the surge flow coefficient to obtain the predicted flow margin of the compressor during the operation phase.

7. The method according to any one of claims 1-5, characterized in that, The method further includes: The compressor surge margin surface for each operating stage is obtained by fitting the key parameter point set and predicted surge margin of the compressor under each operating stage. The surge margin surfaces under each of the aforementioned operating stages are visualized.

8. A surge margin prediction device for a compressor, characterized in that, The device includes: The acquisition module is used to acquire the set of key parameter points obtained by load step iteration under each operating stage of the compressor; wherein, the load step iteration is based on the multi-physics coupling of the compressor; The mapping module is used to map each set of key parameter points to the performance map of the temperature label to obtain the corresponding mapped performance map; The fitting module is used to fit discrete points within the same temperature layer in each of the mapping performance graphs to determine the thermal performance curves of the compressor in each of the operating stages. The determination module is used to obtain the surge critical point of each of the hot performance curves, and determine the predicted surge margin of the compressor in each of the operating stages based on the surge critical point of each of the hot performance curves and the set of key parameter points.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images