Optimization design method for air entraining self-circulation groove of air compressor

Abstract

The invention relates to the technical field of ship power superchargers, in particular to an air-entraining self-circulation groove optimization design method for an air compressor, which comprises the following steps: firstly, establishing a parameterized model containing a plurality of key dimensions such as the axial position of a backflow groove and the axial position of a suction groove, and generating a corresponding air compressor flow field simulation model; performing aerodynamic performance simulation based on the model to obtain pressure ratio and efficiency data; then, an objective function used for evaluating the deviation between a simulation result and a target value is constructed according to the design requirement of the gas compressor; and finally, carrying out iterative optimization on the target function by adopting a simulated annealing particle swarm algorithm, and automatically adjusting the structural parameters of the air-entraining self-circulation tank until a parameter combination meeting a global optimal condition is obtained. The method effectively solves the technical problem that the global optimization of the comprehensive performance of the gas compressor is difficult to realize due to the fact that the mutual coupling influence among size parameters cannot be considered in a traditional control variable method, and realizes the collaborative optimization of the stable working range, the pressure ratio and the efficiency of the gas compressor.

Description

Technical Field

[0001] This invention relates to the field of marine power supercharger technology, and in particular to an optimized design method for a compressor bleed air self-circulation tank. Background Technology

[0002] Centrifugal compressors are one of the core components of turbochargers, and their performance directly affects key indicators such as the turbocharger's pressure ratio and operating range. With the increase in diesel engine power density, the pressure ratio of the matching exhaust gas turbochargers has gradually increased, which can easily lead to a narrowing of the compressor's stable operating range and even cause surge, resulting in failures such as blade breakage. To broaden the compressor's stable operating range, casing treatment technology has been widely adopted. Among them, the bleed air self-circulation casing has gained attention in engineering applications due to its simple structure, ease of manufacturing, and relatively small pressure ratio loss while broadening the stable operating range.

[0003] Currently, the design of bleed air self-circulation tanks typically employs the controlled variable method, which involves adjusting individual tank dimensional parameters one by one and analyzing their impact on the compressor's operating range, pressure ratio, and efficiency to gradually determine each dimension. However, this method does not consider the inter-coupling effects between various dimensional parameters, making it difficult to achieve overall coordination between pressure ratio, efficiency, and stable operating range. Consequently, it fails to obtain a globally optimal tank structure design, resulting in limited improvement in the overall compressor performance.

[0004] Therefore, how to provide a method that can comprehensively consider the coupling effects of multiple parameters and realize the global optimization design of the air-purifying self-circulating tank has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized design method for the compressor bleed air self-circulation tank, which solves the problem that the existing design method cannot take into account the mutual coupling effect between the dimensional parameters of each tank, resulting in the compressor's overall performance not reaching the global optimum.

[0006] To achieve the above objectives, the present invention provides an optimized design method for a compressor bleed air self-circulation tank, comprising the following steps: Establish a parametric model that includes the axial position of the return channel, the axial position of the suction channel, the width of the return channel, the width of the suction channel, the width of the bypass channel, and the height of the bypass channel, and generate the corresponding compressor flow field simulation model. Based on the flow field simulation model, aerodynamic performance is calculated using fluid simulation software to obtain the compressor's pressure ratio and efficiency at different flow rates. Based on the compressor design requirements, target pressure ratio and target efficiency are set, and combined with the pressure ratio and efficiency data obtained from simulation, an objective function is constructed with the structural parameters of the bleed air self-circulation tank as independent variables. The objective function is iteratively optimized using a simulated annealing particle swarm optimization algorithm. The structural parameters of the air-purifying self-circulating tank are adjusted until the objective function value meets the preset tolerance, and the optimal combination of structural parameters is output.

[0007] This involves establishing a parametric model that includes the axial position of the return channel, the axial position of the suction channel, the width of the return channel, the width of the suction channel, the width of the bypass channel, and the height of the bypass channel, and generating a corresponding compressor flow field simulation model, specifically including: The axial position of the reflux groove is the axial distance from the reflux groove to the leading edge of the impeller, and the axial position of the suction groove is the axial distance from the suction groove to the leading edge of the impeller.

[0008] This involves establishing a parametric model that includes the axial position of the return channel, the axial position of the suction channel, the width of the return channel, the width of the suction channel, the width of the bypass channel, and the height of the bypass channel, and generating a corresponding compressor flow field simulation model, specifically including: Parametric modeling was implemented using a script program, and the flow field region containing the compressor impeller, diffuser, and bleed air self-circulation structure was meshed using a mesh generation module.

[0009] Based on the aforementioned flow field simulation model, aerodynamic performance calculations are performed using fluid simulation software to obtain the compressor's pressure ratio and efficiency at different flow rates, specifically including: Fluid simulation was performed using NUMECA software, with the SA model as the turbulence model, and the three-dimensional Reynolds-averaged Navier-Stokes equations were solved.

[0010] Specifically, based on the compressor design requirements, target pressure ratio and target efficiency are set. Combined with the pressure ratio and efficiency data obtained from simulations, an objective function is constructed with the structural parameters of the bleed air self-circulation tank as independent variables. This function includes: The objective function is expressed as: ,in, For flow weighting coefficient, The efficiency weighting coefficient is N, where N is the number of data points extracted from the simulation. For target pressure ratio, For target efficiency, For simulating pressure ratio, For simulation efficiency.

[0011] Among them, the flow weighting coefficient Efficiency weighting coefficient The sum is 1, and adjustments are made according to the compressor design requirements.

[0012] Specifically, the objective function is iteratively optimized using a simulated annealing particle swarm optimization algorithm. The structural parameters of the air-purifying self-circulating tank are adjusted until the objective function value meets a preset tolerance, and the optimal combination of structural parameters is output. The parameters of the simulated annealing particle swarm algorithm include the number of particles, inertia weight, acceleration constant, and annealing coefficient.

[0013] This invention discloses an optimized design method for a compressor bleed air self-circulation tank. First, a parametric model is established, including multiple key dimensions such as the axial position of the return tank and the axial position of the suction tank, and a corresponding compressor flow field simulation model is generated. Then, aerodynamic performance simulation is performed based on this model to obtain pressure ratio and efficiency data. Next, an objective function is constructed according to the compressor design requirements to evaluate the deviation between the simulation results and the target values. Finally, a simulated annealing particle swarm optimization algorithm is used to iteratively optimize the objective function, automatically adjusting the structural parameters of the bleed air self-circulation tank until a parameter combination satisfying the global optimum is obtained. This effectively solves the technical problem that the traditional control variable method cannot consider the mutual coupling effects between various dimensional parameters, thus making it difficult to achieve the global optimum of the compressor's overall performance. It achieves coordinated optimization of the compressor's stable operating range, pressure ratio, and efficiency. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0015] Figure 1 This is a structural diagram of the compressor induced air circulation tank of the present invention.

[0016] Figure 2 This is a flowchart of the compressor bleed air self-circulation tank optimization design method of the present invention.

[0017] Figure 3 This is the flow field simulation model of the self-circulating air compressor of the present invention.

[0018] Figure 4 This is an example diagram showing the compressor performance calculation results of the present invention.

[0019] Figure 5 This is a flowchart of the iterative optimization process of the self-circulating air tank of the present invention.

[0020] Figure 6 This is a schematic diagram of the simulated annealing particle swarm algorithm of the present invention.

[0021] Figure 7 This is a performance curve of the compressor before optimization of the self-circulating tank according to the present invention.

[0022] Figure 8 This is the compressor performance curve after optimization of the self-circulating tank according to the present invention.

[0023] Figure 9 This is a flowchart of the steps in the optimized design method of the compressor bleed air self-circulation tank of the present invention.

[0024] In the diagram: 1a - compressor impeller, 1b - impeller casing, 1c - air inlet recirculation tank. Detailed Implementation

[0025] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0026] The first embodiment of this application is as follows: Please see Figures 1 to 9 ,in, Figure 1 This is a structural diagram of the compressor induced air circulation tank of the present invention. Figure 2 This is a flowchart of the compressor bleed air self-circulation tank optimization design method of the present invention. Figure 3 This is the flow field simulation model of the self-circulating air compressor of the present invention. Figure 4 This is an example diagram showing the compressor performance calculation results of the present invention. Figure 5 This is a flowchart of the iterative optimization process of the self-circulating air tank of the present invention. Figure 6 This is a schematic diagram of the simulated annealing particle swarm algorithm of the present invention. Figure 7 This is a performance curve of the compressor before optimization of the self-circulating tank according to the present invention. Figure 8 This is the compressor performance curve after optimization of the self-circulating tank according to the present invention. Figure 9 This is a flowchart of the steps in the optimized design method of the compressor bleed air self-circulation tank of the present invention.

[0027] This invention provides an optimized design method for a compressor bleed air self-circulation tank, comprising the following steps: S1: Establish a parametric model that includes the axial position of the return channel, the axial position of the suction channel, the width of the return channel, the width of the suction channel, the width of the bypass channel, and the height of the bypass channel, and generate the corresponding compressor flow field simulation model. S2: Based on the flow field simulation model, aerodynamic performance is calculated using fluid simulation software to obtain the pressure ratio and efficiency of the compressor at different flow rates; S3: Based on the compressor design requirements, set the target pressure ratio and target efficiency, and combine the pressure ratio and efficiency data obtained from simulation to construct an objective function with the structural parameters of the bleed air self-circulation tank as independent variables; S4: The objective function is iteratively optimized using the simulated annealing particle swarm optimization algorithm. The structural parameters of the air-purifying self-circulating tank are adjusted until the objective function value meets the preset tolerance, and the optimal combination of structural parameters is output.

[0028] Specifically, such as Figure 1As shown, the present invention optimizes the structure of the compressor induced draft self-circulation groove. The optimized structural parameters are: l1 - distance from the axial position of the return groove to the impeller leading edge (axial position of the return groove), l2 - distance from the axial position of the suction groove to the impeller leading edge (axial position of the suction groove), d1 - width of the return groove, d2 - width of the suction groove, h2 - height of the bypass channel, and h1 - width of the bypass channel.

[0029] like Figure 2 As shown, the compressor bleed air self-circulation tank optimization design method includes the following steps: parametric modeling of the compressor in the bleed air self-circulation tank, modeling in the form of scripts or command streams, which can quickly establish the corresponding compressor performance simulation model as the self-circulation tank parameters change. In a specific embodiment of the present invention, a Python script program is used to define the above-mentioned structural dimension parameters and generate the model, and the NUMECA / AutoGrid5 mesh generation module can be used to mesh the flow field region containing the compressor impeller, diffuser and bleed air self-circulation structure, thereby quickly establishing a compressor performance simulation model that changes with parameters; compressor performance simulation is performed using fluid simulation software (such as NUMEC). A) Calculate the performance of the self-circulating tank compressor to obtain the compressor flow-pressure ratio (efficiency) relationship. In a preferred embodiment, the SA turbulence model is used to solve the three-dimensional Reynolds-averaged Navier-Stokes equations, obtaining the compressor flow-pressure ratio and flow-efficiency relationships. Construct an objective function, which is achieved by calculating the deviation between the simulation results (compressor pressure ratio, efficiency) under different flow rates and the target values ​​(target pressure ratio and target efficiency, target pressure ratio and efficiency). Iterative optimization is performed using a simulated annealing particle swarm optimization algorithm to intelligently adjust the structural parameters of the bleed air self-circulating tank, calculate the objective function value, and iterate repeatedly until the objective function value meets the given requirements. Obtain the optimal parameters, and the calculation terminates to obtain the global optimal solution. Through the above steps, the global optimization of the bleed air self-circulating tank structure can be quickly achieved.

[0030] like Figure 3 As shown, the compressor performance simulation model consists of the compressor impeller casing and the fluid domain between the compressor blades. The compressor blades are set as the rotating domain, and the compressor inlet section and intake recirculation structure are set as the stationary domain. The stationary domain and the rotating domain are connected by a rotating-stationary interface.

[0031] like Figure 4 As shown, the compressor performance calculation results mainly include compressor flow rate and pressure ratio (η). k The correspondence between ) and efficiency (η).

[0032] As shown in the following formula, the objective function F is composed of flow weighting coefficients. Efficiency weighting coefficient The number of data points N extracted by simulation, the target pressure ratio Target efficiency Simulated pressure ratio Simulation efficiency Composition, in which the compressor target pressure ratio Target efficiency The weighting coefficients in the objective function are determined based on the diesel engine's operating characteristic curves and are generally provided by the diesel engine manufacturer. , The weighting factor is typically set to 0.5. However, it can be adjusted according to compressor design requirements. For compressors with high isobaric ratio requirements in medium- and low-speed diesel engine turbochargers, the weighting factor can be increased. Up to 0.7; for compressors with high efficiency requirements, such as low-speed diesel engine turbochargers, the efficiency can be increased. Up to 0.7. But it still needs to meet the following requirements. .

[0033]

[0034] like Figure 5 As shown, in the iterative optimization process of the bleed air self-circulation tank, the parameters of the bleed air self-circulation tank are first randomly initialized within a given range; then, the aforementioned parameterized model is used to simulate the compressor flow field performance, obtaining the compressor flow-pressure ratio and flow-efficiency curves; subsequently, the pressure ratio obtained from the simulation is calculated using the aforementioned constructed objective function F. ,efficiency Compared with the target pressure ratio Target efficiency The percentage deviation is (see Equation 1). If the objective function F is greater than the set value (usually 1%), the simulated annealing particle swarm optimization (SAPSO) algorithm is used to change the structural parameters of the bleed air self-circulation tank and perform iterative optimization until the objective function is less than the given error, thereby obtaining the optimal parameter combination of the bleed air self-circulation tank.

[0035] like Figure 6 As shown, the Simulated Annealing Particle Swarm Optimization (SAPSO) algorithm combines the advantages of the annealing algorithm, possessing the ability of probabilistic mutation, which can improve the local convergence problem of the particle swarm optimization algorithm, and integrating the fast convergence speed of the particle swarm optimization algorithm, thus possessing the ability to quickly find global optimization. Its algorithmic approach is as follows: First, at time t=0, the particle velocity and direction are randomly assigned; second, the fitness P of each particle is evaluated. i And store the position of the best-fit particle at P. g Then, calculate the initial temperature T0 of the annealing algorithm based on the best fitness; then, calculate the fitness of each particle in the annealing algorithm at the current temperature; then, use the roulette wheel algorithm to select one from the individual optima to replace the global optimum and determine whether it meets the objective function requirements. If it does, the calculation ends; otherwise, proceed to the next step; update the velocity and direction of each particle and anneal, repeat the above steps until the iteration termination condition is met.

[0036] In the specific implementation of this invention, the optimization process includes: (1) At the start of the calculation (t=0), the parameters are randomly initialized, including the distance l1 from the axial position of the return channel to the impeller leading edge. 0 The distance l2 from the axial position of the suction groove to the leading edge of the impeller 0 , reflux groove width d1 0 d2 of suction groove 0 Bypass channel width h1 0 Bypass channel height h2 0 .

[0037] (2) Based on the above parameters, a simulation analysis model was established, and the structural parameters of the air-purifying self-circulating tank were calculated as l1. 0 l2 0 d1 0 d2 0 h1 0 h2 0 Compressor flow rate-pressure ratio and flow rate-efficiency diagrams (see attached diagram) Figure 7 Based on the simulation analysis results, the percentage deviation F calculated according to formula (1) is 0.3, which does not meet the design requirements.

[0038] (3) Set the parameters of the simulated annealing particle swarm optimization algorithm: number of particles N=50, inertia weight w=0.5, acceleration constant C1=C2=2.0, annealing coefficient α=0.9, and perform global iterative optimization. For each iteration, change the corresponding self-circulating slot parameter, denoted as l1. t l2 t d1 t d2 t h1 t h2 t .

[0039] (4) When the iteration termination condition (F=0.009) is met, the compressor flow-pressure ratio and flow-efficiency are as follows: Figure 8 As shown, the design requirements are met. Record the structural parameters l1, l2, d1, d2, h1, and h2 of the self-circulating tank at this time to complete the optimized design of the self-circulating tank.

[0040] The compressor bleed air self-circulation tank optimization design method of this invention solves the problem that the compressor performance cannot achieve global optimum due to the mutual influence of the shape and position parameters of the compressor bleed air self-circulation tank. The optimization objective function constructed by this invention comprehensively considers the compressor flow rate, pressure ratio, and efficiency, achieving comprehensive compressor performance optimization. The parametric modeling method of this invention can efficiently complete the modification of the compressor fluid calculation model, enabling rapid modeling and flow field simulation. The intelligent optimization algorithm adopted by this invention can quickly find the global optimum solution, reduce computation time, and avoid getting trapped in local convergence.

[0041] The above-disclosed embodiments are merely one or more preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments and equivalent changes made in accordance with the claims of this application still fall within the scope of this application.

Claims

1. A method for optimizing the design of a compressor bleed air self-circulation tank, characterized in that, Includes the following steps: Establish a parametric model that includes the axial position of the return channel, the axial position of the suction channel, the width of the return channel, the width of the suction channel, the width of the bypass channel, and the height of the bypass channel, and generate the corresponding compressor flow field simulation model. Based on the flow field simulation model, aerodynamic performance is calculated using fluid simulation software to obtain the compressor's pressure ratio and efficiency at different flow rates. Based on the compressor design requirements, target pressure ratio and target efficiency are set, and combined with the pressure ratio and efficiency data obtained from simulation, an objective function is constructed with the structural parameters of the bleed air self-circulation tank as independent variables. The objective function is iteratively optimized using a simulated annealing particle swarm optimization algorithm. The structural parameters of the air-purifying self-circulating tank are adjusted until the objective function value meets the preset tolerance, and the optimal combination of structural parameters is output.

2. The compressor bleed air self-circulation tank optimization design method as described in claim 1, characterized in that, A parametric model is established, including the axial position of the return channel, the axial position of the suction channel, the width of the return channel, the width of the suction channel, the width of the bypass channel, and the height of the bypass channel. A corresponding compressor flow field simulation model is then generated, specifically including: The axial position of the reflux groove is the axial distance from the reflux groove to the leading edge of the impeller, and the axial position of the suction groove is the axial distance from the suction groove to the leading edge of the impeller.

3. The compressor bleed air self-circulation tank optimization design method as described in claim 2, characterized in that, A parametric model is established, including the axial position of the return channel, the axial position of the suction channel, the width of the return channel, the width of the suction channel, the width of the bypass channel, and the height of the bypass channel. A corresponding compressor flow field simulation model is then generated, specifically including: Parametric modeling was implemented using a script program, and the flow field region containing the compressor impeller, diffuser, and bleed air self-circulation structure was meshed using a mesh generation module.

4. The compressor bleed air self-circulation tank optimization design method as described in claim 3, characterized in that, Based on the aforementioned flow field simulation model, aerodynamic performance calculations are performed using fluid simulation software to obtain the compressor's pressure ratio and efficiency at different flow rates, specifically including: Fluid simulation was performed using NUMECA software, with the SA model as the turbulence model, and the three-dimensional Reynolds-averaged Navier-Stokes equations were solved.

5. The compressor bleed air self-circulation tank optimization design method as described in claim 4, characterized in that, Based on the compressor design requirements, target pressure ratio and target efficiency are set. Combining the pressure ratio and efficiency data obtained from simulations, an objective function is constructed with the structural parameters of the bleed air self-circulation tank as independent variables. Specifically, this includes: The objective function is expressed as: ,in, For flow weighting coefficient, The efficiency weighting coefficient is N, where N is the number of data points extracted from the simulation. For target pressure ratio, For target efficiency, For simulating pressure ratio, For simulation efficiency.

6. The compressor bleed air self-circulation tank optimization design method as described in claim 5, characterized in that, Flow weighting coefficient Efficiency weighting coefficient The sum is 1, and adjustments are made according to the compressor design requirements.

7. The compressor bleed air self-circulation tank optimization design method as described in claim 6, characterized in that, The objective function is iteratively optimized using a simulated annealing particle swarm optimization algorithm. The structural parameters of the induced draft gas self-circulation tank are adjusted until the objective function value meets a preset tolerance. The optimal combination of structural parameters is then output, specifically including: The parameters of the simulated annealing particle swarm algorithm include the number of particles, inertia weight, acceleration constant, and annealing coefficient.

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