Variable structure turbine data-based supercharger efficiency evaluation method and device

Abstract

This application relates to the field of turbocharger efficiency prediction technology, and discloses a turbocharger efficiency evaluation method and device based on variable structure turbine data. The method first establishes a reliable radial turbine numerical model through high-precision three-dimensional simulation and experimental verification; then, it uses sensitivity analysis and Latin hypercube sampling to screen key impeller geometric parameters and construct an efficient sample dataset; the core is the application of a neural network algorithm to establish a nonlinear prediction model with turbine reduced speed and key geometric parameters as input and turbine efficiency as output, thereby quickly and accurately obtaining the variable structure turbine efficiency characteristics under all operating conditions; finally, by dynamically identifying the real-time operating conditions of the turbocharger, and combining the turbine efficiency predicted by the neural network with the compressor efficiency calculated by actual measurements, an accurate and efficient evaluation of the overall efficiency of the variable structure turbocharger is achieved, effectively solving the problems of high prediction difficulty and low accuracy of traditional methods.

Description

Technical Field

[0001] This application relates to the field of turbocharger efficiency prediction technology, and for example to a turbocharger efficiency evaluation method and device based on variable structure turbine data. Background Technology

[0002] Turbochargers are critical energy conversion devices widely used in aerospace, marine propulsion, and industrial power, and their efficiency directly impacts the performance and economy of power systems. Accurately predicting the efficiency characteristics of turbochargers with different structural configurations is crucial during design and optimization. However, the efficiency of variable-structure turbochargers is influenced by a variety of complex factors, including geometric parameters (such as impeller shape and blade length) and operating conditions (such as speed and flow rate), making prediction extremely difficult. Traditional methods include experimental measurement and numerical simulation. The former is accurate but costly and time-consuming, while the latter is computationally intensive and limited by model accuracy. With the development of artificial intelligence, neural network algorithms have been widely applied due to their nonlinear mapping and self-learning capabilities. Although neural network-based prediction methods exist, those for variable-structure turbochargers, especially those comprehensively considering geometric parameters and real-time data, are still relatively few, and their accuracy and efficiency need improvement.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application. Summary of the Invention

[0004] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0005] The turbocharger efficiency evaluation method, device, and storage medium based on variable structure turbocharger data provided in this disclosure can solve the problems of high difficulty and low accuracy in predicting the efficiency of variable structure turbochargers.

[0006] This disclosure provides a method for evaluating turbocharger efficiency based on variable structure turbine data, the method including: A three-dimensional aerodynamic simulation model of a runoff turbine was established, and the numerical simulation model of turbine design was verified based on experimental measurements to accurately simulate the complex flow phenomena and performance inside the turbine. Based on the single-factor sensitivity analysis method, the influence weight of the geometric parameters of the impeller inlet and outlet on the efficiency characteristics of the radial turbine is analyzed by CFD three-dimensional simulation, and the key geometric parameters and constraint ranges of the impeller inlet and outlet that affect the efficiency characteristics of the radial turbine are obtained. Based on the Latin hypercube sampling method, we explored the space of key impeller geometric parameters that affect the efficiency of runoff turbines. We selected different turbine reduced speeds and obtained the turbine efficiency under different parameter combinations through three-dimensional calculations. We then obtained a dataset for fitting the relationship between runoff turbine efficiency and key impeller geometric parameters. A neural network algorithm was used to establish a model of the influence of key impeller geometric parameters on the efficiency of the radial turbine under different operating conditions. Based on the turbine reduced speed, impeller outlet rim position, outlet hub position and blade length, the efficiency of the radial turbine was predicted, and the efficiency characteristics of the variable structure turbine under all operating conditions were obtained. The system dynamically identifies the operating conditions of the turbocharger and calculates the compressor operating efficiency based on the measured temperature and pressure at the compressor inlet and outlet, thereby accurately evaluating the efficiency of the variable structure turbocharger.

[0007] This disclosure provides an electronic device that includes at least one processor; and memory that is communicatively connected to at least one processor; The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to perform the above-described turbocharger efficiency evaluation method based on variable structure turbine data.

[0008] This disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the above-described turbocharger efficiency evaluation method based on variable structure turbine data.

[0009] The turbocharger efficiency evaluation method, device, and storage medium based on variable structure turbine data provided in this disclosure can achieve the following technical effects: This disclosure first establishes a reliable numerical model of the radial turbine through high-precision 3D simulation and experimental verification. Then, it uses sensitivity analysis and Latin hypercube sampling to screen out key impeller geometric parameters and construct an efficient sample dataset. The core is to apply a neural network algorithm to establish a nonlinear prediction model with turbine reduced speed and key geometric parameters as input and turbine efficiency as output, thereby quickly and accurately obtaining the efficiency characteristics of the variable structure turbine under all operating conditions. Finally, by dynamically identifying the real-time operating conditions of the turbocharger, and combining the turbine efficiency predicted by the neural network with the compressor efficiency calculated by actual measurement, an accurate and efficient evaluation of the overall efficiency of the variable structure turbocharger is achieved, effectively solving the problems of high prediction difficulty and low accuracy of traditional methods.

[0010] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0011] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein: Figure 1 This is a schematic flowchart of a turbocharger efficiency evaluation method based on variable structure turbine data provided in an embodiment of this disclosure; Figure 2 This is a flowchart illustrating another turbocharger efficiency evaluation method based on variable structure turbine data provided in this disclosure embodiment; Figure 3 This is a schematic diagram of the structure of a turbocharger efficiency evaluation device based on variable structure turbine data provided in an embodiment of this disclosure. Detailed Implementation

[0012] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0013] The terms "first," "second," etc., used in the embodiments of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0014] Unless otherwise stated, the term "multiple" means two or more.

[0015] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0016] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0017] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0018] To address the aforementioned issues, this disclosure provides a method, apparatus, and storage medium for evaluating turbocharger efficiency based on variable structure turbine data.

[0019] The following description, in conjunction with the accompanying drawings, illustrates a method, apparatus, and storage medium for evaluating turbocharger efficiency based on variable structure turbine data, as provided in this disclosure.

[0020] Figure 1 This is a schematic flowchart of a turbocharger efficiency evaluation method based on variable structure turbine data provided in this embodiment of the disclosure.

[0021] Combination Figure 1 As shown, the turbocharger efficiency evaluation method based on variable structure turbine data includes: S101, establish a three-dimensional aerodynamic simulation calculation model for a radial turbine, and verify the numerical simulation model of the turbine design based on experimental measurements, so as to accurately simulate the complex flow phenomena and performance inside the turbine. S102, based on the single-factor sensitivity analysis method, uses CFD three-dimensional simulation to analyze the influence weight of the geometric parameters of the impeller inlet and outlet on the efficiency characteristics of the radial turbine, and obtains the key geometric parameters and constraint range of the impeller inlet and outlet that affect the efficiency characteristics of the radial turbine. S103 explores the space of key impeller geometric parameters affecting the efficiency of runoff turbines based on the Latin hypercube sampling method. Different turbine reduced speeds are selected, and turbine efficiencies under different parameter combinations are obtained through three-dimensional calculations. A dataset is then obtained to fit the relationship between runoff turbine efficiency and key impeller geometric parameters. S104. A neural network algorithm is used to establish a model of the influence of key impeller geometric parameters on the efficiency of the radial turbine under different operating conditions. Based on the turbine reduced speed, impeller outlet rim position, outlet hub position and blade length, the efficiency of the radial turbine is predicted and the efficiency characteristics of the variable structure turbine under all operating conditions are obtained. S105 dynamically identifies the turbocharger's operating conditions and calculates the compressor's operating efficiency based on the measured temperature and pressure at the compressor inlet and outlet, thereby accurately assessing the efficiency of the variable structure turbocharger.

[0022] In some embodiments, the above-mentioned three-dimensional aerodynamic simulation calculation model of the runoff turbine is established based on ANSYS CFX software, and the prototype turbine is modeled in three dimensions using the impeller and volute design software CFturbo based on the measured turbine geometric parameters.

[0023] In some embodiments, the control equations for the three-dimensional aerodynamic simulation of the turbine are selected from the Reynolds-averaged Navier-Stokes equations, and the turbulence model adopts the shear stress transport model. The working medium inside the turbine is considered to be an incompressible ideal gas, and its physical properties are corrected according to the total inlet temperature conditions. The inlet and outlet boundary conditions are selected based on the total temperature and pressure at the inlet and the static pressure at the outlet, and their values ​​are selected based on experimental data.

[0024] In some embodiments, the above method further includes calculating the total static efficiency of the runoff turbine based on the three-dimensional simulation results, satisfying the formula: , Where T represents temperature, P represents pressure, subscript 1 represents the inlet section of the volute, subscript 2 represents the interface between the volute and the impeller (which is both the volute outlet and the impeller inlet), and subscript 3 represents the outlet section of the impeller. T 3S It is the isentropic temperature at the impeller outlet; In the three-dimensional calculation, it is assumed that the gas flow inside the turbine is an ideal adiabatic process, i.e., the gas adiabatic index is... remain unchanged. The temperature is isentropic, and the total temperature is... (Stagnation temperature), total pressure (Stagnation pressure) is related to flow velocity, and the calculation formula is: , , , Where K represents the adiabatic index and Ma represents the flow velocity. For quality flow, The density of the fluid passing through the cross-sectional area. Where is the local speed of sound, k is the specific heat ratio, R is the gas constant, and T is the thermodynamic temperature of the gas.

[0025] In some embodiments, the single-factor sensitivity analysis method described above analyzes the influence of each impeller geometry parameter on turbine efficiency individually, evaluates the sensitivity of each geometry parameter to the efficiency improvement of low expansion ratio radial turbines based on the maximum efficiency improvement rate, selects the geometry parameters that have a significant impact on radial turbine efficiency as key parameters for turbine efficiency characteristic prediction, and obtains the constraint range of each geometry parameter.

[0026] In some embodiments, the above-described Latin hypercube sampling method explores the space of geometric parameters of impeller outlet rim, hub position and blade length that have a significant impact on turbine efficiency. The turbine impeller geometry is determined based on different combinations of geometric parameters. The turbine efficiency is obtained through three-dimensional calculation under different equivalent speed conditions representing low, medium and high turbine speeds, and used to train the turbine efficiency prediction model.

[0027] In some embodiments, the established model of the influence relationship between the key geometric parameters of the impeller on the efficiency of the radial turbine under different operating conditions is a backpropagation neural network; The input parameters of the backpropagation neural network include the normalized turbine reduced speed, impeller outlet rim position, outlet hub position, and blade length, while the output parameter is set to the turbine's total static efficiency. The backpropagation neural network is a three-layer neural network with one hidden layer and three neurons in each hidden layer; the transfer function between layers is the Sigmoid function, which satisfies the following formula: , in, It is a weighted sum of the neuron's inputs. It is the connection weight between the i-th and j-th neurons. It is the output of the j-th neuron. These are the bias weights between neurons in each layer, and the weights are adjusted using the gradient descent method.

[0028] In some embodiments, the operating efficiency of the compressor and the efficiency of the booster satisfy the following formula: ,in, For compressor efficiency, This refers to the compressor inlet temperature. This refers to the compressor outlet temperature. The compressor pressure ratio. The adiabatic index, For turbocharger efficiency, For variable structure turbine efficiency. For turbocharger mechanical efficiency.

[0029] This disclosure accurately assesses the turbocharger efficiency of the variable structure turbine by dynamically identifying the turbocharger's operating conditions through real-time acquisition of the temperature, pressure, and flow rate before and after the turbine and compressor, calculating the variable structure turbine efficiency based on a neural network prediction model, and calculating the compressor operating efficiency based on experimental data.

[0030] Specifically, in response to the shortcomings of existing technologies, this invention proposes a turbocharger efficiency evaluation method based on variable structure turbine data. This method can predict the radial turbine efficiency based on turbine geometry parameters and operating speed, and calculate the compressor operating efficiency based on real-time operating data, thereby achieving accurate evaluation of the efficiency of turbochargers with different structural forms.

[0031] Step 1: Establish a three-dimensional aerodynamic simulation model of the radial turbine. Verify the numerical simulation model of the turbine design based on experimental measurements to accurately simulate the complex flow phenomena and performance inside the turbine. Step 2: Based on single-factor sensitivity analysis, analyze the influence weights of the inlet and outlet geometric parameters of the radial turbine impeller on the efficiency characteristics through CFD three-dimensional simulation, obtaining the key inlet and outlet geometric parameters and constraint ranges affecting the efficiency characteristics of the radial turbine. Step 3: Explore the space of key impeller geometric parameters affecting the efficiency of the radial turbine based on the Latin hypercube sampling method. Select different turbine reduced speeds and respectively... The turbine efficiency under different parameter combinations is obtained through three-dimensional calculation, and a dataset is acquired to fit the relationship between the radial turbine efficiency and the key geometric parameters of the impeller. In the fourth step, a neural network algorithm is applied to establish a model of the influence of the key geometric parameters of the impeller on the radial turbine efficiency under different operating conditions. Based on the turbine reduced speed, impeller outlet rim position, outlet hub position, and blade length, the radial turbine efficiency is predicted, and the efficiency characteristics of the variable structure turbine under all operating conditions are obtained. In the fifth step, the turbocharger operating conditions are dynamically identified, and the compressor operating efficiency is calculated based on the measured temperature and pressure at the compressor inlet and outlet, thereby accurately evaluating the efficiency of the variable structure turbocharger.

[0032] Further, in step one of this invention, the three-dimensional aerodynamic simulation calculation model of the radial turbine is established based on ANSYS CFX software, and the prototype turbine is modeled in three dimensions using the impeller and volute design software CFturbo based on measured turbine geometric parameters. The governing equations for the turbine's three-dimensional numerical simulation are the Reynolds-averaged Navier-Stokes equations, and the turbulence model uses a shear stress transport model. The working medium inside the turbine is considered an incompressible ideal gas, and its properties are corrected based on the inlet total temperature conditions. The inlet and outlet boundary conditions are selected as inlet total temperature and total pressure, and outlet static pressure, the values ​​of which are selected based on experimental data. Based on the above parameter settings, the flow process inside the turbine is numerically simulated. According to the three-dimensional simulation calculation results, the total-static efficiency of the radial turbine is calculated according to the following formula: , In this calculation, subscript 1 represents the inlet section of the volute, subscript 2 represents the interface between the volute and the impeller (which is both the volute outlet and the impeller inlet), and subscript 3 represents the outlet section of the impeller. The three-dimensional calculation assumes that the gas flow inside the turbine is an ideal adiabatic process, i.e., the gas adiabatic index is... remain unchanged. The temperature is isentropic, and the total temperature is... (Stagnation temperature), total pressure (Stagnation pressure) is related to flow velocity, and the calculation formula is: , , , Based on mesh independence analysis, the mesh count for a single-channel blade was set to 850,000, which is not only sufficiently detailed to capture flow details but also avoids excessive computational cost. To further verify the accuracy of the calculation results of the three-dimensional numerical simulation model of the turbocharger, steady-state performance under different turbine speeds was compared based on turbine performance test data, with the boundary conditions of the turbine numerical simulation remaining consistent with the experimental conditions. Verification through the numerical simulation model ensured a high degree of consistency between the three-dimensional numerical simulation results and experimental results in terms of both turbine flow capacity and turbine efficiency. The established three-dimensional aerodynamic simulation model of the radial turbine can accurately simulate the complex flow phenomena and performance inside the turbine.

[0033] Furthermore, in step two of this invention, the single-factor sensitivity analysis method analyzes the influence of each impeller geometric parameter on turbine efficiency individually, evaluates the sensitivity of each geometric parameter to the efficiency improvement of low expansion ratio radial turbines based on the maximum efficiency improvement rate, selects the geometric parameters that have a significant impact on the efficiency of radial turbines as key parameters for turbine efficiency characteristic prediction, and obtains the constraint range of each geometric parameter.

[0034] Furthermore, in step three of this invention, the Latin hypercube sampling method explores the spatial geometry of the impeller outlet rim, hub position, and blade length, which significantly affect turbine efficiency. The sampling intervals for the geometric parameters are as follows: outlet hub diameter interval is [15, 35] (mm); blade length variation interval is [-8, 8] (mm); and outlet rim diameter interval is [58, 78] (mm). Based on different combinations of geometric parameters, the turbine impeller geometry is determined, and the turbine efficiency is obtained through three-dimensional calculation under different reduced speed conditions representing low, medium, and high turbine speeds, which is used to train the turbine efficiency prediction model.

[0035] Furthermore, in step four of this invention, the input parameters of the backpropagation neural network include the normalized turbine reduced speed, impeller outlet rim position, outlet hub position, and blade length, while the output parameter is set to the turbine's total static efficiency. The neural network structure is a three-layer neural network with one hidden layer and three neurons in the hidden layer. The transfer function between layers is the Sigmoid function. , , In the formula, It is a weighted sum of the neuron's inputs. It is the connection weight between the i-th and j-th neurons. It is the output of the j-th neuron. These are the bias weights between neurons in each layer, and the weights are adjusted using the gradient descent method. Based on the established neural network prediction model, the efficiency of the radial turbine under different speeds and expansion ratios is calculated to obtain the efficiency characteristics of the radial turbine under all operating conditions. This model has good accuracy in predicting turbine efficiency and good generalization ability.

[0036] Furthermore, in step five of this invention, the formulas for calculating the compressor operating efficiency and the booster efficiency are as follows: , , In the formula, For compressor efficiency, This refers to the compressor inlet temperature. This refers to the compressor outlet temperature. The compressor pressure ratio. The adiabatic index, For turbocharger efficiency, For variable structure turbine efficiency. For turbocharger mechanical efficiency.

[0037] By collecting real-time temperature, pressure, and flow rate data before and after the turbine and compressor, the turbocharger's operating conditions are dynamically identified. The efficiency of the variable structure turbine is calculated based on a neural network prediction model, and the compressor operating efficiency is calculated based on experimental data, thereby accurately evaluating the turbocharger efficiency of the variable structure turbine.

[0038] Compared with the prior art, the present invention has the following beneficial effects: the method solves the problem of high difficulty and low accuracy in predicting the efficiency characteristics of variable structure turbochargers. The neural network prediction model trained based on three-dimensional aerodynamic simulation data can predict the efficiency of variable structure turbines according to turbine geometric parameters and operating speed. Combined with real-time operating data, the compressor operating efficiency is calculated, thus realizing accurate prediction of turbocharger efficiency under different structural forms and different operating conditions.

[0039] Figure 2 This is a flowchart illustrating another turbocharger efficiency evaluation method based on variable structure turbine data provided in this disclosure, combined with... Figure 2 ,right Figure 1 The methods described in the text will be further described.

[0040] The implementation process of this invention is as follows: First, a three-dimensional aerodynamic simulation model of the radial turbine was established. Based on experimental measurements, the numerical simulation model of the turbine design was verified to accurately simulate the complex flow phenomena and performance inside the turbine. The three-dimensional aerodynamic simulation model of the radial turbine was established using ANSYS CFX software, and the prototype turbine was modeled in three dimensions using the impeller and volute design software CFturbo based on measured turbine geometric parameters. The governing equations for the three-dimensional numerical simulation of the turbine were the Reynolds-averaged Navier-Stokes equations, and the turbulence model adopted the shear stress transport model. The working medium inside the turbine was considered an incompressible ideal gas, and its properties were corrected according to the inlet total temperature conditions. The inlet and outlet boundary conditions were selected as inlet total temperature and total pressure, and outlet static pressure, the values ​​of which were selected based on experimental data. Based on the above parameter settings, the flow process inside the turbine was numerically simulated. Based on the three-dimensional simulation results, the total-static efficiency of the radial turbine was calculated according to the following formula: , In this calculation, subscript 1 represents the inlet section of the volute, subscript 2 represents the interface between the volute and the impeller (which is both the volute outlet and the impeller inlet), and subscript 3 represents the outlet section of the impeller. The three-dimensional calculation assumes that the gas flow inside the turbine is an ideal adiabatic process, i.e., the gas adiabatic index is... remain unchanged. The temperature is isentropic, and the total temperature is... (Stagnation temperature), total pressure (Stagnation pressure) is related to flow velocity, and the calculation formula is: , , , Based on mesh independence analysis, the mesh count for a single-channel blade was set to 850,000, which is not only sufficiently detailed to capture flow details but also avoids excessive computational cost. To further verify the accuracy of the calculation results of the three-dimensional numerical simulation model of the turbocharger, steady-state performance under different turbine speeds was compared based on turbine performance test data, with the boundary conditions of the turbine numerical simulation remaining consistent with the experimental conditions. Verification through the numerical simulation model ensured a high degree of consistency between the three-dimensional numerical simulation results and experimental results in terms of both turbine flow capacity and turbine efficiency. The established three-dimensional aerodynamic simulation model of the radial turbine can accurately simulate the complex flow phenomena and performance inside the turbine.

[0041] Then, through CFD three-dimensional simulation, the influence of each geometric parameter of the radial turbine impeller on turbine efficiency was analyzed individually based on the single-factor sensitivity analysis method. The sensitivity and influence weight of each geometric parameter on the efficiency improvement of low expansion ratio radial turbine were evaluated based on the maximum efficiency improvement rate. The impeller inlet and outlet geometric parameters that have a significant impact on the efficiency of radial turbine were selected as key parameters for turbine efficiency characteristic prediction, and the constraint range of each geometric parameter was obtained.

[0042] Subsequently, the spatial geometry of the impeller outlet rim, hub position, and blade length, which significantly affect the efficiency of the radial turbine, was explored using the Latin hypercube sampling method. The sampling intervals for the geometric parameters were: outlet hub diameter [15, 35] (mm); blade length variation [-8, 8] (mm); and outlet rim diameter [58, 78] (mm). Based on different combinations of geometric parameters, the turbine impeller geometry was determined. Turbine efficiency was obtained through three-dimensional calculations under different reduced speed conditions representing low, medium, and high turbine speeds, thus acquiring a dataset for training a turbine efficiency prediction model based on geometric parameters.

[0043] Then, a neural network algorithm was applied to establish a model of the influence of key impeller geometric parameters on the efficiency of the radial turbine under different operating conditions. Based on the turbine reduced speed, impeller outlet rim position, outlet hub position, and blade length, the radial turbine efficiency was predicted. The input parameters of the backpropagation neural network included normalized turbine reduced speed, impeller outlet rim position, outlet hub position, and blade length, while the output parameter was set as the turbine's total static efficiency. The neural network structure was a three-layer neural network with one hidden layer and three neurons in each hidden layer. The Sigmoid function was chosen as the transfer function between layers. , In the formula, It is a weighted sum of the neuron's inputs. It is the connection weight between the i-th and j-th neurons. It is the output of the j-th neuron. These are the bias weights between neurons in each layer, and the weights are adjusted using the gradient descent method.

[0044] The efficiency of the radial turbine under different speeds and expansion ratios is calculated based on the established neural network prediction model, and the efficiency characteristics of the radial turbine under all operating conditions are obtained. This model has good accuracy in predicting turbine efficiency and has good generalization ability.

[0045] Finally, by collecting real-time temperature, pressure, and flow rate before and after the turbine and compressor, the turbocharger operating conditions are dynamically identified. The efficiency of the variable structure turbine is calculated based on a neural network prediction model, and the compressor operating efficiency is calculated based on experimental data, thereby accurately evaluating the turbocharger efficiency of the variable structure turbine.

[0046] The formulas for calculating compressor operating efficiency and turbocharger efficiency are as follows: , In the formula, For compressor efficiency, This refers to the compressor inlet temperature. This refers to the compressor outlet temperature. The compressor pressure ratio. The adiabatic index, For turbocharger efficiency, For variable structure turbine efficiency. For turbocharger mechanical efficiency.

[0047] Combination Figure 3 As shown in the illustration, this disclosure also provides a turbocharger efficiency evaluation device 300 based on variable structure turbine data, including a processor 304 and a memory 301. Optionally, the system may further include a communication interface 302 and a bus 303. The processor 304, communication interface 302, and memory 301 can communicate with each other via the bus 303. The communication interface 302 can be used for information transmission. The processor 304 can call logical instructions in the memory 301 to execute the turbocharger efficiency evaluation method based on variable structure turbine data described in the above embodiments.

[0048] Furthermore, the logic instructions in the aforementioned memory 301 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0049] The memory 301, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as the program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 304 executes functional applications and data processing by running the program instructions / modules stored in the memory 301, thereby implementing the turbocharger efficiency evaluation method based on variable structure turbine data in the above embodiments.

[0050] The memory 301 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 301 may include high-speed random access memory and may also include non-volatile memory.

[0051] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured as a turbocharger efficiency evaluation method based on variable structure turbine data.

[0052] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0053] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of this disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0054] The foregoing description and accompanying drawings fully illustrate embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. As used in the description of the embodiments, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used herein means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the terms “comprise” and its variations “comprises” and / or “comprising” refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Unless otherwise specified, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes the element. In this document, each embodiment may focus on describing the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, then the relevant parts can be referred to the description of the method section.

[0055] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0056] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0057] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0058] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0059] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0060] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0061] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0062] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0063] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0064] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0065] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for evaluating turbocharger efficiency based on variable structure turbine data, characterized in that, The method includes: A three-dimensional aerodynamic simulation model of a runoff turbine was established, and the numerical simulation model of turbine design was verified based on experimental measurements to accurately simulate the complex flow phenomena and performance inside the turbine. Based on the single-factor sensitivity analysis method, the influence weight of the geometric parameters of the impeller inlet and outlet on the efficiency characteristics of the radial turbine is analyzed by CFD three-dimensional simulation, and the key geometric parameters and constraint ranges of the impeller inlet and outlet that affect the efficiency characteristics of the radial turbine are obtained. Based on the Latin hypercube sampling method, we explored the space of key impeller geometric parameters that affect the efficiency of runoff turbines. We selected different turbine reduced speeds and obtained the turbine efficiency under different parameter combinations through three-dimensional calculations. We then obtained a dataset for fitting the relationship between runoff turbine efficiency and key impeller geometric parameters. A neural network algorithm was used to establish a model of the influence of key impeller geometric parameters on the efficiency of the radial turbine under different operating conditions. Based on the turbine reduced speed, impeller outlet rim position, outlet hub position and blade length, the efficiency of the radial turbine was predicted, and the efficiency characteristics of the variable structure turbine under all operating conditions were obtained. The system dynamically identifies the operating conditions of the turbocharger and calculates the compressor operating efficiency based on the measured temperature and pressure at the compressor inlet and outlet, thereby accurately evaluating the efficiency of the variable structure turbocharger.

2. The method according to claim 1, characterized in that, The three-dimensional aerodynamic simulation model of the radial turbine was established based on ANSYS CFX software, and the prototype turbine was modeled in three dimensions using the impeller and volute design software CFturbo based on the measured turbine geometric parameters.

3. The method according to claim 2, characterized in that, The control equations for the three-dimensional aerodynamic simulation of the turbine are the Reynolds-averaged Navier-Stokes equations, and the turbulence model is the shear stress transport model. The working medium inside the turbine is considered to be an incompressible ideal gas, and its physical properties are corrected according to the total inlet temperature conditions. The inlet and outlet boundary conditions are selected based on the total temperature and pressure at the inlet and the static pressure at the outlet, and their values ​​are selected based on experimental data.

4. The method according to claim 1, characterized in that, The method further includes calculating the total static efficiency of the runoff turbine based on the three-dimensional simulation results, satisfying the formula: ， Where T represents temperature, P represents pressure, subscript 1 represents the inlet section of the volute, subscript 2 represents the interface between the volute and the impeller (which is both the volute outlet and the impeller inlet), and subscript 3 represents the outlet section of the impeller. T 3S It is the isentropic temperature at the impeller outlet; In the three-dimensional calculation, it is assumed that the gas flow inside the turbine is an ideal adiabatic process, i.e., the gas adiabatic index is... remain unchanged. remain unchanged. (Stagnation temperature), total pressure (Stagnation pressure) is related to flow velocity, and the calculation formula is: ， ， ， Where K represents the adiabatic index and Ma represents the flow velocity. For quality flow, The density of the fluid passing through the cross-sectional area. Where is the local speed of sound, k is the specific heat ratio, R is the gas constant, and T is the thermodynamic temperature of the gas.

5. The method according to claim 1, characterized in that, The single-factor sensitivity analysis method analyzes the influence of each impeller geometry parameter on turbine efficiency individually. It evaluates the sensitivity of each geometry parameter to the efficiency improvement of low expansion ratio radial turbines based on the maximum efficiency improvement rate. The geometry parameters that have a significant impact on the efficiency of radial turbines are selected as key parameters for predicting turbine efficiency characteristics, and the constraint range of each geometry parameter is obtained.

6. The method according to claim 1, characterized in that, The Latin hypercube sampling method is used to explore the space of geometric parameters of impeller outlet rim, hub position and blade length that have a significant impact on turbine efficiency. The turbine impeller geometry is determined based on different combinations of geometric parameters. The turbine efficiency is obtained through three-dimensional calculation under different equivalent speed conditions representing low, medium and high turbine speeds, and used to train the turbine efficiency prediction model.

7. The method according to claim 1, characterized in that, The established model of the influence relationship between the key geometric parameters of the impeller on the efficiency of the radial turbine under different operating conditions is a backpropagation neural network. The input parameters of the backpropagation neural network include the normalized turbine reduced speed, impeller outlet rim position, outlet hub position, and blade length, while the output parameter is set to the turbine's total static efficiency. The backpropagation neural network is a three-layer neural network with one hidden layer and three neurons in each hidden layer; the transfer function between layers is the Sigmoid function, which satisfies the following formula: ， in, It is a weighted sum of the neuron's inputs. It is the connection weight between the i-th and j-th neurons. It is the output of the j-th neuron. These are the bias weights between neurons in each layer, and the weights are adjusted using the gradient descent method.

8. The method according to claim 1, characterized in that, The operating efficiency of the compressor and the efficiency of the booster satisfy the following formula: ， in, For compressor efficiency, This refers to the compressor inlet temperature. This refers to the compressor outlet temperature. The compressor pressure ratio. The adiabatic index, For turbocharger efficiency, For variable structure turbine efficiency. For turbocharger mechanical efficiency.

9. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-8.

Images