A whole machine variable dimension simulation method based on a circumferential average flow model

Abstract

The application discloses a whole machine variable dimension simulation method based on a circumferential average through-flow model, belongs to the technical field of numerical simulation and multi-scale modeling of an aero-engine, and solves the problems that in the prior art, the simulation efficiency and the local flow detail accuracy are difficult to be considered, and the traditional full three-dimensional calculation resource consumption is too large and the whole machine performance cannot be quickly evaluated. The application first establishes a zero-dimensional whole machine and a two-dimensional component model of an aero-engine, reduces the dimension by using a circumferential average N-S equation, embeds the two-dimensional model into the zero-dimensional model, solves the equation group by equivalent replacement of variables and reconstruction, realizes synchronous convergence of the whole machine and the component model, and improves the simulation accuracy and efficiency. The application adopts a full coupling mode, combines two-dimensional dimension reduction modeling, realizes minute-level convergence and simulation error ≯ 5%, improves the simulation accuracy, improves the calculation efficiency, can provide rich S2 flow field information, and reduces the research and development cost and period.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of numerical simulation and multi-scale modeling of aero-engines, and particularly relates to a whole-machine variable-dimension simulation method based on a circumferential average through-flow model. BACKGROUND

[0002] With the rapid improvement of computing device capability and the rapid development of numerical simulation technology, numerical simulation technology has been applied to the design of various components of aero-engines as an economic, effective and reliable means to replace physical prototype testing to comprehensively evaluate and verify the structure and performance.

[0003] At present, the performance characteristic evaluation of the whole machine is more zero-dimensional calculation, which uses the working relationship of various components of the engine and the component characteristics to solve the steady-state characteristics and transient-state characteristics of the engine, but the accuracy of zero-dimensional calculation depends on the engineering experience model obtained from a large number of component and whole machine tests; at the same time, due to the limitation of calculation conditions and the like, it is difficult to perform three-dimensional high-precision numerical simulation of the performance of the main flow passage of the whole machine.

[0004] In order to solve the above problems in whole machine simulation, various whole machine variable-dimension simulation methods are proposed, the variable-dimension simulation technology can use the component characteristics solved based on the high-precision simulation model for lower-precision whole machine simulation, establish a multi-precision simulation model of the aero-engine, and magnify some components or subsystems in the system, thereby allowing researchers to study the flow details and complex flow field structures of the components or subsystems in the whole machine environment and their influence on the performance of the whole machine, thereby improving the numerical simulation accuracy of the aero-engine and greatly reducing the research and development period and cost.

[0005] There are three types of current main variable-dimension simulation methods: weak coupling method, iterative coupling and complete coupling. The parameter transmission in the weak coupling method is one-way, which is difficult to ensure that the results are simultaneously convergent between different precision levels, the convergence speed and stability of the iterative coupling are affected by data processing methods and general characteristic maps and the like, and the complete coupling method can theoretically obtain completely convergent results, but because the high-dimensional simulation model is deeply embedded in the whole machine zero-dimensional simulation, the high-dimensional simulation model needs to be called multiple times, if the 0D whole machine-3D component coupling mode is adopted, it will inevitably lead to the problems of long calculation time and low calculation efficiency, which cannot meet the rapid iteration requirements of the design scheme in the engine engineering development. SUMMARY

[0006] The present application proposes a whole-machine variable-dimension simulation method based on a circumferential average through-flow model, which solves the problem that the simulation efficiency and local flow detail accuracy are difficult to be considered in the prior art, and the traditional full three-dimensional calculation resource consumption is too large and the whole machine performance cannot be quickly evaluated.

[0007] A whole machine variable dimension simulation method based on a circumferential average through-flow model, the method comprising the following steps:

[0008] Step one, establishing an aero-engine zero-dimensional whole machine simulation model, the model being constructed by means of a component characteristic map and flow continuity, power balance, and static pressure balance constraints to build a nonlinear equation set describing the working state of the whole machine;

[0009] Step two, two-dimensional dimension reduction modeling of a target component, a quasi-three-dimensional S2 through-flow model being established by means of a circumferential average N-S equation, i.e. implicit terms in a full three-dimensional equation set being converted into explicit source terms, and the source terms being modeled by means of an empirical model of a blade surface normal pressure gradient and a tangential friction force;

[0010] Step three, complete coupling and embedding of the two-dimensional component model into the zero-dimensional whole machine model, equivalent replacement of auxiliary variables in the zero-dimensional whole machine model by physical variables of the two-dimensional model, reconstruction of the nonlinear equation set and solution, and realization of synchronous convergence of the whole machine and the component model.

[0011] Further, in step one, the nonlinear equation set describing the working state of the whole machine is:

[0012]

[0013] In the formula, V i represents an iteration variable in a table, E i is a residual term in a balance equation, the number of balance equations and the corresponding aero-engine type being related;

[0014] Further, after step one, there are further steps:

[0015] Step one, solution of the nonlinear equation set describing the working state of the whole machine by means of a Netwon-Raphson iteration method, and obtaining of performance parameters of each component and the whole machine.

[0016] Further, in step two, flow viscosity and circumferential non-uniformity are ignored, a circumferential pressure gradient related term is modeled as a non-viscous blade force, a viscous stress related term is modeled as a viscous blade force, and the non-viscous blade force and viscous blade force models are established by means of an empirical / semi-empirical modeling equation.

[0017] Further, in step two, the implicit terms in the full three-dimensional equation set are converted into explicit source terms, as shown in the following formula,

[0018]

[0019] In the formula, each term is defined as follows:

[0020]

[0021] Further, in step three, the auxiliary variables of the zero-dimensional model include beta and the specific speed for characteristic map interpolation, and the physical variables of the two-dimensional model include the rotating speed, the inlet total temperature / total pressure and the outlet back pressure, wherein beta is an auxiliary function defined for facilitating the characteristic map interpolation and has no actual physical meaning.

[0022] Further, in step three, in the solving process of the reconstructed nonlinear equation set, whether the component flow calculated by the two-dimensional model is equal to the outlet flow of the upstream component of the zero-dimensional model is judged as the basis for model convergence.

[0023] Further, in step three, the iteration variables and the number of balance equations of the zero-dimensional model remain unchanged after coupling, ensuring the uniqueness of the solution.

[0024] A storage medium, which stores a computer program, the computer program is executed by a processor to implement the whole machine variable dimension simulation method based on the circumferential average flow model.

[0025] A computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executes the program to implement the whole machine variable dimension simulation method based on the circumferential average flow model.

[0026] The beneficial effects of the present application are as follows:

[0027] 1. The variable dimension simulation method proposed in the present application is based on a full coupling method, compared with the weak coupling and iterative coupling methods, the method can realize the simultaneous convergence of different dimension simulation models and obtain completely converged simulation results, and the simulation accuracy is higher.

[0028] 2. Since the two-dimensional simulation has smaller calculation amount than the full three-dimensional simulation and is easy to implement, the zero-dimensional whole machine-two-dimensional component variable dimension simulation method proposed in the present application has higher calculation efficiency, can realize minute-level calculation convergence, and solves the problems of long calculation time and low calculation efficiency caused by multiple calls of high-dimensional simulation models in the full coupling method.

[0029] 3. The two-dimensional simulation has its unique advantages: the two-dimensional simulation can provide more abundant S2 flow field information; at the same time, the two-dimensional calculation amount is small and easy to implement; and for specific cases, the two-dimensional simulation can use the loss model and the blockage model obtained from the test data to correct the flow field results, and avoid the amplification of the flow field calculation error caused by the limitation of the turbulence model. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 The whole machine variable dimension performance simulation flow framework and the control method flow chart in the embodiments of the present application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0032] A whole-machine variable-dimension simulation method based on a circumferential average through-flow model, the method comprising the following steps:

[0033] Step one, establishing an aero-engine zero-dimensional whole-machine simulation model, the model being constructed by means of component characteristic maps and flow continuity, power balance and static pressure balance constraints to build a nonlinear equation set describing the working state of the whole machine;

[0034] Step two, two-dimensional dimension reduction modeling of a target component, a quasi-three-dimensional S2 through-flow model being established by means of circumferential average N-S equations, i.e. implicit terms in a full three-dimensional equation set being converted into explicit source terms, and the source terms being modeled by means of an empirical model of blade surface normal pressure gradient and tangential friction force;

[0035] Step three, complete coupling of the two-dimensional component model into the zero-dimensional whole-machine model, equivalent replacement of auxiliary variables in the zero-dimensional whole-machine model by physical variables of the two-dimensional model, reconstruction of the nonlinear equation set and solution, and realization of synchronous convergence of the whole-machine and component models.

[0036] Specifically, in order to ensure the accuracy of variable-dimension simulation calculation while effectively reducing the consumption of computing resources, the present application proposes a zero-dimensional whole-machine-two-dimensional component complete coupling variable-dimension simulation method, which is fast in evaluation speed (realizes minute-level calculation convergence), high in evaluation accuracy (simulation error ≯ 5%), and convenient for combination with engineering practice.

[0037] Firstly, the present application establishes a component-based engine zero-dimensional simulation model by means of an object-oriented modeling method, which can meet the demand for performance simulation of a conventional aero-engine. The simulation model of each component is established by means of a component characteristic map, and the air path and shaft connection mode between components are defined to build a whole-machine simulation model, and a nonlinear equation set describing the working state of each component of the engine is established based on constraints such as flow continuity, power balance and static pressure balance,

[0038]

[0039] In the formula, V i represents the iteration variable in the table, E iTo balance the residual term in the equation, the number of balance equations and the specific engine type are related; finally, the non-linear equation set is solved by Newton-Raphson iteration method to obtain the performance parameters of each component and the whole machine.

[0040] Secondly, the S2 flow calculation based on the circumferential average N-S equation is adopted in the application, after the circumferential average of the N-S equation set, the equation set is reduced to quasi-three-dimensional, i.e. the implicit term in the full three-dimensional equation set is converted into an explicit source term, as shown in the following formula,

[0041]

[0042] Each term in the equation is defined as follows,

[0043]

[0044] These source terms can be divided into three categories: circumferential non-uniformity, circumferential pressure gradient related term and viscous stress related term. At present, the direct treatment is to ignore the flow viscosity, to replace the above three types of source terms with the normal pressure gradient and tangential friction force on the surface of the blade, and to establish the normal pressure and tangential friction force model on the surface of the blade with the experience / semi-experience modeling equation. Temporarily ignore the circumferential non-uniformity, model the circumferential pressure gradient related term as the blade force without viscosity (the normal pressure on the surface of the blade), model the viscous stress related term as the viscous blade force (the tangential friction force on the surface of the blade), and establish the blade force without viscosity and the viscous blade force model with the experience / semi-experience modeling equation.

[0045] Finally, the application adopts a fully coupled method to establish a zero-dimensional-two-dimensional variable dimension simulation model. The fully coupled method directly uses the component high-fidelity simulation model to replace the corresponding component zero-dimensional simulation model to construct the engine multi-dimensional simulation model. Compared with the weak coupling and iterative coupling method, the fully coupled method is more concise in form, but the cost is that the component high-fidelity simulation model is deeply embedded in the engine simulation model, which destroys the integrity of the engine zero-dimensional simulation model. In the engine multi-dimensional simulation model, the change of the component performance calculation method directly affects the construction and solution of the engine nonlinear equation set. Therefore, when using the fully coupled method, the solution scheme needs to be designed according to the characteristics of the whole machine zero-dimensional and component high-fidelity simulation model, and the nonlinear equation set describing the joint work of each component of the engine is reconstructed. The difficulty lies in quickly solving the nonlinear equation set under the condition that the component high-fidelity simulation model is deeply embedded in the nonlinear equation set solving process.

[0046] Here, the coupling of the fan two-dimensional model and the whole machine zero-dimensional model is taken as an example to introduce the full coupling method. In the zero-dimensional simulation model of the fan, the pressure ratio, efficiency, flow rate, margin and other performance parameters are usually obtained by interpolation on the fan characteristic diagram with the channel ratio, beta and rotating speed as independent variables, wherein the beta is an auxiliary function defined for the convenience of characteristic diagram interpolation and has no actual physical meaning. The two-dimensional simulation model of the fan adopts the rotating speed, inlet total temperature / total pressure and outlet back pressure as inputs, and obtains the performance parameters such as the pressure ratio, efficiency, flow rate, margin and detailed flow field distribution of the fan through CFD solving. Therefore, the key to constructing the zero-dimensional-two-dimensional coupling model lies in equivalent replacement of the independent variables of the zero-dimensional model and the independent variables of the two-dimensional model, and the calculation logic of the original whole machine zero-dimensional simulation is not changed as much as possible.

[0047] The whole machine variable dimension simulation method based on the circumferential average through-flow model provided by the application, the core of which is to establish a zero-dimensional whole machine and a two-dimensional component model and realize effective coupling. By adopting the circumferential average N-S equation to establish a quasi-three-dimensional S2 through-flow model, a two-dimensional reduced dimension modeling is performed on the target component, the implicit term in the full three-dimensional equation set is converted into an explicit source term, and an empirical model is used for modeling processing, which not only simplifies the calculation process, but also retains the key flow field information. Compared with the traditional full three-dimensional simulation, this reduced dimension modeling greatly reduces the calculation amount, so that it can also run efficiently under limited computing resources, and improves the calculation efficiency. In the model coupling aspect, the two-dimensional component model is fully coupled and embedded into the zero-dimensional whole machine model, the auxiliary variables in the zero-dimensional whole machine model are equivalent replaced by the physical variables of the two-dimensional model, the nonlinear equation set is reconstructed and solved. This process realizes the synchronous convergence of the whole machine and the component model, compared with the weak coupling and iterative coupling methods, the full coupling method of the application can obtain a fully converged simulation result, which significantly improves the simulation accuracy. In the solving process, whether the component flow rate calculated by the two-dimensional model is equal to the outlet flow rate of the upstream component in the zero-dimensional model is used as the model convergence basis, and the number of iteration variables and balance equations of the zero-dimensional model remains unchanged after coupling, which ensures the uniqueness of the solution and further guarantees the reliability and stability of the simulation result. In addition, the two-dimensional simulation itself has unique advantages, it can provide rich S2 flow field information, and for specific cases, the loss model, blockage model and the like obtained from the test data can be used to modify the flow field result, effectively avoiding the problem that the calculation error of the flow field is amplified due to the limitation of the turbulence model, which provides strong support for accurate analysis of the performance of the component and the whole machine.

[0048] Further, in step one, the nonlinear equation set describing the working state of the whole machine is:

[0049]

[0050] In the formula, V i represents the iteration variable in the table, Ei The number of balance equations and the corresponding engine type are related to the residual term in the balance equation;

[0051] Specifically, the embodiment explicitly gives a nonlinear equation set describing the working state of the whole machine. The equation set provides an accurate mathematical basis for subsequent simulation calculation and is a key link to realize accurate simulation. By constructing the nonlinear equation set, the working state of each component of the aero-engine and the performance of the whole machine are closely related. The constraint conditions such as flow continuity, power balance and static pressure balance are reflected in the equation, which fully reflects the complex physical relationship inside the engine. This makes it possible to more accurately simulate the running state of the engine under different working conditions and capture the mutual influence and cooperative working mechanism between components during the simulation process. The establishment of the equation set also lays a solid foundation for subsequent solution using Newton-Raphson iteration method. Through iterative solution, the performance parameters of each component and the whole machine can be obtained, which are important basis for evaluating engine performance and optimizing design. In the process of engine design and development, engineers can adjust the structure and component characteristics of the engine according to these accurate performance parameters to improve the performance and reliability of the engine.

[0052] Further, after step one, further comprising:

[0053] Step one, solving the nonlinear equation set describing the working state of the whole machine by Newton-Raphson iteration method to obtain the performance parameters of each component and the whole machine.

[0054] Specifically, the embodiment further perfects the simulation process, which provides that after establishing the zero-dimensional whole engine simulation model and constructing the nonlinear equation set, the performance parameters of each component and the whole engine are obtained by solving the equation set through the Netwon-Raphson iteration method. The design of this process is of great significance to the whole simulation method. As a mature and efficient numerical calculation method, the Netwon-Raphson iteration method plays a key role in the present application. It can use the information of the nonlinear equation set to approach the real solution of the equation set through continuous iteration. In the simulation context of an aero-engine, this means that the performance parameters of each component and the whole engine can be accurately obtained from the complex engine system model. Since the aero-engine involves numerous interrelated parameters and complex physical processes, ordinary solving methods are difficult to cope with, while the iteration method effectively solves this problem with its good convergence and computational efficiency. By using the Netwon-Raphson iteration method for solving, on the one hand, the detailed performance data of each component under different working conditions can be obtained, such as the pressure ratio of the compressor, the efficiency of the turbine, etc., which are crucial for in-depth understanding of the working characteristics of the components and optimizing the component design. On the other hand, the performance parameters of the whole engine, such as thrust, fuel consumption rate, etc., can also be accurately obtained, providing a quantitative basis for evaluating the overall performance of the engine.

[0055] Further, in step two, the circumferential pressure gradient related terms are modeled as inviscid blade forces, the viscous stress related terms are modeled as viscous blade forces, and the inviscid blade force and viscous blade force models are established through empirical / semi-empirical modeling equations.

[0056] Specifically, in the simulation of the complex flow field of an aero-engine, it is difficult to meet the rapid simulation requirements of engineering practice by directly solving the complete three-dimensional N-S equation due to huge calculation amount. The simplification and modeling manner adopted by the present application greatly reduces the calculation complexity under the premise of ensuring a certain accuracy. The simplification of ignoring flow viscosity and circumferential non-uniformity is a reasonable simplification made after comprehensive consideration of engineering practice and calculation efficiency. In many actual working conditions, such simplification will not have a decisive influence on the overall simulation result, but can significantly reduce the calculation amount and improve the simulation speed. The core technology of this step is to reasonably model the circumferential pressure gradient related term and the viscous stress related term, and to establish a model through an empirical / semi-empirical modeling equation. These empirical / semi-empirical modeling equations are obtained based on a large amount of experimental data and theoretical research, and can effectively reflect the actual physical phenomenon. By establishing the inviscid blade force and viscous blade force models through these equations, the action of the blade on the airflow can be accurately simulated in the two-dimensional dimension reduction modeling process. This not only helps to better understand the interaction mechanism between the blade and the airflow, but also provides more reliable component models for subsequent whole-machine performance simulation, thereby improving the accuracy of whole-machine variable dimension simulation and making the simulation result more reflect the actual performance of the engine.

[0057] Further, in step two, the implicit term in the full three-dimensional equation set is converted into an explicit source term, as shown in the following formula,

[0058]

[0059] In the equation, each term is defined as follows:

[0060]

[0061]

[0062] Further, in step three, the auxiliary variables of the zero-dimensional model include β and the bypass ratio for characteristic map interpolation, and the physical variables of the two-dimensional model include the rotating speed, the inlet total temperature / total pressure and the outlet back pressure, wherein β is an auxiliary function defined for the convenience of characteristic map interpolation and has no actual physical meaning.

[0063] Specifically, in the simulation of an aero-engine, the zero-dimensional model and the two-dimensional model have their own characteristics. The zero-dimensional model is widely used for its fast calculation speed and its ability to quickly evaluate the performance of the whole engine, but it is relatively rough and has limited ability to describe the detailed flow field inside the components. The two-dimensional model can provide more detailed flow field information, but the calculation amount is larger. The present application fully utilizes the advantages of the two models by coupling them. Defining the auxiliary variables of the zero-dimensional model (such as beta and the bypass ratio for characteristic map interpolation) and the physical variables of the two-dimensional model (such as the rotational speed, the total temperature / total pressure at the inlet, and the back pressure at the outlet) is an important basis for realizing this coupling. By equivalently replacing these variables, a close relationship between the zero-dimensional whole engine model and the two-dimensional component model can be established, allowing the more accurate component information in the two-dimensional model to be integrated into the whole engine model when reconstructing the nonlinear equation set. This helps to break down the barriers between models of different dimensions and achieve a deep integration of the whole engine and component models, thereby significantly improving the accuracy of the simulation while ensuring computational efficiency. In the design and optimization process of the engine, more accurate simulation results can provide engineers with more reliable basis, helping them better understand the working principle and performance characteristics of the engine, and then optimize the structure and parameters of the engine to improve the performance and reliability of the engine.

[0064] Further, in step three, in the solving process of the reconstructed nonlinear equation set, whether the component flow calculated by the two-dimensional model is equal to the outlet flow of the upstream component of the zero-dimensional model is judged as the basis for model convergence.

[0065] Specifically, in the variable dimension simulation of an aero-engine, model convergence is directly related to the effectiveness of the simulation results. Due to the differences in characteristics and calculation logic between the zero-dimensional whole engine model and the two-dimensional component model, how to ensure that the two models can work in coordination and reach a stable convergence state after coupling is a key problem. The present application selects the component flow calculated by the two-dimensional model and the outlet flow of the upstream component of the zero-dimensional model as the basis for judgment, which is highly scientific and reasonable. The flow parameter is a key indicator reflecting the working state of the engine inside, and its transmission and change between different components reflect the complex physical process inside the engine. By comparing the two flows, the accuracy of the simulation of the flow relationship between components by the coupled zero-dimensional and two-dimensional models can be directly measured. If they are equal or within a reasonable error range, it means that the model can accurately reflect the flow distribution and flow characteristics inside the engine, indicating that the zero-dimensional whole engine model and the two-dimensional component model have reached a coordinated working state under the current calculation conditions, and the simulation results are reliable; otherwise, it means that there is a problem with the model that needs to be further adjusted and optimized. This way of using flow comparison as the basis for convergence not only provides a clear termination condition for simulation calculation, avoiding invalid calculation and improving calculation efficiency, but also helps to find potential problems in the construction and calculation of the model, providing a direction for the improvement and perfection of the model.

[0066] Further, in step three, the iteration variables and the number of balance equations of the zero-dimensional model remain unchanged after coupling, ensuring the uniqueness of the solution.

[0067] Specifically, when constructing the variable-dimension simulation model of an aero-engine, ensuring that the iteration variables and the number of balance equations of the zero-dimensional model remain unchanged can maintain the stability and consistency of the model solution. The zero-dimensional model is the basis for preliminary evaluation of the overall performance, and its iteration variables and balance equations reflect the basic physical relationship and constraint conditions between the components of the engine. When coupled with the two-dimensional component model, if these key elements are changed arbitrarily, the calculation logic of the model will become complex and chaotic, leading to unstable solving process and unpredictable results. Keeping them unchanged is like providing a stable framework for simulation calculation, so that effective calculation can still be carried out based on the original physical logic after adding the high-precision details of the two-dimensional model. From the perspective of computational efficiency, fixing the number of iteration variables and balance equations avoids the additional computational burden caused by frequent changes in the model structure. In each iteration process, the reasonable allocation of computing resources is crucial. If the number of variables and equations changes constantly, the solving algorithm needs to constantly adjust the calculation strategy, which will undoubtedly increase the calculation time and resource consumption. The design of the present application ensures the continuity of the calculation process, so that the computing resources can be concentrated on solving the core calculation tasks after the model is coupled, thereby improving the overall efficiency of the simulation calculation and meeting the demand for rapid evaluation of design schemes in the engineering development of aero-engines. In terms of ensuring the uniqueness of the solution, the number of iteration variables and balance equations plays a key role. In complex aero-engine simulation, the uniqueness of the solution is an important prerequisite to ensure the reliability of the results. If these key factors change arbitrarily, multiple solutions or no solutions may occur, making the simulation results meaningless. The present application maintains the number of iteration variables and balance equations unchanged, and uses the mathematical stability principle to ensure that the simulation model can obtain a unique and determined solution under different working conditions, providing accurate and reliable simulation data for engineers.

[0068] A storage medium, the storage medium has a computer program stored thereon, the computer program is executed by a processor to implement the above-mentioned whole-machine variable-dimension simulation method based on the circumferential average through-flow model.

[0069] Specifically, the storage medium serves as a carrier for the simulation method of the present application, greatly enhancing the technology's communicability and reusability. In the field of aero-engine research and development, different research teams and enterprises often need to repeat similar simulation work. The storage medium solidifies the simulation method of the present application in the form of a computer program, making related technology no longer limited to specific research and development environments and personnel. Whether in academic research, helping researchers quickly build simulation platforms for theoretical verification, or in industrial production, providing efficient simulation tools for engine design optimization, the technology of the present application can be conveniently obtained and applied through the use of the storage medium, thereby avoiding repeated development and saving a large amount of manpower, material resources, and time costs. From the perspective of technology inheritance and development, the storage medium provides a solid foundation for subsequent technological improvement and innovation. With the continuous development of aero-engine technology, the requirements for simulation accuracy and efficiency are also continuously improving. Based on the computer program on the storage medium, researchers can conduct secondary development and optimization on this basis. They can improve the algorithms and models in the program according to new research results and actual needs, further improving the performance of the simulation method. This helps to promote the continuous progress of aero-engine simulation technology, making it better adapt to changing industry demands and promoting the development of the entire aero-engine industry. In addition, the existence of the storage medium also makes the simulation method of the present application more convenient to integrate with other related technologies. In the modern aero-engine research and development process, multiple technical means are often combined, such as computer-aided design (CAD), computer-aided manufacturing (CAM), etc. By integrating the simulation program in the storage medium with these technologies, an integrated process from design to simulation to manufacturing can be achieved, improving the collaboration and efficiency of research and development, and further highlighting the importance of the present application in the field of aero-engine research and development.

[0070] A computer device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned whole-machine variable-dimension simulation method based on a circumferential average through-flow model.

[0071] Specifically, the computer device provides a complete and efficient running environment for the whole machine variable dimension simulation method based on the circumferential average through-flow model. From the perspective of computing performance, the processor, as the core operation unit, has powerful data processing capability and can quickly process the data of the zero-dimensional whole machine simulation model and the two-dimensional component model. When constructing the nonlinear equation set and performing iterative solution, the processor can execute complex algorithms such as the Netwon-Raphson iteration method at a high speed, ensuring the accuracy and timeliness of the calculation. The high-efficiency operation capability solves the problems of slow calculation and low efficiency that may occur when the traditional computing device processes such complex simulation tasks, realizes minute-level calculation convergence, meets the demand for quickly obtaining simulation results in the design of an aero-engine, and enables engineers to evaluate a plurality of design schemes in a short time, greatly accelerating the design iteration speed. The memory provides stable storage and reading support for various data in the simulation process. During simulation running, it stores a large amount of component characteristic map data, engine working condition parameters, and intermediate data in the iteration process, etc. These data are the basis for smooth simulation, and the stable storage of the memory ensures the integrity and accuracy of the data, avoiding the influence of data loss or errors on the simulation results. At the same time, the fast data reading capability enables the processor to obtain the required data in time, further improving the smoothness and efficiency of the entire simulation process. Integrating the simulation method program of the present application into the computer device realizes the deep integration of hardware and software. This integration enables the designers and researchers of the aero-engine to no longer spend a lot of effort on building a complex computing environment, but can conveniently use the variable dimension simulation method of the present application by operating the computer device. This not only reduces the technical use threshold and improves the research and development efficiency, but also enables more researchers and engineers to participate in the research and development of the aero-engine, promotes the wide application and further innovation of the technology, effectively promotes the development of the aero-engine technology, and improves the competitiveness of the entire aero-engine industry.

[0072] When the VCE variable dimension simulation model is established by using the full coupling method, a solution scheme needs to be designed according to the characteristics of the whole machine zero-dimensional and compression component two-dimensional simulation models, and a nonlinear equation set describing the common work of each component of the engine needs to be reconstructed. The whole machine variable dimension performance simulation flow framework and control method (taking the fan component as an example) are as shown in Figure 1

[0073] The specific implementation of the variable dimension scheme is as follows:

[0074] ​1. In the design point simulation analysis, the zero-dimensional model mainly uses the component general characteristic map to get the modeling characteristic map of the compressor at the design point. In the variable dimension simulation based on the full coupling method, since the performance parameters of the compressor components can be calculated according to the two-dimensional model, it is not necessary to model the general characteristic map, but in order not to make too many changes to the original zero-dimensional solver, it is necessary to set an output error at the design point. The definition of the output error of the zero-dimensional model at the design point is as follows: first, a design point R line is given, the error between the input R line and the design point R line is set as the output error at the design point, a design value Ps of the outlet back pressure can be given, and the error between Ps and the input Ps is defined as the output error at the design point. At the same time, it is also necessary to ensure that the two-dimensional model can obtain the required design pressure ratio and efficiency performance parameters at the design value Ps.

[0075] 2. In the non-design point simulation analysis, the aerodynamic parameters (mass flow, total enthalpy, total temperature, total pressure, oil-gas ratio) required by the zero-dimensional model must be reflected in the two-dimensional model. The two-dimensional model also needs physical speed in calculation, and the torque and bleed air parameters can also be directly obtained after the two-dimensional model is calculated. Therefore, the key of the variable dimension simulation based on the full coupling method is to replace R line (the iteration variable) in the compressor zero-dimensional model and the balance equation. From the calculation logic of the two-dimensional model, the inlet conditions are generally given the total temperature and total pressure, which can be calculated by the upstream related components of the zero-dimensional model; the outlet parameters are generally given the static pressure, which cannot be directly obtained by the zero-dimensional model. In the 2D simulation model, when the speed and inlet conditions are determined, the equal speed characteristic line of the compressor can be obtained by giving different back pressures, which is consistent with the idea of determining the characteristic map position by R line in the zero-dimensional model, so static pressure can be used to replace R line . The iteration error of the non-design point balance equation of the zero-dimensional model compressor is obtained according to the flow balance: in a certain iteration calculation of the zero-dimensional simulation model, the upstream component will give a flow value, and the R line value will also give a flow value by looking up the characteristic map, and the error between the two flow values can be used to judge whether the simulation converges. In the two-dimensional model, a flow value is also calculated by giving the back pressure, which also needs to be consistent with the flow value of the upstream component in the zero-dimensional model, so the definition of the non-design point balance equation in the variable dimension simulation of the compression system can remain the same as the original zero-dimensional model. Since the iteration variable and the number of balance equations of the zero-dimensional model do not change in this variable dimension method, the solution is unique.

[0076] In the specific solving process, the flow balance of fan / compressor, turbine and nozzle, the rotor power balance and the static pressure balance equation at the mixing chamber are solved by Newton-Raphson iteration method, and the expression of the iteration algorithm is

[0077] Y (a+1) = Y (a) - [A (a) ] -1 Z (a)

[0078] wherein, a represents the iteration number, A represents the Jacobian matrix:

[0079]

[0080] Whether the iteration converges or not is judged by judging whether the flow calculated by the two-dimensional model and the flow at the outlet of the upstream component are equal. If the flows are not equal, the compressor outlet back pressure of the next iteration can be calculated according to the flow difference, and the total temperature, total pressure and rotating speed of the compressor inlet in the new iteration can be obtained according to the zero-dimensional model, and the compressor two-dimensional model is called again to calculate the performance parameters of the compressor, which are used for the zero-dimensional overall cycle calculation, until the zero-dimensional model and the two-dimensional model reach the convergent state at the same time, and the engine variable-dimension performance parameters and the two-dimensional flow field under the current working condition can be obtained.

[0081] The whole machine variable dimension simulation method based on the circumferential average through-flow model has remarkable beneficial effects. The method effectively overcomes the shortcomings of traditional simulation methods by establishing a zero-dimensional whole machine simulation model and a two-dimensional reduced dimension model of the target component, and completely coupling and embedding the two. Nonlinear equations are constructed using component characteristic maps combined with flow continuity, power balance, and static pressure balance constraints, and then solved by the Netwon-Raphson iteration method to accurately obtain the performance parameters of each component and the whole machine. When the two-dimensional reduced dimension modeling is performed, the quasi-three-dimensional S2 through-flow model is established by using the circumferential average N-S equation, and the source term is reasonably modeled to simplify the calculation while retaining key flow field information. In the coupling process, the zero-dimensional whole machine model auxiliary variables are replaced by the two-dimensional model physical variables to reconstruct and solve the nonlinear equations, realize the synchronous convergence of the whole machine and component model, and have higher simulation accuracy compared with the traditional coupling method. Moreover, the unique advantages of two-dimensional simulation, such as providing rich S2 flow field information and being able to use experimental data to correct the results, further improve the reliability of the simulation. The method is realized by means of a storage medium and a computer device, is convenient for technology dissemination, reuse and integration, significantly improves the efficiency and accuracy of the aero-engine simulation, provides strong support for engine design and research and development, effectively shortens the research and development cycle and reduces the cost, and promotes the continuous progress of aero-engine technology. The method has fast evaluation speed (realizes minute-level calculation convergence), high evaluation accuracy (simulation error≯5%), and is convenient for combination with engineering practice.

[0082] The specific embodiments of the application are described in detail above, but only as examples. The application is not limited to the specific embodiments described above. Any equivalent modifications and substitutions made by those skilled in the art to the application are also within the scope of the application. Therefore, equivalent transformations and modifications made without departing from the spirit and scope of the application should be covered within the scope of the application.

Claims

1. A whole machine variable dimension simulation method based on a circumferential average through-flow model, characterized in that, The method comprises the following steps: Step one, establishing a zero-dimensional whole engine simulation model, the model is constructed by component characteristic map and flow continuity, power balance, static pressure balance constraints to describe the nonlinear equation set of whole engine working state, the nonlinear equation set of whole engine working state is: where V i represents the iteration variable in the table, E i is the residual term in the balance equation, the number of balance equations and the corresponding engine type are related; Step one, solving the nonlinear equation set of whole engine working state by Newton-Raphson iteration method to obtain the performance parameters of each component and whole engine; Step two, two-dimensional dimension reduction modeling of target component, quasi-three-dimensional S2 through-flow model is established by adopting circumferential average N-S equation, that is, implicit terms in full three-dimensional equation set are converted into explicit source terms, and source terms are processed by empirical model of blade surface normal pressure gradient and tangential friction force, in step two, flow viscosity and circumferential non-uniformity are ignored, circumferential pressure gradient related terms are modeled as non-viscous blade force, viscous stress related terms are modeled as viscous blade force, and non-viscous blade force and viscous blade force models are established by empirical / semi-empirical modeling equation, in step two, the implicit terms in full three-dimensional equation set are converted into explicit source terms, as shown in the following formula, Wherein, each term in the equation is defined as follows: ； Step three, completely coupling and embedding the two-dimensional component model into the zero-dimensional whole engine model, replacing the auxiliary variables in the zero-dimensional whole engine model with the physical variables of the two-dimensional model by equivalent replacement, reconstructing the nonlinear equation set and solving, realizing the synchronous convergence of whole engine and component model; the auxiliary variables of the zero-dimensional whole engine model include β and bypass ratio for characteristic map interpolation, the physical variables of the two-dimensional model include rotating speed, inlet total temperature / total pressure and outlet back pressure, wherein β is an auxiliary function defined for convenient characteristic map interpolation, and has no actual physical meaning; in the solving process of the reconstructed nonlinear equation set, whether the component flow calculated by the two-dimensional model is equal to the outlet flow of the upstream component in the zero-dimensional model is judged as the basis for model convergence; the number of iteration variables and balance equations of the zero-dimensional whole engine model remains unchanged after coupling, ensuring the uniqueness of the solution.

2. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the whole engine variable dimension simulation method based on circumferential average through-flow model in claim 1.

3. A computer device, comprising: Comprise: Memory, processor and computer program stored on the memory and executable on the processor, the processor executes the program to realize the whole engine variable dimension simulation method based on circumferential average through-flow model in claim 1.

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