Complete machine variable-dimension simulation method based on circumferential average through-flow model

Abstract

The invention discloses a complete machine variable-dimension simulation method based on a circumferential average flow model, belongs to the technical field of aero-engine numerical simulation and multi-scale modeling, and solves the problem that complete machine simulation efficiency and local flow detail precision are difficult to consider at the same time in the prior art. And traditional full-three-dimensional computing is too high in resource consumption and cannot quickly evaluate the performance of the whole machine. According to the method, a zero-dimensional complete machine and component two-dimensional model of the aero-engine is firstly established, dimensionality reduction is carried out by using a circumferential average N-S equation, the two-dimensional model is embedded into the zero-dimensional model, an equivalent replacement variable reconstruction equation set is solved, synchronous convergence of the complete machine and component models is realized, and simulation precision and efficiency are improved. According to the method, a full coupling mode is adopted, two-dimensional dimension reduction modeling is combined, minute-level convergence is achieved, the simulation error is not larger than 5%, the simulation precision is improved, the calculation efficiency is improved, rich S2 flow field information can be provided, and the research and development cost and period are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical simulation and multi-scale modeling of aero-engines, and in particular to a whole-machine variable-dimensional simulation method based on a circumferential average through-flow model. Background Art

[0002] With the rapid improvement of computing equipment capabilities and the rapid development of numerical simulation technology, numerical simulation technology has been applied to the design of various components of aircraft engines as an economical, effective and reliable means to replace physical prototype tests for comprehensive evaluation and verification of structure and performance.

[0003] Currently, the performance characteristics of the entire engine are mostly evaluated through zero-dimensional calculations, which use the working relationships and component characteristics of the engine components to solve the engine's steady-state and transient characteristics. However, the accuracy of zero-dimensional calculations depends on the engineering experience models obtained from a large number of component and entire engine tests. At the same time, due to limitations such as calculation conditions, it is difficult to perform three-dimensional high-precision numerical simulations of the main flow channel performance of the entire engine.

[0004] In order to solve the above-mentioned problems in whole-machine simulation, a variety of whole-machine variable-dimensional simulation methods have been proposed. Variable-dimensional simulation technology can use component characteristics solved based on high-precision simulation models for lower-precision whole-machine simulation, establish a multi-precision simulation model of an aero-engine, and amplify certain components or subsystems in the system, allowing researchers to study the flow details and complex flow field structures of components or subsystems in a whole-machine environment, as well as their impact on the performance of the whole machine, thereby improving the accuracy of numerical simulation of aero-engines and greatly reducing the R&D cycle and cost.

[0005] Currently, there are three main types of variable-dimensional simulation methods: weak coupling, iterative coupling, and full coupling. The parameter transfer in weak coupling methods is unidirectional, making it difficult to ensure simultaneous convergence between different levels of accuracy. The convergence speed and stability of iterative coupling are affected by factors such as data processing methods and general characteristic diagrams. Full coupling methods can theoretically achieve fully converged results, but because the high-dimensional simulation model is deeply embedded in the zero-dimensional simulation of the entire engine, it requires multiple calls to the high-dimensional simulation model. Using a 0D entire engine-3D component coupling approach inevitably results in long computation times and low efficiency, which does not meet the requirements for rapid iteration of design solutions in engine engineering development. Summary of the Invention

[0006] The present invention proposes a variable-dimensional simulation method for the entire machine based on a circumferential average flow model. By combining a multi-scale coupling strategy with local high-precision correction of the three-dimensional blade model, it solves the problem in the existing technology that it is difficult to strike a balance between the efficiency of the entire machine simulation and the accuracy of local flow details, and that traditional full three-dimensional computing resources consume too much and cannot quickly evaluate the performance of the entire machine.

[0007] A whole-machine variable-dimension simulation method based on a circumferential average throughflow model comprises the following steps:

[0008] Step 1: Establish a zero-dimensional whole-machine simulation model of the aircraft engine, wherein the model constructs a nonlinear equation system describing the whole-machine operating state through component characteristic diagrams and flow continuity, power balance, and static pressure balance constraints;

[0009] Step 2: Perform 2D dimensionality reduction modeling on the target component and establish a quasi-3D S2 flow model using the circumferentially averaged NS equations. This involves converting the implicit terms in the full 3D equations into explicit source terms, and modeling the source terms using empirical models of the normal pressure gradient and tangential friction on the blade surface.

[0010] Step three: fully couple the two-dimensional component model and embed it into the zero-dimensional whole machine model. By equivalently replacing the auxiliary variables in the zero-dimensional whole machine model with the physical variables of the two-dimensional model, reconstruct the nonlinear equation group and solve it to achieve synchronous convergence of the whole machine and component models.

[0011] Furthermore, in step 1, the nonlinear equation group describing the working state of the whole machine is:

[0012]

[0013] Where V i Represents the iteration variable in the table, E i is the residual term in the equilibrium equation. The number of equilibrium equations is related to the corresponding engine configuration;

[0014] Furthermore, after step 1, the method further includes:

[0015] Step 11: Solve the nonlinear equations describing the working state of the whole machine by using the Netwon-Raphson iterative method to obtain the performance parameters of each component and the whole machine.

[0016] Furthermore, in step 2, the flow viscosity and circumferential heterogeneity are ignored, 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.

[0017] Furthermore, in step 2, the implicit terms in the full three-dimensional equations are converted into explicit source terms, as shown in the following formula:

[0018]

[0019] The definitions of each term in the equation are as follows:

[0020]

[0021] Furthermore, in step three, the auxiliary variables of the zero-dimensional model include β and bypass ratio for characteristic diagram interpolation, and the physical variables of the two-dimensional model include speed, inlet total temperature / total pressure, and outlet back pressure, wherein β is an auxiliary function defined to facilitate characteristic diagram interpolation and has no actual physical meaning.

[0022] Furthermore, in step three, during the solution of the reconstructed nonlinear equations, 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 used as a basis for model convergence.

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

[0024] A storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned whole-machine variable-dimensional simulation method based on a circumferential average through-flow model.

[0025] A computer device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned whole-machine variable-dimensional simulation method based on a circumferential average flow model.

[0026] Beneficial effects of the present invention:

[0027] 1. The variable-dimensional simulation proposed in the present invention is based on a full-coupling method. Compared with weak-coupling and iterative-coupling methods, this method can achieve simultaneous convergence of simulation models in different dimensions and obtain fully converged simulation results with higher simulation accuracy.

[0028] 2. Since two-dimensional simulation has less computational complexity and is easier to implement than full three-dimensional simulation, the zero-dimensional whole machine-two-dimensional component variable-dimensional simulation method proposed in the present invention has high computational efficiency and can achieve computational convergence in minutes, solving the problems of long computational time and low computational efficiency caused by multiple calls to high-dimensional simulation models faced by the fully coupled method.

[0029] 3. Two-dimensional simulation has its unique advantages: 2D simulation can provide richer S2 flow field information; at the same time, 2D calculations are small and easy to implement; and for specific cases, 2D simulation can use loss models and blockage models derived from experimental data to correct flow field results, avoiding the amplification of flow field calculation errors caused by the limitations of turbulence models. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of the whole machine variable dimension performance simulation process framework and control method in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] A whole-machine variable-dimension simulation method based on a circumferential average throughflow model comprises the following steps:

[0033] Step 1: Establish a zero-dimensional whole-machine simulation model of the aircraft engine, wherein the model constructs a nonlinear equation system describing the whole-machine operating state through component characteristic diagrams and flow continuity, power balance, and static pressure balance constraints;

[0034] Step 2: Perform 2D dimensionality reduction modeling on the target component and establish a quasi-3D S2 flow model using the circumferentially averaged NS equations. This involves converting the implicit terms in the full 3D equations into explicit source terms, and modeling the source terms using empirical models of the normal pressure gradient and tangential friction on the blade surface.

[0035] Step three: fully couple the two-dimensional component model and embed it into the zero-dimensional whole machine model. By equivalently replacing the auxiliary variables in the zero-dimensional whole machine model with the physical variables of the two-dimensional model, reconstruct the nonlinear equation group and solve it to achieve synchronous convergence of the whole machine and component models.

[0036] Specifically, in order to ensure the calculation accuracy of variable-dimensional simulation while effectively reducing the consumption of computing resources, the present invention proposes a variable-dimensional simulation method with full coupling of zero-dimensional whole machine and two-dimensional components. This method has a fast evaluation speed (achieving calculation convergence in minutes) and high evaluation accuracy (simulation error ≯5%), and is easy to combine with engineering practice.

[0037] First, the present invention uses an object-oriented modeling approach to establish a component-based zero-dimensional engine simulation model, which can meet the needs of conventional aircraft engine performance simulation. Simulation models for each component are established using component characteristic diagrams, and the gas paths and shaft connections between components are defined to build a complete engine simulation model. Based on constraints such as flow continuity, power balance, and static pressure balance, a set of nonlinear equations describing the operating conditions of each engine component is established.

[0038]

[0039] Where V i Represents the iteration variable in the table, E iis the residual term in the balance equation. The number of balance equations is related to the specific engine configuration. Finally, the nonlinear equations are solved by the Netwon-Raphson iterative method to obtain the performance parameters of each component and the entire engine.

[0040] Secondly, the present invention adopts the S2 throughflow calculation based on the circumferentially averaged NS equation. After the circumferential averaging of the NS equation group, the equation group is reduced to quasi-three-dimensional, that is, the implicit terms in the full three-dimensional equation group are converted into explicit source terms, as shown in the following formula:

[0041]

[0042] The definitions of each term in the equation are as follows,

[0043]

[0044] These source terms can be divided into three categories: circumferential inhomogeneity terms, circumferential pressure gradient-related terms, and viscous stress-related terms. Currently, the straightforward approach is to ignore flow viscosity and replace these three source terms with the normal pressure gradient and tangential friction on the blade surface. These terms are then modeled using empirical / semi-empirical modeling equations for the normal pressure and tangential friction on the blade surface. Temporarily ignoring circumferential inhomogeneity, the circumferential pressure gradient-related terms are modeled as inviscid blade forces (normal pressure on the blade surface), and the viscous stress-related terms are modeled as viscous blade forces (tangential friction on the blade surface). These inviscid and viscous blade forces are then modeled using empirical / semi-empirical modeling equations.

[0045] Finally, the present invention adopts a fully coupled method to establish a zero-dimensional-two-dimensional variable-dimensional 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 methods, 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 whole-machine simulation model, which destroys the integrity of the engine zero-dimensional simulation model. In the engine multi-dimensional simulation model, the change in the component performance calculation method directly affects the construction and solution of the engine nonlinear equation group. Therefore, when adopting the fully coupled method, it is necessary to design a solution scheme based on the characteristics of the whole-machine zero-dimensional and component high-fidelity simulation models, and reconstruct the nonlinear equation group that describes the joint operation of the engine components. The difficulty lies in quickly completing the solution of the nonlinear equation group when the component high-fidelity simulation model is deeply embedded in the nonlinear equation group solution process.

[0046] This article introduces the fully coupled method using the coupling of a two-dimensional fan model and a zero-dimensional model of the entire machine as an example. In a zero-dimensional fan simulation model, bypass ratio, β, and speed are typically used as independent variables to interpolate performance parameters such as pressure ratio, efficiency, flow rate, and margin from the fan characteristic diagram. β is an auxiliary function defined to facilitate characteristic diagram interpolation and has no actual physical meaning. A two-dimensional fan simulation model uses speed, inlet total temperature / total pressure, and outlet backpressure as inputs. Through CFD, the fan's performance parameters, such as pressure ratio, efficiency, flow rate, margin, and detailed flow field distribution, are obtained. Therefore, the key to constructing a zero-dimensional-two-dimensional coupled model lies in equivalently replacing the independent variables of the zero-dimensional model with those of the two-dimensional model, while minimizing the need to change the computational logic of the original zero-dimensional simulation of the entire machine.

[0047] The present invention proposes a whole machine variable dimension simulation method based on a circumferential average flow model. The core lies in establishing a zero-dimensional whole machine and a two-dimensional component model and achieving effective coupling. By adopting the circumferential average NS equation to establish a quasi-three-dimensional S2 flow model, the target component is subjected to two-dimensional dimensionality reduction modeling, the implicit terms in the full three-dimensional equation group are converted into explicit source terms, and the empirical model is used for modeling. This dimensionality reduction modeling method greatly reduces the amount of calculation compared to traditional full three-dimensional simulation, making it possible to run efficiently even with limited computing resources, thereby improving computing efficiency. In terms of model coupling, the two-dimensional component model is fully coupled and embedded in the zero-dimensional whole machine model. By equivalently replacing the auxiliary variables in the zero-dimensional whole machine model with the physical variables of the two-dimensional model, the nonlinear equation group is reconstructed and solved. This process achieves the synchronous convergence of the whole machine and component models. Compared with the weak coupling and iterative coupling methods, the full coupling method of the present invention can obtain fully converged simulation results, significantly improving the simulation accuracy. During the solution process, the convergence of the model is determined by determining 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. At the same time, the iterative variables and the number of equilibrium equations in the zero-dimensional model remain unchanged after coupling, ensuring the uniqueness of the solution and further guaranteeing the reliability and stability of the simulation results. In addition, two-dimensional simulation has its own unique advantages. It can provide rich S2 flow field information. For specific cases, the flow field results can be corrected using loss models and blockage models derived from experimental data. This effectively avoids the problem of amplified flow field calculation errors caused by the limitations of turbulence models and provides strong support for accurate analysis of component and overall machine performance.

[0048] Furthermore, in step 1, the nonlinear equation group describing the working state of the whole machine is:

[0049]

[0050] Where V i Represents the iteration variable in the table, Ei is the residual term in the equilibrium equation. The number of equilibrium equations is related to the corresponding engine configuration;

[0051] Specifically, this embodiment clearly provides a set of nonlinear equations that describe the operating state of the entire engine. The provision of this set of equations provides a precise mathematical basis for subsequent simulation calculations and is a key link in achieving accurate simulation. By constructing this set of nonlinear equations, the operating state of each component of the aircraft engine can be closely linked to the performance of the entire engine. Constraints such as flow continuity, power balance, and static pressure balance are reflected in the equations, fully reflecting the complex physical relationships within the engine. This makes it possible to more accurately simulate the operating state of the engine under different working conditions during the simulation process and capture the mutual influence and collaborative working mechanism between the various components. The establishment of this set of equations also lays a solid foundation for the subsequent use of the Netwon-Raphson iterative method for solution. Through iterative solution, the performance parameters of each component and the entire engine can be obtained. These parameters are an important basis for evaluating engine performance and performing design optimization. During the engine design and development process, engineers can make targeted adjustments to the engine structure, component characteristics, etc. based on these precise performance parameters to improve the engine's performance and reliability.

[0052] Furthermore, after step 1, the method further includes:

[0053] Step 11: Solve the nonlinear equations describing the working state of the whole machine by using the Netwon-Raphson iterative method to obtain the performance parameters of each component and the whole machine.

[0054] Specifically, this embodiment further improves the simulation process. It stipulates that after establishing a zero-dimensional aircraft engine simulation model and constructing a nonlinear system of equations, the Netwon-Raphson iterative method is used to solve the system of equations to obtain the performance parameters of each component and the entire engine. The design of this process is of vital importance to the entire simulation method. The Netwon-Raphson iterative method, as a mature and efficient numerical calculation method, plays a key role in this invention. It can leverage information from the nonlinear system of equations to approximate the true solution of the system through continuous iteration. In the context of aircraft engine simulation, this means that the performance parameters of each component and the entire engine can be accurately obtained from the complex engine system model. Because aircraft engines involve numerous interrelated parameters and complex physical processes, conventional solution methods are difficult to address. However, this iterative method, with its excellent convergence and computational efficiency, effectively solves this problem. By using the Netwon-Raphson iterative method, detailed performance data of each component under different operating conditions can be obtained, such as the compressor's pressure ratio and the turbine's efficiency. This data is crucial for gaining a deeper understanding of component operating characteristics and optimizing component design. On the other hand, the performance parameters of the entire machine, such as thrust and fuel consumption rate, can also be accurately obtained, providing a quantitative basis for evaluating the overall performance of the engine.

[0055] Furthermore, in step 2, the flow viscosity and circumferential heterogeneity are ignored, 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, the computational complexity of directly solving the complete three-dimensional NS equations is huge, and it is difficult to meet the rapid simulation requirements of engineering practice. The simplification and modeling processing method adopted by the present invention greatly reduces the computational complexity while ensuring a certain degree of accuracy. Ignoring flow viscosity and circumferential heterogeneity is a reasonable simplification made after comprehensively considering engineering practice and computational efficiency. Under many actual working conditions, this simplification will not have a decisive impact on the overall simulation results, but it can significantly reduce the computational complexity and improve the simulation speed. The core technology of this step is to rationally model the circumferential pressure gradient related terms and the viscous stress related terms, and to establish a model through empirical / semi-empirical modeling equations. These empirical / semi-empirical modeling equations are based on a large amount of experimental data and theoretical research, and can effectively reflect actual physical phenomena. By establishing inviscid blade force and viscous blade force models through these equations, the effect of blades on airflow can be more accurately simulated in the two-dimensional dimensionality reduction modeling process. This not only helps to gain a deeper understanding of the interaction mechanism between blades and airflow, but also provides a more reliable component model for subsequent whole-machine performance simulation, thereby improving the accuracy of whole-machine variable-dimensional simulation and making the simulation results more reflective of the actual engine performance.

[0057] Furthermore, in step 2, the implicit terms in the full three-dimensional equations are converted into explicit source terms, as shown in the following formula:

[0058]

[0059] The definitions of each term in the equation are as follows:

[0060]

[0061]

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

[0063] Specifically, in aircraft engine simulation, zero-dimensional models and two-dimensional models each have their own characteristics. The zero-dimensional model is widely used because of its fast calculation speed and ability to quickly evaluate the performance of the entire machine, but it is relatively rough and has limited ability to describe the detailed flow field inside the component; although the two-dimensional model can provide more detailed flow field information, the calculation amount is large. The present invention gives full play to their advantages by coupling the two. Clarifying the auxiliary variables of the zero-dimensional model (such as β and bypass ratio used for characteristic map interpolation) and the physical variables of the two-dimensional model (such as speed, inlet total temperature / total pressure, outlet back pressure) is an important basis for achieving this coupling. By equivalently replacing these variables, a close connection can be established between the zero-dimensional entire machine model and the two-dimensional component model, so that when reconstructing the nonlinear equation group, more accurate component information in the two-dimensional model can be integrated into the entire machine model. This helps to break the barriers between models of different dimensions and achieve a deep fusion of the entire machine and component models, thereby greatly improving the accuracy of the simulation while ensuring computational efficiency. During the engine design and optimization process, more accurate simulation results can provide engineers with a more reliable basis, helping them better understand the engine's working principles and performance characteristics, and then optimize the engine's structure and parameters to improve engine performance and reliability.

[0064] Furthermore, in step three, during the solution of the reconstructed nonlinear equations, 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 used as a basis for model convergence.

[0065] Specifically, in the variable-dimensional simulation of aircraft engines, model convergence is directly related to the validity of the simulation results. Due to the differences in the characteristics and calculation logic of the zero-dimensional whole machine model and the two-dimensional component model, how to ensure that the two can work in coordination and reach a stable convergence state after coupling is a key issue. The present invention 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 judgment basis, which is highly scientific and reasonable. The flow parameter is a key indicator reflecting the internal working state of the engine. Its transmission and changes between different components reflect the complex physical processes inside the engine. By comparing these two flow rates, it is possible to directly measure whether the simulation of the flow relationship between components after coupling between the zero-dimensional and two-dimensional models is accurate. If the two 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 machine model and the two-dimensional component model have achieved a coordinated working state under the current calculation conditions, and the simulation results are reliable; otherwise, it indicates that there is a problem with the model and further adjustment and optimization are required. This method of using flow comparison as the basis for convergence not only provides a clear termination condition for the simulation calculation, avoids invalid calculations, and improves calculation efficiency, but also helps to discover potential problems in the model construction and calculation process, providing direction for model improvement and perfection.

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

[0067] Specifically, when constructing a variable-dimensional simulation model for an aircraft engine, ensuring that the number of iterative variables and equilibrium equations in the zero-dimensional model remains constant can maintain the stability and consistency of the model solution. The zero-dimensional model serves as the basis for preliminary evaluation of overall engine performance. Its iterative variables and equilibrium equations reflect the fundamental physical relationships and constraints between engine components. When coupled with a two-dimensional component model, arbitrarily changing these key elements can complicate and confuse the model's computational logic, leading to unstable solution processes and unpredictable results. Maintaining these constants provides a stable framework for the simulation, enabling efficient calculations based on the original physical logic even after incorporating high-precision details from the two-dimensional model. From a computational efficiency perspective, fixing the number of iterative variables and equilibrium equations avoids the additional computational burden associated with frequent changes to the model structure. Proper allocation of computing resources is crucial during each iteration. If the number of variables and equations constantly changes, the solution algorithm must constantly adjust its computational strategy, which inevitably increases computational time and resource consumption. This design of the present invention ensures the continuity of the calculation process, allowing computing resources to be concentrated on solving the core computing tasks after model coupling, thereby improving the overall efficiency of simulation calculations and meeting the demand for rapid evaluation of design solutions in aerospace engine engineering development. In terms of ensuring the uniqueness of the solution, the constant number of iterative variables and equilibrium equations plays a key role. In complex aerospace engine simulations, the uniqueness of the solution is an important prerequisite for ensuring the reliability of the results. If these key factors are changed at will, multiple solutions or no solutions may appear, making the simulation results meaningless. The present invention maintains this constant and utilizes the mathematical stability principle to ensure that under different working conditions, the simulation model can obtain a unique and definite solution, providing engineers with accurate and reliable simulation data.

[0068] A storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned whole-machine variable-dimensional simulation method based on a circumferential average through-flow model.

[0069] Specifically, the storage medium, as a carrier for the simulation method of the present invention, greatly enhances the dissemination and reusability of the technology. In the field of aircraft engine R&D, different research teams and companies often need to repeatedly perform similar simulation work. The storage medium solidifies the simulation method of the present invention in the form of a computer program, making the related technology no longer limited to specific R&D environments and personnel. Whether in academic research, helping researchers quickly build simulation platforms for theoretical verification, or in industrial production, providing companies with efficient simulation tools for engine design optimization, the storage medium can conveniently access and apply the technology of the present invention, thus avoiding duplication of development and saving significant 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 aircraft engine technology, the requirements for simulation accuracy and efficiency are also continuously increasing. Based on the computer program on the storage medium, researchers can conduct secondary development and optimization. They can improve the algorithms and models in the program based on new research results and actual needs, further enhancing the performance of the simulation method. This will help promote the continuous advancement of aircraft engine simulation technology, enable it to better adapt to the ever-changing industry needs, and promote the development of the entire aircraft engine industry. In addition, the presence of the storage medium also makes it easier to integrate the simulation method of the present invention with other related technologies. In the process of modern aircraft engine research and development, it is often necessary to combine multiple technical means, such as computer-aided design (CAD) and computer-aided manufacturing (CAM). 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 coordination and efficiency of research and development, and further highlighting the important value of the present invention in the field of aircraft engine research and development.

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

[0071] Specifically, this computer device provides a complete and efficient operating environment for a variable-dimensional whole-machine simulation method based on a circumferentially averaged flow model. From a computing performance perspective, the processor, as the core computing unit, possesses powerful data processing capabilities, enabling rapid processing of data from both zero-dimensional whole-machine simulation models and two-dimensional component models. When constructing nonlinear equations and performing iterative solutions, the processor can execute complex algorithms such as the Netwon-Raphson iteration method at high speed, ensuring accurate and timely calculations. This efficient computing capability overcomes the computational slowness and inefficiency typically associated with traditional computing devices when handling such complex simulation tasks, achieving computational convergence within minutes. This meets the demand for rapid simulation results in aerospace engine design, enabling engineers to evaluate multiple design options in a short period of time and significantly accelerating design iterations. The memory provides stable storage and access support for various data during the simulation process. During simulation execution, it stores a large amount of component characteristic diagram data, engine operating parameters, and intermediate data from the iterative process. This data is essential for the smooth progress of the simulation, and the stable storage of the memory ensures data integrity and accuracy, preventing data loss or errors from impacting the simulation results. At the same time, the fast data reading capability enables the processor to obtain the required data in a timely manner, further improving the fluency and efficiency of the entire simulation process. The simulation method program of the present invention is integrated into the computer device to achieve a deep integration of hardware and software. This integration eliminates the need for aircraft engine designers and developers to spend a lot of energy on building a complex computing environment. They can simply use the variable dimension simulation method of the present invention by operating the computer device. This not only lowers the threshold for technology use and improves R&D efficiency, but also enables more scientific researchers and engineers to participate in the R&D of aircraft engines, promotes the widespread application of technology and further innovation, effectively promotes the development of aircraft engine technology, and enhances the competitiveness of the entire aircraft engine industry.

[0072] When using the fully coupled method to establish a VCE variable-dimensional simulation model, it is necessary to design a solution based on the characteristics of the zero-dimensional simulation model of the entire engine and the two-dimensional simulation model of the compression component, and reconstruct the nonlinear equations that describe the joint operation of the engine components. The process framework and control method of the variable-dimensional performance simulation of the entire engine (taking the fan component as an example) are as follows: Figure 1 shown.

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

[0074] 1. In the design point simulation analysis, the zero-dimensional model mainly uses the universal characteristic diagram of the components to obtain the modeled characteristic diagram of the compressor at the design point. In the variable dimension simulation based on the fully coupled method, since the performance parameters of the compressor components can be calculated based on the two-dimensional model, there is no need to model the universal characteristic diagram. However, in order to avoid making too many changes to the original zero-dimensional solver, it is necessary to set an output error at the design point. Combined with the definition of the zero-dimensional model design point output error: First, given a design point R line , input R line and design point R line The error between the two values is defined as the output error at the design point. A design value Ps for the outlet back pressure can be given, and the error between it and the input Ps is defined as the output error at the design point. At the same time, it is necessary to ensure that the two-dimensional model can achieve the required performance parameters such as the design pressure ratio and efficiency 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 requires physical speed during calculation. After the two-dimensional model calculation is completed, the torque and bleed air parameters can also be directly obtained. Therefore, the key to the variable dimension simulation based on the fully coupled method is to convert the R line (Iteration variable) and the equilibrium equation are replaced. From the calculation logic of the two-dimensional model, we can analyze: the inlet conditions generally give the total temperature and total pressure, which can be calculated by the upstream related components of the zero-dimensional model; the outlet parameters generally give 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 constant speed characteristic line of the compressor can be obtained by giving different back pressures, which is different from the zero-dimensional model through R line The idea of determining the position of the characteristic diagram is the same, so the static pressure can be used to determine the R line Replace. The iteration error of the zero-dimensional model compressor non-design point balance equation is obtained based on the flow balance: in a certain iteration calculation of the zero-dimensional simulation model, the upstream component will give a flow value, and combined with R line By looking up the characteristic diagram, a flow value is also obtained. The error between these two flow rates can be used to determine simulation convergence. In the 2D model, a flow rate is also calculated based on a given backpressure. This flow rate must also be consistent with the flow rate of the upstream component in the zero-dimensional model. Therefore, the definition of the off-design point equilibrium equations in the variable-dimensional simulation of the compression system can remain consistent with the original zero-dimensional model. Because the iteration variables and the number of equilibrium equations in the zero-dimensional model remain unchanged in this variable-dimensional method, the solution is unique.

[0076] In the specific solution process, the flow balance of the fan / compressor, turbine, tail nozzle, rotor power balance and static pressure balance equations at the mixing chamber are solved by the Netwon-Raphson iterative method. The expression of the iterative algorithm is:

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

[0078] Where a represents the number of iterations and A represents the Jacobian matrix:

[0079]

[0080] The convergence of the iteration is determined by determining whether the compressor flow rate calculated by the two-dimensional model is equal to the flow rate at the outlet of its upstream component. If the flow rates are not equal, the compressor outlet backpressure for the next iteration can be calculated based on the flow difference. At the same time, the zero-dimensional model can be used to obtain the total temperature, total pressure, and speed at the compressor inlet for the next iteration. The two-dimensional compressor model is then called again to calculate the compressor performance parameters, which are used in the zero-dimensional overall cycle calculation. This is done until both the zero-dimensional and two-dimensional models reach convergence. The engine variable dimensional performance parameters and two-dimensional flow field under the current operating conditions can be obtained.

[0081] The present invention proposes a whole-machine variable-dimensional simulation method based on a circumferentially averaged flow model, which has significant beneficial effects. This method effectively overcomes the shortcomings of traditional simulation methods by establishing a zero-dimensional whole-machine simulation model of an aircraft engine and a two-dimensional dimensionality reduction model of the target component, and fully coupling and embedding the two. By using the component characteristic diagram combined with flow continuity, power balance, and static pressure balance constraints to construct a nonlinear equation group, and then solving it through the Netwon-Raphson iterative method, the performance parameters of each component and the whole machine can be accurately obtained. When modeling with two-dimensional dimensionality reduction, the circumferentially averaged NS equation is used to establish a quasi-three-dimensional S2 flow model, and the source term is reasonably modeled to simplify the calculation while retaining key flow field information. In the coupling process, by equivalently replacing the auxiliary variables of the zero-dimensional whole-machine model with the physical variables of the two-dimensional model, the nonlinear equation group is reconstructed and solved to achieve synchronous convergence of the whole-machine and component models. Compared with traditional coupling methods, the simulation accuracy is higher. In addition, the unique advantages of two-dimensional simulation, such as providing rich S2 flow field information and the ability to use experimental data to correct results, further improve the reliability of the simulation. This method, implemented using storage media and computer equipment, facilitates technology dissemination, reuse, and integration, significantly improving the efficiency and accuracy of aeroengine simulations. This provides strong support for engine design and development, effectively shortening R&D cycles, reducing costs, and driving the continuous advancement of aeroengine technology. This method offers rapid evaluation (achieving computational convergence within minutes) and high accuracy (simulation error ≤ 5%), making it easy to integrate with engineering practice.

[0082] While the specific embodiments of the present invention have been described in detail above, these are intended to be exemplary only, and the present invention is not limited thereto. Any equivalent modifications or substitutions to the present invention that would be apparent to those skilled in the art are also within the scope of the present invention. Therefore, any equivalent modifications or substitutions made without departing from the spirit and scope of the present invention are intended to be encompassed within the scope of the present invention.

Claims

1. A whole machine variable dimension simulation method based on a circumferential average flow model, characterized in that: The method comprises the following steps: Step 1: Establish a zero-dimensional whole-machine simulation model of the aircraft engine, wherein the model constructs a nonlinear equation system describing the whole-machine operating state through component characteristic diagrams and flow continuity, power balance, and static pressure balance constraints; Step 2: Perform 2D dimensionality reduction modeling on the target component and establish a quasi-3D S2 flow model using the circumferentially averaged NS equations. This involves converting the implicit terms in the full 3D equations into explicit source terms, and modeling the source terms using empirical models of the normal pressure gradient and tangential friction on the blade surface. Step three: fully couple the two-dimensional component model and embed it into the zero-dimensional whole machine model. By equivalently replacing the auxiliary variables in the zero-dimensional whole machine model with the physical variables of the two-dimensional model, reconstruct the nonlinear equation group and solve it to achieve synchronous convergence of the whole machine and component models.

2. The whole machine variable dimension simulation method based on the circumferential average flow model according to claim 1 is characterized in that: In step 1, the nonlinear equations describing the working state of the whole machine are: Where V i Represents the iteration variable in the table, E i is the residual term in the equilibrium equation. The number of equilibrium equations is related to the corresponding engine configuration.

3. The whole machine variable dimension simulation method based on the circumferential average flow model according to claim 2 is characterized in that: After step one, it also includes: Step 11: Solve the nonlinear equations describing the working state of the whole machine by using the Netwon-Raphson iterative method to obtain the performance parameters of each component and the whole machine.

4. The whole machine variable dimension simulation method based on the circumferential average flow model according to claim 3 is characterized in that: In step 2, the flow viscosity and circumferential heterogeneity are ignored, 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.

5. The whole machine variable dimension simulation method based on the circumferential average flow model according to claim 4 is characterized in that: In step 2, the implicit terms in the full three-dimensional equations are converted into explicit source terms, as shown in the following formula: The definitions of each term in the equation are as follows:

6. The whole machine variable dimension simulation method based on the circumferential average flow model according to claim 5 is characterized in that: In step three, the auxiliary variables of the zero-dimensional model include β and bypass ratio for characteristic diagram interpolation, and the physical variables of the two-dimensional model include rotational speed, inlet total temperature / total pressure, and outlet back pressure. Among them, β is an auxiliary function defined to facilitate characteristic diagram interpolation and has no actual physical meaning.

7. The whole machine variable dimension simulation method based on the circumferential average flow model according to claim 6 is characterized in that: In step three, during the solution of the reconstructed nonlinear equations, 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 used as a basis for model convergence.

8. The whole machine variable dimension simulation method based on the circumferential average flow model according to claim 7 is characterized in that: In step three, the iteration variables and the number of equilibrium equations of the zero-dimensional model remain unchanged after coupling to ensure the uniqueness of the solution.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the whole machine variable dimension simulation method based on the circumferential average flow model described in any one of claims 1 to 8 is implemented.

10. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the whole-machine variable-dimensional simulation method based on the circumferential average flow model as described in any one of claims 1 to 8.

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