Design method for automatically reversely solving geometric model

Abstract

The invention provides a design method for automatically reversely solving a geometric model, and relates to the field of gas turbine design. The design method comprises the following steps: step 1, determining physical boundary conditions, namely determining boundary physical conditions according to the requirements of a geometric model; 2, mathematical description is carried out on the geometric model, and variable parameters required by mathematical description of the geometric model are determined; step 3, target variable determination: determining the target variable with the optimal performance according to design requirements; and step 4, reverse solution of the geometric model: aiming at mathematical description of the geometric model and determination of a target variable, establishing a closed equation, solving the geometric model, and carrying out automatic optimization on the structure of the geometric model through iteration of an algorithm.

Description

Technical Field

[0001] The present invention relates to the field of gas turbine design, and in particular to a design method for automatic inverse geometric model. Background Art

[0002] Gas turbines are key power equipment in aviation, power generation, and marine applications, and their performance directly impacts overall efficiency and reliability. The flow structure design of the three core components of a gas turbine—the compressor, combustor, and turbine—plays a decisive role in its overall performance. However, traditional design methods often require extensive manual intervention and trial and error, resulting in long design cycles and low efficiency, making it difficult to obtain a geometric model that optimizes performance within a limited timeframe.

[0003] Some designers use an inverse solution approach to solve design problems, thereby reducing the design cycle and improving design efficiency. Domestic patent applications for methods to solve inverse problems are gradually increasing. Patent 202011194365.4 invents an NSGA-II-based pseudo-S1 flow surface inverse problem optimization method. Its purpose is to automatically search for inverse problem load inputs that can optimize the aerodynamic performance of the compressor through genetic algorithms, solving the problem that the inverse problem input loads previously required high designer experience. Patent 202210918134.6 invents a high-reliability aerodynamic design method for a one-dimensional inverse problem of an axial-flow turbine. Its purpose is to provide a high-reliability aerodynamic design method for a one-dimensional inverse problem of an axial-flow turbine, providing a better design method for the one-dimensional inverse problem design of turbines.

[0004] However, current existing technology designs have low efficiency and poor performance. Summary of the Invention

[0005] A brief overview of the present invention is provided below to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important aspects of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is simply to present certain concepts in a simplified form as a prelude to the more detailed description discussed later.

[0006] In view of this, the present invention provides an automatic inverse geometry design method (AIGDM method) to at least solve the above-mentioned problems.

[0007] According to one aspect of the present invention, a design method for automatic inverse geometric model is provided, comprising the following steps: step one, determination of physical boundary conditions, determination of boundary physical conditions according to the requirements of the geometric model; step two, mathematical description of the geometric model, determination of variable parameters required for the mathematical description of the geometric model; step three, determination of target variables, determination of target variables with optimal performance according to design requirements; step four, inverse solution of the geometric model, establishment of closed equations based on the mathematical description of the geometric model and determination of the target variables, solution of the geometric model, and automatic optimization of the geometric model structure through algorithm iteration.

[0008] Furthermore, the step one specifically includes: determining the key geometric parameters, operating parameters, and performance parameters of the component's geometric structure, wherein the key geometric parameters are the construction dimensions of the geometric model structure, the operating parameters are the working environment conditions of the geometric model, and the performance parameters are the performance index constraints that the geometric model needs to achieve under the operating parameters.

[0009] Furthermore, the step 2 specifically includes: mathematically describing the geometric model, wherein the mathematical description is to determine variable parameters based on the known key geometric parameters; and determining the number of equations required to solve the geometric model.

[0010] Furthermore, the mathematical description is described using functional equations such as multi-order spline curves.

[0011] Furthermore, the step three specifically includes: determining the variables and objective functions to be optimized; the optimized variables refer to the variables associated with the performance indicators through the objective function after optimizing and screening the geometric parameters of the complex geometric model.

[0012] Furthermore, the step four specifically includes: solving the mathematical description function of the geometric model according to the optimization variables and the optimal target variables, and automatically optimizing the geometric model structure through algorithm iteration; the solution method adopts CFD numerical simulation, genetic algorithm, iterative algorithm and the like.

[0013] This invention provides a method for automatically inversely analyzing geometric models for the three core flow structures of a gas turbine: the compressor, combustor, and turbine. Given physical boundary conditions, the method automatically inversely analyzes the flow structure's geometric model based on a set optimal performance target variable. This significantly improves flow structure design efficiency and effectively achieves the extreme value of the "set optimal performance target variable"—the expected optimal performance solution.

[0014] The present invention proposes a method for automatically inversely solving geometric models, which, compared to existing technologies, is as follows: Traditionally, the geometric solution of gas turbine flow structures is based on trial and error. The flow structure trial and error design process relies entirely on the designer's experience and skills, making it difficult to obtain a satisfactory design solution in a short period of time, and requiring a large amount of numerical simulation and physical testing. However, the present invention proposes a method for automatically inversely solving geometric models, which, given physical boundary conditions, can automatically inversely solve the geometric model of the flow structure according to the set optimal performance target variables. This method not only significantly improves the efficiency of flow structure design, but also truly obtains the extreme value of the set optimal performance target variable, that is, the expected optimal performance solution.

[0015] These and other advantages of the present invention will become more apparent from the following detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention may be better understood by referring to the following description taken in conjunction with the accompanying drawings, in which the same or similar reference numerals are used throughout the drawings to represent the same or similar components. The accompanying drawings, together with the following detailed description, are incorporated in and form a part of this specification and are used to further illustrate preferred embodiments of the present invention and to explain the principles and advantages of the present invention. In the drawings:

[0017] Figure 1 1 is a schematic diagram illustrating a solution process of a design method for automatically inversely solving a geometric model according to the present invention;

[0018] Figure 2 Schematic diagram of an inverse problem solution example, where (a) is a schematic diagram of a solution example, and (b) is a schematic diagram of a solution example;

[0019] Figure 3 Flowchart for solving inverse problem examples;

[0020] Figure 4 It is the optimal solution when the inlet and outlet resistance loss is used as the optimization objective function.

[0021] Those skilled in the art will appreciate that the elements in the drawings are shown for simplicity and clarity only and are not necessarily drawn to scale. For example, the dimensions of some elements in the drawings may be exaggerated relative to other elements to facilitate a better understanding of the embodiments of the present invention. DETAILED DESCRIPTION

[0022] Exemplary embodiments of the present invention are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of an actual implementation are described in this specification. However, it should be understood that in the process of developing any such actual implementation, many implementation-specific decisions must be made in order to achieve the developer's specific goals, such as meeting system and business-related constraints, which may vary from implementation to implementation. Furthermore, it should be understood that while development work may be complex and time-consuming, it will be a routine task for those skilled in the art who benefit from this disclosure.

[0023] It is also necessary to explain here that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show the device structure and / or processing steps closely related to the solution according to the present invention, while other details that are not closely related to the present invention are omitted.

[0024] An embodiment of the present invention provides a design method for automatically inverse-finding a geometric model, comprising the following steps:

[0025] Step 1: Determine the physical boundary conditions. According to the requirements of the geometric model, determine the boundary physical conditions; Step 2: Mathematically describe the geometric model and determine the variable parameters required for the mathematical description of the geometric model; Step 3: Determine the target variables. According to the design requirements, determine the target variables with optimal performance; Step 4: Inverse solve the geometric model. Based on the mathematical description of the geometric model and the determination of the target variables, establish a closed equation, solve the geometric model, and automatically optimize the geometric model structure through algorithm iteration.

[0026] like Figure 1 As shown, in step 1, the physical boundary conditions are determined, that is, the boundary physical conditions are determined according to the requirements of the geometric model.

[0027] As an example, the physical boundary conditions in step one are determined based on the solution requirements, such as inlet flow, velocity, temperature, pressure, etc.

[0028] For example, step one may specifically include: determining the key geometric parameters, operating parameters, and performance parameters of the component's geometric structure, wherein the key geometric parameters are the dimensions of the geometric model structure, the operating parameters are the working environment conditions of the geometric model, and the performance parameters are the performance index constraints that the geometric model needs to achieve under the operating parameters.

[0029] Next, in step 2, the geometric model is mathematically described, that is, the variable parameters required for the mathematical description of the geometric model are determined.

[0030] As an example, the purpose of mathematically describing the geometric model in step 2 is to establish and determine the variable parameters of the geometric model and determine the number of equations required to solve the geometric model.

[0031] The numerical sequence description is, for example, determining the variable parameters based on the known key geometric parameters, and can be described by using functional equations such as multi-order spline curves.

[0032] In step three, the target variables are determined, that is, the target variables with optimal performance are determined according to the design requirements.

[0033] As an example, in step three, the variables and objective functions that need to be optimized are determined, such as maximum efficiency, minimum resistance loss, optimal temperature distribution, etc.

[0034] The optimized variables refer to, for example, variables associated with the performance indicators through the objective function after optimizing and screening the geometric parameters of the complex geometric model.

[0035] In step 4, the inverse solution of the geometric model is completed, that is, based on the mathematical description of the geometric model and the determination of the target variables, a closed equation is established to solve the geometric model, and the geometric model structure is automatically optimized through the iteration of the algorithm.

[0036] As an example, in step 4, the mathematical description function of the geometric model can be solved according to the selected optimization variables and the optimal target variables, and the geometric model structure can be automatically optimized through the iteration of the algorithm.

[0037] The solution method may be, for example, CFD numerical simulation, genetic algorithm, iterative algorithm, etc.

[0038] Those skilled in the art will appreciate that embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. It should be understood that any number of elements in the accompanying drawings is for illustrative purposes only and not limiting, and any nomenclature is for distinction only and does not have any limiting meaning.

[0039] Preferred Embodiments

[0040] In the preferred embodiment, steps one to four are performed as described below.

[0041] In step 1, set the physical boundary conditions of the gas turbine compressor, combustion chamber, and turbine. Then, proceed to step 2.

[0042] In step 2, the geometric model is mathematically described, the variable parameters required for the mathematical description of the geometric model are determined, the target variables are determined, and the target variables with optimal performance are determined according to the design requirements.

[0043] Then, in step three, an automatic inverse geometric model design method is used to automatically inverse the optimal flow structure geometric model through computer simulation and optimization algorithms under the set physical boundary conditions and target variables.

[0044] Then, in step 4, the inverse geometric model is verified to ensure that its performance meets the expected goals.

[0045] In this preferred embodiment, the physical boundary conditions of the gas turbine compressor, combustion chamber, and turbine in step 1 need to be specifically analyzed based on the actual working conditions and the required design structure.

[0046] In this preferred embodiment, the optimal performance target variable in step 2 needs to be specifically analyzed according to the desired design geometry, such as pressure ratio, efficiency, temperature distribution, etc.;

[0047] In this preferred embodiment, the computer simulation and optimization algorithm in step 3 uses CFD numerical simulation, genetic algorithm, iterative algorithm and the like to automatically solve the results;

[0048] In this preferred embodiment, the inversely solved geometric model in step 4 can be verified by using numerical simulation and various experimental methods.

[0049] In this preferred embodiment, Figure 2 An implementation example of the automatic inverse geometric model design method proposed in this patent is given, and the specific content is as follows:

[0050] There are two flagpoles fixed on the same plane, with a rigid flag between the flagpoles, and the length of the flag is fixed. Now there is wind blowing from the side, such as Figure 2 As shown in (a), Figure 2 (a) is a schematic diagram of the solution example. Figure 2 (b) is the coordinate analysis of the inverse problem solution example, which gives the top view of the example and describes it using a two-dimensional Cartesian coordinate system, where point 1 is the location of flagpole 1 with coordinates (x0, y0), point 2 is the location of flagpole 2 with coordinates (x1, y1), the length of the rigid flag is S, and the direction of the wind speed v makes an angle α with the two flagpoles. What shape of the flag is required to minimize the drag loss?

[0051] In this preferred embodiment, Figure 3 A flow chart of an embodiment of a design method for automatic inverse geometric model is given.

[0052] Step 1: Determine known boundary conditions;

[0053] According to the embodiment of the present invention, (1) the position coordinates (x0, y0) of flagpole 1; (2) the position coordinates (x1, y1) of flagpole 2; and (3) the length S of the rigid flag can be determined.

[0054] Step 2: mathematically describe the geometric model and determine the variable parameters required for the mathematical description of the geometric model;

[0055] The geometric shape of the rigid flag is selected as a cubic spline curve, that is, the geometric model of the rigid flag is determined to be y=ax 3 +bx 2 +cx+d, if the geometric structure model needs to be determined, the four parameters a, b, c, and d need to be determined;

[0056] Step 3: Determine the target variable. According to the design requirements, determine the target variable with optimal performance;

[0057] The optimal target variable is selected as the minimum value of resistance loss. Since the purpose is to solve the minimum value of resistance loss, the optimal target variable is the total pressure loss coefficient.

[0058] Step 4: Using the automatic inverse geometric model design method, under the set physical boundary conditions and target variables, the optimal flow structure geometric model is automatically inversely derived through computer simulation and optimization algorithms;

[0059] Based on the mathematical description of the geometric model and the determination of the target variables, a closed equation is established:

[0060]

[0061] y′(x0)=tanθ (4)

[0062] And the direct relationship between drag loss and angle is constructed according to the closed equation:

[0063] f(θ)~ΔP(5)

[0064] Figure 4 A schematic diagram of the optimal solution with the inlet and outlet resistance loss as the optimization objective function is given, and the optimal result that meets the objective is given to the geometric model structure through algorithm iteration.

[0065] It should be noted that the present invention is applicable to the design of key geometric parameters of the three major components of a gas turbine: the compressor, combustion chamber, and turbine, such as compressor blade profile design, compressor aerodynamic design, combustion chamber diffuser design, flame tube design, and turbine moving and stationary blade design.

[0066] Although the present invention has been described with respect to a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A design method for automatic inverse geometric model, characterized in that: The following steps are involved: Step 1: Determine the physical boundary conditions. Determine the boundary physical conditions according to the requirements of the geometric model. Step 2: mathematically describe the geometric model and determine the variable parameters required for the mathematical description of the geometric model; Step 3: Determine the target variable. According to the design requirements, determine the target variable with optimal performance. Step 4: Inverse solution of the geometric model. Based on the mathematical description of the geometric model and the determination of the target variables, a closed equation is established to solve the geometric model, and the geometric model structure is automatically optimized through algorithm iteration.

2. The method for designing an automatic inverse geometric model according to claim 1, characterized in that The step 1 specifically includes: Determine the key geometric parameters, operating parameters, and performance parameters of the component's geometric structure, where the key geometric parameters are the dimensions of the geometric model structure, the operating parameters are the working environment conditions of the geometric model, and the performance parameters are the performance index constraints that the geometric model needs to achieve under the operating parameters.

3. A design method for automatic reverse geometric model according to claim 1 or 2, characterized in that The second step specifically includes: mathematically describing the geometric model, wherein the mathematical description is to determine variable parameters based on the known key geometric parameters; and determining the number of equations required to solve the geometric model.

4. A method for designing an automatic inverse geometric model according to claim 3, wherein: The mathematical description is described using functional equations such as multi-order spline curves.

5. The method for designing an automatic inverse geometric model according to claim 1 or 2, characterized in that The step three specifically includes: determining the variables and objective functions to be optimized; the optimized variables refer to the variables associated with the performance indicators through the objective function after optimizing and screening the geometric parameters of the complex geometric model.

6. The method for designing an automatic inverse geometric model according to claim 5, characterized in that The step four specifically includes: solving the mathematical description function of the geometric model according to the optimization variables and the optimal target variables, and automatically optimizing the geometric model structure through algorithm iteration; the solution method adopts CFD numerical simulation, genetic algorithm, iterative algorithm and the like.

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