Method and apparatus for acquiring positions of measurement points of combustion chamber of turbine engine

Abstract

Disclosed in the present application are a method and apparatus for acquiring the positions of measurement points of a combustion chamber of a turbine engine. By minimizing a condition number of a design matrix A, the minimum number of measurement points and an optimal measurement position are determined by using a global optimization algorithm. In the embodiments of the present application, on the basis of data of the minimum number of measurement points and an optimal measurement position that are determined, a temperature field of a turbine engine can be reconstructed, thereby solving the problems with respect to the low efficiency and high costs of conventional rotary-rake-based methods during combustion chamber measurement, and reducing the test cycle and cost during turbine engine development.

Description

Method and device for obtaining measurement point position of turbine engine combustion chamber Technical Field

[0001] The present application relates to, but is not limited to, gas turbine engine technology, and in particular to a method and device for obtaining the position of a measurement point in a combustion chamber of a turbine engine. Background Art

[0002] Gas turbine engines are used in a wide variety of applications, including aircraft propulsion, power generation, and marine propulsion. Accurately measuring the circumferential temperature variation at the combustion chamber exit in gas turbine engines is crucial to ensure efficient engine performance and reduce emissions.

[0003] Due to the complexity of the flow field in the combustion chamber, the temperature field on the combustion chamber outlet cross section is complex and unstable, with large radial and circumferential variations. In other words, the temperature field has an inhomogeneous characteristic with non-constant field strength, and varies greatly along the circumferential and radial directions. Measuring these temperature changes is challenging.

[0004] In order to quantify the temperature non-uniformity of the turbine engine combustion chamber, how to simply and accurately obtain the measurement point position of the turbine engine combustion chamber has become a technical problem that needs to be solved urgently.

[0005] SUMMARY OF THE INVENTION

[0006] The present application provides a method and device for obtaining the position of a measuring point in a combustion chamber of a turbine engine, which can solve any of the above-mentioned technical problems.

[0007] The present invention provides a method for obtaining a position of a measuring point in a combustion chamber of a turbine engine, comprising:

[0008] Determining a dominant wave number, where the dominant wave number is obtained based on analysis of high-precision computational fluid dynamics simulation results, or is obtained based on the number of fuel injectors or the number of downstream high-pressure turbine inlet guide vanes;

[0009] Determine the number of measuring points in the turbine engine combustion chamber according to the number of dominant wave numbers, wherein the number of measuring points is not less than 2N+1, where N is the number of dominant wave numbers;

[0010] The measurement point locations of the turbine engine combustion chamber are determined using a global optimization algorithm by minimizing the condition number of the design matrix A.

[0011] In an exemplary embodiment, the dominant wave number is equal to the number of the fuel injectors.

[0012] In an exemplary embodiment, when the temperature field of the turbine engine combustion chamber is reconstructed using a single wave number approximation, the number of measurement points in the turbine engine combustion chamber is 6;

[0013] In the case of reconstructing the temperature field of the turbine engine combustion chamber using a multi-wavenumber approximation of four wavenumbers, the number of measurement points in the turbine engine combustion chamber is 10.

[0014] In an exemplary embodiment, determining the measurement point position of the turbine engine combustion chamber using a global optimization algorithm includes:

[0015] According to the geometric constraints of the probe placement, the objective function f obj is: f obj =k(θ,W n )+fconstraint; where k(θ,W n ) indicates that the condition number k of the design matrix A is the dominant wave number W n and a function of the measured position θ; fconstraint is the value of the constraint function when considering the geometric constraints of the probe placement. When the position of a probe violates the constraint, a penalty or cost will be added to the constraint function to prevent the probe from being placed in the prohibited area.

[0016] In an exemplary embodiment, for the case of determining the optimal probe position in a completely new experimental design, based on the constraints of the probe position in the turbine engine combustion chamber, the global optimization algorithm is used to make the objective function and the condition number k converge to the minimum value to find the optimal probe position.

[0017] In an exemplary embodiment, in the case of determining the optimal probe position based on historical test data, an optimal test data subset is selected based on the existing historical data for use in evaluating the turbine engine combustion chamber outlet temperature distribution factor; based on the constraints of the turbine engine combustion chamber probe position and the existing historical data, the global optimization algorithm is used to converge the objective function and the condition number k to a minimum value to find the optimal probe position.

[0018] In an exemplary embodiment, when the temperature field of the turbine engine combustion chamber is reconstructed using a single wave number approximation, the condition number k corresponding to the optimal measurement point position is less than 2;

[0019] For the case of using multiple wave numbers less than or equal to four wave numbers to approximate the temperature field of the turbine engine combustion chamber, the condition number k corresponding to the optimal measurement point position is less than 4.

[0020] In an exemplary embodiment, the global optimization algorithm includes: a particle swarm optimization algorithm, a simulated annealing algorithm, a genetic algorithm, or an evolutionary algorithm.

[0021] In an exemplary embodiment, the design matrix A is:

[0022] Among them, W n,1 、W n,2 …W n,N represents the N dominant wave numbers, θ=(θ1,θ2,θ3,…θ m ) represents the m measuring points at different circumferential positions.

[0023] In an exemplary embodiment, the condition number k of the design matrix A is: k = ‖A‖‖A + ‖; Among them, A + is the inverse of the square matrix A, or the Moore-Penrose generalized inverse of a non-square matrix A.

[0024] An embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute any of the above-mentioned methods for obtaining the position of a measurement point in a turbine engine combustion chamber.

[0025] An embodiment of the present application further provides a device for obtaining the position of a measurement point in a combustion chamber of a turbine engine, comprising a memory and a processor, wherein the memory stores the following instructions that can be executed by the processor: for executing the steps of any of the above-mentioned methods for obtaining the position of a measurement point in a combustion chamber of a turbine engine.

[0026] The embodiment of the present application further provides a device for obtaining the position of a measuring point in a combustion chamber of a turbine engine, comprising: a first processing module, a second processing module, and a determination module; wherein,

[0027] A first processing module is configured to determine a dominant wave number, where the dominant wave number is obtained based on analysis of high-precision computational fluid dynamics simulation results, or is obtained based on the number of fuel injectors or the number of downstream high-pressure turbine inlet guide vanes;

[0028] A second processing module is used to determine the number of measurement points in the turbine engine combustion chamber according to the number of dominant wave numbers, wherein the number of measurement points is not less than 2N+1, where N is the number of dominant wave numbers;

[0029] The determination module is used to determine the measurement point positions of the turbine engine combustion chamber by minimizing the condition number of the design matrix A using a global optimization algorithm.

[0030] Through the embodiments of this application, by minimizing the condition number of the design matrix A and using a global optimization algorithm to determine the minimum number of measurement points and optimal measurement positions, the measurement point positions in the turbine engine combustion chamber are simply and accurately obtained. The data based on the minimum number of measurement points and optimal measurement positions determined by the embodiments of this application can be used to reconstruct the temperature field of the turbine engine, solving the low efficiency and high cost of traditional rotating rake-based methods for combustion chamber measurement, thereby reducing the test cycle and cost during turbine engine development.

[0031] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0032] Summary of the Figures

[0033] The accompanying drawings are used to provide a further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0034] FIG1 is a flow chart of a method for obtaining a measurement point position in a combustion chamber of a turbine engine according to an embodiment of the present application;

[0035] FIG2 is a schematic diagram showing changes in the objective function value during optimization operation in one embodiment of the present application;

[0036] FIG3 is a schematic diagram showing changes in the objective function value during optimization in another embodiment of the present application;

[0037] FIG4 is a schematic diagram of the composition structure of an apparatus for obtaining the position of a measuring point in a combustion chamber of a turbine engine according to an embodiment of the present application.

[0038] Details

[0039] To make the purpose, technical solutions and advantages of this application more clear, the embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of this application can be combined with each other in any way.

[0040] To facilitate understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The accompanying drawings provide embodiments of the present application. However, the present application may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application.

[0042] Placing probes within the rotating components of an aircraft engine (the compressor and turbine) reduces the number of measurement points, optimizes probe placement, and achieves excellent results. The combustion chamber of an aircraft engine, located between the compressor and turbine, has a structure significantly different from that of the rotating components. Its design goal is to maintain stable combustion and a uniform outlet temperature field under all conditions. Drawing on methods used in compressors and turbines, in order to reconstruct the combustion chamber temperature field, it is necessary to determine the dominant wave number, the minimum number of measurement points, and the optimal probe placement for various combustion chamber scenarios.

[0043] To this end, an embodiment of the present application provides a method for obtaining the position of a measurement point in a combustion chamber of a turbine engine, as shown in FIG1 , which may include:

[0044] Step 100: Determine the dominant wave number, where the dominant wave number is obtained based on analysis of high-precision computational fluid dynamics simulation results, or is obtained based on the number of fuel injectors or the number of downstream high-pressure turbine inlet guide vanes.

[0045] The circumferential temperature variation at the outlet of the combustion chamber of a turbine engine is affected by several factors, the main factor being the discrete characteristics of the fuel and dilution air jets, which leads to obvious hot spots at the outlet of the combustion chamber. In addition, the potential energy of the high-pressure turbine (HPT) blades will cause temperature variations at the outlet of the combustion chamber. In an exemplary embodiment, in actual applications, the dominant wave number can be obtained based on the analysis of high-precision simulation results of computational fluid dynamics. In another exemplary embodiment, actual applications often face the situation of incomplete prior information. For example, in the case where there is no other available information except the geometric structure of the combustion chamber and the downstream high-pressure turbine, the selection criteria for selecting the dominant wave number may include, for example: 1) the number of injectors; 2) the number of blades of the downstream high-pressure turbine inlet guide vanes.

[0046] Analyzing the main mechanism of circumferential flow non-uniformity in the combustion chamber and selecting the appropriate wave number are prerequisites for reconstructing the temperature field. For the combustion chamber outlet temperature field with obvious hot spots, the dominant wavelet in the central channel is based on the discreteness of the injectors. Therefore, in one embodiment, the dominant wave number is equal to the number of injectors. In the near-end wall area, there are multiple mechanisms that lead to temperature non-uniformity, and single wave number, double wave number, triple wave number and quadruple wave number are used to approximate the combustion chamber outlet temperature distribution respectively. Among them, the dominant wave number refers to the wave number (Wavenumber) of the dominant beam, that is, the number of times the beam is repeated along the circumference. In the near-mid-high area where the hot spots are located, the single wave number approximation produces a more representative hot spot pattern than the result obtained using the multi-wave number approximation. This shows that for combustion chambers with obvious hot spots at the outlet, the single wave number approximation method can accurately evaluate the combustion chamber outlet temperature factor.

[0047] Theoretically, the steady-state (time-averaged) temperature field at the combustion chamber outlet cross section with a radius of R and a spatial period of 2π can be described by an infinite sequence of different wave numbers as shown in formula (2):

[0048] In formula (1), T(θ) represents the temperature field at a radius of R and a position of θ, c0 represents the DC component of the temperature signal, and W n represents the wave number, A and The table shows the amplitude and phase of the temperature signal, and the subscript i is used to indicate the parameter related to the i-th wave number. The subscript i is used to indicate the parameter related to the i-th wave number, then formula (1) can be converted to the following formula (1):

[0049] Taking the combustion chamber of an aircraft engine as an example, the temperature flow field in the combustion chamber of an aircraft engine is usually dominated by a few wave numbers along the circumferential direction. Therefore, there is no need to use the infinite number of wave numbers described in equations (1) or (2). The circumferential flow in the temperature flow field of the combustion chamber can be approximated by a few (e.g., N) dominant wave numbers, as shown in equation (3):

[0050] In formula (3), T(θ) represents the temperature field at a radius of R and a position of θ, c0 represents the DC component of the temperature signal, and W n represents the selected dominant wave number, and Among them, A and denote the amplitude and phase of the temperature measurement, respectively. The subscript j indicates the parameter associated with the jth wavenumber, and N denotes the number of dominant wavenumbers.

[0051] In this way, after determining the amplitude and phase of the dominant wave number, formula (3) can be used to reconstruct the circumferential temperature field. At this time, the number of unknown coefficients in the equation is reduced from infinity to 2N+1.

[0052] In an exemplary embodiment, in order to solve the equation containing 2N+1 unknowns, the minimum number of data points required to be measured at different circumferential positions includes: θ = (θ1, θ2, θ3, ... θ m ).

[0053] Step 101: Determine the number of measuring points in the turbine engine combustion chamber according to the number of dominant wave numbers, wherein the number of measuring points is no less than 2N+1, where N is the number of dominant wave numbers.

[0054] To solve the N dominant wave numbers in the equation, at least 2N+1 measurement points at different circumferential positions are required, that is, θ=(θ1,θ2,θ3,…θ m), (m ≥ 2N + 1). The system can be described as: AF = T, where A is called the design matrix with dimensions m × (2N + 1), F is a vector containing 2N + 1 unknown coefficients, and T is an m-element vector containing all measured data points from different circumferential positions. The mathematical expressions of A, F, and T are as follows:

[0055] Among them, W n,1 、W n,2 …W n,N represents N dominant wave numbers, θ=(θ1,θ2,θ3,…θ m ) represents m measuring points at different circumferential positions.

[0056] To solve the N dominant wavenumbers described by the equation, the number of data points in vector T must be greater than the number of unknown coefficients, that is, m ≥ 2N + 1. However, in practical applications, due to the uncertainty of T(θ), the reconstructed signal contains errors. Assessing the confidence of the reconstructed signal is crucial, which requires additional data points in T(θ). Therefore, at least 2N + 2 measurement points are required to characterize the N dominant wavenumbers. However, this results in an overdetermined system with more equations than unknowns.

[0057] In an exemplary embodiment, in actual application, the confidence interval of the reconstructed signal can be improved by increasing the number of measurement points / probes. For example, when N=4, the number of measurement points / probes m=10. In one embodiment, for single wave number approximation, at least six probes can be selected to generate a reconstructed signal with an appropriate confidence interval, that is, when N=, the number of measurement points / probes m=6. In one embodiment, for the case of using single wave number approximation to reconstruct the temperature field of the turbine engine combustion chamber, the number of measurement points in the turbine engine combustion chamber is 6; for the case of using multi-wave number approximation of four wave numbers to reconstruct the temperature field of the turbine engine combustion chamber, the number of measurement points in the turbine engine combustion chamber is 10.

[0058] Step 102: Determine the measurement point locations of the turbine engine combustion chamber using a global optimization algorithm by minimizing the condition number of the design matrix A.

[0059] Increasing the number of measurement locations can improve the confidence in the reconstructed signal, but this also means higher measurement instrument costs, increased congestion due to probe spacing limitations, and sometimes higher risks. Therefore, it is of great value to understand the confidence gain that improves with increasing measurement locations. For example, the effect of the number of measurement locations on the confidence of the reconstructed signal was studied using the QinetiQ aerospace engine full annular combustion chamber dataset. Five different circumferential locations were selected, including five cases of six, eight, ten, twelve, and fourteen, and the single wavenumber approximation was applied in all cases. Overall, all five cases produced temperature distribution shapes similar to the experiment, and there was no obvious trend indicating a benefit with increasing the number of measurement points. It can be seen that for the single wavenumber approximation, at least six locations are required to produce a reconstructed temperature field with good fidelity, and the benefit of increasing the measurement count is uncertain.

[0060] In the field of numerical analysis, the condition number of a function measures how the output of the function changes with small changes in the input parameters. The condition number of the design matrix A is the dominant wave number W n and the measured position θ. The condition number of the design matrix A can vary from 1 to infinity. Systems with large condition numbers can result in excessive errors in the reconstructed signal. There are many formulas for calculating the matrix condition number, taking the vector and matrix norm calculation using the two-norm as an example. The condition number k is calculated using k = ‖A‖‖A + ‖ to calculate, where A + is the inverse of the square matrix A, or the Moore-Penrose generalized inverse of a non-square matrix A.

[0061] In the embodiment of the present application, the condition number k of the design matrix A is determined by the probe position θ and the selected dominant wave number W n OK. A selected set of W n The condition number of the design matrix A measures the sensitivity of the reconstructed temperature to the probe placement error. Therefore, the goal of searching for the optimal measurement point layout position (also called probe layout position) is to minimize the condition number of the design matrix A.

[0062] In an exemplary embodiment, the global optimization algorithm may include, but is not limited to, a particle swarm optimization algorithm, a simulated annealing algorithm, a genetic algorithm, or an evolutionary algorithm.

[0063] In a preferred embodiment, particle swarm optimization (PSO) can be used to search for the optimal probe position corresponding to the condition number that produces the minimum design matrix A. Because PSO has a high convergence efficiency, PSO is a good optimization technique for solving global optimization problems.

[0064] In an illustrative example, for a case where there is a significant hot spot at the exit of a turbine engine combustion chamber, a high-fidelity temperature distribution can be generated using a single wavenumber approximation method.

[0065] In one exemplary embodiment, when a single wavenumber is used to approximate the temperature field of a turbine engine combustion chamber, the condition number corresponding to the optimal probe position is less than 2. In another exemplary embodiment, when a multi-wavenumber method with less than or equal to four wavenumbers is used to approximate the temperature field of a turbine engine combustion chamber, the condition number corresponding to the optimal probe position is less than 4.

[0066] The method for obtaining measurement point locations in a turbine engine combustion chamber, provided in an embodiment of the present application, minimizes the condition number of a design matrix A and uses a global optimization algorithm to determine the minimum number of measurement points and optimal measurement locations, allowing for simple and accurate acquisition of measurement point locations in the turbine engine combustion chamber. The data obtained using the minimum number of measurement points and optimal measurement locations determined in this embodiment of the present application can be used to reconstruct the turbine engine's temperature field, resolving the low efficiency and high cost of traditional rotating rake-based combustion chamber measurement methods and reducing the testing cycle and costs during turbine engine development.

[0067] The following describes how to determine the optimal measurement location by minimizing the condition number of the design matrix A, combining two scenarios: a new experimental design and one based on historical test data.

[0068] Scenario 1: Determining the optimal probe position in a new experimental design.

[0069] In an exemplary embodiment, determining the probe position of the turbine engine combustion chamber using a global optimization algorithm in step 102 includes: considering the geometric constraints of the probe placement position in actual application, that is, the objective function f obj In one embodiment, to determine the optimal probe position in a new experimental design, based on the constraints of the probe position in the turbine engine combustion chamber, a global optimization algorithm can be used to converge the objective function and the condition number k to a minimum value to find the optimal probe position.

[0070] For a new experiment where the measurement locations can be freely chosen, the goal of the search is to minimize the condition number of the design matrix A. The objective function is thus: obj =k(θ,W n )+fconstraint;

[0071] Among them, fconstraint is the value of the constraint function when considering the geometric constraints of placing probes. When the position of a probe violates the constraint, a "penalty" or "cost" will be added to the constraint function to prevent the probe from being placed in the prohibited area.

[0072] The two main constraints of a turbine engine combustion chamber include the minimum spacing between adjacent measurements and the restricted area caused by external casing fixtures such as bleed holes and actuators. The probes in a turbine engine combustion chamber are usually mounted on the outer casing through various instrument ports. Therefore, setting a minimum probe spacing is necessary for practical operation. The calculation of the minimum probe spacing is shown in formula (4): |Δθ j,i |=|θ j -θ i |≥θ min (4)

[0073] In formula (4), θ i and θ j represent the circumferential positions of the i-th probe and the j-th probe, Δθ j,i represents the distance between the i-th probe and the j-th probe, θ min Indicates the minimum allowed probe spacing.

[0074] In an exemplary embodiment, in many cases, it may not be possible to install the probe in certain locations due to the presence of fixtures or obstacles in certain areas. Therefore, it is necessary to set constraints to prevent the probe from being placed within these ranges. The setting of the constraints is shown in formula (5):

[0075] In formula (5), and Denote the minimum axial position and maximum circumferential position of the pth obstacle, respectively. During the optimization process, if the position of a probe violates the above constraints, a "penalty" or "cost" will be added to the constraint function to prevent the probe from being placed in these areas where there are fixtures or obstacles.

[0076] In one embodiment, taking the outlet temperature field of a QinetiQ aircraft engine full annular combustion chamber as an example, the input information of the measurement position is as follows:

[0077] Dominant Wave: W n =[20,40,60,80];

[0078] Number of measurement points: m = 10;

[0079] In this embodiment, the constraints may include:

[0080] Minimum measurement distance: θ min =2.5°;

[0081] obstacle:

[0082] Using the above input information, the PSO algorithm is used to search for the best set of measurement positions. In this embodiment, assuming that the selected particle swarm size is 100, the optimization is run for 1000 iterations. Figure 2 shows the change of the objective function value during the optimization run. As shown in Figure 2, the horizontal axis represents the particle swarm size, and the vertical axis represents the objective function f obj It can be seen that during the first 100 iterations, the objective function value decreases rapidly, and the curve is quite flat in the subsequent iterations, which indicates that the selection of 200 iterations is appropriate. In the embodiment of the optimal measurement position and condition number of the new experimental design, the optimal measurement position and the corresponding condition number are shown in Table 1.

[0083] Table 1

[0084] Scenario 2: Determine the optimal probe location based on historical test data.

[0085] In an exemplary embodiment, the use of a global optimization algorithm to determine the probe position of a turbine engine combustion chamber in step 102 includes: selecting an optimal test data subset based on existing historical data for evaluating the turbine engine combustion chamber outlet temperature distribution factor. At this time, based on the constraints of the turbine engine combustion chamber probe position and the existing historical data, a global optimization algorithm can be used to make the objective function and the condition number k converge to the minimum value to find the optimal probe position.

[0086] If there is already some historical test data, the PSO algorithm can be used to search for the best test data subset from the complete measurement set. Taking the outlet temperature field of the full annular combustion chamber of QinetiQ's aircraft engine as an example, the input information of the measurement position is as follows:

[0087] Dominant Wave: W n =[20,40,60,80];

[0088] Number of measurement points: m = 10;

[0089] The positions of the existing probes are: θ=0°:2.5°:360°.

[0090] Still taking the selected particle swarm size of 100 and 1000 iterations as an example, the PSO optimization process is shown in FIG3 . In the embodiment of using the best measurement position and condition number of the existing measurement positions, the obtained best measurement position and corresponding condition number are shown in Table 2 .

[0091] Table 2

[0092] From the calculations of the above two scenarios, it can be seen that using the method for obtaining the position of the measuring point in the combustion chamber of a turbine engine provided in the embodiment of the present application, a probe is arranged in the combustion chamber of the turbine engine, and the objective function and the condition number converge to reasonable values ​​quickly within the expected number of iterations, and the optimal probe arrangement position is found. The method for obtaining the position of the measuring point in the combustion chamber of a turbine engine provided in the embodiment of the application is suitable for determining the optimal probe position in the combustion chamber of a turbine engine.

[0093] The present application also provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute any of the above-mentioned methods for obtaining the position of a measurement point in a turbine engine combustion chamber.

[0094] The present application further provides a device for obtaining the position of a measurement point in a combustion chamber of a turbine engine, comprising a memory and a processor, wherein the memory stores the following instructions that can be executed by the processor: for executing the steps of any of the above-mentioned methods for obtaining the position of a measurement point in a combustion chamber of a turbine engine.

[0095] FIG4 is a schematic diagram of the structure of the device for obtaining the position of a measuring point in a combustion chamber of a turbine engine according to an embodiment of the present application. As shown in FIG4 , the device may include: a first processing module, a second processing module, and a determination module; wherein,

[0096] A first processing module is configured to determine a dominant wave number, where the dominant wave number is obtained based on analysis of high-precision computational fluid dynamics simulation results, or is obtained based on the number of fuel injectors or the number of downstream high-pressure turbine inlet guide vanes;

[0097] A second processing module is used to determine the number of measurement points in the turbine engine combustion chamber according to the number of dominant wave numbers, wherein the number of measurement points is not less than 2N+1, where N is the number of dominant wave numbers;

[0098] The determination module is used to determine the measurement point positions of the turbine engine combustion chamber by minimizing the condition number of the design matrix A using a global optimization algorithm.

[0099] In an exemplary embodiment, when using a single wave number to approximate the reconstruction of the temperature field of the turbine engine combustion chamber, the number of measurement points in the turbine engine combustion chamber is 6; when using a multi-wave number approximation of four wave numbers to reconstruct the temperature field of the turbine engine combustion chamber, the number of measurement points in the turbine engine combustion chamber is 10.

[0100] In an exemplary embodiment, the determining module may be configured to:

[0101] In the case of determining the optimal probe position in a completely new experimental design, based on the constraints of the probe position in the turbine engine combustion chamber, a global optimization algorithm is used to make the objective function and the condition number k converge to the minimum value to find the optimal probe position.

[0102] When the optimal probe position is determined based on historical test data, the best test data subset is selected based on the existing historical data for evaluating the temperature distribution factor at the outlet of the turbine engine combustion chamber. Based on the constraints of the probe position in the turbine engine combustion chamber and the existing historical data, a global optimization algorithm is used to converge the objective function and the condition number k to the minimum value to find the optimal probe position.

[0103] In one embodiment, the objective function is: obj =k(θ,W n )+fconstraint;

[0104] Among them, fconstraint is the value of the constraint function when considering the geometric constraints of placing probes. When the position of a probe violates the constraint, a "penalty" or "cost" will be added to the constraint function to prevent the probe from being placed in the prohibited area.

[0105] In one embodiment, the condition number k is k=||A||A + ‖ to calculate, where A + is the inverse of the square matrix A, or the Moore-Penrose generalized inverse of a non-square matrix A.

[0106] The apparatus for obtaining measurement point locations in a turbine engine combustion chamber, provided in an embodiment of the present application, uses a global optimization algorithm to determine the minimum number of measurement points and optimal measurement locations by minimizing the condition number of a design matrix A. This allows for simple and accurate acquisition of measurement point locations in the turbine engine combustion chamber. The data obtained using the minimum number of measurement points and optimal measurement locations determined in this embodiment of the present application can be used to reconstruct the turbine engine's temperature field, resolving the low efficiency and high cost of traditional rotating rake-based combustion chamber measurement methods and reducing the testing cycle and costs during turbine engine development.

[0107] Although the embodiments disclosed in this application are as described above, the contents described are merely embodiments adopted to facilitate understanding of this application and are not intended to limit this application. Any person skilled in the art to which this application belongs may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application. However, the scope of patent protection of this application shall still be based on the scope defined by the attached claims.

Claims

1. A method for obtaining the measuring point positions in a combustion chamber of a turbofan engine, characterized in that, Including: Determine the dominant wave number, which is obtained based on the analysis of the high-precision computational fluid dynamics simulation results or according to the number of fuel injectors or the number of blades of the inlet guide vane of the downstream high-pressure turbine; Determine the number of measuring points in the turbine engine combustion chamber according to the number of dominant wave numbers, where the number of measuring points is not less than 2N + 1, and N is the number of dominant wave numbers; Determine the measuring point positions in the turbine engine combustion chamber by minimizing the condition number of the design matrix A using a global optimization algorithm.

2. The method according to claim 1, wherein The dominant wave number is equal to the number of fuel injectors.

3. The method according to claim 1, wherein For the case of reconstructing the temperature field of the turbine engine combustion chamber using single-wave number approximation, the number of measuring points in the turbine engine combustion chamber is 6; For the case of reconstructing the temperature field of the turbine engine combustion chamber using multi-wave number approximation with four wave numbers, the number of measuring points in the turbine engine combustion chamber is 10.

4. The method according to claim 1, wherein The determining the measuring point positions in the turbine engine combustion chamber using the global optimization algorithm includes: Determine the objective function f according to the geometric constraint conditions of the probe placement position obj as: f obj = k(θ, W n ) + fconstraint; where k(θ, W n ) means that the condition number k of the design matrix A is a function of the dominant wave number W n and the measurement position θ; fconstraint is the value of the constraint function when considering the geometric constraints for placing the probe.

5. The method according to claim 4, wherein, For the case of determining the optimal probe position in a completely new experimental design, according to the constraint conditions of the probe position in the turbine engine combustion chamber, through the global optimization algorithm, the objective function and the condition number k converge to the minimum value to find the optimal probe position.

6. The method according to claim 4, wherein, For the case of determining the optimal probe position based on historical test data, select the best test data subset based on the existing historical data for evaluating the temperature distribution factor at the outlet of the turbine engine combustion chamber; according to the constraint conditions of the probe position in the turbine engine combustion chamber and the existing historical data, through the global optimization algorithm, the objective function and the condition number k converge to the minimum value to find the optimal probe position.

7. The method according to claim 1, 5 or 6, wherein For the case of reconstructing the temperature field of the turbine engine combustion chamber using single-wave number approximation, the condition number k corresponding to the optimal measuring point position is less than 2; For the case of reconstructing the temperature field of the turbine engine combustion chamber using multi-wave number approximation with less than or equal to four wave numbers, the condition number k corresponding to the optimal measuring point position is less than 4.

8. The method according to claim 1, 5 or 6, characterized in that The global optimization algorithm includes: particle swarm optimization algorithm, or simulated annealing algorithm, or genetic algorithm or evolutionary algorithm.

9. The method according to claim 4, wherein The design matrix A is as follows: Among them, W n,1 , W n,2 …W n,N represent the N dominant wave numbers, and θ = (θ1, θ2, θ3, … θ m ) represents m measuring points at different circumferential positions.

10. The method according to claim 9, wherein, The condition number k of the design matrix A is: k = ‖A‖‖A + ‖; where A + is the inverse of the square matrix A or the Moore-Penrose generalized inverse matrix of the non-square matrix A.

11. A computer-readable storage medium storing computer-executable instructions for executing the method for obtaining the measuring point positions in the turbine engine combustion chamber according to any one of claims 1 to 10.

12. An apparatus for implementing the acquisition of the measuring point positions in a turbine engine combustion chamber, comprising a memory and a processor, wherein, Instructions executable by a processor are stored in a memory: for performing the steps of the method for obtaining the measuring point positions in the turbine engine combustion chamber according to any one of claims 1 to 10.

13. A device for obtaining the measuring point positions of a turbine engine combustion chamber, characterized in that, Including: A first processing module, a second processing module, and a determination module; where The first processing module is used to determine the dominant wave number, which is obtained based on the analysis of the high-precision computational fluid dynamics simulation results or according to the number of fuel injectors or the number of blades of the inlet guide vane of the downstream high-pressure turbine; The second processing module is used to determine the number of measuring points in the turbine engine combustion chamber according to the number of dominant wave numbers, where the number of measuring points is not less than 2N + 1, and N is the number of dominant wave numbers; A determination module, configured to use a global optimization algorithm to determine the measuring point positions of a turbine engine combustion chamber by minimizing the condition number of a design matrix A.

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