An adaptive interpolation method and device for multi-section turbomachinery blades

Abstract

The present invention discloses an adaptive interpolation method and device for a multi-section turbine machinery blade, comprising: obtaining the contour data of each section of the original model of the blade; determining the geometric data of each section of the blade according to the contour data; determining the area on the blade where the interpolation point needs to be adjusted by curvature analysis or image analysis according to the geometric data, and obtaining multiple interpolation point adjustment areas; adjusting the points on each interpolation point adjustment area, and then performing error analysis on each initially adjusted area obtained, determining multiple interpolation correction areas according to the error analysis, adjusting the points on each interpolation correction area, obtaining an optimized model of the blade, and determining whether the optimized model reaches a preset accuracy, if not, the optimized model continues to adjust the interpolation points until the obtained optimized model reaches the preset accuracy. The present invention can optimize the blade model adaptively and efficiently.

Description

Technical Field

[0001] The present invention belongs to the field of aerospace technology, and in particular relates to an adaptive interpolation method and device for multi-section turbine machinery blades. Background Art

[0002] With the development of modern industry, especially the growing demand for high-performance turbomachinery in aerospace, energy generation and other fields, blades, as one of the core components of turbomachinery, face unprecedented challenges in design and manufacturing technology. In traditional design methods, blade design relies on experienced engineers to manually adjust parameters, which is not only time-consuming but also difficult to meet increasingly complex design requirements.

[0003] Over the past few decades, with the advancement of computer-aided design (CAD), computer-aided engineering (CAE), and computer-aided manufacturing (CAM) technologies, designers have gradually begun to use numerical simulation and finite element analysis to optimize blade design. However, with the increasing complexity of blade design, especially in the design of multi-section blades, the traditional fixed interpolation method has gradually exposed its limitations, such as the inability to effectively handle complex geometric changes, low computational efficiency, and difficulty in ensuring surface quality. In the process of designing and manufacturing blades for multi-section turbomachinery (such as aircraft engines or industrial turbines), a common technical challenge is how to efficiently and accurately process data of complex geometries. Traditional design methods often rely on fixed grids or manual adjustments, which are not only time-consuming but also prone to errors, especially when accurate interpolation of different sections is required. In this case, any minor design changes may cause the entire blade model to be recalculated, greatly increasing development time and cost.

[0004] In this context, the industry's demand for an interpolation method that can automatically adapt to different geometric features, perform efficient calculations, and ensure surface quality has become increasingly urgent. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides an adaptive interpolation method and device for multi-section turbine machinery blades.

[0006] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] The present invention provides an adaptive interpolation method for multi-section turbine machinery blades, comprising:

[0008] Acquire the contour data of each cross section of the original model of the blade to be processed;

[0009] Determining geometric data of each cross section of the blade to be processed according to the contour data, wherein the geometric data of each cross section includes coordinates of points constituting the cross section;

[0010] According to the geometric data, by curvature analysis or image analysis, determining the area on the blade to be processed where the interpolation points need to be adjusted, and obtaining a plurality of interpolation point areas to be adjusted;

[0011] Adjusting each point on the interpolation point to-be-adjusted region to obtain a plurality of initially adjusted regions corresponding one-to-one to the plurality of interpolation point to-be-adjusted regions;

[0012] Performing error analysis on each initially adjusted region, determining multiple interpolation correction regions based on the error analysis results, and adjusting points on each interpolation correction region;

[0013] Based on all adjusted areas and all unadjusted areas, an optimization model of the blade to be processed is obtained, and it is determined whether the optimization model of the blade to be processed reaches a preset accuracy. If not, the interpolation points of the optimization model of the blade to be processed continue to be adjusted until the obtained optimization model reaches the preset accuracy.

[0014] The present invention also provides an adaptive interpolation device for a multi-section turbine machinery blade, comprising:

[0015] A data acquisition module, used to acquire the contour data of each cross section of the original model of the blade to be processed;

[0016] A preprocessing module, used to determine the geometric data of each cross section of the blade to be processed according to the contour data, wherein the geometric data of each cross section includes the coordinates of the points constituting the cross section;

[0017] An adaptive difference module is used to determine, based on the geometric data, the area on the blade to be processed where the interpolation points need to be adjusted through curvature analysis or image analysis, and obtain multiple interpolation point areas to be adjusted; adjust the points on each interpolation point area to be adjusted to obtain multiple initially adjusted areas corresponding to the multiple interpolation point areas to be adjusted; perform error analysis on each initially adjusted area, and determine multiple interpolation correction areas based on the error analysis results, and adjust the points on each interpolation correction area; obtain the optimization model of the blade to be processed based on all adjusted areas and all unadjusted areas, and determine whether the optimization model of the blade to be processed reaches a preset accuracy. If not, continue to adjust the interpolation points of the optimization model of the blade to be processed until the obtained optimization model reaches the preset accuracy.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention can flexibly adjust between different cross-sections through adaptive interpolation, better adapt to geometric changes, and improve the flexibility of blade design. In addition, through adaptive interpolation, the geometric shape of the blade can be accurately controlled, thereby improving the overall efficiency and reliability of the turbomachinery. When optimizing the blade, the present invention only adjusts the data of the affected part, avoiding unnecessary global recalculation and significantly improving the iteration speed during design. The present invention adjusts the interpolation points on the cross-section through error analysis, thereby ensuring the continuity and smoothness of the blade surface and improving the aerodynamic performance. The present invention has a high degree of automation, reduces dependence on manual adjustment, lowers the design threshold, and enables non-professionals to efficiently complete the design task, thereby reducing manual dependence.

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of an adaptive interpolation method for multi-section turbine machinery blades provided by an embodiment of the present invention;

[0022] Figure 2 It is a schematic diagram of a curvature distribution diagram on a blade provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0024] Figure 1 is a flow chart of an adaptive interpolation method for multi-section turbine machinery blades provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0025] S101 , obtaining contour data of each cross section of an original model of a blade to be processed.

[0026] It should be noted that the model in the present invention is a three-dimensional geometric model.

[0027] Here, the blade to be processed may be a turbine blade to be interpolated.

[0028] S102 . Determine geometric data of each cross section of the blade to be processed according to the contour data, wherein the geometric data of each cross section includes coordinates of points constituting the cross section.

[0029] Here, the geometric data of each section includes vertex coordinates, edge information, normal vectors, and geometric features.

[0030] S103, according to the geometric data, through curvature analysis or image analysis, determine the area on the blade to be processed where the interpolation points need to be adjusted, and obtain multiple interpolation point areas to be adjusted.

[0031] S104: adjusting each point on the interpolation point to-be-adjusted region to obtain a plurality of initially adjusted regions corresponding one-to-one to the plurality of interpolation point to-be-adjusted regions.

[0032] S105 , performing error analysis on each initially adjusted region, determining a plurality of interpolation correction regions according to the error analysis results, and adjusting points on each interpolation correction region.

[0033] S106. Obtain an optimization model of the blade to be processed based on all adjusted areas and all unadjusted areas, and determine whether the optimization model of the blade to be processed reaches a preset accuracy. If not, continue to adjust the interpolation points of the optimization model of the blade to be processed until the obtained optimization model reaches the preset accuracy.

[0034] For the above S101, the contour data of each section of the original model of the blade to be processed can be directly obtained from the design file of the original model of the blade to be processed, or the contour of the original model of the blade to be processed can be extracted to obtain the contour data of each section. The contour data of each section is composed of multiple points, each point is a coordinate point in a three-dimensional coordinate system, and the three-dimensional coordinate system is established with the center point of the blade as the origin.

[0035] In some embodiments, the above S102 can be specifically implemented as follows: performing data preprocessing on the contour data of each cross-section of the original model of the blade to be processed to obtain preprocessed data, re-obtaining the original model of the blade to be processed based on the preprocessed data, performing geometric recognition on the re-obtained original model of the blade to be processed to obtain the geometric data of each cross-section of the blade to be processed, wherein the geometric data of each cross-section includes the coordinates of the points constituting the cross-section.

[0036] In the actual measurement or design process, the data often contains various noises, which will affect the subsequent processing and analysis. Therefore, illustratively, data preprocessing can be filtering the contour data of each section of the original model of the blade to be processed, for example, using one or more of a low-pass filter, a high-pass filter, a band-pass filter and a median filter to filter the contour data of each section of the original model of the blade to be processed to obtain preprocessed data. The low-pass filter is used to remove high-frequency noise and retain low-frequency signals. The high-pass filter is used to remove low-frequency noise and retain high-frequency signals. The band-pass filter is used to retain only signals within a specific frequency range. The median filter is used to remove salt and pepper noise and is suitable for nonlinear noise.

[0037] In another exemplary embodiment, data preprocessing may be filtering and coordinate transformation of the contour data of each cross section of the original model of the blade to be processed. Exemplarily, the coordinate transformation may include one or more of translation, rotation and scaling. Through coordinate translation, each point in the contour data may be translated to the vicinity of the origin of the above-mentioned three-dimensional coordinate system, wherein the formula for coordinate translation is: (x′, y′, z′) = (x-x0, y-y0, z-z0), wherein x0, y0 and z0 represent the translation amounts of the x, y and z axes respectively, (x′, y′, z′) represents the coordinates of a point after translation, and (x, y, z) represents the coordinates of a point before translation. Through coordinate rotation, each point in the contour data may be rotated to a standard direction, and by performing coordinate rotation on each point in the contour data of each cross section of the original model of the blade to be processed, the main axis of the blade to be processed may be aligned with any one of the x, y and z axes of the above-mentioned three-dimensional coordinate system; wherein the formula for coordinate rotation is: Where R is the rotation coefficient, (x′, y′, z′) represents the coordinates of a point after rotation, and (x, y, z) represents the coordinates of a point before rotation. Through coordinate scaling, each point in the contour data of each section of the original model of the blade to be processed can be scaled to a uniform scale to make the scales of all points consistent; where the formula for coordinate rotation is: (x′, y′, z′) = (sx·x, sy·y, sz·z), where sx, sy and sz represent the scaling amounts of the x, y and z axes respectively, (x′, y′, z′) represents the coordinates of a point after scaling, and (x, y, z) represents the coordinates of a point before scaling.

[0038] It should be noted that the specific data preprocessing method can be determined according to actual needs.

[0039] In some embodiments, the above S103 can be implemented through steps S1031 to S1033:

[0040] S1031. According to the geometric data of each cross section of the blade to be processed, a curvature analysis algorithm is used to calculate the curvature value on each cross section of the blade to be processed.

[0041] Exemplarily, the curvature analysis algorithm may be a quadratic differential method, and the specific method for calculating the curvature value is not described in detail here.

[0042] S1032. Draw a curvature distribution diagram on the blade to be processed using the matplotlib library according to the calculated curvature value.

[0043] S1033. By setting the curvature threshold, determine from the curvature distribution map the area whose curvature value does not meet the curvature threshold, and obtain multiple interpolation point adjustment areas.

[0044] Exemplarily, the set curvature threshold includes two different curvature thresholds, and the two different curvature thresholds constitute a curvature range. Each area in the curvature distribution map where the curvature value is less than the lower limit value of the curvature range can be used as an interpolation point area to be adjusted, and each area in the curvature distribution map where the curvature value is greater than the upper limit value of the curvature range can be used as an interpolation point area to be adjusted. In this way, two or more interpolation point areas to be adjusted can be obtained.

[0045] For example, Figure 2 It is a curvature distribution diagram on a blade. The dark red area in the curvature distribution diagram represents an area on the blade where the curvature value is greater than the upper limit value of the curvature range, and the light red area represents an area where the curvature value is less than the lower limit value of the curvature range.

[0046] In the case where the above S103 is implemented through steps S1031 to S1033, the above S104 is implemented through S1041:

[0047] S1041. For each interpolation point to be adjusted area whose curvature value is less than the lower limit of the curvature range, reduce the number of points on the area; and, for each interpolation point to be adjusted area whose curvature value is greater than the upper limit of the curvature range, use an interpolation algorithm to add interpolation points in the area, so as to obtain multiple initially adjusted areas corresponding one by one to the multiple interpolation point to be adjusted areas.

[0048] Exemplarily, for each interpolation point to be adjusted region whose curvature value is greater than the upper limit of the curvature range, an interpolation point can be inserted between every two existing points in the region, wherein the coordinates of the interpolation point to be inserted can be calculated based on the coordinates of the two existing points using an interpolation algorithm. In another exemplary embodiment, for each interpolation point to be adjusted region whose curvature value is greater than the upper limit of the curvature range, the number of interpolation points that need to be added in the region can also be determined based on the curvature value of the region. For example, the larger the curvature value of the region, the more interpolation points need to be added in the region, and these interpolation points can be inserted evenly in the region; in this way, the model of the blade to be processed is refined by inserting additional interpolation points in each region with a larger curvature. After adding interpolation points to an interpolation point to be adjusted region whose curvature value is greater than the upper limit of the curvature range, an initially adjusted region can be obtained.

[0049] Exemplarily, for each interpolation point to be adjusted region whose curvature value is less than the lower limit of the curvature range, a preset number of points can be evenly deleted from the region to reduce the amount of data, thereby improving the efficiency of subsequent calculations. In another exemplary embodiment, for each interpolation point to be adjusted region whose curvature value is less than the lower limit of the curvature range, the density of the points in the region can be detected first, and then, according to the detection results, some points in the region with a density higher than the set density threshold are deleted to reduce the amount of data while fully ensuring the overall quality of the model. Specifically, when deleting points, one point can be deleted every few points, or the key points can be detected using a corresponding algorithm, and then all non-key points or some non-key points can be deleted. After deleting points in an interpolation point to be adjusted region whose curvature value is less than the lower limit of the curvature range, an initially adjusted region can be obtained.

[0050] In some embodiments, the above S103 can be implemented through steps S10 to S11:

[0051] S10, converting the geometric data of each cross section of the blade to be processed into a visualized two-dimensional image to obtain a plurality of two-dimensional images.

[0052] Here, existing visualization tools can be used to convert the geometric data of each cross section of the blade to be processed into a visualized two-dimensional image.

[0053] S11. Using image segmentation technology, a key area of ​​the blade to be processed is identified according to a plurality of two-dimensional images, and each identified key area is used as an interpolation point to be adjusted area, thereby obtaining a plurality of interpolation point to be adjusted areas.

[0054] Specifically, the edge detection algorithm can be used to identify the edges in the image, the Hough transform can be used to detect straight lines and curves, and the SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features) in the feature point detection algorithm can be used to detect key points in the image, so as to identify each key area of ​​the blade to be processed. Here, each key area can be an area with manual markings or some areas that are automatically identified, which can be set according to actual needs. Exemplarily, a key area can be the leading edge area, trailing edge area, or tip area of ​​the blade to be processed. In another exemplary embodiment, a key area can also be a stress concentration area.

[0055] In some embodiments, in order to reduce the amount of data to improve subsequent computing efficiency, each non-critical area can also be used as an interpolation point area to be adjusted. Specifically, the area other than the critical area in the leaf to be processed can be divided, and each area obtained by the division can be used as a non-critical area, and each non-critical area can also be used as an interpolation point area to be adjusted.

[0056] In the case where the above S103 is implemented through steps S10 to S11, the above S104 is implemented through S20:

[0057] S20 , using an interpolation algorithm to add interpolation points in each interpolation point to-be-adjusted region, to obtain a plurality of initially adjusted regions corresponding one-to-one to the plurality of interpolation point to-be-adjusted regions.

[0058] Exemplarily, for each key area, an interpolation point can be inserted between every two existing points in the area, wherein an interpolation algorithm can be used to calculate the coordinates of the interpolation point to be inserted based on the coordinates of the two existing points. In another exemplary embodiment, for each key area, the number of interpolation points that need to be added in the area can also be determined based on the curvature value of the area. For example, when the curvature value of the area is larger, the number of interpolation points that need to be added in the area is more, and these interpolation points can be inserted evenly in the area; in this way, the model of the blade to be processed is refined by inserting additional interpolation points in the key area. After adding interpolation points in a key area, an initially adjusted area can be obtained.

[0059] In some embodiments, when each non-critical area is also used as an interpolation point to be adjusted area, illustratively, for each non-critical area, a preset number of points can be evenly deleted from the area to reduce the amount of data, thereby improving the efficiency of subsequent calculations; in another exemplary embodiment, for each non-critical area, the density of the points in the area can be first detected, and then, according to the detection results, some points in the area with a density higher than a set density threshold are deleted to reduce the amount of data while fully ensuring the overall quality of the model. Specifically, when deleting points, one point can be deleted every few points, or the corresponding algorithm can be used to detect the key points first, and then all or part of the non-critical points can be deleted. After deleting points in a non-critical area, an initially adjusted area can be obtained.

[0060] In some embodiments, the above S105 can be implemented through steps S1051 to S1054:

[0061] S1051 . For each initially adjusted region, determine the error of the initially adjusted region or the error of each sub-region in the initially adjusted region through error analysis.

[0062] Specifically, S1051 can be implemented by any one of steps A, B, C, and D:

[0063] Step A: For each initially adjusted region, the average value of the square of the error between each point on the initially adjusted region and the coordinates of a corresponding point in the ideal model of the blade to be processed is calculated, and the average value is used as the error of the initially adjusted region.

[0064] Exemplarily, the expression for the mean value of the squared error is:

[0065]

[0066] Where n represents the number of samples, that is, all points on the initial adjusted area. Represents the coordinates of the i-th point on the initially adjusted area, y i Represents the true value of the i-th point (i.e., the coordinates of a corresponding point in the ideal model).

[0067] Step B: for each initially adjusted region, calculate the error between the coordinates of each point on the initially adjusted region and a corresponding point in the ideal model, and take the maximum error as the error of the initially adjusted region.

[0068] Exemplarily, the expression of the maximum error is:

[0069]

[0070] Among them, max represents the maximum value operation.

[0071] Step C: for each initially adjusted region, calculate the absolute error between the coordinates of each point on the initially adjusted region and a corresponding point in the ideal model, and take the average value of the absolute errors as the error of the initially adjusted region.

[0072] For example, the expression of absolute error is:

[0073]

[0074] Among them, |.| means finding the absolute value.

[0075] Step D: for each initially adjusted region, determine the region within a preset range centered on each point in the initially adjusted region to obtain multiple sub-regions, and calculate the principal curvature of each sub-region in the initially adjusted region as the error of each sub-region.

[0076] Exemplarily, the calculation formula of the principal curvature of a three-dimensional surface is:

[0077]

[0078] Among them, f′(x) is the first-order derivative of f(x), which represents the slope of the curve, and f″(x) is the second-order derivative of f(x), which represents the rate of change of the slope of the curve.

[0079] Here, the curvature near each interpolation point can be calculated through step D. The sub-region with a larger curvature usually has a larger error, which facilitates the subsequent correction of the interpolation point according to the error.

[0080] S1052. Take each initially adjusted region whose error is greater than a first preset error threshold as an interpolation correction region, and take each initially adjusted region whose error is less than a second preset error threshold as an interpolation correction region; or, take each sub-region whose error is greater than the first preset error threshold as an interpolation correction region, and take each sub-region whose error is less than the second preset error threshold as an interpolation correction region, to obtain multiple interpolation correction regions; wherein the second preset error threshold is less than the first preset error threshold.

[0081] In some embodiments, a visualization tool (such as Matplotlib, Paraview, etc.) may be used to draw an error distribution graph to intuitively display areas with larger errors.

[0082] S1053: Add an interpolation point in each interpolation correction region where the error is greater than a first preset error threshold.

[0083] In some cases, due to the uneven distribution of interpolation points, that is, the interpolation points are too sparse in some areas and cannot accurately capture the local geometric features, resulting in a large error in the area. Therefore, interpolation points can be added in the area with large errors. Specifically, interpolation points can be added in each interpolation correction area in the manner described above.

[0084] In some cases, different interpolation algorithms are suitable for different geometric features, and selecting an inappropriate interpolation algorithm may also lead to large errors. Therefore, the newly added interpolation points in each interpolation correction area where the error is greater than the first preset error threshold can be deleted, and the interpolation algorithm (such as spline interpolation, B-spline interpolation, radial basis function interpolation, etc.) can be reselected, and interpolation points can be re-added in the interpolation correction area.

[0085] In some cases, when there is noise in the data, the interpolation result may be inaccurate, resulting in a large error. Therefore, for each interpolation correction area where the error is greater than the first preset error threshold, the newly added interpolation points in each interpolation correction area where the error is greater than the first preset error threshold may be deleted, and then the points in the interpolation correction area from which the newly added interpolation points have been deleted may be preprocessed, such as filtering, smoothing, etc., to reduce the impact of noise. Afterwards, interpolation points may be re-added to the interpolation correction area based on the preprocessed data.

[0086] It should be noted that one or more of the above-mentioned methods may be used to adjust the points in each interpolation correction area whose error is greater than the first preset error threshold.

[0087] S1054. For each interpolation correction region whose error is less than a second preset error threshold, reduce the points in the region.

[0088] Specifically, the deletion method described above may be used to reduce the points in each interpolation correction area whose errors are less than the second preset error threshold, so as to reduce the amount of data and improve subsequent calculation efficiency.

[0089] In some embodiments, when there is no area with an error greater than the first preset error threshold, only for each interpolation correction area with an error less than the second preset error threshold, the points on the area are reduced, and then step S106 is executed; in some embodiments, when there is no area with an error greater than the first preset error threshold, step S106 can also be executed directly.

[0090] In some embodiments, the above S106 can be implemented through steps S1061 to S1064:

[0091] S1061. Determine the geometric data of each cross section of the optimized model of the blade to be processed.

[0092] Specifically, the geometric data of each cross section of the optimization model can be determined according to the data of the optimization model of the blade to be processed.

[0093] S1062. Calculate the curvature value on each cross section of the optimized model of the blade to be processed using a curvature analysis algorithm according to the geometric data of each cross section of the optimized model of the blade to be processed.

[0094] S1063. According to the calculated curvature value, use the matplotlib library to draw a curvature distribution diagram on the optimized model of the blade to be processed.

[0095] S1064, analyzing whether the curvature value of the preset area in the curvature distribution map reaches the set curvature value. If so, the optimization model of the blade to be processed reaches the preset accuracy; otherwise, the optimization model of the blade to be processed does not reach the preset accuracy.

[0096] Specifically, it is possible to analyze whether the curvature values ​​of some key areas in the curvature distribution map reach the corresponding set curvature values. If the curvature values ​​of some key areas in the curvature distribution map all reach the corresponding set curvature values, it means that the optimization model of the blade to be processed has reached the preset accuracy. At this time, the optimization can be stopped; if the curvature values ​​of some key areas in the curvature distribution map do not reach the corresponding set curvature values, then based on the currently obtained optimization model of the blade to be processed, the above method can be used to continue to determine a new optimization model, and then it is re-determined whether the accuracy of the new optimization model reaches the preset accuracy, until the optimization model of the blade to be processed that reaches the preset accuracy is finally obtained.

[0097] In some embodiments, before executing the above S105 and executing the above S106, the surface of the optimized model of the blade to be processed can also be finely smoothed by using surface reconstruction technology and smoothing algorithm according to the points of all adjusted areas and all unadjusted areas to eliminate any irregularities caused by the interpolation process and ensure that the surface of the blade is smooth, thereby obtaining the smoothed optimized model of the blade to be processed, and then executing the above S106 based on the obtained smoothed optimized model of the blade to be processed. Specifically, the algorithm used in the surface reconstruction technology can be the Gauss surface reconstruction algorithm, and the smoothing algorithm can be the Laplace smoothing algorithm.

[0098] In some embodiments, after obtaining an optimized model of the blade to be processed that reaches a preset accuracy, computer-aided design (CAD) software can be used for visual verification to verify whether the surface quality and aerodynamic characteristics meet the expected standards, and output a data file that can be used for CNC machining. The data file for CNC machining is mainly used to exchange geometric data between different CAD systems. The data file for CNC machining contains the complete geometric information of the blade model and can be read and edited by various CAD software. The data file for CNC machining includes:

[0099] Geometric data: including vertex coordinates, edges, faces and other geometric information of the blade model.

[0100] Material Information: Specify the material properties of the blade, such as material type, density, elastic modulus, etc.

[0101] Processing parameters: including cutting speed, feed speed, tool radius and other processing parameters.

[0102] Process information: includes processing sequence, processing path, tool change point and other process information.

[0103] Tolerance requirements: Specify the processing tolerance of each part to ensure processing accuracy.

[0104] The method provided by the present invention has the following advantages:

[0105] 1) Improved blade surface quality: The adaptive interpolation method of the present invention increases the density of interpolation points in key areas, significantly improving the smoothness of the blade surface. The intelligent distribution of interpolation points reduces surface discontinuities, making the blade surface more uniform and without obvious mutations. The adaptive interpolation method of the present invention can accurately control the position and number of interpolation points, ensure the accuracy of the blade geometry, and avoid geometric distortion caused by unreasonable distribution of interpolation points in traditional methods.

[0106] 2) Optimized blade aerodynamic performance: Improving the surface quality of the blade directly improves the aerodynamic performance of the blade. The smooth surface reduces the resistance in air flow and improves the efficiency of the turbomachinery. Improving the surface smoothness helps reduce turbulent losses, thereby improving the operating efficiency and stability of the turbomachinery.

[0107] 3) Simplified manufacturing process: The high-quality surface data obtained through adaptive interpolation can reduce the workload of correction in subsequent processing and simplify the manufacturing process. By reducing the additional processing steps, the overall manufacturing cost can be reduced and the production efficiency can be improved.

[0108] 4) Enhanced design flexibility: The adaptive interpolation method of the present invention can flexibly cope with blades of different geometric shapes, and can obtain good interpolation effects regardless of whether the cross-section is simple or complex. Some interpolation parameters in the adaptive interpolation method of the present invention can be adjusted according to specific needs, so that designers can customize according to actual needs, which enhances the flexibility of design.

[0109] 5) Improved computational efficiency: The adaptive interpolation method of the present invention can perform efficient computations, thereby being able to quickly process large amounts of data, even when processing blades with complex geometric shapes. The adaptive interpolation method of the present invention uses a real-time feedback mechanism, and the program can quickly adjust the interpolation strategy to ensure that each interpolation operation achieves the best effect.

[0110] 6) Extend the service life of equipment: The improvement of surface smoothness not only improves aerodynamic performance, but also reduces wear during operation and extends the service life of turbine machinery blades. Higher surface quality and better geometric accuracy make the blades more stable and reliable during long-term operation.

[0111] 7) Simplify maintenance work: Improved surface quality reduces the failure rate during operation, reduces maintenance workload, and makes equipment maintenance easier.

[0112] 8) Environmentally friendly: Higher aerodynamic efficiency means lower energy consumption, thereby reducing energy waste and having a positive impact on environmental protection.

[0113] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0114] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0115] In the specification, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality of components. Certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good effects.

[0116] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. An adaptive interpolation method for multi-section turbine machinery blades, characterized in that: include: Acquire the contour data of each cross section of the original model of the blade to be processed; Determining geometric data of each cross section of the blade to be processed according to the contour data, wherein the geometric data of each cross section includes coordinates of points constituting the cross section; According to the geometric data, by curvature analysis or image analysis, determining the area on the blade to be processed where the interpolation points need to be adjusted, and obtaining a plurality of interpolation point areas to be adjusted; Adjusting each point on the interpolation point to-be-adjusted region to obtain a plurality of initially adjusted regions corresponding one-to-one to the plurality of interpolation point to-be-adjusted regions; Performing error analysis on each initially adjusted region, determining multiple interpolation correction regions based on the error analysis results, and adjusting points on each interpolation correction region; Based on all adjusted areas and all unadjusted areas, an optimization model of the blade to be processed is obtained, and it is determined whether the optimization model of the blade to be processed reaches a preset accuracy. If not, the interpolation points of the optimization model of the blade to be processed continue to be adjusted until the obtained optimization model reaches the preset accuracy.

2. The adaptive interpolation method for multi-section turbomachinery blades according to claim 1, characterized in that: The method of determining the area on the blade to be processed where the interpolation points need to be adjusted by curvature analysis or image analysis based on the geometric data, and obtaining a plurality of interpolation point areas to be adjusted, includes: According to the geometric data of each cross section of the blade to be processed, a curvature analysis algorithm is used to calculate the curvature value on each cross section of the blade to be processed; According to the calculated curvature value, the curvature distribution diagram on the leaf to be processed is drawn using the matplotlib library; By setting the curvature threshold, the area whose curvature value does not meet the curvature threshold is determined from the curvature distribution map, and a plurality of interpolation point adjustment areas are obtained.

3. The adaptive interpolation method for multi-section turbomachinery blades according to claim 2, characterized in that: The curvature threshold includes two different curvature thresholds, and the two different curvature thresholds constitute a curvature range; the curvature threshold is set to determine the area whose curvature value does not meet the curvature threshold from the curvature distribution map, and obtain multiple interpolation point adjustment areas, including; Each area in the curvature distribution map whose curvature value is less than the lower limit value of the curvature range is taken as an interpolation point area to be adjusted, and each area in the curvature distribution map whose curvature value is greater than the upper limit value of the curvature range is taken as an interpolation point area to be adjusted, thereby obtaining multiple interpolation point areas to be adjusted.

4. The adaptive interpolation method for multi-section turbomachinery blades according to claim 1, characterized in that: The method of determining the area on the blade to be processed where the interpolation points need to be adjusted by curvature analysis or image analysis based on the geometric data, and obtaining a plurality of interpolation point areas to be adjusted, includes: Converting the geometric data of each cross section of the blade to be processed into a visualized two-dimensional image to obtain a plurality of two-dimensional images; By using image segmentation technology, the key area of ​​the blade to be processed is identified according to the multiple two-dimensional images, and each identified key area is used as an interpolation point to be adjusted area to obtain multiple interpolation point to be adjusted areas.

5. The adaptive interpolation method for multi-section turbomachinery blades according to claim 3, characterized in that: The step of adjusting the points on each interpolation point to-be-adjusted region to obtain a plurality of initially adjusted regions corresponding one-to-one to the plurality of interpolation point to-be-adjusted regions comprises: For each interpolation point to be adjusted area whose curvature value is less than the lower limit of the curvature range, the number of points on the area is reduced, and for each interpolation point to be adjusted area whose curvature value is greater than the upper limit of the curvature range, an interpolation algorithm is used to add interpolation points in the area to obtain multiple initially adjusted areas corresponding one by one to the multiple interpolation point to be adjusted areas.

6. The adaptive interpolation method for multi-section turbomachinery blades according to claim 4, characterized in that: The step of adjusting the points on each interpolation point to-be-adjusted region to obtain a plurality of initially adjusted regions corresponding one-to-one to the plurality of interpolation point to-be-adjusted regions comprises: An interpolation algorithm is used to add interpolation points in each interpolation point to-be-adjusted region, so as to obtain a plurality of initially adjusted regions corresponding one-to-one to the plurality of interpolation point to-be-adjusted regions.

7. The adaptive interpolation method for multi-section turbomachinery blades according to claim 1, characterized in that: The error analysis is performed on each initially adjusted region, and a plurality of interpolation correction regions are determined according to the error analysis results, and points on each interpolation correction region are adjusted, including: For each initially adjusted region, determining an error of the initially adjusted region or an error of each sub-region in the initially adjusted region through error analysis; Each initially adjusted region with an error greater than a first preset error threshold is taken as an interpolation correction region, and each initially adjusted region with an error less than a second preset error threshold is also taken as an interpolation correction region; or each sub-region with an error greater than the first preset error threshold is taken as an interpolation correction region, and each sub-region with an error less than the second preset error threshold is also taken as an interpolation correction region, to obtain a plurality of interpolation correction regions; wherein the second preset error threshold is less than the first preset error threshold; Adding an interpolation point in each interpolation correction region where the error is greater than the first preset error threshold; For each interpolation correction region where the error is less than the second preset error threshold, the points in the region are reduced.

8. The adaptive interpolation method for multi-section turbomachinery blades according to claim 7, characterized in that: The step of determining the error of the initially adjusted region or the error of each sub-region in the initially adjusted region by error analysis includes: Calculate the average value of the square of the error between each point on the initially adjusted region and the coordinates of a corresponding point in the ideal model of the blade to be processed, and use the average value as the error of the initially adjusted region; or, Calculate the error between the coordinates of each point on the initially adjusted region and a corresponding point in the ideal model, and take the maximum error as the error of the initially adjusted region; or, Calculate the absolute error between the coordinates of each point on the initially adjusted region and a corresponding point in the ideal model, and use the average value of the absolute errors as the error of the initially adjusted region; or, An area within a preset range centered at each point in the initially adjusted area is determined to obtain a plurality of sub-areas, and a principal curvature of each sub-area is calculated as an error of each sub-area.

9. The adaptive interpolation method for multi-section turbomachinery blades according to claim 1, characterized in that: The step of determining whether the optimized model of the blade to be processed reaches a preset accuracy comprises: Determining geometric data of each cross section of the optimized model of the blade to be processed; According to the geometric data of each cross section of the optimized model of the blade to be processed, a curvature analysis algorithm is used to calculate the curvature value on each cross section of the optimized model of the blade to be processed; According to the calculated curvature value, a curvature distribution diagram on the optimized model of the blade to be processed is drawn using the matplotlib library; It is analyzed whether the curvature value of the preset area in the curvature distribution map reaches the set curvature value. If so, the optimization model of the blade to be processed reaches the preset accuracy; otherwise, the optimization model of the blade to be processed does not reach the preset accuracy.

10. An adaptive interpolation device for multi-section turbine machinery blades, characterized in that: include: A data acquisition module, used to acquire the contour data of each cross section of the original model of the blade to be processed; A preprocessing module, used to determine the geometric data of each cross section of the blade to be processed according to the contour data, wherein the geometric data of each cross section includes the coordinates of the points constituting the cross section; An adaptive difference module is used to determine, based on the geometric data, the area on the blade to be processed where the interpolation points need to be adjusted through curvature analysis or image analysis, and obtain multiple interpolation point areas to be adjusted; adjust the points on each interpolation point area to be adjusted to obtain multiple initially adjusted areas corresponding to the multiple interpolation point areas to be adjusted; perform error analysis on each initially adjusted area, and determine multiple interpolation correction areas based on the error analysis results, and adjust the points on each interpolation correction area; obtain the optimization model of the blade to be processed based on all adjusted areas and all unadjusted areas, and determine whether the optimization model of the blade to be processed reaches a preset accuracy. If not, continue to adjust the interpolation points of the optimization model of the blade to be processed until the obtained optimization model reaches the preset accuracy.

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