A method for generating and detecting random roughness on turbine surface based on Gaussian noise wave

Abstract

The application provides a turbine surface random roughness generation and detection method based on Gaussian noise waves, first, a node set of a turbine blade structure to which random roughness is to be added is established, and normal vectors of each node on each surface patch are determined; second, according to a turbine surface roughness generation height, a variance and a mean of the Gaussian noise waves are obtained; then, the nodes are controlled to move along a displacement direction according to a Gaussian noise wave function, and a node displacement amount is obtained; thereafter, a turbine blade structure surface roughness statistic after the Gaussian noise waves is calculated, and a correction coefficient of a Gaussian noise wave standard deviation is calculated; finally, the nodes are controlled to move along the displacement direction by using the correction coefficient, and a surface roughness turbine blade structure is obtained. The application can efficiently and controllably generate a turbine random rough surface by adjusting the surface node positions after topology by using a Gaussian function, a displacement height and a surface normal vector, and is convenient for carrying out related research on turbine mechanical fine gas-thermal performance analysis and evaluation with a surface roughness.

Description

Technical Field

[0001] This invention relates to the field of refined thermal analysis and performance evaluation of turbomachinery, specifically a method for generating and detecting random roughness on turbine surfaces based on Gaussian noise. Background Technology

[0002] Because surface damage is unavoidable during the manufacturing and use of aero-engine turbines, the actual turbine aero-thermal performance often deviates significantly from the design specifications. For example, turbine surface roughness is increased by the application of thermal barrier coatings during manufacturing and by the deposition of particulate matter during operation. Surface roughness on the turbine's outer surface and within its bores severely weakens film cooling and enhances convective heat transfer outside the turbine, placing a greater thermal load on the turbine and threatening its safe operation. Therefore, how to study turbine cooling performance under rough surface conditions has become a primary challenge in refined turbine design and cooling margin analysis, and is an indispensable part of turbine cooling design.

[0003] Adding rough elements to the turbine surface is a prerequisite for studying the surface roughness of turbine gas-thermal performance. Currently, regular rough elements or artificial arrangement methods are mainly used. In the paper "Surface roughness effects on external heat transfer of a hp turbine vane" (Journal of Turbomachinery, 2005, Vol. 127, No. 1, pp. 200-208), the authors modeled turbine surface roughness by adding regular frustums to the surface. The advantage of this method is that the size and spacing of the rough elements can be controlled manually. However, when the scale is small, this method requires high machining accuracy, and its effectiveness in equipping actual random roughness is widely questioned. In the paper "Effects of arealistically rough surface on vane heat transfer including the influence of turbulence condition and Reynolds number" (Journal of Turbomachinery, 2012, Vol. 134, Article No. 021013), the authors generated a rough surface by artificially arranging sandpaper. This method cannot precisely control the roughness, and the roughness depends entirely on the experience of the machining personnel, and the research results are difficult to reproduce. Summary of the Invention

[0004] To address the current limitations of only being able to arrange regular rough elements on turbine surfaces and the difficulty in controlling the arrangement of random roughness artificially, this invention proposes a method for generating and detecting random roughness on turbine surfaces based on Gaussian noise. By adjusting the positions of surface nodes after topology re-adjustment using Gaussian functions, displacement height, and surface normal vectors, a random rough surface on the turbine can be generated efficiently and controllably. Furthermore, the statistical parameters of the generated random rough surface are recalculated to complete the detection of the generated surface, thereby facilitating research on the refined gas-thermal performance analysis and evaluation of turbine machinery with surface roughness.

[0005] The technical solution of this invention is as follows:

[0006] The method for generating and detecting random roughness on turbine surfaces based on Gaussian noise includes the following steps:

[0007] Step 1: Establish a set of nodes for the turbine plate structure to be added with random coarsening, and determine the normal vector of each surface where each node is located;

[0008] Step 2: Based on the set turbine surface roughness, generate the height. Following the 3σ principle of normal distribution, obtain the variance and mean of the Gaussian noise:

[0009] μ = 0, σ = h / 3, X ~ N(0, h) 2 / 9)

[0010] Where μ is the variance of the Gaussian noise, σ is the standard deviation of the Gaussian noise, h is the set roughness generation height, and X is the generated Gaussian noise function.

[0011] Step 3: Using the Gaussian noise function obtained in Step 2, control the nodes in Step 1 to move along the displacement direction, and obtain the node displacement Δh. i , where Δh i This represents the displacement of the i-th node; the displacement direction is the resultant vector of the normal vectors of all the faces containing the node.

[0012] Step 4: Based on the displacement of each node obtained in Step 3, calculate the surface roughness statistics of the turbine plate structure after Gaussian noise filtering, including:

[0013] Arithmetic mean roughness Ra: Where I represents the number of nodes;

[0014] Root mean square deviation of profile Rq:

[0015] Maximum profile peak height Rp: Rp = Max(Δh) i );

[0016] Maximum contour valley depth Rv: Rv=|Min(Δh) i )|;

[0017] Maximum profile height Rz: Rz = Rp + Rv;

[0018] Step 5: Based on the surface roughness statistics of the turbine blade structure obtained in Step 4 and the set turbine surface roughness generation height, according to the formula...

[0019] α=2·h / Rz

[0020] Calculate the correction factor α for the standard deviation of Gaussian noise;

[0021] Step 6: Using the correction coefficient α of the Gaussian noise standard deviation obtained in Step 5, the node in Step 1 is moved again along the displacement direction to obtain a surface roughness turbine plate structure that is exactly the same as the set turbine surface roughness generation height.

[0022] Furthermore, the specific process of step 1 is as follows:

[0023] Step 1.1: In the 3D software, create a model of the turbine plate to be added with random roughness;

[0024] Step 1.2: Determine the number of topological patches, and retopologically re-topologically re-topologically re-represent the turbine plate model using triangular patches to obtain a finely divided turbine plate structure;

[0025] Step 1.3: In the finely divided turbine plate structure obtained in Step 1.2, determine the position of each node of the triangular facet and the normal vector of each facet containing each node.

[0026] Furthermore, in step 3, the expression for the movement of the node along the displacement direction is:

[0027]

[0028] Where n i (x0, y0, z0) represents the initial coordinates of the i-th node being (x0, y0, z0), and n i (x,y,z) represents the coordinates of the i-th node after it has been moved. Let N(0,1) be the resultant vector of the unit normal vectors of each facet containing the node, and let N(0,1) be a standard normal distribution.

[0029] Furthermore, in step 3, Δh i =[σ·N(0,1)+μ] i .

[0030] Furthermore, in step 6, the expression for controlling the movement of the node along the displacement direction using the correction coefficient α is:

[0031]

[0032] Beneficial effects

[0033] The method for generating and detecting random roughness on turbine surfaces based on Gaussian noise proposed in this invention has the following advantages:

[0034] 1. By retopologically modifying the turbine blades and adding Gaussian noise based on the actual roughness of the turbine, the difficulty of generating random roughness is greatly reduced. This method can replace the traditional regular roughness element equivalent method and is more in line with engineering practice.

[0035] 2. The standard deviation and mean of Gaussian noise can be designed according to the research range of the actual roughness of the turbine. The surface roughness turbine blade structure can be generated through a controllable normal distribution function, which can avoid the uncontrollability and difficulty in reproducibility of generating rough surfaces by artificial arrangement.

[0036] 3. After the rough surface is generated, the corresponding statistical parameters can be calculated. By adding the correction coefficient of Gaussian noise standard deviation to the nodal displacement, a surface rough turbine blade structure with the same roughness generation height as set can be obtained.

[0037] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0038] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0039] Figure 1 This is the STL geometric model of the original air film pore in the embodiment of the present invention;

[0040] Figure 2 This is the STL geometric model of the air film pores after retopology in an embodiment of the present invention;

[0041] Figure 3 This is the STL geometric model of the air film aperture after adding noise in the embodiment of the present invention;

[0042] Figure 4 The coordinate displacement and statistical parameters of the air film pores in this embodiment of the invention;

[0043] Figure 5 The circular shape of the intersection line of the air film orifice flow direction section in the embodiment of the present invention;

[0044] Figure 6 This embodiment of the invention provides a computational domain for flat film cooling with random roughness within the holes.

[0045] Figure 7In this embodiment of the invention, the flat plate air film cooling effect distribution has random roughness inside the hole;

[0046] Figure 8 The spread-average air film cooling effect of the flat plate with random roughness inside the hole in the embodiment of the present invention is shown. Detailed Implementation

[0047] The embodiments of the present invention are described in detail below. These embodiments are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0048] The method for generating and detecting random roughness on turbine surfaces based on Gaussian noise in this embodiment includes the following steps:

[0049] Step 1: Establish a set of nodes for the turbine plate structure to be randomly coarsened, and determine the normal vector of each surface where each node is located; specifically, this includes the following steps:

[0050] Step 1.1: In the 3D software, create a turbine plate model to be added with random roughening; for example... Figure 1 As shown, the turbine film pore sheet structure to be added is established using the STL geometry file format.

[0051] Step 1.2: Determine the number of topological patches based on the structural complexity, the set roughness generation height, and the roughness element spacing. Retopologically re-topologize the turbine plate model using triangular patches to obtain a finely divided turbine film perforated plate structure.

[0052] Step 1.3: The retopologically restructured STL sheet body file contains the node positions and face normal vector information of the triangular facets. Therefore, in the finely divided turbine sheet body structure obtained in Step 1.2, the position of each node of the triangular facet and the normal vector of each facet containing each node are determined.

[0053] Step 2: Based on the set turbine surface film porosity, generate the height and, according to the 3σ principle of normal distribution, obtain the variance and mean of the Gaussian noise:

[0054] μ = 0, σ = h / 3, X ~ N(0, h) 2 / 9)

[0055] Where μ is the variance of Gaussian noise, σ is the standard deviation of Gaussian noise, h is the set roughness generation height, and X is the generated Gaussian noise function; in this embodiment, the diameter of the air film hole is 4 mm, the set roughness generation height of the air film hole is 0.32 mm, the mean of the normal distribution is 0, the standard deviation of the normal distribution is 0.1067, and the normal distribution is X~N(0,0.01138).

[0056] Step 3: Using the Gaussian noise function obtained in Step 2, control the nodes in Step 1 to move along the resultant vector of the unit normal vectors of their respective facets, thus obtaining a surface roughness film pore structure with Gaussian noise characteristics, as shown below. Figure 3 As shown, the expression for node movement is:

[0057]

[0058] Where n i (x0, y0, z0) represents the initial coordinates of the i-th node being (x0, y0, z0), and n i (x,y,z) represents the coordinates of the i-th node after it has been moved. Let N(0,1) be the resultant vector of the unit normal vectors of each facet containing the node, and let N(0,1) be a standard normal distribution.

[0059] Furthermore, noise information was extracted from the STL file for the surface rough film porous sheet structure to obtain the nodal displacement Δh. i :

[0060] Δh i =[σ·N(0,1)+μ] i

[0061] Where Δh i This represents the displacement of the i-th node.

[0062] Step 4: Based on the displacement of each node obtained in Step 3, calculate the surface roughness statistics of the turbine plate structure after Gaussian noise processing to achieve surface roughness, including:

[0063] Arithmetic mean roughness Ra: Where I represents the number of nodes; in this embodiment, the arithmetic mean roughness of the surface roughness of the air film porous sheet is 0.064 mm;

[0064] Root mean square deviation of profile Rq: In this embodiment, the root mean square deviation of the surface roughness of the air film porous sheet is 0.080 mm;

[0065] Maximum profile peak height Rp: Rp = Max(Δh) i In this embodiment, the maximum profile peak height of the surface roughness air film porous sheet is 0.289 mm.

[0066] Maximum contour valley depth Rv: Rv=|Min(Δh) i In this embodiment, the maximum contour valley depth of the surface roughness air film porous sheet is 0.291 mm.

[0067] Maximum profile height Rz: Rz = Rp + Rv; In this embodiment, the maximum profile height of the rough surface air film perforated sheet is 0.580 mm.

[0068] Step 5: Based on the surface roughness statistics of the turbine blade structure obtained in Step 4 and the set turbine surface roughness generation height, according to the formula...

[0069] α=2·h / Rz

[0070] Calculate the correction factor α for the standard deviation of Gaussian noise; in this embodiment, the correction factor for the standard deviation of Gaussian noise is 1.0934.

[0071] Step 6: Using the correction coefficient α of the Gaussian noise standard deviation obtained in Step 5, re-control the movement of the nodes in Step 1 along the resultant vector of the unit normal vectors of their respective facets, to obtain a turbine blade structure with the exact same surface roughness generation height as the set turbine surface roughness. The expression for controlling the node movement using the correction coefficient α is:

[0072]

[0073] like Figure 4 and Figure 5 In this embodiment, a surface-roughened air film pore sheet with a maximum roughness height of 0.32 mm was obtained through the above generation and detection.

[0074] Simulation examples:

[0075] The surface of the randomly roughened film cooling plate obtained in step 6 is used for 3D modeling to obtain a flat plate film cooling computational domain with random roughness inside the holes. Mesh generation and 3D simulation calculations are performed on the flat plate film cooling computational domain to obtain the specific distribution of the flat plate film cooling effect under the influence of the roughness inside the holes, as follows:

[0076] The computational domain was divided using tetrahedral elements, and the independence of the mesh generation was verified and the relevant computational conditions were set. Specifically, the SSTγ-θ model was selected as the turbulence model, the main flow adopted a velocity inlet and a pressure outlet, the cold air adopted a mass flow rate inlet, and the cold air blowing ratio was 1.0.

[0077] like Figure 7 and Figure 8 The present invention compares the gas film cooling efficiency and spanwise average gas film cooling efficiency of the gas film pores with randomly rough surfaces and smooth pores. After roughening the pores, the spanwise coverage of the gas film at various downstream locations is significantly reduced, and the spanwise average gas film cooling efficiency is significantly lower than that of the smooth pores.

[0078] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A method for generating and detecting random roughness on turbine surfaces based on Gaussian noise, characterized in that: Includes the following steps: Step 1: Establish a set of nodes for the turbine plate structure to be added with random coarsening, and determine the normal vector of each surface where each node is located; Step 2: Based on the set turbine surface roughness, generate the height. Following the 3σ principle of normal distribution, obtain the variance and mean of the Gaussian noise: ， ， Where μ is the variance of the Gaussian noise, σ is the standard deviation of the Gaussian noise, and h is the set roughness generation height. This is the generated Gaussian noise function; Step 3: Using the Gaussian noise function obtained in Step 2, control the nodes in Step 1 to move along the displacement direction, and obtain the node displacement. ,in This represents the displacement of the i-th node; the displacement direction is the resultant vector of the normal vectors of all the faces containing the node. Step 4: Based on the displacement of each node obtained in Step 3, calculate the surface roughness statistics of the turbine plate structure after Gaussian noise filtering, including: Arithmetic mean roughness : ,in The number of nodes; Root mean square deviation of profile : ; Maximum profile peak height : ; Maximum contour valley depth : ; Maximum height of the outline : ; Step 5: Based on the surface roughness statistics of the turbine blade structure obtained in Step 4 and the set turbine surface roughness generation height, according to the formula... Correction factor for calculating the standard deviation of Gaussian noise ; Step 6: Use the correction factor of the Gaussian noise standard deviation obtained in Step 5. The node in step 1 is moved again along the displacement direction to obtain a surface roughness turbine blade structure that is exactly the same as the set turbine surface roughness generation height.

2. The method for generating and detecting random roughness of turbine surfaces based on Gaussian noise according to claim 1, characterized in that: The specific process of step 1 is as follows: Step 1.1: In the 3D software, create a model of the turbine plate to be added with random roughness; Step 1.2: Determine the number of topological patches, and retopologically re-topologically re-topologically re-represent the turbine plate model using triangular patches to obtain a finely divided turbine plate structure; Step 1.3: In the finely divided turbine plate structure obtained in Step 1.2, determine the position of each node of the triangular facet and the normal vector of each facet containing each node.

3. The method for generating and detecting random roughness of turbine surfaces based on Gaussian noise according to claim 1, characterized in that: In step 3, the expression for the movement of the node along the displacement direction is: in Indicates that the initial coordinates of the i-th node are , The coordinates of the i-th node after the movement are: , It is the resultant vector of the unit normal vectors of all faces containing the node. It follows a standard normal distribution; And the nodal displacement is: in This represents the displacement of the i-th node.

4. The method for generating and detecting random roughness of turbine surfaces based on Gaussian noise according to claim 3, characterized in that: In step 6, the correction factor is used. The expression for controlling the movement of the node along the displacement direction is: 。

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