Method and system for simulating and predicting running noise of multi-blade centrifugal fan

Abstract

The invention discloses a running noise simulation prediction method and system for a multi-blade centrifugal fan, and belongs to the technical field of simulation prediction.The running noise simulation prediction method comprises the steps that firstly, after an initial surface grid is generated for a fan three-dimensional model, a boundary layer grid is generated on the initial surface grid based on a near-wall area; after the local curvature of each surface grid node and the thickness of a first boundary layer are analyzed, the local growth rate is corrected, the number of layers needed by each surface grid node for generating a boundary layer grid and the thickness of each boundary layer are determined, the layer thickness increase of the boundary layer of a high-curvature area is dynamically inhibited, and an adaptive boundary layer grid is obtained; the flow details of the strong curvature area are accurately captured, and the accuracy of simulation prediction is effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of simulation prediction, and in particular to a method and system for simulating and predicting the operating noise of a multi-blade centrifugal fan. Background Art

[0002] Operation noise is one of the important factors that affect user satisfaction with multi-blade centrifugal fans. It can also reflect the safety performance of the fan and whether the design structure is reasonable. Therefore, simulation prediction of the operation noise of multi-blade centrifugal fans is extremely important.

[0003] The simulation prediction method for the operation noise of the fan generally includes the construction of a three-dimensional model of the fan, meshing the model, and then testing it using simulation technology (such as large eddy simulation technology); in the existing technology, most of them start from the subsequent simulation prediction technology level to optimize and improve the simulation accuracy. However, in the step of meshing the model, the quality of the meshing also directly affects the calculation accuracy of the simulation. For example, if the quality of the mesh generation is not good, it may lead to the inability to capture the detailed features of the flow field (such as eddy currents, boundary layer separation, etc.), thereby affecting the accuracy of the noise simulation prediction. Therefore, how to improve the accuracy of the noise simulation prediction from the level of mesh generation is an urgent problem to be solved. Summary of the invention

[0004] In order to solve the technical problems existing in the prior art, the present invention provides a method for simulating and predicting the operating noise of a multi-blade centrifugal fan, comprising the following steps: Obtain the geometric parameters of the multi-blade centrifugal fan and construct a three-dimensional model of the fan; Generating an initial surface mesh for the wind turbine three-dimensional model, wherein the initial surface mesh is an unstructured mesh; Generate a boundary layer mesh based on an initial surface mesh of a near-wall region in the three-dimensional model of the wind turbine; The large eddy simulation technology is used to simulate and predict the running noise of the three-dimensional wind turbine model after meshing, and the simulated noise spectrum is obtained; The generating of the boundary layer mesh based on the initial surface mesh of the near-wall region in the three-dimensional model of the wind turbine is specifically: Extracting the initial surface mesh of the near-wall region as the second surface mesh; Obtaining the local curvature of each surface mesh node of the second surface mesh by discrete quadratic surface fitting method; Obtain the first boundary layer thickness of each surface mesh node, and correct the local growth rate according to the local curvature of each surface mesh node; According to the first boundary layer thickness and local growth rate of each surface grid node, the required number of layers of each surface grid node to generate the boundary layer grid is determined; According to the local growth rate and the required number of layers of each surface mesh node and the thickness of each boundary layer of each surface mesh node; According to the required number of layers of each surface mesh node and the thickness of each boundary layer, each surface mesh node is stretched layer by layer along its normal direction to generate a prismatic layer mesh.

[0005] Furthermore, the first boundary layer thickness of each surface grid node is obtained as follows: ; in, It is expressed as the first boundary layer thickness of the i-th surface mesh node, is represented by the preset dimensionless wall distance, Expressed as fluid kinematic viscosity, Expressed as the wall shear stress at the i-th surface mesh node, Expressed as fluid density.

[0006] Furthermore, the local growth rate is corrected according to the local curvature of each surface mesh node, specifically: ; ; ; in, It is expressed as the corrected local growth rate of the i-th surface mesh node, Expressed as the preset base growth rate, Expressed as the preset curvature attenuation coefficient, is expressed as the local curvature of the i-th surface mesh node, is the reference radius of curvature, It is expressed as the mean curvature, and I represents the number of surface mesh nodes of the second surface mesh.

[0007] Furthermore, the required number of layers of the boundary layer mesh generated by each surface mesh node is determined according to the first layer boundary layer thickness and the local growth rate of each surface mesh node, specifically: ; in, represents the number of layers required to generate the boundary layer mesh for the i-th surface mesh node, and H represents the preset total boundary layer thickness. If the value is not an integer, it is rounded up.

[0008] Furthermore, the method of analyzing the thickness of each boundary layer of each surface grid node according to the local growth rate and the required number of layers of each surface grid node also includes: performing constraint verification according to the required number of layers corresponding to each surface grid node and the preset total boundary layer thickness to determine whether the following conditions are met: ; If not satisfied, the user is notified to readjust the preset basic growth rate and / or the preset curvature attenuation coefficient.

[0009] Furthermore, the analysis of the thickness of each boundary layer of each surface grid node is specifically as follows: ; in, Expressed as the thickness of the j-th boundary layer at the i-th surface mesh node.

[0010] Regarding the subscript parameter j in the above formula, it should be noted that, for each surface mesh node, the value range of the corresponding parameter j is from 1 to the required number of layers of the corresponding surface mesh node.

[0011] Furthermore, the generating of the initial surface mesh for the three-dimensional model of the wind turbine is specifically performed by a quadtree / octree method or a Delaunay triangulation algorithm.

[0012] The present invention also provides an operation noise simulation prediction system for a multi-blade centrifugal fan, comprising: A model building module is used to obtain geometric parameters of a multi-blade centrifugal fan and build a three-dimensional model of the fan; A meshing module, used to generate an initial surface mesh for the three-dimensional model of the wind turbine, and to generate a boundary layer mesh based on the initial surface mesh of the near-wall region in the three-dimensional model of the wind turbine; The simulation prediction module is used to simulate and predict the running noise of the wind turbine three-dimensional model after meshing by using the large eddy simulation technology to obtain the simulated noise spectrum; The generating of the boundary layer mesh based on the initial surface mesh of the near-wall region in the three-dimensional model of the wind turbine is specifically: Extracting the initial surface mesh of the near-wall region as the second surface mesh; Obtaining the local curvature of each surface mesh node of the second surface mesh by discrete quadratic surface fitting method; Obtain the first boundary layer thickness of each surface mesh node, and correct the local growth rate according to the local curvature of each surface mesh node; According to the first boundary layer thickness and local growth rate of each surface mesh node, the required number of layers of the boundary layer mesh generated by each surface mesh node is determined; According to the local growth rate and the required number of layers of each surface mesh node and the thickness of each boundary layer of each surface mesh node; According to the required number of layers of each surface mesh node and the thickness of each boundary layer, each surface mesh node is stretched layer by layer along its normal direction to generate a prismatic layer mesh.

[0013] Furthermore, the local growth rate is corrected according to the local curvature of each surface mesh node, specifically: ; ; ; in, It is expressed as the corrected local growth rate of the i-th surface mesh node, Expressed as the preset base growth rate, Expressed as the preset curvature attenuation coefficient, is expressed as the local curvature of the i-th surface mesh node, is the reference radius of curvature, It is expressed as the mean curvature, and I represents the number of surface mesh nodes of the second surface mesh.

[0014] Furthermore, the required number of layers of the boundary layer mesh generated by each surface mesh node is determined according to the first layer boundary layer thickness and the local growth rate of each surface mesh node, specifically: ; in, represents the required number of layers to generate the boundary layer mesh for the i-th surface mesh node, It is represented by the first boundary layer thickness of the i-th surface mesh node, and H is represented by the preset total boundary layer thickness. If the value is not an integer, it is rounded up.

[0015] Compared with the prior art, the present invention has the following beneficial effects: By first generating an unstructured surface mesh that can adapt well to complex geometric shapes, the boundary layer mesh generation problem caused by the complexity of the wall geometry can be effectively avoided. When the boundary layer mesh is subsequently generated, its fit with the wall can be ensured, thereby improving the accuracy and stability of the simulation test. The local curvature of each grid node is corrected to the corresponding local growth rate, and the adaptive growth rate is obtained in different places, which dynamically suppresses the thickness growth of the high curvature area, more accurately captures the flow details in the strong curvature area, and effectively improves the accuracy of simulation prediction; By dynamically configuring the number of layers for each surface mesh node according to the local growth rate of each surface mesh node, it is possible to increase the number of layers in areas with higher curvature to cover the same total thickness, and reduce the number of layers in areas with low curvature to avoid over-stretching, further effectively improving the accuracy and efficiency of simulation predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 It is a flow chart of a method for simulating and predicting the operating noise of a multi-blade centrifugal fan of the present invention; Figure 2 It is a flow chart of step S3 in a method for simulating and predicting operating noise of a multi-blade centrifugal fan of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0021] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0022] Embodiment 1 See also Figure 1 As shown, the present invention provides a method for simulating and predicting the operating noise of a multi-blade centrifugal fan, which specifically includes the following steps: S1. Obtain geometric parameters of a multi-blade centrifugal fan and construct a three-dimensional model of the fan; S2. generating an initial surface mesh for the wind turbine three-dimensional model, wherein the initial surface mesh is an unstructured mesh; S3, generating a boundary layer mesh based on an initial surface mesh of a near-wall region in the three-dimensional model of the wind turbine; S4. The large eddy simulation technology is used to simulate and predict the operation noise of the three-dimensional wind turbine model after meshing to obtain the simulated noise spectrum.

[0023] See also Figure 2 As shown, step S3, generating a boundary layer mesh based on the initial surface mesh of the near-wall region in the three-dimensional model of the wind turbine, is specifically: T1, extracting the initial surface mesh of the near-wall area as the second surface mesh; T2, obtaining the local curvature of each surface mesh node of the second surface mesh by discrete quadratic surface fitting method; T3, obtaining the first boundary layer thickness of each surface mesh node, and correcting the local growth rate according to the local curvature of each surface mesh node; T4, determining the number of layers required for generating boundary layer meshes for each surface mesh node according to the first layer boundary layer thickness and local growth rate of each surface mesh node; T5. Analyze the thickness of each boundary layer of each surface grid node based on the local growth rate and the required number of layers of each surface grid node; T6. According to the required number of layers of each surface mesh node and the thickness of each boundary layer, each surface mesh node is stretched layer by layer along its normal direction to generate a prismatic layer mesh.

[0024] The purpose of meshing the three-dimensional model of the fan is to ensure that the subsequent large eddy simulation can capture key flow characteristics in the simulation prediction of the multi-blade centrifugal fan.

[0025] In the simulation prediction of the operating noise of a multi-blade centrifugal fan, the near-wall area (usually refers to a thin area close to the blade surface, volute surface and other solid walls, which is part of the turbulent boundary layer and is located near the wall) has a large flow gradient and involves boundary layer phenomena. The use of boundary layer meshes can ensure high resolution and accurately capture flow details.

[0026] In the existing meshing of models, volume meshes including boundary layers are usually generated directly through tools (such as ANSYS Meshing, snappyHexMesh, etc.). However, some near-wall areas usually have complex geometric shapes. Directly generated boundary layer meshes are prone to poor fit with the walls of these complex geometric shapes. The flow characteristics of the near-wall area have a great influence on the results. If the boundary layer mesh does not fit the wall well, it may cause numerical solution instability or difficulty in convergence. Therefore, this solution first generates an unstructured surface mesh that can well adapt to complex geometric shapes, effectively avoiding the boundary layer mesh generation problem caused by the complexity of wall geometry, and ensures its fit with the wall when generating the boundary layer mesh subsequently, thereby improving the accuracy and stability of the simulation test.

[0027] The near-wall area may be calibrated manually.

[0028] Obtaining the local curvature of a grid node by discrete quadratic surface fitting belongs to the prior art and will not be described in detail here.

[0029] The method of obtaining the first boundary layer thickness of each surface grid node is specifically as follows: ; in, It is expressed as the first boundary layer thickness of the i-th surface mesh node, is represented by the preset dimensionless wall distance, Expressed as fluid kinematic viscosity, Expressed as the wall shear stress at the i-th surface mesh node, Expressed as fluid density.

[0030] In the simulation prediction of the operation noise of a multi-blade centrifugal fan, the above-mentioned fluid kinematic viscosity and fluid density are set according to the fluid required by the user in the simulation, and in the simulation prediction of the operation noise of a multi-blade centrifugal fan, the fluid is usually air.

[0031] The boundary layer is the area close to the wall where the fluid velocity gradually increases from zero to the mainstream velocity. The boundary layer flow characteristics are affected by many factors, including fluid properties (such as viscosity and density) and wall conditions (such as shear stress). Therefore, this scheme combines the wall shear stress, fluid viscosity, fluid density and dimensionless wall distance to obtain the first boundary layer thickness, which can fully reflect the flow characteristics while ensuring the applicability of the turbulence model, accurately capturing the flow details near the wall and avoiding overly dense or sparse grids.

[0032] The local growth rate is corrected according to the local curvature of each surface mesh node, specifically: , , ; in, It is expressed as the corrected local growth rate of the i-th surface mesh node, Expressed as the preset base growth rate, Expressed as the preset curvature attenuation coefficient, is expressed as the local curvature of the i-th surface mesh node, is the reference radius of curvature, It is expressed as the mean curvature, and I represents the number of surface mesh nodes of the second surface mesh.

[0033] The growth rate represents the exponential growth rate of the thickness of each layer of the boundary layer, which determines the distribution density and change trend of the grid when it expands outward from the surface of the object. In the existing boundary layer grid generation technology, the geometric curvature change is ignored, and the global fixed growth rate is directly adopted. All regions use the same layer thickness growth rule, which can easily lead to layer thickness mismatch problems in strong curvature areas (such as the leading edge of centrifugal fan blades and the tongue of the volute), which may lead to the inability to obtain sufficient resolution in these areas, and ultimately reduce the accuracy of noise simulation prediction structure.

[0034] This solution corrects the local curvature of each grid node to the corresponding local growth rate, obtains adapted growth rates in different places, dynamically suppresses the growth of layer thickness in high curvature areas, more accurately captures the flow details in strong curvature areas, and effectively improves the accuracy of simulation predictions.

[0035] The method of determining the number of required layers of boundary layer meshes generated by each surface mesh node according to the first layer boundary layer thickness and the local growth rate of each surface mesh node is specifically as follows: ; in, represents the number of layers required to generate the boundary layer mesh for the i-th surface mesh node, and H represents the preset total boundary layer thickness. If the value is not an integer, it is rounded up.

[0036] In boundary layer mesh generation, "number of layers" is a key parameter, which specifically refers to the number of mesh units extending outward from the surface of the object. These mesh units are arranged layer by layer to capture the flow characteristics in the boundary layer. If the number of layers is insufficient, the flow gradient in the boundary layer cannot be fully resolved, resulting in inaccurate calculation results. If the number of layers is too large, the required computing resources will increase, greatly affecting the simulation prediction efficiency. In the existing boundary layer mesh generation, the use of a global fixed number of layers is likely to lead to insufficient actual total thickness in high curvature areas or over-stretching in low curvature areas, seriously affecting the accuracy and efficiency of simulation prediction. This solution dynamically configures the number of layers for each surface mesh node according to the local growth rate of each surface mesh node, which can increase the number of layers in areas with higher curvature to cover the same total thickness, and reduce the number of layers in low curvature areas to avoid over-stretching, effectively improving the accuracy and efficiency of simulation prediction.

[0037] In some embodiments, step T4 further includes: Constraint verification is performed based on the required number of layers corresponding to each surface mesh node and the preset total boundary layer thickness to determine whether the following conditions are met: ; If not satisfied, the user is notified to readjust the preset basic growth rate and / or the preset curvature attenuation coefficient.

[0038] The analysis of the thickness of each boundary layer of each surface grid node is specifically as follows: ; in, Expressed as the thickness of the j-th boundary layer at the i-th surface mesh node.

[0039] For the subscript parameter j in the above formula, it should be noted that for each surface mesh node, the value range of the corresponding parameter j is 1 to the required number of layers of the corresponding surface mesh node. .

[0040] In some embodiments, an initial surface mesh is generated for the three-dimensional model of the wind turbine, which may be generated by a quadtree / octree method or a Delaunay triangulation algorithm.

[0041] Embodiment 2 The present invention also provides an operation noise simulation prediction system of a multi-blade centrifugal fan provided by the present invention, which specifically includes: A model building module is used to obtain geometric parameters of a multi-blade centrifugal fan and build a three-dimensional model of the fan; A meshing module, used to generate an initial surface mesh for the three-dimensional model of the wind turbine, and to generate a boundary layer mesh based on the initial surface mesh of the near-wall region in the three-dimensional model of the wind turbine; The simulation prediction module is used to simulate and predict the running noise of the wind turbine three-dimensional model after meshing by using the large eddy simulation technology to obtain the simulated noise spectrum; The generating of the boundary layer mesh based on the initial surface mesh of the near-wall region in the three-dimensional model of the wind turbine is specifically: Extracting the initial surface mesh of the near-wall region as the second surface mesh; Obtaining the local curvature of each surface mesh node of the second surface mesh by discrete quadratic surface fitting method; Obtain the first boundary layer thickness of each surface mesh node, and correct the local growth rate according to the local curvature of each surface mesh node; According to the first boundary layer thickness and local growth rate of each surface grid node, the required number of layers of each surface grid node to generate the boundary layer grid is determined; According to the local growth rate and the required number of layers of each surface mesh node and the thickness of each boundary layer of each surface mesh node; According to the required number of layers of each surface mesh node and the thickness of each boundary layer, each surface mesh node is stretched layer by layer along its normal direction to generate a prismatic layer mesh.

[0042] The method of obtaining the first boundary layer thickness of each surface grid node is specifically as follows: ; in, It is expressed as the first boundary layer thickness of the i-th surface mesh node, is represented by the preset dimensionless wall distance, Expressed as fluid kinematic viscosity, Expressed as the wall shear stress at the i-th surface mesh node, Expressed as fluid density.

[0043] The local growth rate is corrected according to the local curvature of each surface mesh node, specifically: ; in, It is expressed as the corrected local growth rate of the i-th surface mesh node, Expressed as the preset base growth rate, Expressed as the preset curvature attenuation coefficient, is expressed as the local curvature of the i-th surface mesh node, is the reference radius of curvature, It is expressed as the mean curvature, and I represents the number of surface mesh nodes of the second surface mesh.

[0044] The method of determining the number of required layers of boundary layer meshes generated by each surface mesh node according to the first layer boundary layer thickness and the local growth rate of each surface mesh node is specifically as follows: ; in, represents the number of layers required to generate the boundary layer mesh for the i-th surface mesh node, and H represents the preset total boundary layer thickness. If the value is not an integer, it is rounded up.

[0045] And according to the required number of layers corresponding to each surface grid node and the preset total boundary layer thickness, constraint verification is performed to determine whether the following conditions are met: ; If not satisfied, the user is notified to readjust the preset basic growth rate and / or the preset curvature attenuation coefficient.

[0046] The analysis of the thickness of each boundary layer of each surface grid node is specifically as follows: ; in, Expressed as the thickness of the j-th boundary layer at the i-th surface mesh node.

[0047] Embodiment 3 The present invention also provides an electronic device, including: a processor, a sending device, an input device, an output device and a memory. The processor can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the technical solution provided in the embodiment of the present application. The memory can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM), and is used to store computer program code. The computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any of the above possible implementation methods.

[0048] Embodiment 4 The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0049] The beneficial effects of the present invention are: By first generating an unstructured surface mesh that can adapt well to complex geometric shapes, the boundary layer mesh generation problem caused by the complexity of the wall geometry can be effectively avoided. When the boundary layer mesh is subsequently generated, its fit with the wall can be ensured, thereby improving the accuracy and stability of the simulation test. The local curvature of each grid node is corrected to the corresponding local growth rate, and the adaptive growth rate is obtained in different places, which dynamically suppresses the thickness growth of the high curvature area, more accurately captures the flow details in the strong curvature area, and effectively improves the accuracy of simulation prediction; By dynamically configuring the number of layers for each surface mesh node according to the local growth rate of each surface mesh node, it is possible to increase the number of layers in areas with higher curvature to cover the same total thickness, and reduce the number of layers in areas with low curvature to avoid over-stretching, further effectively improving the accuracy and efficiency of simulation predictions.

[0050] In the description of the specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials 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, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0051] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (RandomAccess Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0052] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for simulating and predicting the operating noise of a multi-blade centrifugal fan, characterized in that: The following steps are involved: Obtain the geometric parameters of the multi-blade centrifugal fan and construct a three-dimensional model of the fan; Generating an initial surface mesh for the wind turbine three-dimensional model, wherein the initial surface mesh is an unstructured mesh; Generate a boundary layer mesh based on an initial surface mesh of a near-wall region in the three-dimensional model of the wind turbine; The large eddy simulation technology is used to simulate and predict the running noise of the three-dimensional wind turbine model after meshing, and the simulated noise spectrum is obtained; The generating of the boundary layer mesh based on the initial surface mesh of the near-wall region in the three-dimensional model of the wind turbine is specifically: Extracting the initial surface mesh of the near-wall region as the second surface mesh; Obtaining the local curvature of each surface mesh node of the second surface mesh by discrete quadratic surface fitting method; Obtain the first boundary layer thickness of each surface mesh node, and correct the local growth rate according to the local curvature of each surface mesh node; According to the first boundary layer thickness and local growth rate of each surface mesh node, the required number of layers of the boundary layer mesh generated by each surface mesh node is determined; According to the local growth rate and the required number of layers of each surface mesh node and the thickness of each boundary layer of each surface mesh node; According to the required number of layers of each surface mesh node and the thickness of each boundary layer, each surface mesh node is stretched layer by layer along its normal direction to generate a prismatic layer mesh.

2. The method for simulating and predicting the operating noise of a multi-blade centrifugal fan according to claim 1, characterized in that: The method of obtaining the first boundary layer thickness of each surface grid node is specifically as follows: ； in, It is expressed as the first boundary layer thickness of the i-th surface mesh node, is represented by the preset dimensionless wall distance, Expressed as fluid kinematic viscosity, Expressed as the wall shear stress at the i-th surface mesh node, Expressed as fluid density.

3. The method for simulating and predicting the operating noise of a multi-blade centrifugal fan according to claim 2, characterized in that: The local growth rate is corrected according to the local curvature of each surface mesh node, specifically: ； ； ； in, It is expressed as the corrected local growth rate of the i-th surface mesh node, Expressed as the preset base growth rate, Expressed as the preset curvature attenuation coefficient, is expressed as the local curvature of the i-th surface mesh node, is the reference radius of curvature, It is expressed as the mean curvature, and I represents the number of surface mesh nodes of the second surface mesh.

4. The method for simulating and predicting the operating noise of a multi-blade centrifugal fan according to claim 3, characterized in that: The method of determining the number of required layers of boundary layer meshes generated by each surface mesh node according to the first layer boundary layer thickness and the local growth rate of each surface mesh node is specifically as follows: ； in, represents the number of layers required to generate the boundary layer mesh for the i-th surface mesh node, and H represents the preset total boundary layer thickness. If the value is not an integer, it is rounded up.

5. The method for simulating and predicting the operating noise of a multi-blade centrifugal fan according to claim 4, characterized in that: The method further comprises: performing constraint verification according to the required number of layers and the preset total boundary layer thickness corresponding to each surface grid node, and determining whether the following are satisfied: ； If not satisfied, the user is notified to readjust the preset basic growth rate and / or the preset curvature attenuation coefficient.

6. The method for simulating and predicting the operating noise of a multi-blade centrifugal fan according to claim 4, characterized in that: The analysis of the thickness of each boundary layer of each surface grid node is specifically as follows: ； in, Expressed as the thickness of the j-th boundary layer at the i-th surface mesh node.

7. The method for simulating and predicting the operating noise of a multi-blade centrifugal fan according to claim 1, characterized in that: The initial surface mesh is generated for the three-dimensional model of the wind turbine, specifically by a quadtree / octree method or a Delaunay triangulation algorithm.

8. A multi-blade centrifugal fan operation noise simulation prediction system, using the multi-blade centrifugal fan operation noise simulation prediction method according to any one of claims 1 to 7, characterized in that: include: A model building module is used to obtain geometric parameters of a multi-blade centrifugal fan and build a three-dimensional model of the fan; A meshing module, used to generate an initial surface mesh for the three-dimensional model of the wind turbine, and to generate a boundary layer mesh based on the initial surface mesh of the near-wall region in the three-dimensional model of the wind turbine; The simulation prediction module is used to simulate and predict the running noise of the wind turbine three-dimensional model after meshing by using the large eddy simulation technology to obtain the simulated noise spectrum; The generating of the boundary layer mesh based on the initial surface mesh of the near-wall region in the three-dimensional model of the wind turbine is specifically: Extracting the initial surface mesh of the near-wall region as the second surface mesh; Obtaining the local curvature of each surface mesh node of the second surface mesh by discrete quadratic surface fitting method; Obtain the first boundary layer thickness of each surface mesh node, and correct the local growth rate according to the local curvature of each surface mesh node; According to the first boundary layer thickness and local growth rate of each surface mesh node, the required number of layers of the boundary layer mesh generated by each surface mesh node is determined; According to the local growth rate and the required number of layers of each surface mesh node and the thickness of each boundary layer of each surface mesh node; According to the required number of layers of each surface mesh node and the thickness of each boundary layer, each surface mesh node is stretched layer by layer along its normal direction to generate a prismatic layer mesh.

9. The operating noise simulation prediction system of a multi-blade centrifugal fan according to claim 8, characterized in that: The local growth rate is corrected according to the local curvature of each surface mesh node, specifically: ； ； ； in, It is expressed as the corrected local growth rate of the i-th surface mesh node, Expressed as the preset base growth rate, Expressed as the preset curvature attenuation coefficient, is expressed as the local curvature of the i-th surface mesh node, is the reference radius of curvature, It is expressed as the mean curvature, and I represents the number of surface mesh nodes of the second surface mesh.

10. The operating noise simulation prediction system of a multi-blade centrifugal fan according to claim 9, characterized in that: The method of determining the number of required layers of boundary layer meshes generated by each surface mesh node according to the first layer boundary layer thickness and the local growth rate of each surface mesh node is specifically as follows: ； in, represents the required number of layers to generate the boundary layer mesh for the i-th surface mesh node, It is represented by the first boundary layer thickness of the i-th surface mesh node, and H is represented by the preset total boundary layer thickness. If the value is not an integer, it is rounded up.

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