Robust optimization design method for multi-blade centrifugal fan of range hood

Abstract

The application discloses a kind of robust optimization design methods for multi-wing centrifugal fan of range hood, comprising: (1) clean fan geometric parameterization modeling: selecting fan flow-through portion to carry out geometric parameterization characterization, to realize the accurate description of fan geometry;(2) fouling fan geometric parameterization modeling: the flow-through portion of fan of different running years is measured with high precision to establish parameterization characterization model, and the influence of oil dirt accumulation on fan aerodynamic acoustic characteristics is studied;(3) fan robust optimization proposition establishment and solution: with the maximum fan performance in a certain time of fouling operation, the minimum annual performance attenuation rate as the optimization goal, with fan geometric structure parameters as design variables, with aerodynamic noise and structural parameter range as constraint conditions, the optimization proposition is established to obtain the optimal fan geometric design scheme.The robust optimization design method of the application ensures that the fan can also maintain efficient and stable operation under the condition of irregular cleaning, and improves the market competitiveness of the product.

Description

Technical Field

[0001] This invention relates to the field of gas dynamics, and in particular to a robust optimization design method for a multi-blade centrifugal fan used in range hoods. Background Technology

[0002] With the improvement of living standards, range hoods are widely used in people's lives. The huge market demand has driven the rapid development of the range hood industry, while also placing higher demands on the performance indicators and daily cleaning and maintenance of range hoods.

[0003] Multi-blade centrifugal fans are widely used in range hoods due to their advantages such as low noise, small overall size, and large flow coefficient. Their performance has a significant impact on the range hood's performance. During use, cooking fumes enter the impeller through the fan's channels, causing grease buildup on the blades. The thickness of this grease increases over time, altering the geometry of the flow path and leading to performance degradation, significantly reducing smoke extraction efficiency and compromising the machine's safe and reliable operation. Currently, the industry standard for household range hoods is to clean multi-blade centrifugal fans every 1-2 years to prevent performance deterioration caused by long-term grease buildup. However, cleaning multi-blade fan blades typically requires professional expertise, increasing costs for users. Preliminary surveys indicate that over 80% of consumers do not regularly have their range hoods professionally cleaned, posing potential risks such as low smoke extraction efficiency, high operating noise, short lifespan, and poor safety and reliability. Summary of the Invention

[0004] To address the problem of performance degradation of multi-blade centrifugal fan impellers due to grease buildup in existing technologies, this invention proposes a robust optimization design method for multi-blade centrifugal fans used in range hoods. This method fully considers the impact of grease buildup on the impeller's performance parameters. By optimizing the fan's geometric design parameters, the performance parameters of the multi-blade centrifugal fan remain at a high level in both clean and grease-covered states. Furthermore, the performance parameters of the multi-blade centrifugal fan exhibit a slower rate of decline with grease buildup, thus extending the blade cleaning cycle.

[0005] The objective of this invention is achieved through the following technical solution: A robust optimization design method for a multi-blade centrifugal fan used in range hoods includes the following steps: 1) According to the theoretical design method of multi-blade centrifugal fans, the geometric structure description of multi-blade centrifugal fans is based on their flow components, which include impeller, volute and collector; 2) The geometric structure of the flow components of multi-blade centrifugal fans with different operating years was measured and reverse modeled to obtain multiple multi-blade centrifugal fan models with different degrees of fouling. The multi-blade centrifugal fan models were subjected to simulation experiments based on computational fluid dynamics. Based on the simulation results and sensitivity analysis, the influence of fouling on the aerodynamic performance and acoustic characteristics of the fan was studied. The fouling areas that are sensitive to the fan performance were identified, and structural parameters were selected for parameterized characterization of the fouling areas. 3) Optimize the parameters of the fouling area that is sensitive to the performance of the fan. The optimization objective is to maximize the weighted average performance of the fan under fouling conditions within a specific time period and minimize the annual performance degradation rate of the fan within a specific time period. The design variables are the geometric structural parameters of the cleanroom fan, and the constraints are aerodynamic noise limits and the range of geometric structural parameters obtained through empirical design methods of structural parameters. The optimization method is used to solve this robust optimization problem and obtain the robust optimal solution.

[0006] A further improvement of this invention is that, in step 1), the impeller consists of multiple forward blades, an impeller front disk, and an impeller rear disk. Its structural parameters are impeller outer diameter, impeller inner diameter, blade inlet angle, maximum curvature of the airfoil centerline, blade outlet angle, number of blades, and blade thickness. A geometric parameterization method is used to describe and control one or more structural parameters of the impeller to obtain a new impeller geometry. The structural parameters of the volute are volute profile, volute tongue profile, and axial height. A geometric parameterization method is used to control the structural parameters of the volute to achieve parameterization of the volute. The structural parameters of the collector are collector profile and collector outlet radius. The structural parameters of the collector are controlled to achieve parameterization of the collector.

[0007] A further improvement of this invention is that the impeller parameterization method includes B-spline curves and Bezier curves. The blade airfoil centerline is obtained by controlling the blade's structural parameters through programming. The blade's structural parameters include the blade inlet angle, the blade outlet angle, and the maximum curvature of the airfoil centerline.

[0008] A further improvement of this invention is that, in step 2), for the parameter characterizing the degree of fouling on the flow passage components of the multi-blade centrifugal fan, the profile arithmetic mean deviation used to evaluate surface roughness is selected. Its definition is as follows:

[0009] In the formula, For sampling length, This represents the distance from each point on the contour of the dirt deposit to the smooth wall surface, and all values ​​are greater than 0.

[0010] A further improvement of the present invention is that, in step 2), parameterization is performed on the fouling area that is sensitive to the performance of the fan. One or more structural parameters of the flow passage component where the fouling area is located are selected to parameterize the flow passage component. Training samples and test samples of the selected structural parameters are generated using a sampling method. The performance index parameters corresponding to each sample point are obtained by simulation experiments based on computational fluid dynamics. A proxy model that approximately reflects the relationship between specific structural parameters and performance of the multi-blade centrifugal fan is established.

[0011] A further improvement of this invention is that it employs a sampling method to uniformly extract within the design space. n There are 1 sample points, denoted as _ . Furthermore, the aerodynamic performance at specific sample points was obtained through computational fluid dynamics-based simulation experiments. The sample point parameters and the corresponding wind turbine performance are used as the input and output of the surrogate model.

[0012] A further improvement of the present invention is that, in step 3), during the process of solving the robust optimization problem, the performance index of each intermediate sample multi-blade centrifugal fan with respect to maximizing the weighted average efficiency of the fan in operation with fouling within a specific time period and minimizing the annual performance degradation rate are obtained by a surrogate model that predicts the performance index of the multi-blade centrifugal fan.

[0013] A further improvement of the present invention is that, in step 3), during the process of solving the robust optimization problem, the design variables are one or more structural parameters selected in the flow passage component where the fouling area sensitive to the fan performance is located.

[0014] A further improvement of the present invention is that, in step 3), the optimization method is a multi-objective genetic algorithm. Based on the optimization method steps adopted, and combined with the simulation experiment based on computational fluid dynamics, the optimization problem is solved to determine the optimal parameters of the multi-blade centrifugal fan.

[0015] A further improvement of the present invention is that, in step 3), the performance of the scale buildup operation includes airflow and efficiency.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: (1) Surveys show that performance and noise are two important factors for users when purchasing range hoods. This invention uses a multi-objective optimization algorithm to improve the performance indicators of the fan, such as efficiency and air volume, and uses noise as a constraint to reduce aerodynamic noise while improving aerodynamic performance indicators such as air volume and efficiency. At the same time, this invention starts with the aerodynamic optimization design of the fan, explores multiple structural parameters that affect the fan performance, and performs in-depth optimization of its key components based on the working principle of the range hood. One of the optimization objectives is to maximize the weighted average performance of the fan under the condition of grease buildup within a specific time. By setting weighting coefficients, it is ensured that the performance indicators of the fan under clean conditions are improved compared with existing indicators.

[0017] (2) Current optimization studies on multi-blade centrifugal fans for range hoods only focus on the aerodynamic and acoustic characteristics of the multi-blade centrifugal fan in a clean state, while ignoring the long-term needs of users for durable performance and simple maintenance of range hoods. This invention considers the problem of oil fumes accumulating on the multi-blade centrifugal fan during operation, leading to changes in the geometric structure of the fan's flow components, deterioration of fan performance, and decreased safety and reliability. The optimization design of this invention obtains the fan performance-sensitive area by reverse modeling and sensitivity analysis of the accumulated fans with different operating years, performs parameterized characterization and further optimization design on this area, and takes maximizing the weighted average performance of the fan with accumulated fumes over a specific time and minimizing the annual performance degradation rate of the fan as one of the main optimization objectives. This makes the optimized fan performance degrade more slowly with the extension of operating time, extending the effective service life of the multi-blade centrifugal fan, extending the fan cleaning cycle, and effectively ensuring the safe and reliable operation of the fan.

[0018] In summary, this invention addresses the problem of performance degradation of multi-blade fan impellers due to grease buildup. It proposes a robust optimization design method for multi-blade fan impellers that takes grease buildup into account, achieving a slower rate of efficiency degradation with grease buildup throughout the 5-year service life of the multi-blade fan, maintaining a consistently high level and extending the cleaning cycle of range hoods from 1-2 years to 5 years. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the main structure of a multi-blade centrifugal fan. Figure 2 This is a schematic diagram of the side structure of a multi-blade centrifugal fan; Figure 3 This is a diagram of the centerline of the blade profile of a multi-bladed centrifugal fan with blades of equal thickness.

[0020] Explanation of reference numerals in the attached figures: 1. Impeller; 2. Forward blade; 3. Impeller front plate; 4. Impeller rear plate; 5. Impeller outer diameter; 6. Impeller inner diameter; 7. Volute; 8. Volute tongue profile; 9. Collector; 10. Blade inlet tangential velocity; 11. Blade inlet angle; 12. Blade inlet rotational speed; 13. Angle between the line connecting the blade outlet to the impeller center and the horizontal axis; 14. Maximum curvature of the airfoil centerline; 15. Chord length of the airfoil; 16. Blade outlet tangential velocity; 17. Blade outlet angle; 18. Blade outlet rotational speed; 19. Blade inlet endpoint; 20. Blade outlet endpoint; 21. Control point 22. Control Points . Detailed Implementation

[0021] Specific embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While specific embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0022] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that different terms may be used to refer to the same component. This specification and claims do not distinguish components based on differences in terminology, but rather on differences in function. The terms "comprising" or "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising but not limited to." The following descriptions are preferred embodiments for carrying out the invention; however, these descriptions are for the purpose of understanding the general principles of the specification and are not intended to limit the scope of the invention. The scope of protection of this invention is determined by the appended claims.

[0023] This invention provides a robust optimization design method for a multi-blade centrifugal fan used in range hoods, comprising the following steps: Step 1: Based on the theoretical design method of multi-blade centrifugal fans, the geometric structure description of multi-blade centrifugal fans is mainly based on their flow components, which include impeller 1, volute 7, and collector 9. The impeller 1 consists of multiple forward blades 2, an impeller front plate 3, and an impeller rear plate 4. Its structural parameters mainly include impeller outer diameter 5, impeller inner diameter 6, blade inlet angle 11, maximum curvature of the blade centerline 14, blade outlet angle 17, number of blades, and blade thickness. By describing and controlling one or more structural parameters of the impeller 1 using geometric parameterization methods, a new impeller geometry can be obtained. The structural parameters of the volute 7 mainly include the volute profile, volute tongue profile 8, and axial height. By controlling the structural parameters of the volute using geometric parameterization methods, the parameterization of the volute can be achieved. The structural parameters of the collector 9 mainly include the collector profile and collector outlet radius. By controlling the structural parameters of the collector, the parameterization of the collector can be achieved.

[0024] Step 2: Perform high-precision measurement and reverse modeling of the flow components of multi-blade centrifugal fans with different operating years to obtain multiple multi-blade centrifugal fan models with different degrees of fouling. Conduct simulation experiments based on computational fluid dynamics on the multi-blade centrifugal fan models. Based on the simulation results and sensitivity analysis, study the impact of fouling on the aerodynamic performance and acoustic characteristics of the fan, identify the fouling areas that are sensitive to the fan's performance, and focus on selecting structural parameters for parametric characterization of these areas.

[0025] Step 3: The optimization objective of the robust optimization problem of the fan is to maximize the weighted average of the fan's fouling operation performance (including but not limited to air volume and efficiency) within a specific time period and minimize the annual performance degradation rate of the fan within a specific time period. The design variables are the geometric structural parameters of the cleanroom fan, and the constraints are aerodynamic noise limits and the range of geometric structural parameters obtained through empirical design methods of structural parameters. The optimization method is used to solve this robust optimization problem and obtain the robust optimal solution.

[0026] Furthermore, the impeller parameterization method includes, but is not limited to, B-spline curves and Bezier curves. The blade's airfoil centerline can be obtained by programming to control the blade's structural parameters (including but not limited to blade inlet angle 11°, blade outlet angle 17°, and maximum blade curvature).

[0027] Furthermore, the focus is on parametrically characterizing the fouling areas that are sensitive to the performance of the fan. One or more structural parameters of the flow passage component in the area are selected to parametrically characterize the flow passage component. Training and test samples of the selected structural parameters are generated using sampling methods (including but not limited to Latin hypercube and uniform design methods). Performance index parameters corresponding to each sample point are obtained by simulation experiments based on computational fluid dynamics. A surrogate model that approximately reflects the relationship between specific structural parameters and performance of the multi-blade centrifugal fan is established.

[0028] Furthermore, in solving the robust optimization problem, the performance indices of each intermediate sample multi-blade centrifugal fan regarding maximizing the weighted average efficiency of the fan during fouling operation within a specific time period and minimizing the annual performance degradation rate are obtained by a surrogate model that predicts the performance indices of the multi-blade centrifugal fan.

[0029] Furthermore, in solving the robust optimization problem, the design variables are one or more structural parameters selected in the flow passage components where the fouling area, which is sensitive to the performance of the fan, is located.

[0030] Furthermore, the optimization method includes, but is not limited to, a multi-objective genetic algorithm. Based on the steps of the optimization method adopted, and combined with simulation experiments based on computational fluid dynamics, the optimization problem is solved to determine the optimal parameters of the multi-bladed centrifugal fan.

[0031] Example To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. The accompanying drawings do not constitute a limitation on the embodiments of the present invention.

[0032] To better understand, such as Figure 1 As shown, the main flow passage components of the multi-blade centrifugal fan include, but are not limited to, impeller 1, volute 7, and collector 9. The main structural parameters of impeller 1 include impeller outer diameter 5, impeller inner diameter 6, blade inlet angle 11 (the angle between the blade inlet tangential velocity 10 and the blade inlet rotational velocity 12), maximum curvature of the blade centerline 14, blade outlet angle 17 (the angle between the blade outlet tangential velocity 16 and the blade outlet rotational velocity 18), number of blades, and blade thickness. The blade centerline is parameterized using a non-uniform B-spline curve method, thereby controlling the structural parameters of blade 2 (including but not limited to the angle 13 between the line connecting the blade outlet to the impeller center and the horizontal axis, maximum curvature of the blade centerline 14, and blade outlet angle 17) to obtain the blade centerline and avoid unreasonable blade shapes.

[0033] By keeping the blade inlet endpoint 19 and the blade inlet angle 11 constant, and after determining the angle 13 between the line connecting the blade outlet to the impeller center and the horizontal axis, the position of the blade outlet endpoint 20 can be determined. Furthermore, by determining the blade outlet angle 17 and the maximum curvature 14 of the blade centerline, the control point can be ensured. 21 and control points When the line connecting points 22 is parallel to the chord length 15 of the leaf shape, the control point can be determined. 21 and control points Position 22. The location where the maximum curvature of the leaf centerline is 14 is determined by the nodal vector. Decision, node vector The generation follows the Hartley-Judd algorithm, with node interval lengths as follows:

[0034] In the formula, n = Number of control points - 1 ( n =3), k The degree of the spline curve ( k =2), (In this patent) =3,4). Four of the control points are at the blade inlet end. 19. Control Points 21. Control Points 22 and blade exit end 20; Node value:

[0035] In the formula, ( =3), and , .

[0036] Introducing basis functions To define a non-uniform B-spline curve:

[0037] In the formula, For the first indivual k Basis functions of subspline curves k =2), ( n =3).

[0038] The expression for a non-uniform B-spline curve:

[0039] The non-uniform B-spline curve can describe the blade centerline. To generate a reasonable blade profile, a Python program was used to implement a control program to obtain the blade centerline using the non-uniform B-spline curve. By adjusting the values ​​of three design parameters (angle 13° between the line connecting the blade exit to the impeller center and the horizontal axis, maximum curvature of the blade centerline 14°, and blade exit angle 17°), a suitable range for these three design parameters was determined. Combined with the selected impeller outer diameter 5°, impeller inner diameter 6°, blade thickness, and number of blades, a new impeller geometry can be obtained.

[0040] The geometry of the volute 7 can be determined by structural parameters such as the volute tongue profile 8, the volute profile, and the axial height of the volute. The design method of the volute profile is based on the assumptions of equal circumferential flow distribution and constant angular momentum of the fluid flowing inside the volute, thus deriving a logarithmic spiral or an Archimedean spiral. The volute profile can be controlled by adjusting the diffusion angle of the spiral. The volute tongue profile 8 is often designed as an arc, and the parameterization of the volute 7 structure can be achieved by adjusting the radius of the arc. The geometry of the collector 9 can be determined by structural parameters such as the collector profile and the collector outlet radius. Controlling the structural parameters of the collector 9 can achieve parameterization of the collector, enabling precise description and flexible control of the fan geometry.

[0041] High-precision measurements and reverse modeling were performed on the flow components of various multi-blade centrifugal fans that had been in operation for different years, resulting in multiple multi-blade centrifugal fan models with different degrees of fouling. For the parameters characterizing the degree of fouling on the flow components of the multi-blade centrifugal fan, the arithmetic mean deviation of the profile was selected to evaluate the surface roughness. Its definition is as follows:

[0042] In the formula, For sampling length, Here, is the profile offset. This represents the distance from each point on the contour of the scale deposit to the smooth wall surface; all values ​​are greater than 0. When the sampling length... Enough hours This precisely reflects the magnitude of local roughness caused by fouling; in a clean state, the surface roughness of the flow passage components of the multi-bladed centrifugal fan is 0. Through computational fluid dynamics-based simulation experiments on the fan models, aerodynamic performance parameters such as airflow and efficiency were obtained for each fan model. The fouling conditions of each flow passage component of the fan model were varied one by one to further explore the sensitivity of the fan's aerodynamic performance to changes in fouling. Based on the simulation results and sensitivity analysis, the impact of fouling on the fan's aerodynamic performance and acoustic characteristics was studied, identifying the fouling-sensitive region. In the subsequent robust optimization design process, structural parameters were selected specifically for parametric characterization of this region.

[0043] Robust optimization design aims to improve the aerodynamic performance of cleanroom fans while minimizing the degradation caused by fouling, ensuring that the fans maintain excellent aerodynamic performance even under fouling conditions. The objective function is set as follows: The weighted average of the fan's performance (including but not limited to airflow and efficiency) over a specific period of time is maximized by maximizing the performance of the fan in terms of scale buildup.

[0044] Minimize the annual performance degradation rate of the wind turbine over a specific period of time:

[0045] Robust design variables are one or more structural parameters selected from the flow components where the fouling areas are sensitive to fan performance. By changing these structural parameters, the geometry of the flow components can be flexibly controlled. Constraints include geometric constraints and noise constraints. Geometric constraints are the maximum dimensional limitations of the range hood housing on the flow components, and the range of structural parameters obtained through empirical design methods. Noise constraints can be obtained from the national standard GB / T 17713-2011, "Rainbow Hoods".

[0046] The robust optimization design of the aforementioned multi-bladed centrifugal fan can be reduced to a multi-objective optimization problem. In solving this problem, the Kriging model is used as an approximate model to replace simulation experiments. For each intermediate sample multi-bladed centrifugal fan, the performance indices for maximizing the weighted average efficiency of the fan during fouling operations within a specific time period and minimizing the annual performance degradation rate within that time period can be obtained from the Kriging model, which predicts the performance indices of the multi-bladed centrifugal fan. The input to the Kriging model consists of one or more structural parameters selected from the flow passage components where the fouling-sensitive areas of the fan are located, and the output is the corresponding aerodynamic performance of the fan. For the training and testing samples of the Kriging model, 60 sets of sample points are randomly generated using the optimal Latin hypercube in the experimental design method, denoted as […]. The goal was to ensure that the sample points were evenly distributed within the design space consisting of parameter variation ranges, and to obtain the aerodynamic performance of these 60 sample points using computational fluid dynamics-based simulation experiments, denoted as . Fifty samples were selected from the sample space as training samples to establish the response relationship between design parameters and performance in the Kriging model. The Kriging model achieves this by treating the objective function as a Gaussian process. Dimensional variables In response The mapping relationship between them, about variables and The Kriging model can be described as follows:

[0047] In the formula, It is the mean of a Gaussian process. It is an error term that conforms to a Gaussian distribution, i.e. For any two points and In this regard, their error terms are considered to be correlated, and the following correlation function is introduced.

[0048] In the formula, and These are undetermined coefficients, and their optimal values ​​can be obtained by maximizing the likelihood function of the sample points.

[0049] Then for any unknown point Its predicted value can be expressed as:

[0050] In the formula, yes The estimated value can be expressed as:

[0051] In the formula, This is the correlation coefficient matrix for each sample point, and its elements are: , yes A vector whose elements , It is the response value vector of the sample points, and its size is .

[0052] The remaining 10 samples in the sample space are used as training samples to reflect the accuracy of the Kriging model. The average relative error between the established Kriging model and the aerodynamic performance results calculated by the simulation experiment based on computational fluid dynamics is compared. When the average relative error is about 0.027%, it is within the range of engineering allowable error.

[0053] The population size of the multi-objective genetic algorithm was set to 100, and the maximum number of generations was set to 500. The Geatpy toolbox in the Python programming library was used to perform mathematical optimization. A series of robust optimal solutions to the optimization problem were obtained, and the geometric structure of the multi-bladed centrifugal fan that maximized the weighted average performance of the fan during fouling operations and minimized the annual performance degradation rate within a specific time period was determined.

[0054] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A robust optimization design method for a multi-blade centrifugal fan used in range hoods, characterized in that, Includes the following steps: 1) According to the theoretical design method of multi-blade centrifugal fans, the geometric structure description of multi-blade centrifugal fans is based on their flow components, which include impeller, volute and collector; 2) The geometric structure of the flow components of multi-blade centrifugal fans with different operating years was measured and reverse modeled to obtain multiple multi-blade centrifugal fan models with different degrees of fouling. The multi-blade centrifugal fan models were subjected to simulation experiments based on computational fluid dynamics. Based on the simulation results and sensitivity analysis, the influence of fouling on the aerodynamic performance and acoustic characteristics of the fan was studied. The fouling areas that are sensitive to the fan performance were identified, and structural parameters were selected for parameterized characterization of the fouling areas. 3) Optimize the parameters of the fouling area that is sensitive to the performance of the fan. The optimization objective is to maximize the weighted average performance of the fan in the fouling area within a specific time and minimize the annual performance degradation rate of the fan within a specific time. The design variables are the geometric structural parameters of the cleanroom fan, and the constraints are aerodynamic noise limits and the range of geometric structural parameters obtained through empirical design methods of structural parameters. The robust optimization design is solved using optimization methods to obtain the robust optimal solution.

2. The robust optimization design method for a multi-blade centrifugal fan for a range hood according to claim 1, characterized in that, In step 1), the impeller consists of multiple forward blades, an impeller front plate, and an impeller rear plate. Its structural parameters include the impeller outer diameter, impeller inner diameter, blade inlet angle, maximum curvature of the airfoil centerline, blade outlet angle, number of blades, and blade thickness. A geometric parameterization method is used to describe and control one or more of the impeller's structural parameters to obtain a new impeller geometry. The volute's structural parameters are the volute profile, volute tongue profile, and axial height. A geometric parameterization method is used to control the volute's structural parameters to achieve parameterization of the volute. The collector's structural parameters are the collector profile and collector outlet radius. Controlling the collector's structural parameters achieves parameterization of the collector.

3. The robust optimization design method for a multi-blade centrifugal fan for a range hood according to claim 2, characterized in that, Impeller parameterization methods include B-spline curves and Bézier curves. By programming, the structural parameters of the blade are controlled to obtain the blade's airfoil centerline. The blade's structural parameters include the blade inlet angle, the blade outlet angle, and the maximum curvature of the airfoil centerline.

4. The robust optimization design method for a multi-blade centrifugal fan for a range hood according to claim 1, characterized in that, In step 2), for the parameter characterizing the degree of fouling on the flow passage components of the multi-blade centrifugal fan, the profile arithmetic mean deviation is selected to evaluate the surface roughness. Its definition is as follows: In the formula, For sampling length, This represents the distance from each point on the contour of the dirt deposit to the smooth wall surface, and all values ​​are greater than 0.

5. A robust optimization design method for a multi-blade centrifugal fan for a range hood according to claim 4, characterized in that, In step 2), parameterization is performed on the fouling areas that are sensitive to the performance of the fan. One or more structural parameters of the flow passage component where the fouling area is located are selected to parameterize the flow passage component. Training samples and test samples of the selected structural parameters are generated using a sampling method. The performance index parameters corresponding to each sample point are obtained by simulation experiments based on computational fluid dynamics. A surrogate model that approximately reflects the relationship between specific structural parameters and performance of the multi-blade centrifugal fan is established.

6. A robust optimization design method for a multi-blade centrifugal fan for a range hood according to claim 5, characterized in that, A sampling method is used to uniformly extract samples within the design space. n There are 1 sample points, denoted as _ . Furthermore, the aerodynamic performance at specific sample points was obtained through computational fluid dynamics-based simulation experiments. The sample point parameters and the corresponding wind turbine performance are used as the input and output of the surrogate model.

7. A robust optimization design method for a multi-blade centrifugal fan for a range hood according to claim 5, characterized in that, In step 3), during the process of solving the robust optimization problem, the performance indicators of each intermediate sample multi-blade centrifugal fan regarding maximizing the weighted average efficiency of the fan in operation with fouling within a specific time period and minimizing the annual performance degradation rate are obtained by the surrogate model that predicts the performance indicators of the multi-blade centrifugal fan.

8. A robust optimization design method for a multi-blade centrifugal fan for a range hood according to claim 5, characterized in that, In step 3), during the process of solving the robust optimization problem, the design variables are one or more structural parameters selected in the flow passage components where the fouling area sensitive to the fan performance is located.

9. A robust optimization design method for a multi-blade centrifugal fan for a range hood according to claim 5, characterized in that, In step 3), the optimization method is a multi-objective genetic algorithm. Based on the optimization method steps adopted and combined with simulation experiments based on computational fluid dynamics, the optimization problem is solved to determine the optimal parameters of the multi-blade centrifugal fan.

10. A robust optimization design method for a multi-blade centrifugal fan for a range hood according to claim 1, characterized in that, In step 3), the performance of the scale buildup operation includes airflow and efficiency.

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