Optimization design method for multiphase pump blade of bionic bird airfoil structure and bionic blade

Abstract

The invention provides an optimal design method for a multiphase pump blade of a bionic bird airfoil structure and a bionic blade, and belongs to the technical field of optimal design of fluid machinery. The method comprises the following steps: firstly, determining the distribution form of a bionic flange sawtooth structure arranged on the tail edge of the blade and the groove length of a bionic wing feather surface groove structure arranged on the suction surface of the blade and close to the tail edge area; further parametrically designing the groove width ratio, the number, the width, the depth gradient and the section shape of the groove structure on the surface of the bionic wing feather to form a composite bionic blade; and then, by taking pressurization and gas gathering degree as targets, performing automatic optimization and performance checking on bionic structure parameters through experimental design, a neural network agent model and a multi-target optimization algorithm until design requirements are met. According to the method, two bionic structures are synergistically applied to multiphase pump blade design, tail edge gas-liquid separation and blade surface gas accumulation are effectively inhibited through systematized and intelligent flow optimization, and the operation efficiency and stability of the pump are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of fluid machinery optimization design technology, and in particular to a method for optimizing the design of hybrid pump blades with a biomimetic bird-wing structure and the biomimetic blades. Background Technology

[0002] Mixed-flow pumps are critical transport equipment in fields such as petrochemicals, and their blade performance directly affects system efficiency and stability. In actual operation, due to the differences in physical properties between the gas and liquid phases, a low-pressure zone easily forms at the tail of the blade's suction surface, leading to gas accumulation, flow separation, and the induction of vortices, resulting in significant problems such as decreased efficiency and increased vibration. Although biomimicry has provided new approaches to solving this problem (e.g., serrated edges on bird wings can suppress flow separation, and wing feather grooves can stabilize near-wall turbulence), existing technologies still have significant shortcomings.

[0003] First, the application of the structure is one-sided, often simply transplanting a single biomimetic structure without adapting it to the gas-liquid two-phase flow characteristics of mixed-transport pumps, thus failing to systematically solve the problems of gas accumulation and flow instability. Second, the design method lacks a systematic approach, lacking clear design criteria based on flow mechanisms for key geometric parameters such as serration distribution and groove parameters, relying on trial and error, which is inefficient. Third, the performance optimization capability is lacking, with a lack of automatic optimization methods that combine multiple parameters and target actual performance indicators, which restricts the development of high-performance blades.

[0004] In summary, existing technologies are limited by shortcomings such as the limited application of biomimetic structures, reliance on experience for design parameters, and a lack of systematic and intelligent optimization methods. These limitations make it difficult to fundamentally solve the efficiency degradation and operational instability problems caused by gas accumulation at the trailing edge of mixed-transfer pump blades during the transport process. Therefore, there is an urgent need to propose a systematic, precise, and efficient biomimetic blade optimization design method to improve the overall performance of mixed-transfer pumps. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized design method for mixed-transfer pump blades with a biomimetic bird-wing structure and biomimetic blades, which solves the efficiency and stability problems caused by gas accumulation and trailing edge flow separation in traditional mixed-transfer pump blades.

[0006] To achieve the above objectives, this invention provides an optimized design method for hybrid pump blades with a biomimetic bird wing-shaped structure. The hybrid pump blade includes a suction surface, a pressure surface, a leading edge, and a trailing edge. The suction surface and trailing edge are provided with a biomimetic bird wing-shaped structure, which includes a biomimetic serrated edge structure and a biomimetic feather surface groove structure. The specific design steps are as follows: Step S1: Determine the distribution pattern of the biomimetic serrated edge structure set on the trailing edge of the blade; Step S2: Determine the groove length of the biomimetic wing feather surface groove structure set in the region near the trailing edge of the blade suction surface; Step S3: Select the groove width ratio and number of the biomimetic wing feather surface groove structure, and determine the groove structure width and its variation gradient along the streamline direction; Step S4: Determine the depth distribution of the groove structure on the surface of the biomimetic wing feather, including the groove depth variation gradient and groove depth limit; Step S5: Determine the cross-sectional shape of the groove structure on the surface of the biomimetic wing feathers; Step S6: Perform performance verification on the designed mixed-transfer pump blades. If the performance meets the design standards, the design is terminated; otherwise, optimize the design by using the key parameters of the biomimetic flange sawtooth structure and the biomimetic feather surface groove structure as optimization variables, and the pressure boosting of the mixed-transfer pump and the degree of gas accumulation in the impeller as optimization objectives, until the performance meets the requirements.

[0007] Preferably, in step S1, the biomimetic flange serration structure is distributed in a sinusoidal curve form along the blade span direction, and its functional relationship is as follows: ; ; in, These are coordinates in the local coordinate system. These are absolute coordinates. The amplitude of the sine curve is given, and its value range is given. ; This refers to the thickness of the blade's trailing edge. The period of the sine curve is given, and its value range is given. , The height of the flow channel at the outlet end of the groove structure; The angle between the local coordinate system and the absolute coordinate system; denoted as the outer radius of the blade.

[0008] Preferably, in step S2, the groove length of the biomimetic wing feather surface groove structure is... Determined by the spatial distribution based on the instability characteristics of gas-liquid two-phase flow, specifically including: Step S21: Obtain the distribution of gas-liquid two-phase flow characteristic parameters inside the impeller of the mixed-transfer pump under set operating conditions through model tests or numerical calculation methods. Step S22: If the model test method is used, the gas-liquid two-phase flow image inside the impeller is obtained by high-speed visualization, and the corresponding unstable region of gas-liquid two-phase flow is identified based on the gas stagnation and turbulence phenomenon at the suction surface of the blade trailing edge. When using numerical calculation methods, the gas phase volume fraction distribution characteristics on the suction surface at the blade trailing edge are extracted to determine the unstable region of the gas-liquid two-phase flow: ; in, This indicates the range of the unstable region of gas-liquid two-phase flow at the trailing edge of the suction surface of the blade along the arc direction of the blade. This is the distance from the mid-arc direction of the leading edge of the blade; This refers to a segment of arc length along the mid-arc direction of the blade and the trailing edge of the blade's suction surface, ranging from 40% to 60% of the total arc length of the blade. The inlet gas content of the mixed-transfer pump; The position of the trailing edge region of the blade's suction surface along the mid-curve direction. Local gas content at the location; Step S23: The arc length covering the unstable region of gas-liquid two-phase flow in the arc direction of the blade is determined as the groove length of the groove structure. .

[0009] Preferably, in step S3, the groove width ratio Defined as the groove width of a single groove structure Total width of the slot The ratio satisfies ; Number of groove structures on the surface of biomimetic wing feathers satisfy ; Determine the distance from the trailing edge of the blade Time channel height and groove structure width for: ; ; in, This is a correction factor, and its value range is... ; and The height of the flow channel at the inlet and outlet ends of the groove structure; The gradient of the groove structure width variation is : .

[0010] Preferably, in step S4, the depth of the trailing edge side groove is... satisfy , The thickness of the blade trailing edge; the gradient of the trailing edge lateral groove depth. as follows: ; in, This is the gradient shrinkage coefficient, and its value range is... ; Distance from the trailing edge of the blade The groove depth of the groove structure as follows: ; Further restrictions are placed on the depth of the groove: .

[0011] Preferably, in step S5, the cross-section of the biomimetic wing feather surface groove structure perpendicular to the mid-arc line consists of three parts: a lower sidewall, an upper sidewall, and an arc-shaped groove bottom, exhibiting an asymmetrical shape, specifically including: The lower sidewall is tilted downward relative to the blade surface normal. : The upward tilt angle of the upper sidewall relative to the blade surface normal : ; The bottom of the groove has an arc-shaped profile with a radius of curvature that decreases along the mid-arc line, and is located a short distance from the trailing edge of the blade. The radius of curvature at that time is : ; in, Distance from the trailing edge of the blade The sag of the arc-shaped groove bottom, taking the value ; The lower sidewall and the bottom of the arc-shaped groove, as well as the upper sidewall and the bottom of the arc-shaped groove, are connected by a smooth transition; the minimum radius of curvature of the transition area is not less than 0.15 times the groove depth.

[0012] Preferably, the optimized design in step S6 specifically includes: Step S61: Select the period of the sine curve of the biomimetic flange sawtooth structure. and the amplitude of the sine curve The groove length of the biomimetic wing feather surface groove structure Slot width ratio Number of groove structures Tail edge side groove depth and the gradient of the trailing edge groove depth of the blade To optimize variables, a mixed-transport pump was selected for boosting. and the degree of gas accumulation inside the impeller To optimize the objective; Step S62: Generate sample schemes and perform numerical calculations using the optimal Latin hypercube experimental design method to obtain the optimization objectives corresponding to each sample scheme, construct and normalize the optimization scheme dataset; divide the optimization scheme dataset into training set and validation set, establish a neural network surrogate model with optimization variables as input and optimization objectives as output, and use a genetic algorithm to optimize the connection weights and biases of the neural network surrogate model. Step S63: Perform multi-objective optimization based on the surrogate model to obtain the Pareto non-dominated solution set; the optimization process introduces an adaptive search strategy based on the characteristics of gas-liquid two-phase flow. Step S64: Perform performance verification on candidate solutions in the Pareto non-dominated solution set. If the design criteria are not met, supplement sample points to update the sample set and neural network surrogate model, and repeat the optimization until the performance of the optimized model is improved.

[0013] Preferably, the multi-objective optimization algorithm uses a neural network surrogate model as the objective function evaluator for optimization, and the output Pareto optimal solution set consists of solutions that are not mutually dominant on the two objectives of pressurization and relative gas concentration. Among them, turbocharging The calculation formula is as follows: ; in, and The pressure difference between the inlet and outlet of the mixed-transfer pump impeller; Degree of gas accumulation inside the impeller The calculation formula is as follows: ; in, The inlet gas content of the mixed-transfer pump; Normalized axial distance; Distance from impeller inlet Local gas content value on the axial section; This refers to the effective area of ​​the axial cross-section of the impeller of the mixed-transfer pump.

[0014] Preferably, in the multi-objective optimization iteration process, the specific logic of introducing an adaptive search strategy based on the characteristics of gas-liquid two-phase flow is as follows: Define the linear decay coefficient with iteration : ; in, This represents the number of iteration steps. The maximum number of iterations; Generate random numbers Define search control factors With dynamic weighting factor : ; ; Based on the spatial distribution criterion of the gas-liquid two-phase flow instability characteristics in the region near the trailing edge of the blade suction surface in step S2, an instability coefficient is constructed. : ; in, The length of the groove; This refers to a segment of arc length along the mid-arc direction of the blade and the trailing edge of the blade's suction surface, ranging from 40% to 60% of the total arc length of the blade. Based on the instability coefficient Adaptive adjustment of local search probability threshold The stronger the instability of the gas content at the trailing edge, the stronger the search effect. ; Introduction Random numbers between ,when and At that time, execute search mode one: ; in, The dominant solution is randomly selected from the Pareto non-dominated set at this time; As a control factor; For dynamic weighting factors; For the number of iterations Solution at that time; For the number of iterations Solution at that time; when and At that time, execute search mode two: ; in, It is a constant used to define the shape of the spiral; It is a random number in (-1, 1); when Execute search mode three: ; in, The solution is randomly generated; After each iteration, the newly generated individuals are compared with the historical Pareto non-dominated solution set to determine their dominance relationship, and the non-dominated solution set is updated accordingly. During the update process, the coefficient that makes the tail gas content unstable is preferentially retained. Smaller nondominated solutions.

[0015] The present invention also provides a biomimetic blade for a mixed-transport pump, which is designed based on the above-mentioned optimized design method for a mixed-transport pump blade with a biomimetic bird wing structure.

[0016] This invention does not simply involve setting biomimetic grooves on the blade surface. Instead, it addresses the flow mechanism in mixed-transfer pumps where gas tends to accumulate at the suction surface of the blade trailing edge. By identifying the unstable regions in the gas-liquid two-phase flow, it directionally arranges biomimetic winglet groove structures in key areas to actively control the gas phase migration path. Therefore, the beneficial technical effects of this invention, employing the aforementioned biomimetic bird-wing structure for mixed-transfer pump blade optimization design and biomimetic blade, are as follows: This invention incorporates a composite biomimetic system comprising a biomimetic serrated edge structure and a biomimetic feather surface groove structure on the suction surface and trailing edge of the blade. The biomimetic serrated edge structure, distributed along the trailing edge in a sinusoidal curve along the blade span, effectively disrupts trailing edge vortices and suppresses gas-liquid phase separation. The biomimetic feather surface groove structure is precisely positioned on the suction surface through numerical simulation or experimentation. Its design utilizes a systematic selection of key parameters such as groove width ratio, number of grooves, trailing edge groove depth, and width gradient to further reduce gas accumulation on the blade surface and synergistically enhance the suppression of flow separation.

[0017] Based on this, this invention takes improving the pressurization capacity of the mixed-transfer pump and reducing the degree of gas accumulation within the impeller as its clear optimization objectives. It constructs a systematic optimization process encompassing optimal Latin hypercube experimental design, neural network surrogate models, and multi-objective intelligent optimization algorithms. This process significantly reduces the workload associated with traditional trial-and-error methods, ensures the globality and reliability of the design solution, and greatly shortens the development cycle. Ultimately, this invention effectively solves the performance degradation problem caused by gas accumulation and trailing edge flow separation in mixed-transfer pump blades from both structural design and system optimization perspectives, significantly improving the pump's operating efficiency and stability. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the optimized design method for the blades of a hybrid pump with a biomimetic bird-wing structure, as described in this invention. Figure 2 A schematic diagram of a biomimetic wing flange serration structure; Figure 3 These are the experimental results of the mixed-transfer pump; Figure 4 A schematic diagram showing the arrangement of grooves on the surface of biomimetic wing feathers; Figure 5 This is a schematic diagram of a cross-sectional view of the groove structure on the surface of a biomimetic wing feather. Figure 6 A schematic diagram for calculating the parameters of gas accumulation degree inside the impeller; Figure 7 The image shows a comparison between the optimized gas-liquid two-phase flow pump flow field with the original flow field, featuring a biomimetic structure. Figure 7 In the diagram, (a) represents the flow field before optimization. Figure 7 (b) in the figure represents the optimized flow field.

[0019] Figure Labels 1. Blade suction surface; 2. Blade pressure surface; 3. Blade leading edge; 4. Blade trailing edge; 11. Bionic wing feather surface groove structure; 41. Bionic wing edge serrated structure; 111. Lower sidewall; 112. Upper sidewall; 113. Arc-shaped groove bottom. Detailed Implementation

[0020] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0022] Example 1 In this embodiment, the blade of the mixed pump includes a suction surface 1, a pressure surface 2, a leading edge 3, and a trailing edge 4. The suction surface 1 and the trailing edge 4 are provided with biomimetic structures. The biomimetic structure is a biomimetic bird wing-shaped structure, including a biomimetic wing edge serration structure 41 and a biomimetic wing feather surface groove structure 11.

[0023] like Figure 1 As shown, the specific design steps for the hybrid pump blade with a biomimetic bird-wing structure are as follows: Step S1: Determine the distribution of the biomimetic serrated edge structure 41 set on the trailing edge of the blade.

[0024] In this embodiment, the biomimetic serrated edge structure 41 is specifically disposed on the trailing edge 4 of the blade and distributed in a sinusoidal curve along the blade span direction, such as... Figure 2 As shown, the functional relationship is as follows: ; ; in, These are coordinates in the local coordinate system. These are absolute coordinates. The amplitude of the sine curve is given, and its value range is given. ; This refers to the thickness of the blade's trailing edge. The period of the sine curve is given, and its value range is given. , The height of the flow channel at the outlet end of the groove structure; The angle between the local coordinate system and the absolute coordinate system; denoted as the outer radius of the blade.

[0025] Step S2: Determine the groove length of the biomimetic wing feather surface groove structure 11 set on the suction surface of the blade.

[0026] groove length The spatial distribution of unstable gas-liquid two-phase flow characteristics inside the impeller of the mixed-transfer pump under set operating conditions was obtained through model tests or numerical calculations.

[0027] When using the model test method, high-speed visualization is used to obtain images of the gas-liquid two-phase flow inside the impeller, and the corresponding unstable regions of gas-liquid two-phase flow are identified based on the gas stagnation and turbulence at the suction surface of the blade trailing edge.

[0028] When using numerical calculation methods, the gas phase volume fraction distribution characteristics on the suction surface at the trailing edge of the blade are extracted to determine the unstable region of the gas-liquid two-phase flow: ; in, The range of the unstable region of gas-liquid two-phase flow at the trailing edge of the suction surface of the blade along the mid-curve direction; This is the distance from the mid-arc direction of the leading edge of the blade; This refers to a segment of arc length along the mid-arc direction of the blade and the trailing edge of the blade's suction surface, ranging from 40% to 60% of the total arc length of the blade. The inlet gas content of the mixed-transfer pump; The position of the trailing edge region of the blade's suction surface along the mid-curve direction. The local gas content at that location.

[0029] In this embodiment, the gas-liquid flow field image of the mixed-transfer pump was obtained experimentally, as shown below. Figure 3 As shown, the arc length covering the unstable region of gas-liquid two-phase flow in the arc direction of the blade is determined as the groove length of the groove structure. mm: Step S3: Select the groove width ratio and number of the biomimetic wing feather surface groove structure 11, and determine the groove structure width and its variation gradient along the streamline direction.

[0030] In this embodiment, the slot width ratio Defined as the groove width of a single groove structure Total width of the slot The ratio satisfies .

[0031] The number of biomimetic wing feather surface groove structures 11 satisfy .

[0032] This embodiment takes Select the number of groove structures .

[0033] Since the height of the flow channel in the mixed-transfer pump gradually decreases along the streamline direction, the width of the groove structure also gradually decreases along the streamline direction, thus determining the distance from the blade trailing edge. Time channel height and groove structure width for: ; ; Since there will be some machining error when actually slotting the blades, a correction factor was introduced in advance. The range of values ​​is In this embodiment, it is set to 1.05; and The flow channel heights at the inlet and outlet ends of the groove structure are set to 10mm and 8mm respectively in this embodiment. Figure 4 As shown.

[0034] Ultimately, the gradient of the groove structure width variation can be determined as follows: .

[0035] Step S4: Determine the depth distribution of the groove structure on the surface of the biomimetic wing feather, including the groove depth variation gradient and groove depth limit.

[0036] In this embodiment, to accommodate the gradually increasing thickness of the fluid boundary layer on the blade, the groove depth of the groove structure gradually increases along the streamline direction. Therefore, the groove depth on the trailing edge side of the blade is selected. , The thickness of the blade trailing edge is 1.8 mm.

[0037] Selecting the groove depth variation gradient It satisfies the following relation: ; in, This is the gradient shrinkage coefficient, and its value range is... In this embodiment, the value is 0.5.

[0038] Finally, the depth of the groove structure can be determined: ,and .

[0039] Step S5: Determine the cross-sectional shape of the groove structure on the surface of the biomimetic wing feathers, such as... Figure 5 As shown.

[0040] In this embodiment, the cross-section of the biomimetic wing feather surface groove structure perpendicular to the mid-arc direction is composed of three parts: a lower sidewall 111, an upper sidewall 112, and an arc-shaped groove bottom 113, exhibiting an asymmetrical shape. The lower sidewall 111 and the arc-shaped groove bottom 113, as well as the upper sidewall 112 and the arc-shaped groove bottom 113, are smoothly connected. The minimum radius of curvature of the transition region is 0.15 times the groove depth. The lower sidewall 111 is tilted downwards relative to the blade surface normal at an angle... : The upper sidewall 112 is tilted upward relative to the blade surface in the normal direction. : .

[0041] The bottom of the groove has an arc-shaped profile with a radius of curvature that decreases along the mid-arc line, and is located a short distance from the trailing edge of the blade. The radius of curvature at that time is : ; in, Distance from the trailing edge of the blade The sag of the arc-shaped groove bottom, taking the value .

[0042] Step S6: Perform performance verification on the designed biomimetic bird-wing structure hybrid pump blades. If the performance meets the design standards, the design is terminated; otherwise, optimize the design of the biomimetic bird-wing structure hybrid pump blades until the performance meets the requirements.

[0043] In this embodiment, if the performance of the designed biomimetic bird-wing-shaped hybrid pump blades does not meet the requirements, the specific logic for optimization design is as follows: Step S61: Select the period of the sine curve of the biomimetic flange sawtooth structure. and the amplitude of the sine curve The groove length of the biomimetic wing feather surface groove structure Slot width ratio Number of groove structures Tail edge side groove depth and the gradient of the trailing edge groove depth of the blade To optimize variables, a mixed-transport pump was selected for boosting. and the degree of gas accumulation inside the impeller To optimize the objective; The multi-objective optimization algorithm uses a surrogate model as the objective function evaluator to find the optimal solution. The output Pareto optimal solution set consists of solutions that are not mutually dominant on the two objectives of pressurization and relative gas concentration. Among them, turbocharging The calculation formula is as follows: ; in, and The pressure difference between the inlet and outlet of the mixed-transfer pump impeller; The degree of gas accumulation inside the impeller is as follows Figure 6 As shown, the calculation formula is as follows: ; in, The inlet gas content of the mixed-transfer pump; Normalized axial distance; Distance from impeller inlet Local gas content value on the axial section; This refers to the effective area of ​​the axial cross-section of the impeller of the mixed-transfer pump.

[0044] Step S62: Generate sample schemes and perform numerical calculations using the optimal Latin hypercube experimental design method to obtain the optimization objectives corresponding to each sample scheme, construct and normalize the optimization scheme dataset; divide the optimization scheme dataset into training set and validation set, establish a neural network surrogate model with optimization variables as input and optimization objectives as output, and use a genetic algorithm to optimize the connection weights and biases of the neural network surrogate model to improve the prediction accuracy and generalization ability of the surrogate model; The specific logic is as follows: First, an encoding design is performed, where all parameters to be optimized in the neural network, such as the weights and biases of the input layer and hidden layer, and the hidden layer and output layer, are encoded using real numbers to form chromosomes, with each parameter corresponding to a gene on the chromosome. Second, the population is initialized by randomly generating a certain number of chromosomes to form the initial population. Each chromosome is decoded into the corresponding neural network parameters, and the output results are calculated using the training set data through the neural network. The fitness function is constructed based on the error between the predicted value and the actual optimization target value. Then, selection, crossover, and mutation operations are performed on the current population to generate offspring. The fitness of the offspring is quickly evaluated using the validation set, and elite individuals are retained. This process is iterated until the maximum number of generations or the convergence threshold is met. Finally, the optimal values ​​of the connection weights and biases between the input layer and hidden layer, and between the hidden layer and output layer, are obtained by decoding the gene sequence in the optimal individual.

[0045] Step S63: Perform multi-objective optimization based on the surrogate model to obtain the Pareto non-dominated solution set; the optimization process introduces an adaptive search strategy based on the characteristics of gas-liquid two-phase flow.

[0046] Step S64: Perform performance verification on candidate solutions in the Pareto non-dominated solution set. If the design criteria are not met, supplement sample points to update the sample set and neural network surrogate model, and repeat the optimization until the performance of the optimized model is improved.

[0047] The specific logic is as follows: the multi-objective optimization algorithm uses a neural network surrogate model as the objective function evaluator to find the optimal solution. The output Pareto optimal solution set consists of solutions that are not mutually dominant on the two objectives of pressurization and relative gas concentration. In the multi-objective optimization iteration process, the specific logic of the adaptive search strategy based on the gas-liquid two-phase flow characteristics is as follows: Define the linear decay coefficient with iteration : ; in, This represents the number of iteration steps. The maximum number of iterations; Generate random numbers Define search control factors With dynamic weighting factor : ; ; Based on the spatial distribution criterion of the gas-liquid two-phase flow instability characteristics in the region near the trailing edge of the blade suction surface in step S2, an instability coefficient is constructed. : ; in, The length of the groove; This refers to a segment of arc length along the mid-arc direction of the blade and the trailing edge of the blade's suction surface, ranging from 40% to 60% of the total arc length of the blade. Based on the instability coefficient Adaptive adjustment of local search probability threshold The stronger the instability of the gas content at the trailing edge, the stronger the local search. ; Introduction Random numbers between ,when and At that time, execute search mode one: ; in, The dominant solution is randomly selected from the Pareto non-dominated set at this time; As a control factor; For dynamic weighting factors; For the number of iterations Solution at that time; For the number of iterations Solution at that time; when and At that time, execute search mode two: ; in, It is a constant used to define the shape of the spiral; It is a random number in (-1, 1); when Execute search mode three: ; in, The solution is randomly generated; After each iteration, the newly generated individuals are compared with the historical Pareto non-dominated solution set to determine their dominance relationship, and the non-dominated solution set is updated accordingly. During the update process, the coefficient that makes the tail gas content unstable is preferentially retained. Smaller nondominated solutions are used to improve the efficiency of targeted optimization for trailing gas accumulation problems.

[0048] Figure 7 To compare the flow field of the optimized gas-liquid two-phase flow pump with that of the original gas-liquid two-phase flow pump, a biomimetic bird-wing structure was designed. Figure 7 As can be seen, the degree of gas accumulation at the trailing edge of the blade is significantly reduced, therefore the optimized design is successful, and the process ends here.

[0049] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0050] Therefore, this invention adopts the above-mentioned biomimetic bird wing-shaped structure mixed-transfer pump blade optimization design method and biomimetic blade. By setting up a composite biomimetic system including a biomimetic wing edge serrated structure and a biomimetic wing feather surface groove structure, and establishing a system process from parametric design to optimization, it effectively solves the problem of efficiency decay and operational instability caused by gas accumulation and trailing edge flow separation in mixed-transfer pump blades, and realizes the improvement of pump operating efficiency and stability.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the design of hybrid pump blades with a biomimetic bird-wing-shaped structure, characterized in that, The impeller of the mixed-transfer pump includes a suction surface, a pressure surface, a leading edge, and a trailing edge. The suction surface and trailing edge are designed with a biomimetic bird wing-like structure, which includes a biomimetic serrated edge structure and a wing feather surface groove structure. The specific design steps are as follows: Step S1: Determine the distribution pattern of the biomimetic serrated edge structure set on the trailing edge of the blade; Step S2: Determine the groove length of the biomimetic wing feather surface groove structure set in the region near the trailing edge of the blade suction surface; Step S3: Select the groove width ratio and number of the biomimetic wing feather surface groove structure, and determine the groove structure width and its variation gradient along the streamline direction; Step S4: Determine the depth distribution of the groove structure on the surface of the biomimetic wing feather, including the groove depth variation gradient and groove depth limit; Step S5: Determine the cross-sectional shape of the groove structure on the surface of the biomimetic wing feathers; Step S6: Perform performance verification on the designed mixed-transfer pump blades. If the performance meets the design standards, the design is terminated; otherwise, optimize the design by using the key parameters of the biomimetic flange sawtooth structure and the biomimetic feather surface groove structure as optimization variables, and the pressure boosting of the mixed-transfer pump and the degree of gas accumulation in the impeller as optimization objectives, until the performance meets the requirements.

2. The method for optimizing the design of hybrid pump blades with a biomimetic bird-wing structure according to claim 1, characterized in that, In step S1, the biomimetic serrated edge structure is distributed in a sinusoidal curve along the blade span direction, and its functional relationship is as follows: ； ； in, Local coordinate system coordinates; These are absolute coordinates. The amplitude of the sine curve is given, and its value range is given. ; This refers to the thickness of the blade's trailing edge. The period of the sine curve is given, and its value range is given. , The height of the flow channel at the outlet end of the groove structure; The angle between the local coordinate system and the absolute coordinate system; denoted as the outer radius of the blade.

3. The method for optimizing the design of hybrid pump blades with a biomimetic bird-wing structure according to claim 1, characterized in that, In step S2, the groove length of the biomimetic wing feather surface groove structure Determined by the spatial distribution based on the instability characteristics of gas-liquid two-phase flow, specifically including: Step S21: Obtain the distribution of gas-liquid two-phase flow characteristic parameters inside the impeller of the mixed-transfer pump under set operating conditions through model tests or numerical calculation methods. Step S22: When using the model test method, the gas-liquid two-phase flow image inside the impeller is obtained through high-speed visualization, and the corresponding unstable region of gas-liquid two-phase flow is identified based on the gas stagnation and turbulence phenomenon at the suction surface of the blade trailing edge. When using numerical calculation methods, the gas phase volume fraction distribution characteristics on the suction surface at the trailing edge of the blade are extracted to determine the unstable region of the gas-liquid two-phase flow: ； in, This indicates the range of the unstable region of gas-liquid two-phase flow at the trailing edge of the suction surface of the blade along the arc direction of the blade. This is the distance from the leading edge of the blade along the mid-arc line. This refers to a segment of arc length along the mid-arc direction of the blade and the trailing edge of the blade's suction surface, ranging from 40% to 60% of the total arc length of the blade. The inlet gas content of the mixed-transfer pump; The position of the trailing edge region of the blade's suction surface along the mid-curve direction. Local gas content at the location; Step S23: The arc length covering the unstable region of gas-liquid two-phase flow in the arc direction of the blade is determined as the groove length of the groove structure. .

4. The method for optimizing the design of hybrid pump blades with a biomimetic bird-wing structure according to claim 3, characterized in that, In step S3, the slot width ratio Defined as the groove width of a single groove structure Total width of the slot The ratio satisfies ; Number of groove structures on the surface of biomimetic wing feathers satisfy ; Determine the distance from the trailing edge of the blade Time channel height and groove structure width for: ； ； in, This is a correction factor, and its value range is... ; and The height of the flow channel at the inlet and outlet ends of the groove structure; The gradient of the groove structure width variation is : 。 5. The method for optimizing the design of hybrid pump blades with a biomimetic bird-wing structure according to claim 4, characterized in that, In step S4, the depth of the trailing edge side groove satisfy , The thickness of the blade trailing edge; the gradient of the trailing edge lateral groove depth. as follows: ； in, This is the gradient shrinkage coefficient, and its value range is... ; Distance from the trailing edge of the blade The groove depth of the groove structure as follows: ； Further restrictions are placed on the depth of the groove: .

6. The method for optimizing the design of hybrid pump blades with a biomimetic bird-wing structure according to claim 5, characterized in that, In step S5, the cross-section of the biomimetic wing feather surface groove structure perpendicular to the mid-arc line consists of three parts: a lower sidewall, an upper sidewall, and an arc-shaped groove bottom, exhibiting an asymmetrical shape, specifically including: The lower sidewall is tilted downward relative to the blade surface normal. : The upward tilt angle of the upper sidewall relative to the blade surface normal : ; The bottom of the groove has an arc-shaped profile with a radius of curvature that decreases along the mid-arc line, and is located a short distance from the trailing edge of the blade. The radius of curvature at that time is : ； in, Distance from the trailing edge of the blade The sag of the arc-shaped groove bottom, taking the value ; The lower sidewall and the bottom of the arc-shaped groove, as well as the upper sidewall and the bottom of the arc-shaped groove, are connected by a smooth transition; the minimum radius of curvature of the transition area is not less than 0.15 times the groove depth.

7. The method for optimizing the design of hybrid pump blades with a biomimetic bird-wing structure according to claim 1, characterized in that, The optimization design in step S6 specifically includes: Step S61: Select the period of the sine curve of the biomimetic flange sawtooth structure. and the amplitude of the sine curve The groove length of the biomimetic wing feather surface groove structure Slot width ratio Number of groove structures Tail edge side groove depth and the gradient of the trailing edge groove depth of the blade To optimize variables, a mixed-transport pump was selected for boosting. and the degree of gas accumulation inside the impeller To optimize the objective; Step S62: Generate sample schemes and perform numerical calculations using the optimal Latin hypercube experimental design method to obtain the optimization objectives corresponding to each sample scheme, construct and normalize the optimization scheme dataset; divide the optimization scheme dataset into training set and validation set, establish a neural network surrogate model with optimization variables as input and optimization objectives as output, and use a genetic algorithm to optimize the connection weights and biases of the neural network surrogate model; Step S63: Perform multi-objective optimization based on the surrogate model to obtain the Pareto non-dominated solution set; the optimization process introduces an adaptive search strategy based on the characteristics of gas-liquid two-phase flow. Step S64: Perform performance verification on candidate solutions in the Pareto non-dominated solution set. If the design criteria are not met, supplement sample points to update the sample set and neural network surrogate model, and repeat the optimization until the performance of the optimized model is improved.

8. The method for optimizing the design of hybrid pump blades with a biomimetic bird-wing structure according to claim 7, characterized in that, The multi-objective optimization algorithm uses a neural network surrogate model as the objective function evaluator to find the optimal solution. The output Pareto optimal solution set consists of solutions that are not mutually dominant on the two objectives of pressurization and relative gas concentration. Among them, turbocharging The calculation formula is as follows: ； in, and The pressure difference between the inlet and outlet of the mixed-transfer pump impeller; Degree of gas accumulation inside the impeller The calculation formula is as follows: ； in, The inlet gas content of the mixed-transfer pump; Normalized axial distance; Distance from impeller inlet Local gas content value on the axial section; This refers to the effective area of ​​the axial cross-section of the impeller of the mixed-transfer pump.

9. The method for optimizing the design of hybrid pump blades with a biomimetic bird-wing structure according to claim 7, characterized in that, In the multi-objective optimization iteration process, the specific logic of the adaptive search strategy based on the gas-liquid two-phase flow characteristics is as follows: Define the linear decay coefficient with iteration : ； in, This represents the number of iteration steps. The maximum number of iterations; Generate random numbers Define search control factors With dynamic weighting factor : ； ； Based on the spatial distribution criterion of the gas-liquid two-phase flow instability characteristics in the region near the trailing edge of the blade suction surface in step S2, an instability coefficient is constructed. : ； in, The length of the groove; This refers to a segment of arc length along the mid-arc direction of the blade and the trailing edge of the blade's suction surface, ranging from 40% to 60% of the total arc length of the blade. Based on the instability coefficient Adaptive adjustment of local search probability threshold : ； Introduction Random numbers between ,when and At that time, execute search mode one: ； in, The dominant solution is randomly selected from the Pareto non-dominated set at this time; As a control factor; For dynamic weighting factors; For the number of iterations Solution at that time; For the number of iterations Solution at that time; when and At that time, execute search mode two: ； in, It is a constant used to define the shape of the spiral; It is a random number in (-1, 1); when Execute search mode three: ； in, The solution is randomly generated; After each iteration, the newly generated individuals are compared with the historical Pareto non-dominated solution set to determine their dominance relationship, and the non-dominated solution set is updated accordingly. During the update process, the coefficient that makes the tail gas content unstable is preferentially retained. Smaller nondominated solutions.

10. A biomimetic blade for a mixed-transport pump, characterized in that, The biomimetic blades of the hybrid pump are designed using the optimization design method for the biomimetic bird airfoil structure of the hybrid pump blades according to any one of claims 1 to 9.

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