Rotor blade optimization design method

Abstract

The invention discloses a rotor blade optimization design method which comprises the following steps: firstly, establishing a multi-target blade layout optimization problem, performing parametric modeling on an initial blade, extracting blade layout parameters as design variables, then determining sampling points through experimental design, and calculating target values and constraint values; carrying out variance analysis to obtain sensitivity information of an optimization target to a design variable, carrying out multi-target optimization by adopting an evolutionary algorithm, setting an iteration stop condition to obtain a Pareto leading-edge solution set, carrying out design variable analysis on the selected individual to obtain a design variable analysis result, and based on the design variable analysis result and the sensitivity information, carrying out multi-target optimization on the Pareto leading-edge solution set; and dynamically adjusting the design space until an iteration stop condition is met, and outputting a final optimization scheme. A sand blind suppression target is embedded into a multi-target optimization framework, and a design space dynamic adjustment strategy is introduced, so that the conventional pneumatic / structural performance is ensured, the sand and dust concentration is remarkably reduced, and the environmental adaptability and the flight safety of a rotor wing are improved.

Description

Technical Field

[0001] The present application relates to the technical field of blade optimization, and in particular to a method for optimizing the design of rotor blades. Background Art

[0002] Helicopters operating close to the ground in dry, dusty environments commonly experience "sand blindness." This phenomenon is primarily caused by the ground-effect aerodynamics of the rotor downwash. When the high-speed rotating blades disturb the ground surface, they stir up large quantities of sand and dust particles, forming a dense sand cloud. This sand cloud not only severely obscures the pilot's vision and causes spatial disorientation, but also causes multiple hazards, such as rotor system wear and engine air intake blockage, significantly threatening flight safety and reducing the service life of the equipment.

[0003] Current research on the sand blinding phenomenon faces significant limitations: First, the multiphase coupling mechanism of dust particles, flow fields, and rotor systems is not fully understood; second, existing solutions primarily rely on post-process measures such as blade leading edge protection, failing to control dust entrainment from the aerodynamic design perspective; third, mainstream rotor blade design approaches still prioritize aerodynamic efficiency, structural strength, and noise performance, with sand blind mitigation considered only as a post-design verification check. This "design first, verify later" approach results in most rotor systems lacking inherent sand blinding mitigation capabilities; fourth, current research focuses on numerical simulations of the sand blinding formation process, while inverse design methods for mitigation effectiveness have yet to be established. This traditional design paradigm suffers from a fundamental contradiction: optimizing aerodynamic performance often exacerbates turbulence near the ground, which in turn promotes dust entrainment. The partial improvement solutions disclosed in existing technologies struggle to balance the dual requirements of aerodynamic efficiency and sand blind mitigation, resulting in rotor system performance degradation in dusty environments.

[0004] Therefore, it is urgent to deeply integrate the sand blind formation mechanism and aerodynamic design constraints in the initial layout design stage of the rotor system, develop a blade optimization design method with the ability to actively suppress sand blindness, and solve the problems of flight safety and equipment reliability from the root. Summary of the Invention

[0005] The purpose of this application is to overcome the existing technical defects and provide a rotor blade optimization design method. By embedding the "sand blindness" suppression objective into a multi-objective optimization framework and introducing a dynamic adjustment strategy in the design space, the flight safety risks and performance losses caused by ignoring the sand blindness effect in traditional rotor design are solved.

[0006] The purpose of this application is achieved through the following technical solutions: In a first aspect, the present application proposes a rotor blade optimization design method, the method comprising: a. Establish a multi-objective blade layout optimization problem. The design objectives include conventional aerodynamic / structural / noise performance targets, dust concentration in the pilot's field of view, and blade tip vortex intensity. The design constraints include blade geometry constraints and performance constraints. b. Perform parametric modeling on the initial blades and extract blade layout parameters as design variables; c. Determine the sampling points through experimental design, calculate the target value and constraint value of each sampling point, and then perform variance analysis to obtain the sensitivity information of the optimization target to the design variables; d. Use evolutionary algorithms for multi-objective optimization, set iteration stopping conditions, and obtain the Pareto frontier solution set; e. Select the individual containing the extreme value point of the objective function from the Pareto frontier solution set; f. Perform design variable analysis on the selected individuals to obtain design variable analysis results, and dynamically adjust the design space based on the design variable analysis results and sensitivity information; g. Repeat steps c to f until the iteration stop condition is met and output the final optimization solution.

[0007] In a possible implementation, the dust concentration in the driver's field of view is the number of dust particles per unit visual angle volume in the minimum critical area in front of the driver, where the minimum critical area is the visual range of pitch angle ±25°×yaw angle ±20°.

[0008] In one possible embodiment, the tip vortex intensity The calculation formula is: ,in is the strength coefficient, is the blade load factor, is the blade tip speed, is the average chord length of the blade.

[0009] In one possible implementation, the design space is dynamically adjusted in step f as follows: ,in is the expansion coefficient of the design variable space, is an individual in the Pareto Front, represents the design variable index, Individual index.

[0010] In a possible implementation, the parametric modeling in step b adopts one of CST, FFD or NURBS curve / surface methods.

[0011] In a possible implementation, the dust concentration in the driver's field of view is obtained by rotor CFD / sand particle DEM coupled numerical simulation.

[0012] In a possible implementation, the iteration stopping condition in step d is that the number of optimization iterations reaches a preset threshold or the change in the objective function is less than a set tolerance.

[0013] In a possible embodiment, the experimental design method in step c adopts Latin hypercube sampling or uniform sampling.

[0014] In one possible implementation, the performance constraints include extreme values ​​of hovering efficiency, front lift-to-drag ratio, and noise characteristics.

[0015] In one possible implementation, the design variables include blade twist distribution, chord length distribution, airfoil section parameters, blade tip sweep angle, sweep position, anhedral angle, and anhedral position.

[0016] The above-mentioned main solution of this application and its various further options can be freely combined to form multiple solutions, all of which are solutions that can be adopted and protected by this application. Moreover, in this application, (non-conflicting options) can also be freely combined with each other and with other options. After understanding the solution of this application, those skilled in the art will understand that there are many combinations based on existing technology and common knowledge, all of which are technical solutions to be protected by this application, and this is not an exhaustive list.

[0017] This application discloses a rotor blade optimization design method. First, a multi-objective blade layout optimization problem is established. The initial blade is parametrically modeled and the blade layout parameters are extracted as design variables. Then, sampling points are determined through experimental design and the target and constraint values ​​are calculated. Variance analysis is then performed to obtain the sensitivity information of the optimization target to the design variables. An evolutionary algorithm is used for multi-objective optimization. An iterative stopping condition is set to obtain the Pareto frontier solution set. Design variable analysis is performed on the selected individuals to obtain the design variable analysis results. Based on the design variable analysis results and sensitivity information, the design space is dynamically adjusted until the iterative stopping condition is met and the final optimization solution is output. By embedding the sand blindness suppression objective into the multi-objective optimization framework and introducing a dynamic adjustment strategy for the design space, the dust concentration is significantly reduced while ensuring conventional aerodynamic / structural performance, and the rotor environmental adaptability and flight safety are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A rotor blade optimization design method proposed in an embodiment of the present application is shown.

[0020] Figure 2 A schematic diagram of the driver's visual area is shown. DETAILED DESCRIPTION

[0021] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0022] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of this application.

[0023] In existing technologies, sand blindness poses a serious threat to helicopter flight safety. The surrounding sand cloud can impair pilot judgment and even cause misperceptions, leading to accidents. Furthermore, sand cloud can severely wear rotor blades, clog engines, and reduce component lifespan. To mitigate the hazards of sand blindness and address it through rotor blade design, it's crucial to develop appropriate rotor blade optimization methods.

[0024] In order to solve the above-mentioned technical problems, an embodiment of the present application proposes a rotor blade optimization design method. By embedding the "sand blindness" suppression target (sand and dust concentration in the pilot's field of view and blade tip vortex intensity) into a multi-objective optimization framework and introducing a dynamic adjustment strategy for the design space, the flight safety risk and performance loss problems caused by ignoring the influence of sand blindness in traditional rotor design are solved. It embodies the technical effect of significantly reducing sand and dust concentration, improving rotor environmental adaptability and flight safety while ensuring conventional aerodynamic / structural performance, which will be explained below.

[0025] Please refer to Figure 1 , Figure 1 A rotor blade optimization design method proposed in an embodiment of the present application is shown, and the method includes: a. Establish a multi-objective blade layout optimization problem. The design objectives include conventional aerodynamic / structural / noise performance targets, dust concentration in the pilot's field of view, and blade tip vortex intensity. The design constraints include blade geometry constraints and performance constraints.

[0026] Performance constraints include extreme values ​​of hover efficiency and forward lift-to-drag ratio as well as noise characteristics.

[0027] When formulating a multi-objective blade layout optimization problem, multiple factors must be considered comprehensively. Design objectives encompass not only conventional aerodynamic, structural, and noise performance targets, but also focus on key targets impacting sand blindness: sand concentration in the pilot's field of view and blade tip vortex intensity. Sand concentration in the pilot's field of view is calculated through coupled numerical simulations using rotor CFD and sand particle DEM. These objectives directly reflect the impact of sand blindness on helicopter performance. Furthermore, design constraints are divided into blade geometry constraints and performance constraints. Geometric constraints include specific parameter limits such as blade profile thickness, volume, and trailing edge thickness, while performance constraints address requirements such as hovering efficiency, forward lift-to-drag ratio, and extreme noise characteristics. By rationally combining these objectives and constraints, a complete multi-objective blade layout optimization problem can be formulated, ensuring that the designed rotor blades meet conventional performance requirements while effectively mitigating sand blindness.

[0028] b. Perform parametric modeling on the initial blades and extract the blade layout parameters as design variables.

[0029] The design variables include blade twist distribution, chord length distribution, airfoil profile parameters, tip sweep angle, sweep position, anhedral angle and anhedral position.

[0030] The parametric modeling in step b adopts one of the CST, FFD or NURBS curve / surface methods.

[0031] First, it is necessary to extract the key parameters of the blade layout as design variables , given the initial design space , is the number of design variables, including the blade's twist distribution, chord length distribution, airfoil profile parameters, as well as the blade tip sweep angle, sweep position, anhedral angle, and anhedral position. By selecting an appropriate parameterization method, such as CST (Class / Shape function Transformation), FFD (Free-Form Deformation), or NURBS curves / surfaces, an association between the design variables and the blade's shape can be established. This association enables adjustments to the design variables to be directly reflected in changes in the blade's shape. The given initial design space provides a clear range for the number of design variables, ensuring the controllability and feasibility of the optimization process.

[0032] c. Determine the sampling points through experimental design, calculate the target value and constraint value of each sampling point, and then perform variance analysis to obtain the sensitivity information of the optimization target to the design variables.

[0033] The experimental design method in step c adopts Latin hypercube sampling or uniform sampling.

[0034] First, the number of sampling points and the sampling method need to be determined. Uniform sampling or Latin hypercube sampling can be used. Uniform sampling ensures that the sampling points are evenly distributed throughout the design space, while Latin hypercube sampling reduces the number of required sampling points while maintaining uniformity. After selecting the sampling method and determining the sampling points, a detailed computational analysis is performed on each sampling point. These computational analyses include solving the aerodynamic, structural, and noise performance of the blades, as well as key parameters related to "sand blindness," to obtain the target value and constraint value corresponding to each sampling point. The target value reflects the performance of the design in different performance aspects, while the constraint value ensures that the design meets specific restrictions.

[0035] Next, we perform variance analysis on these sample calculation results. The main purpose of variance analysis is to evaluate the dependence and sensitivity of the optimization objective on each design variable. By analyzing the differences between the target value and the constraint value at each sampling point, we can infer which design variables have a significant impact on the optimization objective.

[0036] d. Use evolutionary algorithm for multi-objective optimization, set the iteration stopping condition, and obtain the Pareto frontier solution set.

[0037] The iteration stopping condition in step d is that the number of optimization iterations reaches a preset threshold or the change in the objective function is less than the set tolerance.

[0038] For multi-objective blade layout optimization, an optimization method based on an evolutionary algorithm is employed. During the optimization process, a population of multiple individuals is initialized, each representing a possible design solution—a specific set of design variable values. Then, through genetic operations such as selection, crossover, and mutation, the population is iteratively updated, gradually approaching the optimal solution.

[0039] During the optimization process, iterative stopping conditions need to be set to determine when to stop the optimization. There are two common iterative stopping conditions: one is when the number of optimization iterations reaches a preset threshold. This means that when the optimization process reaches a specified number of generations, the optimization stops regardless of whether the results have fully converged. The other is when the change in the objective function is less than a set tolerance. This means that when the change in the objective function value over several consecutive generations is less than a preset minimum value, the optimization process is considered to have converged and further iterations can be stopped. In this way, satisfactory optimization results can be achieved while avoiding unnecessary waste of computing resources.

[0040] Ultimately, the optimization results are presented as a Pareto front set of solutions. The Pareto front is a set of optimal solutions in a multi-objective optimization scenario where no other individual can simultaneously improve all objectives. Each solution on the Pareto front outperforms the others in at least one objective and does not degrade them in any other objective. These solutions provide designers with a variety of optimization options, from which they can select the most appropriate blade layout based on specific engineering requirements and priorities.

[0041] e. Select the individual containing the extreme value point of the objective function from the Pareto frontier solution set.

[0042] For multi-objective optimization problems, the optimization result is usually not a single solution, but a set of solutions, called ParetoFront. Select the population individuals that contain the extreme points of the objective function in the Pareto Front. ; f. Perform design variable analysis on the selected individuals to obtain design variable analysis results. Based on the design variable analysis results and sensitivity information, dynamically adjust the design space.

[0043] In step f, the design space is dynamically adjusted to: ,in is the expansion coefficient of the design variable space, is an individual in the Pareto Front, represents the design variable index, Individual index.

[0044] Perform design variable analysis on the individuals selected from the Pareto frontier. Combined with the sensitivity information obtained from the variance analysis in step c, determine the adjustment direction of the design variables. Based on the analysis results, dynamically expand the design space according to the above formula.

[0045] g. Repeat steps c to f until the iteration stop condition is met and output the final optimization solution.

[0046] After the design space is changed, return to step c and re-optimize until the given iteration stop condition is met and the optimization process ends.

[0047] The dust concentration in the driver's field of view is the number of dust particles per unit visual angle volume in the minimum critical area in front of the driver, where the minimum critical area is the visual range of pitch angle ±25° × yaw angle ±20°.

[0048] Figure 2A schematic diagram of the pilot's visual field is shown. In the field of helicopter design, this paper focuses on the pilot's field of view to ensure safe near-ground maneuvers. Since the pilot only needs to discern a specific spatial range ahead, the present invention selects the dust particle density within a certain range within the pilot's field of view as the "sand blindness" performance objective function. Specifically, by reducing the dust concentration within the field of view (FOV) ahead of the cockpit, the hazard posed by sand blindness to flight safety can be effectively mitigated. According to relevant literature, the helicopter pilot's binocular visual field (FOV) is typically 200° × 120° ahead of the cockpit. An area of ​​180° × 55° is referred to as the critical visual cue, which is confined to the cockpit's field of view. During flight, the pilot focuses on this critical visual cue, and the minimum visual area required for effective helicopter control is 40° × 25°. Therefore, as long as the dust concentration within this minimum area meets certain requirements, the pilot can safely perform the flight mission. Based on this, the present invention chooses the sand concentration in this minimum critical area as the optimization target, aiming to ensure that the sand concentration in the area in front of the pilot meets the conditions through rotor design, thereby improving the flight safety and controllability of the helicopter in a dusty environment.

[0049] The dust concentration in the minimum critical area in front of the driver can be expressed by the number of dust particles in this range:

[0050] in, is the number of dust particles within the visual area volume formed by unit yaw angle and unit pitch angle at time t, represents the design variables. It can be calculated through numerical analysis methods such as rotor CFD / sand DEM.

[0051] Tip vortex strength The calculation formula is: ,in is the strength coefficient (for the hover state ), is the blade load factor, is the blade tip speed, is the average chord length of the blade.

[0052] The evolution of the rotor flow field near the surface is dominated by the self-induced behavior of the rotor tip vortex and its underwash, the ground, and the vortex system. The tip vortex, compressed by the ground, increases vorticity. The fusion of the tip vortices leads to vortex core expansion. The mutual induction between the tip vortices results in the aperiodic formation of vortex pairs. The impact of the tip vortex on the ground generates secondary vortex structures and separation bubbles. These effects significantly increase the intensity of the ground jet and shear stress, thereby exerting an upward force on the surface dust.

[0053] The interaction between the rotor tip vortex and the ground generates an upwash that rolls up dust particles. These rolled-up dust particles reach the top of the rotor and are then drawn into the rotor downwash, eventually forming a dust cloud. Therefore, the intensity of the blade tip vortex can be used to characterize the impact of the dust cloud on the helicopter. It can be calculated by rotor CFD or free wake methods.

[0054] In one possible embodiment, a blade optimization example is used to illustrate the specific process. The CST method is used to parameterize the blade layout, taking into account twist and anhedral, while also considering the changes in the airfoil profile of the mid-section of the blade. The twist angle varies from -8° to -14°, and the anhedral angle varies from 0.95R, with a range of 0° to 15°. The optimization design sampling is to reduce the dust concentration in the minimum critical area in front of the pilot as the optimization goal, while not reducing the hovering efficiency of the blade. The rotor collective pitch is 8°, and the optimization design is performed. The optimization problem is described as: .

[0055] The optimization process is carried out using an evolutionary algorithm for the optimization problem. The optimization process is iterated for 20 steps. Table 1 shows the comparison of the initial blade and designed blade layout parameters and performance.

[0056] Table 1

[0057] From the data, it can be seen that the twist angle and the downward angle of the optimized blade have increased. Increasing the negative twist and the downward angle can reduce the intensity of the tip vortex, thereby reducing the degree of dust blowing from the surface, causing the number of sand grains in the minimum critical area in front of the pilot to drop from 1060 to 940, significantly reducing the impact of "sand blindness". At the same time, increasing the negative twist and the downward angle makes the distribution of the rotor disk load more uniform, thereby improving the hovering performance of the rotor.

[0058] Compared with the prior art, the embodiments of the present application have the following beneficial effects: First, the effects of sand blindness were considered during conventional blade optimization. Dust concentration in the pilot's forward field of view and rotor tip vortex intensity were identified as key targets for sand blind mitigation. By optimizing these key targets, the dust concentration in the pilot's field of view and the intensity of the rotor tip vortex were significantly reduced, effectively suppressing sand blindness and improving helicopter flight safety and controllability in complex dusty environments.

[0059] Second, the Pareto frontier is used to obtain design variable information for outstanding individuals and determine the location of design space adjustments. Combined with the sensitivity information obtained from variance analysis, the direction of design space adjustments is determined, thus establishing a dynamic adjustment capability for design variables. This dynamic adjustment capability optimizes the selection and adjustment direction of design variables in real time, improving the efficiency and effectiveness of optimization design and avoiding the optimization limitations caused by a fixed design space.

[0060] Third, by combining the key objective of helicopter "sand blindness" suppression, a rotor blade layout can be designed that achieves the optimal combined performance of both conventional rotor performance and "sand blindness" suppression. This comprehensive performance optimization not only improves the aerodynamic, structural, and noise performance of the rotor blades, but also achieves significant results in "sand blindness" suppression, thereby forming an efficient and high-performance rotor system that meets the multi-faceted requirements of modern helicopter rotor design. Therefore, the method proposed in the embodiment of the present application uses modern numerical optimization methods as the framework basis, combines the key objectives of sand and dust concentration in the pilot's field of vision affected by "sand blindness" and blade tip vortex intensity, and constructs a blade optimization method for suppressing "sand blindness". The influence of "sand blindness" is taken into account during the blade design process, thereby improving the final design of the rotor blade's suppression effect on "sand blindness" and improving the performance and adaptability of the designed rotor. In the optimization process, the dynamic expansion of the design space can also be combined to improve the completeness and accuracy of the optimized design.

[0061] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A rotor blade optimization design method, characterized in that: The method comprises: a. Establish a multi-objective blade layout optimization problem. The design objectives include conventional aerodynamic / structural / noise performance targets, dust concentration in the pilot's field of view, and blade tip vortex intensity. The design constraints include blade geometry constraints and performance constraints. b. Perform parametric modeling on the initial blades and extract blade layout parameters as design variables; c. Determine the sampling points through experimental design, calculate the target value and constraint value of each sampling point, and then perform variance analysis to obtain the sensitivity information of the optimization target to the design variables; d. Use evolutionary algorithms for multi-objective optimization, set iteration stopping conditions, and obtain the Pareto frontier solution set; e. Select the individual containing the extreme value point of the objective function from the Pareto frontier solution set; f. Perform design variable analysis on the selected individuals to obtain design variable analysis results, and dynamically adjust the design space based on the design variable analysis results and sensitivity information; g. Repeat steps c to f until the iteration stop condition is met and output the final optimization solution.

2. The rotor blade optimization design method according to claim 1, characterized in that: The dust concentration in the driver's field of view is the number of dust particles per unit visual angle volume in the minimum critical area in front of the driver, where the minimum critical area is the visual range of pitch angle ±25° × yaw angle ±20°.

3. The rotor blade optimization design method according to claim 1, wherein: Tip vortex strength The calculation formula is: ,in is the strength coefficient, is the blade load factor, is the blade tip speed, is the average chord length of the blade.

4. The rotor blade optimization design method according to claim 1, wherein: In step f, the design space is dynamically adjusted to: ,in is the expansion coefficient of the design variable space, is an individual in the Pareto Front, represents the design variable index, Individual index.

5. The rotor blade optimization design method according to claim 1, wherein: The parametric modeling in step b adopts one of the CST, FFD or NURBS curve / surface methods.

6. The rotor blade optimization design method according to claim 1, wherein: The dust concentration in the driver's field of view is obtained through rotor CFD / sand particle DEM coupled numerical simulation.

7. The rotor blade optimization design method according to claim 1, wherein: The iteration stopping condition in step d is that the number of optimization iterations reaches a preset threshold or the change in the objective function is less than the set tolerance.

8. The rotor blade optimization design method according to claim 1, wherein: The experimental design method in step c adopts Latin hypercube sampling or uniform sampling.

9. The rotor blade optimization design method according to claim 1, wherein: Performance constraints include extreme values ​​of hover efficiency and forward lift-to-drag ratio as well as noise characteristics.

10. The rotor blade optimization design method according to claim 1, wherein: The design variables include blade twist distribution, chord length distribution, airfoil profile parameters, tip sweep angle, sweep position, anhedral angle and anhedral position.

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