Rotor blade structure optimization design method
By using the internal structure of the rotor blade as a design variable and combining dynamic analysis and surrogate models to optimize the rotor blade structure, the problems of large errors and cumbersome processes in traditional design methods are solved. This achieves efficient and accurate optimization of the internal structure of the rotor blade, improving the reliability and efficiency of the design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-09
- Publication Date
- 2026-03-27
AI Technical Summary
Traditional rotor blade optimization design methods cannot accurately consider the internal structure of the blade, resulting in large errors in dynamic analysis and the optimal solution cannot be directly applied to specific structures. The design process is cumbersome and may deviate from the optimal solution.
Using the internal structure of the blade as the design variable, and combining dynamic analysis and surrogate model, the internal structure of the blade is directly optimized through parametric modeling and composite beam section analysis. A surrogate model is established for iterative optimization to obtain the optimal configuration of the internal structure of the blade.
It achieves closed-loop automatic optimization from blade internal structure to rotor dynamics performance, improving the reliability and efficiency of optimization results. The design variables are clearly defined and manufacturable, avoiding the inverse design difficulties of traditional equivalent stiffness optimization, accurately capturing the influence of complex structures, and shortening the design cycle.
Smart Images
Figure CN121744477A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of rotorcraft design technology, and particularly relates to a method for optimizing rotor blade structure design. Background Technology
[0002] The rotor system is the core component of a helicopter, and its dynamic performance directly determines the helicopter's vibration, noise level, and fatigue life. As a key component of the rotor, the structural design of the rotor blades is crucial. Traditional rotor blade optimization design typically focuses on aerodynamic parameters, such as the blade's chord length distribution and twist angle distribution. For structural optimization, a simplified equivalent beam model is often used, equating the complex composite blade profile to a beam with uniform stiffness (such as bending stiffness and torsional stiffness) and mass distribution. Design variables are usually these equivalent stiffness values.
[0003] However, this traditional method has some drawbacks: First, the actual blade structure consists of complex components such as skin, spars, ribs, and foam padding, and its stiffness and mass distribution are highly non-uniform. The equivalent beam model cannot accurately reflect the true cross-sectional characteristics, leading to errors in the dynamic analysis. Second, optimizing using equivalent stiffness as a variable does not directly correspond to the specific, manufacturable internal structural dimensions of the blade. Engineers still need to rely on experience to "re-design" the optimal stiffness value into a specific structural scheme, a process that is not only tedious but may also deviate from the optimal solution.
[0004] Therefore, there is an urgent need for an innovative method that can accurately consider the internal structure of the blade and achieve rapid optimization design. Summary of the Invention
[0005] To address the problem that existing technologies involve "reverse designing" optimal stiffness values into specific structural schemes, a process that is cumbersome and may deviate from the optimal solution, this invention provides a rotor blade structure optimization design method. This method directly uses the internal structure of the blade as design variables, combining dynamic analysis with a surrogate model to achieve efficient optimization of rotor dynamic performance. The technical solution is as follows: Firstly, a method for optimizing rotor blade structure design is provided. The internal structure of the blade is used as the design variable. The internal structure of the blade is analyzed and calculated to obtain the blade profile characteristics. The blade profile characteristics are substituted into the dynamic model for response calculation. A surrogate model is established based on the design variables and the response calculation results. The surrogate model is optimized and iterated to obtain the optimal configuration scheme of the internal structure of the blade.
[0006] Optionally, design variables include skin ply thickness, skin ply angle, and the position and dimensions of the main beam, front edge cladding, counterweight, stiffeners, and rear edge strip.
[0007] Optionally, a parameterized two-dimensional cross-sectional finite element model is generated based on the design variables, and composite material beam section analysis software is used to calculate the model to obtain the blade cross-sectional characteristics.
[0008] Optionally, the blade profile characteristics can be substituted into the dynamic model for response calculation, specifically: The blade profile characteristics output by the composite beam section analysis software are interpolated along the spanwise direction. The interpolated results are then input into the rotor dynamics model. Under a given flight condition (such as forward flight), the dynamic response is calculated to obtain the n / rev vibration load at the hub center, where n is the number of rotor blades and rev is the order.
[0009] Optionally, the dynamic model is based on multibody dynamics or moderately deformable beam theory, which can accurately simulate the coupling effect of the large-scale motion and elastic deformation of the blade.
[0010] Optionally, a surrogate model is established based on the design variables and response calculation results, specifically: The Latin hypercube sampling experimental design method is adopted to generate a certain number of sample points within the constraints of the design variables; For each sample point (i.e. a combination of design variables), determine the blade profile characteristics, substitute the blade profile characteristics into the dynamic model to calculate the response, and obtain the corresponding response value; By using design variables and response calculation results, a surrogate model is trained that can approximately predict the dynamic response corresponding to any combination of structural variables with extremely low computational cost.
[0011] Optionally, the proxy model can be optimized and iterated, specifically as follows: With minimizing the n / rev vibration load at the center of the blade hub as the objective function and blade frequency configuration as the constraint, the surrogate model is globally optimized, and the optimal combination of blade internal structural parameters is finally output, thus obtaining the optimal blade internal structural configuration scheme.
[0012] Optionally, the blade profile characteristics include the coupling stiffness matrix and the mass matrix. The beneficial effects of this invention are at least as follows: This method achieves closed-loop automatic optimization from blade internal structure to rotor dynamics performance, opening up a path for integrated design of "materials-structure-performance." Compared with traditional rotor dynamics optimization methods, it differs fundamentally in the granularity of optimization variables, the precision of analysis tools, and the sophistication of optimization strategies. The main differences are as follows: 1) Design variables are direct and have clear physical meaning: directly optimize manufacturable parameters such as skin thickness and rib position, avoiding the "reverse design" difficulties brought about by traditional equivalent stiffness optimization, and the design results can be directly applied to engineering practice; 2) High analysis accuracy: The composite beam section analysis software can accurately capture the influence of details such as composite ply and complex geometry on the section stiffness / mass distribution, providing high-precision input for subsequent dynamic analysis and fundamentally improving the reliability of optimization results; 3) High optimization efficiency: By introducing a surrogate model, the expensive blade profile characteristic calculation and dynamic coupling analysis process is transformed into a rapid meta-model evaluation, breaking through the computational bottleneck and making detailed structural optimization based on a high-precision model possible within the engineering timescale. Attached Figure Description
[0013] Figure 1 This is a flowchart of a rotor blade structure optimization design method according to the present invention. Detailed Implementation
[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0015] The features and illustrative embodiments of various aspects of the present invention will now be described in detail. Numerous specific details are set forth in the following detailed description to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without requiring some of these specific details. The following description of embodiments is merely intended to provide a better understanding of the invention by illustrating examples of the invention. The invention is by no means limited to any specific setups and methods set forth below, but covers any improvements, substitutions, and modifications to structures, methods, and devices without departing from the spirit of the invention. Well-known structures and techniques are not shown in the drawings and the following description to avoid unnecessarily obscuring the invention.
[0016] It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other, and the various embodiments can be referenced and cited from each other.
[0017] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.
[0018] This invention provides a method for optimizing the design of rotor blade structures, see [link to relevant documentation]. Figure 1 The method mainly includes the following steps: (1) Based on the initial structural design of the blade, select n design profiles along the blade span to determine specific design variable parameters. The design variable parameters may include skin ply thickness, skin ply angle, and the positions of main beam, leading edge cladding, counterweight, stiffener, trailing edge strip, as well as the range of design variable variation. (2) Parametric modeling of the blade structure was performed, including the definition of blade airfoil coordinate points, profile baseline, material, ply, and profile structure. The parametric blade model established the relationship between the structural design scheme and the geometric / physical model features.
[0019] (3) Based on the parametric modeling file in step 2, the composite beam section analysis software is called to calculate the blade profile characteristics. The calculation results include key characteristic parameters such as the blade profile coupling stiffness matrix and mass matrix. (4) Establish a rotor dynamics model based on multibody dynamics or medium deformation beam theory, interpolate the blade profile characteristics calculation results along the blade span, input the interpolated results into the rotor dynamics model, and perform initial dynamic response calculation under a given flight state (such as forward flight) to obtain the n / rev vibration load at the hub center, where n is the number of rotor blades and rev is the order. (5) Determine the optimization design objective and nonlinear constraints. Design objective: Minimize the vibration load at the hub center; Constraint: The low-order wavy, oscillating, and torsional modes of the blades meet the frequency interval requirements. (6) The initial sample input is obtained by using the Latin hypersquare sampling method, and (2) to (4) are repeated to solve for the design objective and nonlinear constraint values; (7) Train the surrogate model using the sample database. The surrogate model adopts the Kriging model. (8) After establishing the initial proxy model, global optimization is performed by adding points criterion and non-gradient optimization algorithm to obtain the potential optimal solution, and the solution is substituted into the rotor dynamics model to calculate the design target and nonlinear constraint value, and added to the training sample library; (9) Repeat (5) to (7) until the number of new samples reaches Nmax; (10) Select feasible solutions from Nmax potential optimal solutions, and the optimal internal structure configuration scheme of the blade.
[0020] For example, one embodiment of the present invention provides a method for optimizing the design of a rotor blade structure. The specific implementation process can be as follows: S1. Define Design Variables: For a typical "C"-shaped beam + skin + rib structure, three key design variables are selected: skin thickness t_skin (range 1.0-2.0mm), distance from the leading edge of the beam web to the leading edge d_spar (range 15%-25% of chord length), and average rib spacing d_rib (range 10%-20% of chord length). These variables determine the bending, torsional stiffness, and mass distribution of the cross-section.
[0021] S2. Parametric Modeling and Blade Profile Characteristic Calculation: Using a Python script, the corresponding finite element model of the blade profile is automatically generated in Abaqus or ANSYS based on the input (t_skin, d_spar, d_rib) values. Then, composite beam section analysis software is called to read the model file, perform calculations, and output the stiffness matrix and mass matrix containing all coupling terms.
[0022] S3. Rotor Dynamics Analysis: Input the VABS calculation results along different blade spanwise profiles (using the same variables or varying them according to a pattern) into the rotor multibody dynamics analysis model. Set the forward flight state (e.g., speed 150km / h), perform transient dynamics simulation, calculate the hub load after stabilization, and extract the hub vertical load component Fz`5 / rev` as the target response value `Fz_5rev`.
[0023] S4. Constructing the surrogate model: Using the optimal Latin hypercube sampling method, 50 initial sample points are generated in the three-dimensional design variable space. S2 and S3 are performed on each sample point to obtain 50 sets of data (t_skin, d_spar, d_rib, Fz_5rev). A kriging model with local estimation characteristics is selected, and these 50 sets of data are used to train the surrogate model. This model can quickly predict the Fz_5rev value corresponding to any combination of (t_skin, d_spar, d_rib).
[0024] S5. Optimization Iteration: With the objective of minimizing Fz_5rev and the constraint that the low-order flapping, yaw, and torsional modes of the propeller meet the frequency spacing requirements, the constructed kriging surrogate model was optimized iteratively using the addition criterion and genetic algorithm within the constraints of the design variables. The genetic algorithm population size was set to 40, and the iteration was performed for 50 generations. The optimization results gave a set of optimal structural variable combinations: t_skin=1.3mm, d_spar=21%c, d_rib=16%c. Fz_5rev was reduced by 18.3% compared to the baseline model, which met the design requirements.
[0025] To verify the results, this optimal solution can be substituted into S2 and S3 for real simulation verification to confirm the prediction accuracy and optimization effect of the surrogate model. This embodiment demonstrates that the method can efficiently and accurately find the optimal internal blade structure configuration for reducing rotor vibration loads.
[0026] This invention provides a method for optimizing the design of rotor blade structures, solving the problems of repeated modifications to blade structures and long design cycles in traditional blade structure design processes. This method enables the rapid and effective generation of blade structure configurations suitable for engineering design.
[0027] The above description merely illustrates embodiments of the present invention and is quite specific and detailed; however, it should not be construed as limiting the scope of the patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Furthermore, any parts of the present invention not described in detail are conventional techniques.
Claims
1. A method of optimizing the design of a rotor blade structure, characterized by, The blade internal structure is taken as a design variable, the blade internal structure is analyzed and calculated, the blade profile characteristics are obtained, the blade profile characteristics are substituted into a dynamic model for response calculation, a surrogate model is established based on the design variable and the response calculation result, and optimization iteration is performed on the surrogate model to obtain an optimal blade internal structure configuration scheme.
2. The method of claim 1, wherein, The design variables include skin ply thickness, skin ply angle, and beam, leading edge iron, weight, stiffener, and trailing edge strip position and size.
3. The method of claim 1, wherein, A parameterized two-dimensional profile finite element model is generated based on the design variables, a composite beam section analysis software is called to calculate the model, and the blade profile characteristics are obtained.
4. The method of claim 1, wherein, The blade profile characteristics are substituted into the dynamic model for response calculation, specifically: The blade profile characteristics output by the composite beam section analysis software are interpolated along the spanwise direction, the interpolated results are taken as input into the rotor dynamics model, and dynamic response calculation is performed under a given flight state to obtain the n / rev vibration load at the hub center, where n is the number of rotor blades and rev is the order.
5. The method of claim 4, wherein, The dynamic model is established based on multi-body dynamics or moderate deformation beam theory, which can accurately simulate the coupling effect of large-scale motion and elastic deformation of the blade.
6. The method of claim 1, wherein, A surrogate model is established based on the design variable and the response calculation result, specifically: The Latin hypercube sampling experimental design method is used to generate a certain number of sample points within the constraint range of the design variable; For each sample point, the blade profile characteristics are determined, the blade profile characteristics are substituted into the dynamic model for response calculation, and the corresponding response value is obtained; A surrogate model is trained using the design variable and the response calculation result, which can approximately predict the corresponding dynamic response of any structure variable combination with extremely low calculation cost.
7. The method of claim 1, wherein, Optimization iteration is performed on the surrogate model, specifically: The minimization of the n / rev vibration load at the hub center is taken as the objective function, the blade frequency configuration is taken as the constraint, the surrogate model is globally optimized, and finally the optimal blade internal structure parameter combination is output, and then the optimal blade internal structure configuration scheme is obtained.
8. The method of claim 3, wherein, The blade profile characteristics include the coupled stiffness matrix and the mass matrix.