Helicopter Rotor Airfoil Design for Dynamic Stall Suppression
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Solution Overview
Problem
Current rotor airfoil designs fail to effectively address dynamic stall caused by changing incoming flows and angles of attack, leading to reduced aerodynamic and control performance in helicopter rotors.
Innovation Solution
A method and system utilizing Latin Hypercube Sampling, Class Shape Transformation, Computational Fluid Dynamics, Kriging modeling, and Non-dominated Sorting Genetic Algorithm II to optimize rotor airfoil design, focusing on dynamic characteristics and suppressing leading edge vortices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional airfoil design methods are used that consider only static characteristics and constant velocity conditions, then the design process is simpler, but the aerodynamic performance deteriorates in actual rotor environments with changing incoming flow and angle of attack
Solution Approach 1:
The patent transitions from static airfoil design to dynamic design by incorporating changing incoming flow and angle of attack conditions. The method uses dynamic stall models and time-dependent CFD simulations to capture the transient aerodynamic behavior of rotor blades during actual operation, making the design adaptive to varying flight conditions.
Solution Approach 2:
The patent changes the design parameters from constant velocity and fixed angle of attack to time-varying parameters including changing incoming flow velocity, rotating blade angles, and dynamic stall conditions. This allows the airfoil design to optimize performance across the full range of operational conditions rather than a single static state.
2Reliability
If dynamic stall is reduced or avoided in rotor environment, then aerodynamic performance and control performance are improved, but this requires complex optimization methods that increase design complexity
Solution Approach 1:
The patent introduces surrogate models as intermediaries between the complex CFD simulations and the optimization algorithm. These surrogate models approximate the aerodynamic performance with reduced computational cost, enabling the NSGA-II multi-objective optimization to efficiently explore the design space without requiring exhaustive CFD calculations for every candidate solution.
Solution Approach 2:
The patent creates simplified copies or representations of the complex aerodynamic system through surrogate models that capture the essential dynamic stall behavior. These models replicate the key performance characteristics without the full computational complexity of detailed CFD simulations, allowing for rapid evaluation and optimization.
3Reliability
If multi-objective optimization using NSGA-II is applied to optimize lift, drag, and moment coefficients simultaneously, then the aerodynamic characteristics are significantly improved, but the computational cost and time increase
Solution Approach 1:
The patent performs preliminary actions by generating initial design candidates using Latin Hypercube Sampling (LHS) to efficiently explore the design space. This statistical sampling method provides a well-distributed initial population for the NSGA-II optimization, reducing the number of iterations needed to converge to optimal solutions compared to random sampling.
Solution Approach 2:
The patent implements feedback mechanisms where the surrogate models provide rapid performance evaluations that feed back into the NSGA-II optimization loop. This allows the optimization algorithm to quickly assess multiple design alternatives, learn from previous evaluations, and progressively converge to optimal solutions without requiring exhaustive computational resources.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The optimized airfoil design significantly improves lift, drag, and moment coefficients, preventing sharp drops and divergence, thereby enhancing dynamic aerodynamic characteristics and reducing dynamic stall.
Implementation Method 1
performing dynamic characteristic simulation on the airfoil according to the characterization equations of the upper and lower airfoil surfaces by using a computational fluid dynamics (CFD) method, to obtain a flow field characteristic of the airfoil
Implementation Method 2
establishing a mapping relationship between the sample point and the flow field characteristic by using a Kriging model
Implementation Method 3
randomly generating a sample point by using a Latin hypercube sampling (LHS) method
Data Source
AI summary
The present disclosure provides a method and system for determining a helicopter rotor airfoil. The method includes: randomly generating a sample point by using a Latin hypercube sampling (LHS) method (S1); determining characterization equations of upper and lower airfoil surfaces of an airfoil based on the airfoil sample point by using a class shape transformation (CST) method (S2); performing dynamic characteristic simulation on the airfoil according to the characterization equations of the upper and lower airfoil surfaces by using a computational fluid dynamics (CFD) method, to obtain a flow field characteristic of the airfoil (S3); establishing a mapping relationship between the sample point and the flow field characteristic by using a Kriging model, and training the mapping relationship by using a maximum likelihood estimation method and an expected improvement (EI) criterion, to obtain a trained mapping relationship (S4); determining an optimal sample point based on the trained mapping relationship by using Non-dominated Sorting Genetic Algorithm II (NSGA-II) (S5); and determining a rotor airfoil based on the optimal sample point (S6). The method performs optimized design on aerodynamic characteristics of the airfoil in a state with a changing incoming flow and a changing angle of attack, and can effectively alleviate dynamic stall in this state.