Propeller Blade Optimization via Multidisciplinary Design Routine
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Solution Overview
Problem
Current propeller design methods are inefficient and fail to optimize across multiple parameters, particularly in minimizing acoustic noise while maximizing aerodynamic performance, due to the sequential disciplinary optimization approach and lack of integrated system design.
Innovation Solution
The implementation of multidisciplinary optimization techniques that unify aerodynamic, structural, electrical, and acoustic analyses in a single optimization routine to design propeller blades, enabling simultaneous optimization of multiple disciplines and rapid generation of CAD models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential disciplinary optimization is used, then each discipline can be optimized in turn, but the overall design efficiency is low and convergence is slow
Solution Approach 1:
The patent merges multiple disciplinary optimization processes (aerodynamic, structural, acoustic, electrical) into a single unified multidisciplinary optimization routine. This allows all disciplines to be optimized simultaneously rather than sequentially, improving both design efficiency and convergence speed while maintaining optimization completeness across all parameters.
Solution Approach 2:
The optimization system is designed to handle multiple disciplines and objectives within a single universal framework. The system can simultaneously optimize for aerodynamic performance, structural integrity, acoustic noise reduction, and electrical power consumption, making it applicable to complex propeller designs that require balanced optimization across multiple competing objectives.
2Productivity
If aerodynamic performance is maximized, then propeller efficiency improves, but acoustic noise signature increases
Solution Approach 1:
The patent employs parameter changes by simultaneously adjusting multiple design parameters (blade geometry, twist distribution, chord length, airfoil selection) to achieve a balance between aerodynamic performance and acoustic noise. The multidisciplinary optimization routine explores the design space to find configurations that maintain high aerodynamic efficiency while minimizing noise-generating features.
Solution Approach 2:
The optimization applies local quality by allowing different regions of the propeller blade to have different characteristics optimized for their specific functions. For example, the root region may be optimized for structural integrity while the tip region is optimized for aerodynamic efficiency and noise reduction, achieving overall system optimization rather than uniform design compromises.
3Reliability
If multiple design parameters are optimized simultaneously, then design robustness improves, but computational complexity increases
Solution Approach 1:
The patent segments the complex multidisciplinary optimization problem into manageable disciplinary sub-problems (aerodynamic analysis, structural analysis, acoustic analysis, electrical analysis) that are solved within an integrated framework. Each discipline can be modeled using established methods, and the segmentation allows the system to handle multiple parameters simultaneously without becoming computationally intractable.
4Device complexity
If traditional propeller design methods are used, then design simplicity is maintained, but design space exploration is limited
Solution Approach 1:
The patent introduces dynamics by implementing an iterative optimization process that dynamically adjusts design parameters based on performance feedback from multiple disciplines. The system can adaptively explore the design space, moving from initial configurations to optimized solutions through repeated cycles of analysis and parameter adjustment, enabling comprehensive design space exploration while maintaining manageable complexity through systematic procedures.
Data Source
AI summary
Processes for optimizing the geometry of a blade for use in a propeller are disclosed. In one exemplary process, an optimization routine that generates new blade geometries based on structural parameters and calculates performance parameters of each blade geometry, including aerodynamic performance parameters, farfield acoustic parameters, and/or electrical power requirements to operate a propeller having the blade geometry, is performed. The optimization routine receives design parameters and weightings from a user and can use one or more surrogate algorithms to map a design space of the weighted values of the design parameters to find their local minima. The optimization routine then determines an optimized blade geometry using a gradient-based algorithm to generate new blade geometries to explore the minima until the weighted values of the design parameters converge at an optimized blade geometry representing the global minima of the design space.