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William A. Crossley
Researcher at Purdue University
Publications - 186
Citations - 2609
William A. Crossley is an academic researcher from Purdue University. The author has contributed to research in topics: Genetic algorithm & Optimization problem. The author has an hindex of 25, co-authored 177 publications receiving 2408 citations. Previous affiliations of William A. Crossley include Arizona State University.
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Journal ArticleDOI
Aerodynamic and Aeroacoustic Optimization of Rotorcraft Airfoils via a Parallel Genetic Algorithm
TL;DR: A parallel genetic algorithm (GA) methodology was developed to generate a family of two-dimensional airfoil designs that address rotorcraft aerodynamic and aeroacoustic concerns and exhibited favorable performance when compared with typical rotorcraft airfoils under identical design conditions using the same analysis routines.
Aerodynamic and aeroacoustic optimization of rotorcraft airfoils via a parallel genetic algorithm
TL;DR: In this article, a parallel GA methodology was developed to generate a family of two-dimensional airfoil designs that address rotorcraft aerodynamic and aero-acoustic concerns.
Journal ArticleDOI
System-of-Systems Inspired Aircraft Sizing and Airline Resource Allocation via Decomposition
TL;DR: In this article, a mixed-integer nonlinear branch-and-bound problem was used to solve the problem of determining the appropriate mix of both existing and yet-to-be-designed systems.
Proceedings ArticleDOI
Morphing airfoil shape change optimization with minimum actuator energy as an objective
TL;DR: In this article, the authors explore a process to link analytical models and optimization tools with design methods to create energy efficient, lightweight wing/structure/actuator combinations for morphing aircraft wings.
Book ChapterDOI
Empirically-Derived Population Size and Mutation Rate Guidelines for a Genetic Algorithm with Uniform Crossover
TL;DR: The results of an empirical study are presented to determine guidelines to assist in choosing appropriate population sizes and mutation rates when using the uniform crossover by examining several parameter combinations on four mathematical functions and one engineering design problem.