Preprint / Version 1

Optimization Algorithms for Aircraft Preliminary Sizing and Cabin Design




AERO, aircraft, aviation, cabin, consumption, costs, design, efficiency, emissions, fuel, Gaussian Random Walk, optimization, Orthogonal Steepest Descent, OSD, Random Monte Carlo, Simulated Annealing


As new aircraft are being designed, optimization of the design parameters becomes necessary to decrease fuel costs and emissions and maximize profits. As opposed to trial-and-error where a design may go through several rounds of testing to improve efficiency, optimization algorithms can save time and effort when implemented properly. Optimization algorithms are of two types: stochastic and deterministic. The stochastic methods used are: Random Monte Carlo, Gaussian Random Walk, and Simulated Annealing. The deterministic method examined is the method of Orthogonal Steepest Descent (OSD). Orthogonal Steepest Descent seems to be the fastest method which is also quite accurate. The next fastest method is Simulated Annealing. The Random Monte Carlo method is less precise by nature, and experiences a greater error and time elapsed because it requires many more iterations to arrive at reasonably small error.


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