Optimization of Low Reynolds Number Airfoils for Martian Rotor Applications Using an Evolutionary Algorithm

The Mars Helicopter (MH) will be flying on the NASA Mars 2020 rover mission scheduled to launch in July of 2020. Research is being performed at the Jet Propulsion Laboratory (JPL) and NASA Ames Research Center to extend the current capabilities and develop the Mars Science Helicopter (MSH) as the next possible step for Martian rotorcraft. The low atmospheric density and the relatively small-scale rotors result in very low chord-based Reynolds number flows over the rotor airfoils. The low Reynolds number regime results in rapid performance degradation for conventional airfoils due to laminar separation without reattachment. Unconventional airfoil shapes with sharp leading edges are explored and optimized for aerodynamic performance at representative Reynolds-Mach combinations for a concept rotor. Sharp leading edges initiate immediate flow separation, and the occurrence of large-scale vortex shedding is found to contribute to the relative performance increase of the optimized airfoils, compared to conventional airfoil shapes. The oscillations are shown to occur independent from laminar-turbulent transition and therefore result in sustainable performance at lower Reynolds numbers. Comparisons are presented to conventional airfoil shapes and peak lift-to-drag ratio increases between 17% and 41% are observed for similar section lift

不確実性下での設計に対するMulti-Fidelity不確定性定量化とSurrogate-Based Memeticアルゴリズム

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 土屋 武司, 東京大学教授 鈴木 真二, 東京大学教授 李家 賢一, 東京大学准教授 大山 聖, 東北大学准教授 下山 幸治University of Tokyo(東京大学

Aeronautical engineering: A continuing bibliography with indexes (supplement 296)

This bibliography lists 592 reports, articles, and other documents introduced into the NASA scientific and technical information system in Oct. 1993. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Aeronautical engineering: A continuing bibliography with indexes (supplement 301)

This bibliography lists 1291 reports, articles, and other documents introduced into the NASA scientific and technical information system in Feb. 1994. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Aeronautical engineering: A continuing bibliography with indexes (supplement 309)

This bibliography lists 212 reports, articles, and other documents introduced into the NASA scientific and technical information system in Oct. 1994. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics