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Propeller Design and Optimization Using a Robust Genetic Algorithm and a Computationally Efficient Solver.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Propeller Design and Optimization Using a Robust Genetic Algorithm and a Computationally Efficient Solver./
作者:	Pastor, Grady.
面頁冊數:	1 online resource (142 pages)
附註:	Source: Masters Abstracts International, Volume: 84-03.
Contained By:	Masters Abstracts International84-03.
標題:	Parents & parenting. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29288787click for full text (PQDT)
ISBN:	9798845454294

Propeller Design and Optimization Using a Robust Genetic Algorithm and a Computationally Efficient Solver.
Pastor, Grady.

Propeller Design and Optimization Using a Robust Genetic Algorithm and a Computationally Efficient Solver. - 1 online resource (142 pages)

Source: Masters Abstracts International, Volume: 84-03.

Thesis (M.Sc.)--Auburn University, 2022.

Includes bibliographical references

A computationally efficient and reliable propeller design tool has been constructed using an advanced real coded Genetic Algorithm (GA) and a mid-fidelity potential flow solver. The GA constructs a population of propeller geometries using a series of Bernstein Polynomials (BP) which have a total of 63 coefficients. This population of propeller geometries is then tested using a reliable and efficient solver. The best four members from the population are then obtained by means of a tournament style selection followed by a round-robin style tournament to determine the true maximums. A following population is then built using the 63 characteristics from the four most optimal members. The process of build, test, select, and build is carried out for several demes or subpopulations which provide the initial population for the main generational loop. After all the deme and main generations have been executed, the GA will provide a propeller that matches the desired thrust input for the specified operating conditions and diameter while maintaining high propulsive efficiencies due to the nature of the fitness function. This thesis describes the technical details of the optimizer, solver, and associated tooling, validation cases for the solver, and sample optimization results.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798845454294Subjects--Topical Terms:

3562799
Parents & parenting.
Index Terms--Genre/Form:

542853
Electronic books.

Propeller Design and Optimization Using a Robust Genetic Algorithm and a Computationally Efficient Solver.
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