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An optimization approach to the desi...

DiIenna, Vito Alfred.

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An optimization approach to the design of a Formula-style racecar through artificial neural network surrogate models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An optimization approach to the design of a Formula-style racecar through artificial neural network surrogate models./
作者:	DiIenna, Vito Alfred.
面頁冊數:	189 p.
附註:	Source: Masters Abstracts International, Volume: 44-05, page: 2429.
Contained By:	Masters Abstracts International44-05.
標題:	Engineering, Automotive. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1434094
ISBN:	9780542635359

An optimization approach to the design of a Formula-style racecar through artificial neural network surrogate models.
DiIenna, Vito Alfred.

An optimization approach to the design of a Formula-style racecar through artificial neural network surrogate models. - 189 p.

Source: Masters Abstracts International, Volume: 44-05, page: 2429.

Thesis (M.E.)--The Cooper Union for the Advancement of Science and Art, 2006.

Simulation-based optimization is the direction being taken by modern automotive engineering design. Simulations reduce the need to build physical prototypes in large numbers, which had traditionally been the source of great time, material, and cost expense for large corporations. This thesis presents a design methodology that links a high-fidelity vehicle simulation package, called CarSim, within a numerical optimization routine in order to automate the vehicle design process. The particular problem addressed was that of minimizing the track time of a Formula-style racecar, subject to fuel consumption, acceleratory, and tire slip angle constraints. The variables examined were transmission upshift schedule, suspension spring rate, and final gear ratio.

ISBN: 9780542635359Subjects--Topical Terms:

1018477
Engineering, Automotive.

An optimization approach to the design of a Formula-style racecar through artificial neural network surrogate models.
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520 $a CarSim utilizes the fundamental equations of vehicle dynamics to calculate vehicle performance. However, a built-in and systematic optimization scheme does not currently exist within the CarSim framework. To address this deficiency, an extensive set of data was produced with CarSim. These data were then used to train Artificial Neural Network models for the vehicle's track time, fuel consumption per lap, minimum and maximum lateral acceleration, and minimum and maximum tire slip angles. (Abstract shortened by UMI.)
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