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Design of air cushion vehicles using artificial intelligence: Expert system and genetic algorithm.

机译：使用人工智能的气垫车设计：专家系统和遗传算法。

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摘要

In the thesis, initial design of an Air Cushion Vehicle (ACV) is performed with the expert system and its skirt system is further optimized with the genetic algorithm. Both the expert system and genetic algorithm are advanced computerized design techniques of artificial intelligence. Those techniues are specifically developed for the ACVs with programming codes in this thesis. Then the main objective is to show the successful implementation of those techniques in the design of ACVs.;The thesis work is divided into two parts. In the first part, the general configuration of ACVs, including the overall dimensions, weight distribution, parametric properties, and several subsystems, is studied and designed by the expert system as an initial design phase. In the second part of the thesis, the skirt system of ACVs is further optimized. In particular, the properties of the bag and finger skirt are optimized for improved ride quality and stability by the genetic algorithm. For the validation of these two artificial intelligence techniques, the CCG (Canadian Coast Guard) 37 ton Waban-Aki and U.S. Navy's 150 ton LCAC (Landing Craft Air Cushion) are selected for the tests. The results of the tests proved that the expert system was successfully implemented and was a powerful tool for the initial design of ACVs. Furthermore, the genetic algorithm optimized the skirt system with significantly improved ride quality and stability. It was also shown that the skirt mass was an important design factor in the heave response of the bag and finger skirt. Hence, this thesis work opened the new possibility of designing ACVs with artificial intelligence techniques.
机译：本文采用专家系统对气垫车进行了初步设计，并通过遗传算法对其裙边系统进行了进一步优化。专家系统和遗传算法都是先进的人工智能计算机化设计技术。这些技术是本文针对带有编程代码的ACV专门开发的。然后主要目的是展示这些技术在ACV设计中的成功实施。论文工作分为两部分。在第一部分中，作为初始设计阶段，专家系统研究和设计了ACV的常规配置，包括总体尺寸，重量分布，参数特性和几个子系统。在论文的第二部分，ACV的裙边系统得到了进一步的优化。特别是，通过遗传算法优化了包和指裙的属性，以提高乘坐质量和稳定性。为了验证这两种人工智能技术，选择了CCT（加拿大海岸警卫队）37吨Waban-Aki和美国海军的150吨LCAC（登陆艇气垫）进行测试。测试结果证明该专家系统已成功实施，并且是ACV初始设计的强大工具。此外，遗传算法优化了裙板系统，从而显着提高了骑行质量和稳定性。还表明，裙质量是包和手指裙的升沉响应的重要设计因素。因此，本文工作为利用人工智能技术设计交流电提供了新的可能性。

著录项

  • 作者

    Jung, Tae-Cheol.;

  • 作者单位

    Ryerson University (Canada).;

  • 授予单位 Ryerson University (Canada).;
  • 学科 Engineering Mechanical.;Artificial Intelligence.
  • 学位 M.Sc.A.
  • 年度 2003
  • 页码 144 p.
  • 总页数 144
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;人工智能理论;
  • 关键词

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