Computational Steering for Trade Space Exploration During Complex Systems Design

该拨款为研究复杂系统设计期间贸易空间探索的新范例提供资金，该范例利用多维可视化和面向设计师的计算导向技术。本研究将形式化贸易空间吸引因子的新概念，设计师在寻找好的设计时隐含地使用它。将制定在图形设计界面中实施吸引器的战略，并将利用与几个工业伙伴共同开发的陆地、海洋、空中和空间应用程序，对复杂的系统设计问题进行拟议方法的测试。这项研究将由宾夕法尼亚州立大学的一个多学科团队进行，该团队包括来自航空航天工程、机械工程和工业工程的教师以及宾夕法尼亚州立大学应用研究实验室的研究人员。工业伙伴关系已经与通用汽车、通用电气和航空航天公司建立，以开发研究的案例研究，并与Phoenix Integration建立伙伴关系，后者将在商业环境中测试软件开发的beta版本。这项研究的结果将通过利用最近在计算能力和速度方面的进步来推进复杂系统设计的方法。通过开发新的贸易空间搜索策略，该研究将为工程设计和多目标决策领域做出重大贡献，该策略弥合了当前半自动优化技术和临时设计空间探索技术之间的差距。技术转让将与我们的工业伙伴一起进行，并通过商业环境中的软件测试进行。这项研究将确定新的培训程序，以教育设计师了解多维可视化和贸易空间探索，案例研究将通过互联网向社区传播。最后，REU补品将被要求让本科生参与研究，并参与宾夕法尼亚州立大学的两个增强多样性的夏季研究项目，这将扩大代表性不足群体对研究的参与。

This grant provides funding to investigate a new paradigm for trade space exploration during complex systems design that utilizes multidimensional visualization and designer-oriented computational steering techniques. The research will formalize the novel idea of trade space attractors, which designers implicitly use when searching for good designs. Strategies for implementing attractors in a graphical design interface will be developed, and the proposed methods will be tested on complex system design problems using land, sea, air, and space applications developed in conjunction with several industrial partners. The research will be conducted by a multidisciplinary team at the Pennsylvania State University that includes faculty from Aerospace Engineering, Mechanical Engineering, and Industrial Engineering and researchers at the Applied Research Laboratory at Penn State. Industrial partnerships have been established with General Motors, General Electric, and Aerospace Corporation to develop case studies for the research, and with Phoenix Integration, who will test a beta-version of software developments in a commercial environment.The results of this research will advance methods for complex systems design by taking advantage of recent advances in computing power and speed. The research will make significant contributions to the field of engineering design and multi-objective decision-making by developing new trade space search strategies that bridge the gap between current techniques for semi-automated optimization and ad hoc design space exploration. Technology transfer will occur with our industrial partners and through software testing in a commercial setting. The research will identify new training procedures for educating designers about multi-dimensional visualization and trade space exploration, and case studies will be disseminated over the Internet to the community. Finally, REU Supplements will be requested to engage undergraduates in the research and participation in two diversity-enhancing summer research programs at Penn State will broaden the participation of under-represented groups in the research.