Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


30591.pdf (1.14 MB)

ETD Abstract Container

Abstract Header

A Decomposition-based Multidisciplinary Dynamic System Design Optimization Algorithm for Large-Scale Dynamic System Co-Design

Sherbaf Behtash, Mohammad
http://orcid.org/0000-0003-1435-5809
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535468984437623

Abstract Details

2018, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Dynamic systems incorporating physical plant and control systems should be designed in an integrated way to yield desirable and feasible solutions. Conventionally, these systems are designed in a sequential manner which often fails to produce system-level optimal solutions. However, combined physical and control system design (co-design) methods are able to manage the interactions between the physical artifact and the control part and consequently yield superior optimal solutions. Small-scale to moderate-scale dynamic systems can be addressed by using existing co-design methods effectively; nonetheless, these methods can be impractical and sometimes impossible to apply to large-scale dynamic systems which may hinder us from determining the optimal solution. This work addresses this issue by developing a new algorithm that combines decomposition-based optimization with a co-design method to optimize large-scale dynamic systems. Specifically, the new formulation applies a decomposition-based optimization strategy known as Analytical Target Cascading (ATC) to a co-design method known as Multidisciplinary Dynamic System Design Optimization (MDSDO) for the co-design of a representative large-scale dynamic system consisting of a plug-in hybrid-electric vehicle (PHEV) powertrain. Moreover, since many of dynamic systems may consist of several time-dependent linking variables among their subsystems, a new consistency measure for the management of such variables has also been proposed. To validate the accuracy of the presented method, the PHEV powertrain co-design problem has been studied with both simultaneous and ATC methods; results from the case studies indicate the new optimization formulation's ability in finding the system-level optimal solution.
Michael Alexander-Ramos, Ph.D. (Committee Chair)
Sam Anand, Ph.D. (Committee Member)
Manish Kumar, Ph.D. (Committee Member)
67 p.
Mechanical Engineering
Decomposition-based Design Optimization; Multidisciplinary Dynamic Systems; Co-Design; Large-Scale Dynamic Systems; Plug-in Hybrid Electric Vehicle

Recommended Citations

Citations

  • Sherbaf Behtash, M. (2018). A Decomposition-based Multidisciplinary Dynamic System Design Optimization Algorithm for Large-Scale Dynamic System Co-Design [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535468984437623

    APA Style (7th edition)

  • Sherbaf Behtash, Mohammad. A Decomposition-based Multidisciplinary Dynamic System Design Optimization Algorithm for Large-Scale Dynamic System Co-Design. 2018. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535468984437623.

    MLA Style (8th edition)

  • Sherbaf Behtash, Mohammad. "A Decomposition-based Multidisciplinary Dynamic System Design Optimization Algorithm for Large-Scale Dynamic System Co-Design." Master's thesis, University of Cincinnati, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535468984437623

    Chicago Manual of Style (17th edition)

ucin1535468984437623
506
© 2018, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.