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A Dimensionality-Reduced First Order Method for Industrial Optimization: A Case Study in Renewable Energy Technologies

[+] Author Affiliations
Shima Hajimirza

California State Polytechnic University, Pomona, CA

Paper No. ES2015-49058, pp. V001T10A001; 8 pages
doi:10.1115/ES2015-49058
From:
  • ASME 2015 9th International Conference on Energy Sustainability collocated with the ASME 2015 Power Conference, the ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2015 Nuclear Forum
  • Volume 1: Advances in Solar Buildings and Conservation; Climate Control and the Environment; Alternate Fuels and Infrastructure; ARPA-E; Combined Energy Cycles, CHP, CCHP, and Smart Grids; Concentrating Solar Power; Economic, Environmental, and Policy Aspects of Alternate Energy; Geothermal Energy, Harvesting, Ocean Energy and Other Emerging Technologies; Hydrogen Energy Technologies; Low/Zero Emission Power Plants and Carbon Sequestration; Micro and Nano Technology Applications and Materials
  • San Diego, California, USA, June 28–July 2, 2015
  • Conference Sponsors: Advanced Energy Systems Division, Solar Energy Division
  • ISBN: 978-0-7918-5684-0
  • Copyright © 2015 by ASME

abstract

We propose and study a dimensionality-reduced first order method for solving complex optimization problems of high-dimensional search space. We demonstrate that the proposed method is very efficient in design problems where the computational bottleneck is mostly due to the time-consuming nature of the forward problem in contrast to the complexity of the function behavior in the search space or other computational overheads. Many industrial problems are of this nature including design problems based on back testing or simulation of an evolutionary equation or a dynamic system in time, frequency or other (hybrid) domains, such as Electromagnetic, Quantum equations, Navier-Stokes PDEs, etc. The premise of efficiency improvement in the proposed framework is a better modelling and utilization of the complexity distribution among the components of an inverse design problem. As a particular case study, we list some of the existing optimization problems related to energy production, distribution and utilization at the industrial level. We briefly overview the different complexity components of these problems at a high level, and make suggestions as what industrial problems can be facilitated through the proposed framework.

Copyright © 2015 by ASME

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