Restricted Research - Award List, Note/Discussion Page

Fiscal Year: 2018

1347  The University of Texas at Arlington  (74852)

Principal Investigator: Ramtin Madani

Total Amount of Contract, Award, or Gift (Annual before 2011): $ 300,000

Exceeds $250,000 (Is it flagged?): Yes

Start and End Dates: 3/1/18 - 2/28/21

Restricted Research: YES

Academic Discipline: Department of Electrical Engineering

Department, Center, School, or Institute: College of Engineering

Title of Contract, Award, or Gift: High-fidelity Optimization for Next-generation Shipboard Power Systems

Name of Granting or Contracting Agency/Entity: Office of Naval Research (ONR)
CFDA Link: DOD
12.300

Program Title: N/A
CFDA Linked: Basic and Applied Scientific Research

Note:

Vision and Significance: Massive penetration of power electronic devices, peculiar aspects of pulsed loads, laser weapons, and electromagnetic rail guns, hostile and uncertain operational environments, and mission- and safety-critical nature of shipboard power systems have introduced major computational challenges for high-fidelity planning, optimization, and decision making. This challenge is recognized by the U.S. Navy as highlighted in 2015 Naval S and T strategy plan and 2015 Naval Power and Energy Systems Technology Development Roadmap. In response, we seek to create scalable and distributed optimization methods to address long standing naval problems including survivability assessment, network reconfiguration, early-stage design evaluation, and fault detection and monitoring. Enhanced optimization methods can boost the efficiency of naval power systems and facilitate resilient operation in hostile and uncertain environments, as highlighted in 2015 Naval Power and Energy Systems Technology Development Roadmap. The proposed research contributes to Ship Systems and Engineering Research thrust, and Naval Energy Resiliency and Sustainability trust in ONR Code 33. Synergistically, it also pertains to the Science of Autonomy thrust in ONR Code 35, and Mathematics, Computers and Information Sciences thrust in ONR Code 31. Technical Contributions: First, we offer a rigorous mathematical formulation for a variety of shipboard optimization problems based on comprehensive models of hybrid power networks. Next, a family of distributed and scalable computational methods solve resulting optimization problems. We then leverage distributed computational platforms to achieve orders-of-magnitude improvements in problems scalability. We will tackle a broad class of computationally hard shipboard power system problems, and create distributed control and optimization methods that are scalable, can be implemented in real-time, and are robust to parametric, topological, and dynamical uncertainties. The direction to be pursued advances the area of optimization theory and introduces a variety of mathematical tools to the power systems literature for the study of navy-centric energy problems. Our methodology will be manifested on long-standing navy problems including survivability assessment, early-stage design evaluation, and network reconfiguration. We leverage modern platforms for distributed computation, to achieve orders-of-magnitude improvements in problems scalability, which allows for the design of perfect planning and real-time strategies based on accurate physical models.

Discussion: No discussion notes

 

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