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Evolutionary Design Optimization for Guided Weapon Concepts Modeling and Simulation

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8651-19-P-0011
Agency Tracking Number: F182-089-0885
Amount: $149,949.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF182-089
Solicitation Number: 2018.2
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2018-10-16
Award End Date (Contract End Date): 2019-10-16
Small Business Information
13766 Hawthorne Blvd.
Hawthorne, CA 90250
United States
DUNS: 028281020
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Amanda Lampton
 (310) 679-2281
 alampton@systemstech.com
Business Contact
 Peter Gondek
Phone: (310) 679-2281
Email: pgondek@systemstech.com
Research Institution
N/A
Abstract

When the United States enters conflicts throughout the world, guided weapons that can eliminate threats while minimizing collateral damage are a necessity for all branches of the DoD. Each weapon is designed for a specific mission, though there is no guarantee that the design is either optimized for that mission or robust to mission changes. The scope of the design parameter space and the ever-evolving missions requiring guided weapons make accomplishing one or both tasks extremely challenging for current design practices. As computational power continues to increase, novel optimization methods that efficiently navigate such highly complex, large, multi-disciplinary design parameter spaces and fitness calculation become a more feasible solution to this problem. Evolutionary algorithms mimic natures approach to developing complex systems through generations of design variations and provide a novel means to optimize design. To this end, the team of Systems Technology, Inc. (STI) and the University of Tennessee Space Institute (UTSI) proposes to develop the Design Algorithm Robust Weapon Interface (DARWIN), a design interface that provides a robust, evolutionary multi-objective/multi-disciplinary optimizer, a design and visualization interface, and an architecture that utilizes modeling and simulation capabilities to evaluate the performance and improve the design from one generation to the next.

* Information listed above is at the time of submission. *

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