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PMOR Suite for Enabling Real-Time Parametric Aeroservoelastic Simulations

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9302-18-C-0016
Agency Tracking Number: F161-026-0333
Amount: $749,731.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: AF161-026
Solicitation Number: 16.1
Timeline
Solicitation Year: 2016
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-26
Award End Date (Contract End Date): 2020-12-26
Small Business Information
566 Glenbrook Drive
Palo Alto, CA 94306-0000
United States
DUNS: 172390481
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Goeric Daeninck
 Senior Research Scientist
 (650) 530-2435
 gdaeninck@cmsoftinc.com
Business Contact
 Frankie Farhat
Phone: (650) 898-9585
Email: ffarhat@cmsoftinc.com
Research Institution
N/A
Abstract

The main objectives of this SBIR Phase II effort are to implement, validate, and deploy at the Air Force Test Center at the Edwards Air Force Base an innovative computational technology based on adaptive, Projection-based Reduced-Order Models (PROMs) that can be operable as: a predictive tool to estimate aeroelastic/aeroservoelastic stability and aerodynamic loads prior to testing; a real-time system that takes inputs from a given live flight test and outputs a corresponding estimate of the aeroelastic/aeroservoelastic stability and aerodynamic loads; and a pilot-in-the-loop aeroservoelastic/aeroelastic simulator. This technology is based on the concept of parametric, fluid-structure PROMs. Its feasibility was demonstrated during Phase I of this effort. For linear and linearized problems, its pillars include a database of PROMs, and real-time algorithms for interpolation on matrix manifolds. For nonlinear problems, its key components include machine learning algorithms for constructing local reduced-order bases, and hyper reduction methods for enabling real-time approximations in these reduced bases. For all applications, it features greedy algorithms for sampling the parameter domain of interest, and a nonparametric probabilistic approach for adapting a computed PROM using test data. Because fluid-structure interaction problems are ubiquitous in engineering, this technology has dualâ€ï¿½use applications and a significant potential for commercial exploitation.

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

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