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Rotorcraft Integrated Electro-Optic/Infrared (EO/IR) Plumes and Effects Signature Modeling

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-19-C-0725
Agency Tracking Number: N181-010-0125
Amount: $1,298,631.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N181-010
Solicitation Number: 18.1
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-23
Award End Date (Contract End Date): 2024-03-01
Small Business Information
13290 Evening Creek Drive South Suite 250
San Diego, CA 92128
United States
DUNS: 133709001
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Nicolas Reveles
 Project Engineer
 (256) 258-8406
Business Contact
 Joshua Davis
Phone: (858) 480-2028
Research Institution

ATA Engineering, Inc., in collaboration with IERUS Technologies, Inc., proposes the continued development of the Rotorcraft Advanced Signature Prediction (RASP) toolkit. This will involve extension of existing widely used, well-validated EO/IR toolsets to rotorcraft through a modular, variable-fidelity, hierarchical approach to signature analysis. To achieve this capability, the project team will integrate the NASA FUN3D unstructured CFD solver with the JANNAF Spectral and In-band Radiometric Imaging of Targets and Scenes (SPIRITS) and Fast Line-of-sight Imagery for Target and Exhaust Signatures (FLITES) EO/IR signature modeling tools, in addition to extending support for Standard Plume Ultraviolet Radiation (SPURC).The Phase II effort will focus on integration with the aforementioned EO/IR solvers and expansion of their support with the RASP toolkit, improving the toolkit’s physics modeling capabilities, and verifying the predictive accuracy of the toolkit. The effort will establish a variable-fidelity approach where modeling components, such as the inclusion of fuselage conjugate heat transfer, may be switched on and off in a hierarchical approach to ascertain the underlying sensitivities to the choice in numerical models. This approach also directly supports situations where advanced modeling parameters may not be known and lower-fidelity models are preferred.

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

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