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Opportunity at Davies Teaching Fellowships (ARL/USMA)

Fundamental Research in Prognostics and Diagnostics for Total Material State Awareness and Condition Based Maintenance of Army Vehicles


Vehicle Technology Directorate-FFP, Vehicle Technology Directorate, Propulsion Division-FFP

RO# Location
AA.37.01.B7810 Aberdeen Proving Ground, MD 210055066


Name E-mail Phone
Ghoshal, Anindya 410.278.7358


ARL conducts basic and applied research involving prognostics and diagnostics for Future Force Protection. The services performed under this request will improve Army vehicle technology resulting in lighter weight, improvement in vehicle lifetime, and cost. The research effort is a critical part of ongoing mission programs toward developing advanced prognostics and diagnostics methodologies for future and current Army vehicles. Without such development, the mission research will be negatively impacted. Ongoing projects include (1) development of computational models and experimental analysis to aid diffractive shielding to mitigate blast/stress waves, (2) development of analytical models and experimental validation to define the acoustic ultrasonic wave interaction with microplasticity zone around cracks, and (3) conducting fundamental experimental and analytical research to identify major drivers of micro-damage precursors in composites under cyclic loading leading to development of self-responsive engineered composite materials for total material state awareness. This research effort will focus on development of advanced experimental and analytical methods for total material state awareness in Army Vehicles. Research proposals are invited to develop improved prognostics and diagnostics (P&D) methods based on the basic sciences (e.g., physics, material science based methods, and damage/fault diagnosis and prognosis). The areas of technical interest include, but are not limited to damage precursors nucleation and propagation, prognostic methods for the prediction of remaining useful life of a component and/or system, advanced sensing and sensor arrays, advanced signal processing techniques, sensor optimization and placement, data fusion, component and/or system level reasoners and reasoning methods, advanced P&D hardware/software, structural health monitoring, and self-responsive engineered composite materials leading to total material state awareness that can reduce the logistics footprint and life-cycle costs while increasing vehicle availability.



Butler S, Gurvich M, Ghoshal A, et al: Journal of Intelligent Material Systems and Structures 22(16): 1857, 2011

Ghoshal A, et al: Microelectronics Reliability 50(6): 857, 2010


Condition based maintenance; Total material state awareness; Structural health monitoring; Integrated vehicle health management; Damage precursors; Rotorcraft; Aircraft; Mechanics; Prognostics and diagnostics;


Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral applicants
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