COMBUSTION RESEARCH & FLOW TECHNOLOGY INC — Department of Defense SBIR Phase II: N152-100
COMBUSTION RESEARCH & FLOW TECHNOLOGY INC — SBIR Phase II award from Department of Defense.
- Amount
- $507,414
- Agency
- Department of Defense · Army
- Program / Phase
- SBIR · Phase II
- Topic
- N152-100
- Solicitation
- 15.2
- NAICS
- —
- Place of performance
- PA
- Period
- 2020-03-12 → 2021-09-01
Description
Over the past few years, Unmanned Aircraft Systems (UAS) have evolved considerably and come to play an integral role in Department of Defense operations, seen by many as a transformational force multiplier providing reliable support in the execution of core Combatant Command (COCOM) missions in hostile environments. A large number of the designs revolve around multirotor configurations with Vertical Take-Off Landing (VTOL) capabilities providing a high degree of redundancy and reliability. However, the design of rotor blades used in these UAS/UAV drone configurations has, till recently, not received much attention and often comprise of simple airfoil shapes and planforms and are plagued by poor performance. The design of these rotors present challenges given that they operate in laminar and transitional low-Reynolds number flight regimes. In the proposed Phase II program, we aim to enhance our understanding by experimentally investigating transition and the development of laminar separation bubble in sectional airfoil designs; validating our analysis and design tools to reliably predict such phenomena and their deleterious effects on performance; optimize airfoil sections providing better lift-to-drag ratios in these regimes of low Reynolds numbers and consequently utilizing these airfoil shapes in propeller designs through optimal planform designs; analyzing and understanding the additional aerodynamic effects of propeller-propeller and propeller-fuselage interactions and altering the design of the airframe to minimize interactional systemic losses, evaluate vehicle performance including the noise from such propeller systems and finally provide a machine-learning interface that can be used to design propellers in the field based on mission requirements.