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These results provide insight for the design of high-speed helicopter rotor blades by examining the sensitivity of airloads to the range of Reynolds numbers encountered in the reverse flow region. The character of the lift curve for the cambered ellipse airfoil changes drastically for Reв‰Ґ3.3Г—105 in both forward and reverse flow. The lift curve of an elliptical airfoil becomes more linear with increasing Reynolds number. In reverse flow, the NACA 0024 exhibits a decrease in the magnitude of the airloads with increasing Reynolds number for в€’3в‰¤в€’О±revв‰¤15 deg. The airloads of thicker airfoils were found to be more sensitive to Reynolds number. This work shows that the airloads for a NACA 0012 in reverse flow are insensitive to Reynolds number due to early flow separation, because it acts as a вЂњthinвЂќ airfoil due to the sharp aerodynamic leading edge. Two airfoils with a sharp geometric trailing edge (a NACA 0012 and a NACA 0024) and two airfoils with a blunt geometric trailing edge (a 24% thick elliptical airfoil and a 26% thick cambered ellipse airfoil) were tested. Time-averaged sectional airloads and surface oil flow visualizations were obtained experimentally for four airfoil cross sections at Reynolds numbers between 3.3Г—105 and 1.0Г—106. The present work was aimed at providing an improved understanding of Reynolds number effects in both forward and reverse flow. The retreating blade of a high-advance-ratio rotor encounters a wide range of Reynolds numbers when passing through the reverse flow region. This result supports the claim that policy iteration remains the superlative solution algorithm for exactly solving computationally tractable Markov decision problems. Moreover, an analysis of solution approaches for the MEDEVAC dispatching problem reveals that the policy iteration algorithm substantially outperforms the linear programming algorithms executed by CPLEX 12.6 with regard to computational effort. These results inform the development and implementation of MEDEVAC tactics, techniques, and procedures by military medical planners. Results indicate that dispatching MEDEVAC units considering the precedence level of requests and the locations of busy MEDEVAC units increases the performance of the MEDEVAC system. Several excursions are examined to identify how the 9-line MEDEVAC request arrival rate and the MEDEVAC flight speeds impact the optimal dispatching policy. Two computational experiments are conducted to examine the impact of selected MEDEVAC problem features on the optimal policy and the system performance measure. A representative planning scenario based on contingency operations in southern Afghanistan is utilized to investigate the differences between the optimal dispatching policy and three practitioner-friendly myopic policies. The MDP model allows the dispatching authority to accept, reject, or queue incoming requests based on a requestвЂ™s classification (i.e., zone and precedence level) and the state of the MEDEVAC system.

A discounted, infinite horizon Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem.

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The objective of this research is to determine how to optimally dispatch MEDEVAC units in response to 9-line MEDEVAC requests to maximize MEDEVAC system performance. Military medical planners must develop dispatching policies that dictate how aerial medical evacuation (MEDEVAC) units are utilized during major combat operations.