T. T. Myers, D. H. Klyde, and R. E. Magdaleno, The Dynamic Icing Detection System (DIDS), presented at AIAA 38th Aerospace Sciences Meeting and Exhibition, Reno, NV, January 10-13, 2000. (AIAA 2000-0364, STI-P-562)The Dynamic Icing Detection System (DIDS) concept is intended to detect an aircraft icing condition in flight. DIDS is based on near real-time identification of degradation in aircraft dynamic characteristics and aerodynamic parameters, e.g., drag coefficient, static margin, elevator effectiveness, etc. Beyond simply detecting icing, the DIDS concept provides a unique capability to quantify degradation in stability and control and flying qualities. Thus, the DIDS can determine the effects of icing whereas ice sensors can only detect the presence of ice. Ideally, however, icing effects data from the DIDS would be fused with onboard ice sensor data and off-board meteorological data to maximize the overall resolution, specificity, accuracy, timeliness and reliability of icing information available to the pilot. This paper presents selected results from an initial analytical study to develop, test and refine DIDS algorithms. Frequency domain parameter estimation methods were applied to a simulation of a Twin Otter aircraft, which included a simple icing model based on NASA icing data. The feasibility of detecting icing was examined for a variety of test input control points and magnitudes, turbulence levels and icing severities. The initial results indicate a significant detectable sensitivity to icing implying feasibility of the DIDS concept. Work is continuing on development, testing and refinement of DIDS

B. L. Aponso, D. Lee, Ph.D., and E. N. Bachelder, Ph.D., Evaluation of a Rotorcraft Autorotation Training Display on a Commercial Flight Training Device, presented at AHS Forum 61, Grapevine, TX, June 3, 2005. (STI-P-655)This paper outlines the application of a real-time trajectory optimization method for guiding a rotorcraft pilot through an autorotation following a total loss of power. Rotorcraft autorotation is a time-critical maneuver that allows very little margin for error in the timing and magnitude of the pilot's control inputs. Preliminary tests strongly indicate that successful autorotations may be performed from well within the unsafe operating area of the height-velocity profile of a helicopter by employing a fast and robust optimal algorithm that provides guidance on control inputs through an intuitive pilot display. The algorithm generates optimal aircraft trajectories and control commands via the direct-collocation optimization method, solved using a commercially available nonlinear programming problem solver. The commanded control inputs generated by optimal control formulation are collective and aircraft pitch which are easily tracked by a pilot or can be converted to control actuator commands for automated operation during autorotation. The formulation of the optimal control problem was carefully tailored to emulate solutions resembling those of an expert pilot, accounting for the performance limitations of the rotorcraft as well as safety concerns.

B. L. Aponso, E. N. Bachelder, and D. Lee, Automated Autorotation for Unmanned Rotorcraft Recovery, presented at AHS International Specialists' Meeting on Unmanned Rotorcraft, Chandler, AZ, January 18-20, 2005. (STI-P-646)This paper outlines the application of a real-time trajectory optimization method for guiding an autonomous unmanned rotorcraft, or a remote operator of an unmanned rotorcraft, through an autorotation in the event of total loss of power. This would allow the recovery of the unmanned rotorcraft including valuable sensor packages and data that may otherwise be lost following a power failure. Initial testing of the concept strongly indicates that successful autorotations may be performed from well within the unsafe operating area of the height-velocity profile of a helicopter by employing a fast and robust optimal algorithm that commands control motion directly in an autonomous rotorcraft or remotely through an intuitive pilot display. The algorithm generates optimal trajectories and control commands via the direct-collocation optimization method, solved using a commercially available nonlinear programming problem solver. Control inputs computed by optimal control formulation are collective and aircraft pitch, which are easily manipulated by a remote pilot or converted to collective and longitudinal cyclic commands. The formulation of the optimal control problem was carefully tailored to enable the solutions to resemble those of an expert pilot, accounting for the performance limitations of the rotorcraft as well as safety concerns.