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A reconfigurable control system is an intelligent control system that detects faults within the system and adjusts its performance automatically to avoid mission failure, save lives, and reduce system maintenance costs. The concept was first successfully demonstrated by NASA between December 1989 and March 1990 on the F-15 flight control system (SRFCS), where software was integrated into the aircraft's digital flight control system to compensate for component loss by reconfiguring the remaining control loop. This was later adopted in the Boeing X-33. Other applications include modular robotics, reconfigurable computing structure, and reconfigurable helicopters. The motivation of this work is to test such control system designs for future long term space missions, more explicitly, the automation of life support systems.

Aircraft incidents and accidents in icing are often the result of degradation in performance and control. However, current ice sensors measure the amount of ice and not the effect on performance and control. No processed aircraft performance degradation information is available to the pilot. In this paper research is reported on a system to estimate aircraft performance and control changes due to ice, then use this information to automatically operate ice protection systems, provide aircraft envelope protection and, if icing is severe, adapt the flight controls. Key to such a safety system would be he proper communication to, and coordination with, the flight crew. This paper reviews the basic system concept, as well as the research conducted in three critical areas; aerodynamics and flight mechanics, aircraft control and identification, and human factors.

Virtual reality is an emerging technology with the potential for many engineering applications including machinery simulation. In this paper the writers describe the hardware and software components of a virtual reality system that simulates machinery. They detail the flow of information that occurs in this system and discuss the functioning of an existing system at the National Center for Supercomputing Applications (NCSA) located at the University of Illinois at Urbana-Champaign. Finally, they describe potential uses of virtual reality in product design, manufacturing, training and marketing.

Neural networks are massively parallel computational models for knowledge representation and information processing. The capabilities of neural networks, namely learning, noise tolerance, adaptivity, and parallel structure make them good candidates for application to a wide range of engineering problems including diagnostics problems. The general approach in developing neural network based diagnostic methods is described through a case study. The development of an acoustic wayside train inspection system using neural networks is described. The study is aimed at developing a neural network based method for detection defective wheels from acoustic measurements. The actual signals recorded when a train passes a wayside station are used to develop a neural network based wheel defect detector and to study its performance. Signal averaging and scoring techniques are developed to improve the performance of the constructed neural inspection system.

The US Army uses a light tactical High-Mobility Multi-Purpose Wheeled Vehicle (HMMWV) which, due to the amount of armor added, requires air conditioning to keep its occupants comfortable. The current system uses R134a in a dual evaporator, remote-mounted condenser, engine-driven compressor system. This vehicle has been adapted to use an environmentally friendly refrigerant (carbon dioxide) to provide performance, efficiency, comfort and logistical benefits to the Army. The unusual thermal heat management issues and the fact that the vehicle is required to operate under extreme ambient conditions have made the project extremely challenging. This paper is a continuation of work presented at the SAE Alternate Refrigerants Symposium held in Phoenix last June [1].