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Beginning flight students were taught landings in a flight simulator with a visual landing display to examine the effects of scene detail, visual augmented guidance, and the number of landing training trials. Transfer as assessed in a criterion simulator configuration showed advantages for larger numbers of training trials, visual augmented guidance, and moderate scene detail. Transfer of training to the aircraft showed advantages for low-scene detail over moderate-scene detail for the number of landing training sessions. Subjects who received equal simulator time practicing an instrument pattern (control group) performed better than the moderate-scene detail group on student assisted landings and number of landing training sessions.

This research compares the responses of a vehicle modeled in the 3D vehicle simulation program SIMON in the HVE simulation operating system against instrumented responses of a 3-axle tractor, 2-axle semi-trailer combination. The instrumented tests were previously described in SAE 2001-01-0139 and SAE 2003-01-1324 as part of a continuous research effort in the area of vehicle dynamics undertaken at the Vehicle Research and Test Center (VRTC). The vehicle inertial and mechanical parameters were measured at the University of Michigan Transportation Research Institute (UMTRI). The tire data was provided by Smithers Scientific Services, Inc. and UMTRI. The series of tests discussed herein compares the modeled and instrumented vehicle responses during quasi-steady state, steady state and transient handling maneuvers, producing lateral accelerations ranging nominally from 0.05 to 0.5 G's.

Using neural networks, an algorithm has been developed to steer a wheel loader vehicle. Mathematical functions have been used in the past in an attempt to model a human in their operation of many types of vehicles. Since such functions can typically only be derived for situations in which the problem domain is thoroughly understood, research continues in an effort to develop a complete “operator model”. Neural Network algorithms were utilized in an attempt to determine the feasibility of accurately modeling the operator of a wheel loader construction vehicle. These algorithms were also used to determine how the control of different vehicle functions might be automated on a wheel loader.

Inlet valve deposits in gasoline engines have a significant effect on engine operation with particular reference to cold starting and driveability. Present methods of quantifying these deposits by weighing them or rating them with the aid of a visual rating scale are recognized as not being reliable indices of the detrimental effect of these deposits. A valve deposit quantification system was developed that relied on the use of machine vision. Algorithms were formulated to track the silhouetted edge profile of a backlit valve from which a valve volume was determined. The valve deposit volume was calculated as the difference in volume between the valve in its clean and coked states. The system was able to detect a minimum coke deposit level of 0.06g at the 95% confidence limit, the accuracy being based on the correlation between the volume as determined by the vision system and the mass of the deposit.

In this paper we demonstrate our work to date on our underactuated two link robot called the Pendubot. First we will overview the Pendubot's design, discussing the components of the linkage and the interface to the PC making up the controller. Parameter identification of the Pendubot is accomplished both by solid modeling methods and energy equation least squares techniques. With the identified parameters, mathematical models are developed to facilitate controller design. The goal of the control is to swing the Pendubot up and balance it about various equilibrium configurations. Two control algorithms are used for this task. Partial feedback linearization techniques are used to design the swing up control. The balancing control is then designed by linearizing the dynamic equations about the desired equilibrium point and using LQR or pole placement techniques to design a stabilizing controller.

An overview of enrollment trends and curricular changes in engineering education in the past ten years. Comments are made about the implications of lower enrollment on quality of education and availability of engineers for the employment market. Discussions of curricular variations summarizes changes such as computerization of engineering studies, expansion of high school preparation, and selection of major studies for students.

A new Aircraft Accident Awareness Program (AAAP) was developed, evaluated, and is available to emergency response service provider organizations (firefighters, emergency medical technicians, trauma center personnel, law enforcement, clergy, coroners, and media) who would be called to an aircraft accident scene. Aircraft accident responder training is a critical factor in accident victim crash survivability and successful life-safety outcomes. This program was designed to teach participants about the unique conditions and safety hazards associated with aircraft crashes. A blend of academic classroom investigation, exposure to airworthy/ unairworthy aircraft including operating systems and components, computer accident simulations, “hands-on” (destructive) extrication protocol training, and participation in simulated in-the-field accident scenarios was used as an instructional delivery model.

Yield potential was predicted and mapped for three corn fields in Central Illinois, using digital aerial color infrared images. Three methods, namely statistical (regression) modeling, genetic algorithm optimization and artificial neural networks, were used for developing yield models. Two image resolutions of 3 and 6 m/pixel were used for modeling. All the models were trained using July 31 image and tested using images from July 2 and August 31, all from 1998. Among the three models, artificial neural networks gave best performance, with a prediction error less than 30%. The statistical model resulted in prediction errors in the range of 23 to 54%. The lower resolution images resulted in better prediction accuracy compared to resolutions higher than or equal to the yield resolution. Images after pollination resulted in better accuracy compared to images before pollination.

Air source heat pumps as the heating system for full electric vehicles are drawing more and more attention in recent years. Despite the high energy efficiency, frost accumulation on the heat pump evaporator is one of the major challenges associated with air source heat pumps. The evaporator needs to be actively defrosted periodically and heat pump heating will be interrupted during defrosting process. Proper defrost control is needed to obtain high average heat pump energy efficiency. In this paper, a new method for generating air source heat pump defrost control policy using reinforcement learning is introduced. This model-free method has several advantages. It can automatically generate optimal defrost control policy instead of requiring manually determination of the control policy parameters and logics.

Artificial ice shapes of various geometric fidelity were tested on a wing model based on the Common Research Model. Low Reynolds number tests were conducted at Wichita State University’s Walter H. Beech Memorial Wind Tunnel utilizing an 8.9% scale model, and high Reynolds number tests were conducted at ONERA’s F1 wind tunnel utilizing a 13.3% scale model. Several identical geometrically-scaled ice shapes were tested at both facilities, and the results were compared at overlapping Reynolds and Mach numbers. This was to ensure that the results and trends observed at low Reynolds number could be applied and continued to high, near-flight Reynolds number. The data from Wichita State University and ONERA F1 agreed well at matched Reynolds and Mach numbers. The lift and pitching moment curves agreed very well for most configurations.

Understanding the aerodynamic impact of swept-wing ice accretions is a crucial component of the design of modern aircraft. Computer-simulation tools are commonly used to approximate ice shapes, so the necessary level of detail or fidelity of those simulated ice shapes must be understood relative to high-fidelity representations of the ice. Previous tests were performed in the NASA Icing Research Tunnel to acquire high-fidelity ice shapes. From this database, full-span artificial ice shapes were designed and manufactured for both an 8.9%-scale and 13.3%-scale semispan wing model of the CRM65 which has been established as the full-scale baseline for this swept-wing project. These models were tested in the Walter H. Beech wind tunnel at Wichita State University and at the ONERA F1 facility, respectively. The data collected in the Wichita St.