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The dielectric barrier discharge (DBD) has seen significantly increased levels of interest for its applications to various aerodynamic problems. The DBD produces stable atmospheric-pressure non-thermal plasma with highly energetic electrons and a variety of ions and neutral species. The resulting plasma often degrades the dielectric barrier between the electrodes of the device, ultimately leading to actuator failure. Several researchers have studied a variety of parameters related to degradation and time-dependent dielectric breakdown of various polymers such as PMMA or PVC that are often used in actuator construction. Many of these studies compare the degradation of these materials to that of borosilicate glass in which it is claimed that there is no observable degradation to the glass. Recent research at West Virginia University has shown that certain actuator operating conditions can lead to degradation of a glass barrier and can ultimately result in failure.

Diesel Particulate Filters (DPFs) are well assessed exhaust aftertreatment devices currently equipping almost every modern diesel engine to comply with the most stringent emission standards. However, an accurate estimation of soot content (loading) is critical to managing the regeneration of DPFs in order to attain optimal behavior of the whole engine-after-treatment assembly, and minimize fuel consumption. Real-time models can be used to address challenges posed by advanced control systems, such as the integration of the DPF with the engine or other critical aftertreatment components or to develop model-based OBD sensors. One of the major hurdles in such applications is the accurate estimation of engine Particulate Matter (PM) emissions as a function of time. Such data would be required as input data for any kind of accurate models. The most accurate way consists of employing soot sensors to gather the real transient soot emissions signal, which will serve as an input to the model.

Recent catastrophic air crashes have shown that physical redundancy is not a foolproof option for failures on Air Data Systems (ADS) on an aircraft providing airspeed measurements. Since all the redundant sensors are subjected to the same environmental conditions in flight, a failure on one sensor could occur on the other sensors under certain conditions such as extreme weather; this class of failure is known in the literature as “common mode” failure. In this paper, different approaches to the problem of detection, identification and accommodation of failures on the Air Data System (ADS) of an aircraft are evaluated. This task can be divided into component tasks of equal criticality as Sensor Failure Detection and Identification (SFDI) and Sensor Failure Accommodation (SFA). Data from flight test experiments conducted using the WVU YF-22 unmanned research aircraft are used.

The Federal Test Procedure (FTP) for heavy-duty engines requires the use of a full-flow tunnel based constant volume sampler (CVS). These are costly to build and maintain, and require a large workspace. A small portable micro-dilution system that could be used on-board, for measuring emissions of in-use, heavy-duty vehicles would be an inexpensive alternative. This paper presents the rationale behind the design of such a portable particulate matter measuring system. The presented micro-dilution tunnel operates on the same principle as a full-flow tunnel, however given the reduced size dilution ratios can be more easily controlled with the micro dilution system. The design targets dilution ratios of at least four to one, in accordance with the ISO 8178 requirements. The unique features of the micro-dilution system are the use of only a single pump and a porous sintered stainless steel tube for mixing dilution air and raw exhaust sample.

Internal combustion diesel engines with optical access (a.k.a. optical engines) increase the fundamental understanding of combustion phenomena. However, optical access requirements result in most optical engines having a different in-cylinder geometry compared with the conventional diesel engine, such as a flat bowl-in-piston combustion chamber. This study investigated the effect of the bowl geometry on the flow motion and emissions inside a conventional heavy-duty direct-injection diesel engine that can operate in both metal and optical-access configurations. This engine was converted to natural-gas spark-ignition operation by replacing the fuel injector with a spark plug and adding a low-pressure gas injector in the intake manifold for fuel delivery, then operated at steady-state lean-burn conditions. A 3D CFD model based on the experimental data predicted that the different bowl geometry did not significantly affect in-cylinder emissions distribution.

Fuel economy is an important issue in urban driving cycle where vehicles are required to operate most of the time at lower power than the average demand. High power fuel cells operate economically at higher loads. Hence, conventional combination of a high power fuel cell and an additional storage device such as ultracapacitor or battery units does not necessarily provide an economic configuration. This paper offers a new configuration that consists of two fuel cells combined with a battery unit to provide a fuel efficient source of power for hybrid electric fuel cell vehicles in urban driving applications. The control algorithm and power management strategy for a combination of two downsized fuel cells and a storage device is provided and its performance of operation is compared with traditional topologies.

Gasoline-powered vehicles compose the vast majority of all light-duty vehicles in the United States. Improving fuel economy is currently a topic of great interest due to the rapid rise in gasoline costs as well as new fuel-economy and greenhouse-gas emissions standards. The Chevrolet Silverado is currently one of the top selling trucks in the U.S. and has been previously modeled using the commercial finite element code LS-DYNA by the National Crash Analysis Center (NCAC). This state-of the art model was employed to examine alternative weight saving configurations using material alternatives and replacement of traditional steel with composite panels. Detailed mass distribution analysis demonstrated the chassis assembly to be an ideal candidate for weight reduction and was redesigned using Aluminum 7075-T6 Alloy and Magnesium Alloy HM41A-F.

A work-based window method has been developed to calculate in-use brake-specific oxides of nitrogen (NOx) emissions for all engine speeds and engine loads. During an in-use test, engine speed and engine torque are read from the engine's electronic control unit, and along with time, are used to determine instantaneous engine power. Instantaneous work is calculated using this power and the time differential in the data collection. Work is then summed until the target amount of work is accumulated. The emissions levels are then calculated for that window of work. It was determined that a work window equal to the theoretical Federal Test Procedure (FTP) cycle work best provides a means of comparison to the FTP certification standard. Also, a failure criterion has been established based on the average amount of power generated in the work window and the amount of time required to achieve the target work window to determine if a particular work window is valid.

Machine learning models were shown to provide faster results but with similar accuracy to multidimensional computational fluid dynamics or in-depth experiments. This study used a support-vector machine (SVM), a set of related supervised learning methods, to predict the dynamic performance (i.e., engine power and torque) of a heavy-duty natural gas spark ignition engine. The single-cylinder four-stroke test engine was fueled with methane. The engine was operated at different spark timings, mixture equivalence ratios, and engine speeds to provide the data for training and testing the proposed SVM. The results indicated that the performance and accuracy of the built regression model were satisfactory, with correlation coefficient quantities all larger than 0.95 and root-mean-square errors close to zero for both training and validation datasets.