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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.

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.

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.

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.

The operation of a conventional passenger car is characterised by increasing or maintaining the kinetic energy, when accelerating or cruising the vehicle, and reducing the kinetic energy by using the brakes. While the energy taken by the friction brakes to slow the vehicle is dissipated into heat, the introduction of Kinetic Energy Recovery Systems (KERS) has permitted the recovery of part of the braking energy. This reduces the amount of energy needed from the internal combustion engine (ICE). The contribution reviews the latest developments in electric KERS (E-KERS), with emphasis to round trip efficiency wheels to wheels and electrification of the powertrain. The contribution considers the opportunity to connect the E-KERS traction battery to other electric machines, such as an electrically assisted turbocharger (E-TC) connected to a motor/generator unit, or an electric water pump (EWP), to further optimise the vehicle operation.

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.

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.

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.

The dielectric barrier discharge (DBD) has been studied significantly in the past two decades for its applications to various aerodynamic problems. The most common aerodynamic applications have been stall/separation control and boundary layer modification. Recently several researchers have proposed utilizing the DBD in various configurations to act as viable propulsion systems for micro and nano aerial vehicles. The DBD produces stable atmospheric-pressure non-thermal plasma in a thin sheet with a preferred direction of flow. The plasma flow, driven by electrohydrodynamic body forces, entrains the quiescent air around it and thus develops into a low speed jet on the order of 10-1 to 101 m/s. Several researchers have utilized DBDs in an annular geometric setup as a propulsion device. Other researchers have used them to alter rectangular duct flows and directional jet devices. This study investigates 2-D duct flows for applications in micro plasma thrusters.

There is an ever growing need in the aircraft industry to increase the performance of a flight vehicle. To enhance performance of the flight vehicle one active area of research effort has been focused on the control of the boundary layer by both active and passive means. An effective flow control mechanism can improve the performance of a flight vehicle by eliminating boundary layer separation at the leading edge (as long as the energy required to drive the mechanism is not greater than the savings). In this paper the effectiveness of a novel active flow control technique known as dynamic roughness (DR) to eliminate flow separation in a stalled NACA 0012 wing has been explored. As opposed to static roughness, dynamic roughness utilizes small time-dependent deforming elements or humps with amplitudes that are on the order of the local boundary layer height to energize the local boundary layer. DR is primarily characterized by the maximum amplitude and operating frequency.

Machine learning algorithms are effective tools to reduce the number of engine dynamometer tests during internal combustion engine development and/or optimization. This paper provides a case study of using such a statistical algorithm to characterize the heat transfer from the combustion chamber to the environment during combustion and during the entire engine cycle. The data for building the machine learning model came from a single cylinder compression ignition engine (13.3 compression ratio) that was converted to natural-gas port fuel injection spark-ignition operation. Engine dynamometer tests investigated several spark timings, equivalence ratios, and engine speeds, which were also used as model inputs. While building the model it was found that adding the intake pressure as another model input improved model efficiency.

To meet the ignition system needs of large bore high pressure lean burn natural gas engines a laser diode side pumped passively Q-switched laser igniter was designed and tested. The laser was designed to produce the optical intensities needed to initiate ignition in a lean burn high brake mean effective pressure (BMEP) engine. The experimentation explored a variety of optical and electrical input parameters that when combined produced a robust spark in air. The results show peak power levels exceeding 2 MW and peak focal intensities above 400 GW/cm2. Future research avenues and current progress with the initial prototype are presented and discussed.

Evermore demanding market and legislative pressures require stationary lean burn natural gas engines to operate at higher efficiencies and reduced levels of emissions. Higher in-cylinder pressures and leaner air/fuel ratios are required in order to meet these demands. The performance and durability of spark plug ignition systems suffer as a result of the increase in spark energy required to maintain suitable engine operation under these conditions. Advancing the state of the art of ignition systems for these engines is critical to meeting increased performance requirements. Laser-spark ignition has shown potential to improve engine performance and ignition system durability to levels required meet or exceed projected requirements. This paper discusses testing which extends previous efforts [1] to include constant fueling knock, misfire, thermal efficiency, and NOx emissions mapping of a single cylinder lean burn natural gas engine.

As unmanned aerial vehicle applications continue their rise in popularity in the public and private sectors, there is an increasing demand in many cases for smaller, more efficient low speed unmanned aerial vehicles (UAVs). Although the primary drivers for the continued performance improvement of smaller UAV platforms tend to be in the areas of electronics miniaturization and improved energy storage, aerodynamics, particularly in the low Reynolds number regime, still have a significant role in the overall performance enhancement of small UAVs. This paper focuses on the study of the nearfield aerodynamic effects of a low-power active flow enhancement technique known as dielectric barrier discharge (DBD) in very low speed/low Reynolds number flows most closely associated with small and micro unmanned aerial vehicles.

This paper explores the feasibility of using in-cylinder pressure-based variables to predict gaseous exhaust emissions levels from a Navistar T444 direct injection diesel engine through the use of neural networks. The networks were trained using in-cylinder pressure derived variables generated at steady state conditions over a wide speed and load test matrix. The networks were then validated on previously “unseen” real-time data obtained from the Federal Test Procedure cycle through the use of a high speed digital signal processor data acquisition system. Once fully trained, the DSP-based system developed in this work allows the real-time prediction of NOX and CO2 emissions from this engine on a cycle-by-cycle basis without requiring emissions measurement.

In-service 1994-2003 heavy-duty trucks were acquired by West Virginia University (WVU), equipped with the WVU Mobile Emissions Measurement System (MEMS) to measure on-road NOx, and driven on road routes near Sabraton, West Virginia, and extending up to Washington, PA to obtain real-world oxides of nitrogen (NOx) emissions data on highways and local roads. The MEMS measured 5Hz NOx, and load was obtained from the electronic control unit. Trucks were loaded to about 95% of their gross vehicle weights. Emissions in g/mi and g/bhp-hr were computed over the various road routes. In addition, some of the trucks were tested 1 to 2 years later to determine emission changes that may have occurred for these trucks. Emission results varied significantly over the different road routes due to different speeds, driving patterns, and road grades.

Performance of a natural gas two-stroke engine incorporated in a 1-kW free-piston oscillating Linear Engine Alternator (LEA) - a household electricity generator - was investigated under different resonant frequencies for pre-design phase purposes. To increase the robustness, power density, and thermal efficiencies, the crank mechanism in free-piston LEA is omitted and all moving parts of the generator operate at a fixed resonant frequency. Flexure springs are the main source of the LEA’s stiffness and the mass-spring dynamics dominates the engine’s speed. The trade-off between the engine’s performance, mass-spring system limits, and power and efficiency targets versus the LEA speed is very crucial and demands a careful investigation specifically at the concept design stages to find the optimum design parameters and operating conditions. CFD modeling was performed to analyze the effects of resonant frequency on the engine’s gas exchange behavior.

Tube Launched-Unmanned Air Vehicles (TL-UAV) are munitions that alter their trajectories during flight to enhance the capabilities by possibly extending range, increasing loiter time through gliding, and/or having guided targeting capabilities. Traditional munition systems, specifically the tube-launched mortar rounds, are not guided. Performance of these "dumb" munitions could be enhanced by updating to TL-UAV and still utilize existing launch platforms with standard propellant detonation firing methods. The ability to actively control the flight path and extend range of a TL-UAV requires multiple onboard systems which need to be identified, integrated, assembled, and tested to meet cooperative function requirements. The main systems, for a mortar-based TL-UAV being developed at West Virginia University (WVU), are considered to be a central hub to process information, aerodynamic control devices, flight sensors, a video camera system, power management, and a wireless transceiver.

Beginning 2007, heavy-duty engine certification would require that in-use emissions from vehicles be measured under ‘real-world’ operating conditions using on-board measurement devices. An on-board portable emissions measurement system called Mobile Emissions Measurement System (MEMS) was developed at West Virginia University (WVU) to record in-use, continuous and brake-specific emissions from heavy-duty diesel-powered vehicles. The objective of this paper is to present a preliminary development of a test data quality assurance methodology for emissions measured using the any portable emissions measurement system (PEMS). The first stage of the methodology requires ensuring the proper operation of the different sensors and transducers during data collection. The second stage is data synchronization and pre-processing. The next stage is systematic checking of possible errors from transducers and sensors.

The high demand for traditional air traffic as well as increased use of unmanned aerial systems (UAS) has resulted in researchers examining alternative technologies which would result in safer, more reliable, and better performing aircraft. Active methods of aerodynamic flow control may be the most promising approach to this problem. Research in the area of aerodynamic control is transitioning from traditional mechanical flow control devices to, among other methods, plasma actuators. Plasma actuators offer an inexpensive and energy efficient method of flow control. Dielectric Barrier Discharge (DBD), one of the most widely studied forms of plasma actuation, employs an electrohydrodynamic (EHD) device which uses dominant electric fields for actuation. Unlike traditional flow control methods, a DBD device operates without moving components or mass injection methods.