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The Army continues to improve its Reliability-based Design Optimization (RBDO) process, expanding from component optimization to system optimization. We are using the massively parallel computing power of the Department of Defense (DoD) High Performance Computing (HPC) systems to simultaneously optimize multiple components which interact with each other in a mechanical system. Specifically, we have a subsystem of a military ground vehicle, consisting of more than four components and are simultaneously optimizing five components of that subsystem using RBDO methods. We do not simply optimize one component at a time, sequentially, and iterate until convergence. We actually simultaneously optimize all components together. This can be done efficiently using the parallel computing environment. We will discuss the results of this optimization, and the advantages and disadvantages of using HPC systems for this work.

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.

The free piston engine combined with a linear electric alternator has the potential to be a highly efficient converter from fossil fuel energy to electrical power. With only a single major moving part (the translating rod), mechanical friction is reduced compared to conventional crankshaft technology. Instead of crankshaft linkages, the motion of the translator is driven by the force balance between the engine cylinder, alternator, damping losses, and springs. Focusing primarily on mechanical springs, this paper explores the use of springs to increase engine speed and reduce cyclic variability. A numeric model has been constructed in MATLAB®/Simulink to represent the various subsystems, including the engine, alternator, and springs. Within the simulation is a controller that forces the engine to operate at a constant compression ratio by affecting the alternator load.

The range-extended electric vehicle (REEV) is a complex nonlinear system powered by internal combustion engine and electricity stored in battery. This research proposed a Multiple Operation Points (MOP) control strategy of REVV based on operation features of REEV and the universal characteristic curve of the engine. The switching logic rules of MOP strategy are designed for the desired transition of the operation mode, which makes the engine running at high efficiency region. A Genetic algorithm (GA) is adapted to search the optimal solution. The fuel consumption is defined as the target cost function. The demand power of engine is defined as optimal variable. The state of charge (SOC) and vehicle speed are selected as the state variables. The dynamic performance of vehicle and cycling life of battery is set as the constraints. The optimal switching parameters are obtained based on this control strategy. Finally, a dynamic simulation model of REEV is developed in Matlab/Simulink.

Continuously variable transmissions promise to improve the performance and drivability of vehicles. The design and implementation of continuously variable transmissions for medium or large displacement (power) engines have been hampered by the power limitations of the belts. A continuously variable transmission with a power split design (CVPST) has been developed to minimize the loading on the belt while providing for increased power transfer compared to existing designs. To aid in the design and development of this CVPST, a simulator program has been developed. The simulator can be used to optimize the CVPST and to compare with other transmissions. Finally, an optimized CVPST design is presented.

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.

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.

Scale-resolving turbulence modeling for engine flow simulation has constantly increased its popularity in the last decade. In contrast to classical RANS modeling, LES-like approaches are able to resolve a larger number of unsteady flow features. In principle, this capability allows to accurately predict some of the key parameters involved in the development and optimization of modern engines such as cycle-to-cycle variations in a DI engine. However, since multiple simulated engine cycles are required to extract reliable flow statistics, the spatial and temporal resolution requirements of pure LES still represent a severe limit for its wider application on realistic engine geometries. In this context, Hybrid URANS-LES methodologies can therefore become a potentially attractive option. In fact, their task is to preserve the turbulence scale-resolving in the flow core regions but at a significantly lower computational cost compared to standard LES.

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.

Linear engine alternator (LEA) design optimization traditionally has been difficult because each independent variable alters the motion with respect to time, and therefore alters the engine and alternator response to other governing variables. An analogy is drawn to a conventional engine with a very light flywheel, where the rotational speed effectively is not constant. However, when springs are used in conjunction with an LEA, the motion becomes more consistent and more sinusoidal with increasing spring stiffness. This avoids some attractive features, such as variable compression ratio HCCI operation, but aids in reducing cycle-to-cycle variation for conventional combustion modes. To understand the cycle-to-cycle variations, we have developed a comprehensive model of an LEA with a 1kW target power in MATLAB®/Simulink, and an LEA corresponding to that model has been operated in the laboratory.

The control and design optimization of a Free Piston Engine Generator (FPEG) has been found to be difficult as each independent variable changes the piston dynamics with respect to time. These dynamics, in turn, alter the generator and engine response to other governing variables. As a result, the FPEG system requires an energy balance control algorithm such that the cumulative energy delivered by the engine is equal to the cumulative energy taken by the generator for stable operation. The main objective of this control algorithm is to match the power generated by the engine to the power demanded by the generator. In a conventional crankshaft engine, this energy balance control is similar to the use of a governor and a flywheel to control the rotational speed. In general, if the generator consumes more energy in a cycle than the engine provides, the system moves towards a stall.