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A Hybrid Projectile (HP) is a round that transforms into a UAV after being launched. Some HP's are fired from a rifled barrel and must be de-spun and wings-level for lifting surfaces to be deployed. Control surfaces and controllers for de-spinning and wings-leveling were required for initial design of an HP 40 mm. Wings, used as lifting surfaces after transformation, need to be very close to level with the ground when deployed. First, the tail surface area needed to de-spin a 40 mm HP was examined analytically and simulated. Next, a controller was developed to maintain a steady de-spin rate and to roll-level the projectile in preparation of wing deployment. The controller was split into two pieces, one to control de-spin, and the other for roll-leveling the projectile. An adaptable transition point for switching controllers was identified analytically and then adjusted by using simulations.

The rollover threshold for a partially filled tanker truck carrying fluid cargo is of great importance due to the catastrophic nature of accidents involving such vehicles, particularly when payloads are toxic and flammable. In this paper, a method for determining the threshold of rollover stability of a specific tanker truck is presented using finite element analysis methods. This approach allows the consideration of many variables which had not been fully incorporated in past models, including nonlinear spring behavior and tank flexibility. The program uses simple mechanical pendulums to simulate the fluid sloshing affects, beam elements to match the torsional and bending stiffness of the tank, and spring damper elements to simulate the suspension. The finite element model of the tanker truck has been validated using data taken by the U.S. Army Aberdeen Test Center (ATC) on a M916A1 tractor/ Etnyre model 60PRS 6000 gallon trailer combination.

Hydrogen Internal Combustion Engine (ICE) vehicles using a traditional ICE that has been modified to use hydrogen fuel are an important mid-term technology on the path to the hydrogen economy. Hydrogen-powered ICEs that can run on pure hydrogen or a blend of hydrogen and compressed natural gas (CNG) are a way of addressing the widespread lack of hydrogen fuelling infrastructure in the near term. Hydrogen-powered ICEs have operating advantages as all weather conditions performances, no warm-up, no cold-start issues and being more fuel efficient than conventional spark-ignition engines. The Wankel engine is one of the best ICE to be converted to run hydrogen. The paper presents some details of an initial investigation of the CAD and CAE modeling of a novel design where two jet ignition devices per rotor are replacing the traditional two spark plugs for a faster and more complete combustion.

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.

Turbulence modeling is a key aspect for the accurate simulation of ICE related fluid flow phenomena. RANS-based turbulence closures are still the preferred modeling framework among industrial users, mainly because they are robust, not much demanding in terms of computational resources and capable to extract ensemble-averaged information on a complete engine cycle without the need for multiple cycles simulation. On the other hand, LES-like approaches are gaining popularity in recent years due to their inherent scale-resolving nature, which allows the detailed modeling of unsteady flow features such as cycle-to-cycle variations in a DI engine. An LES requires however a large number of simulated engine cycles to extract reliable flow statistics, which coupled to the higher spatial and temporal resolution compared to RANS still poses some limits to a wider application of such methodology on realistic engine geometries.

A two-equation Zonal-DES (ZDES) approach has been recently proposed by the authors as a suitable hybrid URANS/LES turbulence modeling alternative for Internal Combustion Engine flows. This approach is conceptually simple, as it is all based on a single URANS-like framework and the user is only required to explicitly mark which parts of the domain will be simulated in URANS, DES or LES mode. The ZDES rationale was initially developed for external aerodynamics applications, where the flow is statistically steady and the transition between zones of different types usually happens in the URANS-to-DES or URANS-to-LES direction. The same “one-way” transition process has been found to be fairly efficient also in steady-state internal flows with engine-like characteristics, such as abrupt expansions or intake ports with fixed valve position.

This paper presents a sensitivity analysis-based study aimed at robustly calibrating the parameters of an adaptive energy management strategy designed for a Plugin Hybrid Electric Vehicle (PHEV). The supervisory control is developed from the Pontryagin's Minimum Principle (PMP) approach and applied to a model of a GM Chevrolet Volt vehicle. The proposed controller aims at minimizing the fuel consumption of the vehicle over a given driving mission, by achieving a blended discharge strategy over the entire cycle. The calibration study is conducted over a wide set of driving conditions and it generates a look-up table and two constant values for the three controller parameters to be used in the in-vehicle implementation. Finally, the calibrated adaptive control strategy is validated against real driving cycles showing the effectiveness of the calibration approach.

Diesel particulate filters (DPFs) are recognized as the most efficient technology for particulate matter (PM) reduction, with filtration efficiencies in excess of 90%. Design guidelines for DPFs typically are: high removal efficiency, low pressure drop, high durability and capacity to resist high temperature excursions during regeneration events. The collected mass inside the trap needs to be periodically oxidized to regenerate the DPF. Thus, an in-depth understanding of filtration and regeneration mechanisms, together with the ability of predicting actual DPF conditions, could play a key role in optimizing the duration and number of regeneration events in case of active DPFs. Thus, the correct estimation of soot loading during operation is imperative for effectively controlling the whole engine-DPF assembly and simultaneously avoidingany system failure due to a malfunctioning DPF. A viable way to solve this problem is to use DPF models.

Increasingly stringent oxides of nitrogen (NOx) and particulate matter (PM) regulations worldwide have prompted considerable activity in developing emission control technology to reduce the emissions of these two constituents from heavy-duty diesel engines. NOx has come under particular scrutiny by regulators in the US and in Europe with the promulgation of very stringent regulation by both the US Environmental Protection Agency (EPA) and the European Union (EU). In response, heavy-duty engine manufacturers are considering Selective Catalytic Reduction (SCR) as a potential NOx reduction option. While SCR performance has been well established through engine dynamometer evaluation under laboratory conditions, there exists little data characterizing SCR performance under real-world operating conditions over time. This project evaluated the field performance of ten SCR units installed on heavy-duty Class 8 highway and refuse trucks.

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.

The Wankel engine for Unmanned Aerial Vehicle (UAV) applications delivers advantages vs. piston engines of simplicity, smoothness, compactness and high power-to-weight ratio. The use of computational fluid dynamic (CFD) and computer aided engineering (CAE) tools may permit to address the major downfalls of these engines, namely the slow and incomplete combustion due to the low temperatures and the rotating combustion chambers. The paper proposes the results of CAD/CFD/CAE modelling of a Wankel engine featuring tangential jet ignition to produce faster and more complete combustion.

Internal combustion engines with optical access (a.k.a. optical engines) provide additional information in the quest for understanding the fundamental in-cylinder combustion phenomena. However, most optical engines have flat bowl-in-piston combustion chamber to optimize the visualization process, which is different, for example, from the traditional re-entrant bowl in compression ignition engines. A conventional heavy-duty direct-injection compression ignition engine was converted to spark ignition operation by replacing the fuel injector with a spark plug in both optical and metal setups to investigate the effect of the bowl geometry on flame propagation. Experimental data from steady-state lean-burn conditions was used to develop and validate a 3D CFD model of the engine. Numerical simulation results show that flame propagation in the radial direction was similar for both combustion chambers despite their different geometries.

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 combustion process in spark-ignition engines can vary considerably cycle by cycle, which may result in unstable engine operation. The phenomena amplify in natural gas (NG) spark-ignition (SI) engines due to the lower NG laminar flame speed compared to gasoline, and more so under lean burn conditions. The main goal of this study was to investigate the main sources and the characteristics of the cycle-by-cycle variation in heavy-duty compression ignition (CI) engines converted to NG SI operation. The experiments were conducted in a single-cylinder optically-accessible CI engine with a flat bowl-in piston that was converted to NG SI. The engine was operated at medium load under lean operating conditions, using pure methane as a natural gas surrogate. The CI to SI conversion was made through the addition of a low-pressure NG injector in the intake manifold and of a NG spark plug in place of the diesel injector.

3D CFD spark-ignition IC engine simulations are extremely complex for the regular user. Truly-predictive CFD simulations for the turbulent flame combustion that solve fully coupled transport/chemistry equations may require large computational capabilities unavailable to regular CFD users. A solution is to use a simpler phenomenological model such as the G-equation that decouples transport/chemistry result. Such simulation can still provide acceptable and faster results at the expense of predictive capabilities. While the G-equation is well understood within the experienced modeling community, the goal of this paper is to document some of them for a novice or less experienced CFD user who may not be aware that phenomenological models of turbulent flame combustion usually require heavy tuning and calibration from the user to mimic experimental observations.

Among the key technologies currently being used for reducing emissions of oxides of nitrogen, Exhaust Gas Recirculation (EGR) has been found to be very effective for light duty diesel engines. However, EGR results in a sharp increase in particulate matter emissions in heavy-duty diesel engines. The presence of increased levels of particulate matter in the engine has led to increased wear of engine parts such as cylinder liners, piston rings, valve train system and bearings. A statistically designed experiment was developed to examine the effects of soot contaminated engine oil on wear of engine components. A three-body wear machine was designed and developed to simulate and estimate the extent of wear. The three oil properties studied were phosphorous level, dispersant level and sulfonate substrate level. The above three variables were formulated at two levels: High (1) and Low (-1). This resulted in a 23 matrix (8 oil blends).

Optical motion capture (OMC) is a relatively new experimental tool used in many branches of science and engineering. Despite OMC’s widespread use, literature and practical procedures on the quantification of error and uncertainty in OMC systems for rigid bodies are currently underdeveloped. However, in most studies involving error and uncertainty quantification, the OMC volumes are relatively small (maximum length of 2m in any dimension) and involve expensive experimental apparatuses. Therefore, a cost-effective procedure to quantify the positional errors and uncertainties present in a large volume OMC system is presented. The procedure utilizes the kinematics of a wooden block traveling through air to predict errors and uncertainties in the OMC system by only collecting trajectory data.

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.

Unmanned aerial vehicle (UAV) design aspects are as broad as the missions they are used to support. In some cases, the UAV mission scope can impose design constraints that can be difficult to achieve. This paper describes recent work performed at West Virginia University (WVU) to support repeated flight testing of a single-use UAV platform with emphasis on the highly specialized wings required to help meet the overall airframe mass properties constrained by the project sponsor. The wings were fabricated using a molded polyurethane (PU) foam as the base material which was supported by several different types of rigid and flexible substructures, skins, and matrix-infused fiber elements. Different ratios of infused fiber mass to PU foam were tested and additional tungsten masses were added to the wings to achieve the correct total mass and mass distribution of the wings.

As a part of the 1996 FutureCar Challenge competition, West Virginia University converted a 1996 Chevrolet Lumina to a series hybrid electric vehicle. This technical report summarizes the modifications made to the vehicle during 1997, the second year of the competition, and details the present state of development of this second-generation hybrid electric vehicle. In particular, the vehicle's powertrain configuration, component selection, control strategy for all modes of operation, emissions control strategies, vehicle structure and design modifications, and suspension design and modifications are all detailed. Also discussed, are the operational use of this vehicle and its intended market. The projected performance of the vehicle, obtained from computer simulations, is discussed in the light of results obtained from testing during 1996 and 1997.