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Electric drives, whether in battery electric vehicles (BEVs) or various other applications, are an important part of modern transportation. Traditionally, physics-based models based on steady-state mapping of electric drives have been used to evaluate their behavior under transient conditions. Hardware-in-the-Loop (HIL) testing seeks to provide a more accurate representation of a component’s behavior under transient load conditions that are more representative of real world conditions it will operate under, without requiring a full vehicle installation. Oak Ridge National Laboratory (ORNL) developed such a HIL test platform capable of subjecting electric drives to both conventional steady-state test procedures as well as transient experiments such as vehicle drive cycles.

This work explores pathways to achieve diesel-like, high-efficiency combustion with stoichiometric 3-way catalyst compatible spark ignition (SI). A high stroke-to-bore engine design (1.5:1) with cooled exhaust gas recirculation (EGR) and high compression ratio (rc) was used to improve engine efficiency by up to 30% compared with a production turbocharged gasoline direct injection spark ignition engine. To achieve efficiency improvements, engine experiments were coupled with computational fluid dynamics simulations to guide and explain experimental trends between the original engine and the high stroke-to-bore ratio design (1.5:1). The effects of EGR and late intake valve closing (IVC) and fuel characteristics are investigated through their effects on knock mitigation. Direct injection of 91 RON E10 gasoline, 99 RON E0 gasoline, and liquified petroleum gas (i.e., propane/autogas) were evaluated with geometric rc ranging from 13.3:1 to 16.8:1.

A computational study using multi-dimensional CFD modeling was performed to investigate the effects of physical properties on diesel engine combustion characteristics with bio-diesel fuels. Properties of typical bio-diesel fuels that were either calculated or measured are used in the study and the simulation results are compared with those of conventional diesel fuels. The sensitivity of the computational results to individual physical properties is also investigated, and the results provide information about the desirable characteristics of the blended fuels. The properties considered in the study include liquid density, vapor pressure, surface tension, liquid viscosity, liquid thermal conductivity, liquid specific heat, latent heat, vapor specific heat, vapor diffusion coefficient, vapor viscosity and vapor thermal conductivity. The results show significant effects of the fuel physical properties on ignition delay and burning rates at various engine operating conditions.

Fatigue behavior of dissimilar ultrasonic spot welds in lap-shear specimens of magnesium AZ31B-H24 and hot-dipped-galvanized mild steel sheets is investigated based on experimental observations, closed-form stress intensity factor solutions, and a fatigue life estimation model. Fatigue tests were conducted under different load ranges with two load ratios of 0.1 and 0.2. Optical micrographs of the welds after the tests were examined to understand the failure modes of the welds. The micrographs show that the welds mainly fail from kinked fatigue cracks growing through the magnesium sheets. The optical micrographs also indicate that failure mode changes from the partial nugget pullout mode under low-cycle loading conditions to the transverse crack growth mode under high-cycle loading conditions. The closed-form stress intensity factor solutions at the critical locations of the welds are used to explain the locations of fatigue crack initiation and growth.

To make informed decisions about travel and vehicle purchase, consumers need unbiased and accurate information of the fuel economy they will actually obtain. In the past, the EPA fuel economy estimates based on its 1984 rules have been widely criticized for overestimating on-road fuel economy. In 2008, EPA adopted a new estimation rule. This study compares the usefulness of the EPA's 1984 and 2008 estimates based on their prediction bias and accuracy and attempts to improve the prediction of on-road fuel economies based on consumer and vehicle attributes. We examine the usefulness of the EPA fuel economy estimates using a large sample of self-reported on-road fuel economy data and develop an Individualized Model for more accurately predicting an individual driver's on-road fuel economy based on easily determined vehicle and driver attributes. Accuracy rather than bias appears to have limited the usefulness of the EPA 1984 estimates in predicting on-road MPG.

To provide useful information for automakers to design successful plug-in hybrid electric vehicle (PHEV) products and for energy and environmental analysts to understand the social impact of PHEVs, this paper addresses the question of how many of the U.S. consumers, if buying a PHEV, would prefer what electric ranges. The Market-oriented Optimal Range for PHEV (MOR-PHEV) model is developed to optimize the PHEV electric range for each of 36,664 sampled individuals representing U.S. new vehicle drivers. The optimization objective is the minimization of the sum of costs on battery, gasoline, electricity and refueling hassle.

Reactivity Controlled Compression Ignition (RCCI) is an engine combustion strategy that produces low NO and PM emissions with high thermal efficiency. Previous RCCI research has been investigated in single-cylinder heavy-duty engines. The current study investigates RCCI operation in a light-duty multi-cylinder engine at 3 operating points. These operating points were chosen to cover a range of conditions seen in the US EPA light-duty FTP test. The operating points were chosen by the Ad Hoc working group to simulate operation in the FTP test. The fueling strategy for the engine experiments consisted of in-cylinder fuel blending using port fuel-injection (PFI) of gasoline and early-cycle, direct-injection (DI) of diesel fuel. At these 3 points, the stock engine configuration is compared to operation with both the original equipment manufacturer (OEM) and custom-machined pistons designed for RCCI operation.

It is rare for an attempt towards optimization at the fleet-level when consideration is given to the sheer number of seemingly unpredictable interactions among vehicles and infrastructure in congested urban areas. To close the gap, we introduce a simulation based framework to explore the impact of speed synchronization on fuel economy improvement for fleets in traffic. The framework consists of traffic and vehicle modules. The traffic module is used to simulate driver behavior in urban traffic; and the vehicle module is employed to estimate fuel economy. Driving schedule is the linkage between these two modules. To explore the impact, a connected vehicle technology sharing vehicle speed information is used for better fuel economy of a fleet including six vehicles. In all scenarios analyzed, the leading vehicle operates under the EPA Urban Dynamometer Driving Schedule (UDDS), while the other five vehicles follow the leader consecutively.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

Metal additive manufacturing has high potential to produce automobile parts, due to its shape flexibility and unique material properties. On the other hand, residual stress which is generated by rapid solidification causes deformation, cracks and failure under building process. To avoid these problems, understanding of internal residual stress distribution is necessary. However, from the view point of measureable area, conventional residual stress measurement methods such as strain gages and X-ray diffractometers, is limited to only the surface layer of the parts. Therefore, neutron which has a high penetration capability was chosen as a probe to measure internal residual stress in this research. By using time of flight neutron diffraction facility VULCAN at Oak Ridge National Laboratory, residual stress for mono-cylinder head, which were made of aluminum alloy, was measured non-distractively. From the result of precise measurement, interior stress distribution was visualized.

Given the importance of the fuel-injection process on the combustion and emissions performance of gasoline direct injected engines, there has been significant recent interest in understanding the fluid dynamics within the injector, particularly around the needle and through the nozzles. The pressure losses and transients that occur in the flow passages above the needle are also of interest. Simulations of these injectors typically use the nominal design geometry, which does not always match the production geometry. Computed tomography (CT) using x-ray and neutron sources can be used to obtain the real geometry from production injectors, but there are trade-offs in using these techniques. X-ray CT provides high resolution, but cannot penetrate through the thicker parts of the injector. Neutron CT has excellent penetrating power but lower resolution.

Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems.

In this study we identify components of a typical biodiesel fuel and estimate both their individual and mixed thermo-physical and transport properties. We then use the estimated mixture properties in computational simulations to gauge the extent to which combustion is modified when biodiesel is substituted for conventional diesel fuel. Our simulation studies included both conventional diesel combustion (DI) and premixed charge compression ignition (PCCI). Preliminary results indicate that biodiesel ignition is significantly delayed due to slower liquid evaporation, with the effects being more pronounced for DI than PCCI. The lower vapor pressure and higher liquid heat capacity of biodiesel are two key contributors to this slower rate of evaporation. Other physical properties are more similar between the two fuels, and their impacts are not clearly evident in the present study.

The effect of biodiesel content on homogeneous charge compression ignition (HCCI) engine performance has been investigated both experimentally and by computer simulation. Combustion experiments were performed in a single cylinder HCCI engine using blends of soy biodiesel in ultra low sulfur diesel, with concentrations ranging from 0 to 50 vol% and equivalence ratios (Φ) from 0.38 to 0.48. Data from the engine tests included combustion analysis and exhaust composition analysis with standard gaseous emissions equipment. The engine utilized a custom port fuel injection strategy to provide highly premixed charges of fuel and air, making it possible to compare the results with single zone chemical kinetics simulations that were performed using CHEMKIN III, with a reaction set including 670 species and over 3000 reactions.

The heat treatment of steel parts is an essential step in the manufacturing of high-performance components for a variety of commercial and military products. Distortion in the size and shape of parts resulting from the heat treatment process is a pervasive manufacturing problem that causes higher finishing costs, excessive scrap and rework, long delivery times, and negative environmental impact. To date, techniques that have been developed to reduce or eliminate heat treatment distortion are largely based on experience and have been limited to trial and error. This presentation describes the philosophy and results of an ongoing collaborative project to develop a methodology and computer simulation capability to predict ferrous alloy component response (distortion, residual stress, and microstructure) to industrial heat treatment processes for automotive, truck, bearing, and aerospace applications.

The computer-aided-design and analysis of a robust casting process requires the optimization of both mold filling and solidification. A number of commercial casting codes are available for modeling the fluid flow during mold filling and the heat transfer during solidification. The next generation casting process models will build on present capabilities to allow the prediction of microporosity and other defects and microstructure. This paper will discuss the issues involved in the development of next generation casting process models and present results from a computer model for microporosity prediction that is based on first principles, and will take into account alloy composition, alloy microstructure, the initial hydrogen content of the liquid alloy, and the resistance to inter-dendritic fluid flow to feed shrinkage.

Current political and environmental concerns are driving renewed efforts to develop techniques for improving the efficiency of internal combustion engines. A detailed thermodynamic analysis of an engine and its components from a 1st and 2nd Law perspective is necessary to characterize system losses and to identify efficiency opportunities. We have developed a method for performing this analysis using simulation results from commercially available engine-modeling software packages such as WAVE® from Ricardo, Inc., and GT-Power™ from Gamma Technologies, Inc. Results from the simulation are post-processed to compute thermodynamic properties such as internal energy, enthalpy, entropy, and availability (or exergy) which are required to perform energy and availability balances for the system. This analysis is performed for all major engine components (turbocharger, intercooler, EGR cooler, etc.) and for the engine as a whole as a function of crank angle over an entire engine cycle.

To address the growing need for accurate predictions of combustion phasing and emissions for development of advanced engines, a more accurate definition of model fuels and their associated chemical-kinetics mechanisms are necessary. Wide variations in street fuels require a model-fuel blending methodology to allow simulation of fuel-specific characteristics, such as ignition timing, emissions, and fuel vaporization. We present a surrogate-blending technique that serves as a practical modeling tool for determination of surrogate blends specifically tailored to different real-fuel characteristics, with particular focus on model fuels for gasoline engine simulation. We start from a palette of potential model-fuel components that are based on the characteristic chemical classes present in real fuels. From this palette, components are combined into a surrogate-fuel blend to represent a real fuel with specific fuel properties.

While gasoline surrogate development has progressed in the areas of more complex surrogate mixtures and in kinetic modeling tools and mechanism development, it is generally recognized that further development is still needed. This paper represents a small step in supporting this development by providing comparisons between experimental engine data and surrogate-based kinetic models. In our case, the HCCI engine data comes from a port-injected, single-cylinder research engine with intake-air heating for combustion phasing control. Timing sweeps were run at constant fuel rate for three market gasolines and five surrogate mixtures. Modeling was done using the CHEMKIN software with a gasoline mechanism set containing 1440 species and 6572 reactions. Five pure compounds were selected for the surrogate blends and include iso-octane, n-heptane, toluene, methylcyclohexane, and 1-hexene.

To accurately predict emissions as well as combustion phasing in a homogeneous charge compression ignition (HCCI) engine, detailed chemistry needs to be used in Computational Fluid Dynamics (CFD) modeling. In this work, CFD simulations of an Oak Ridge National Laboratory (ORNL) gasoline HCCI engine have been performed with full coupling to detailed chemistry. Engine experiments using an E30 gasoline surrogate blend were performed at ORNL, which included measurements of several trace species in the exhaust gas. CFD modeling using a detailed mechanism for the same fuel composition used in the experiments was also performed. Comparisons between data and model are made over a range of intake temperatures. The (experiment & model) surrogate blend consists of 33 wt % ethanol, 8.7 % n-heptane and 58.3 % iso-octane. The data and simulations involve timing sweeps using intake temperature to control combustion phasing at a constant fuel rate.