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Φ-sensitivity is a fuel characteristic that has important benefits for the operation and control of low-temperature gasoline combustion (LTGC) engines. A fuel is φ-sensitive if its autoignition reactivity varies with the fuel/air equivalence ratio (φ). Thus, multiple-injection strategies can be used to create a φ-distribution that leads to several benefits. First, the φ-distribution causes a sequential autoignition that reduces the maximum heat release rate. This allows higher loads without knock and/or advanced combustion timing for higher efficiencies. Second, combustion phasing can be controlled by adjusting the fuel-injection strategy. Finally, experiments show that intermediate-temperature heat release (ITHR) increases with φ-sensitivity, increasing the allowable combustion retard and improving stability. A detailed mechanism was applied using CHEMKIN to understand the chemistry responsible for φ-sensitivity.

As Electric and Hybrid Electric Vehicles (EVs and HEVs) become more prevalent, there is a need to change the power source from gasoline on the vehicle to electricity from the grid in order to mitigate requirements for onboard energy storage (battery weight) as well as to reduce dependency on oil by increasing dependency on the grid (our coal, gas, and renewable energy instead of their oil). Traditional systems for trains and buses rely on physical contact to transfer electrical energy to vehicles in motion. Until recently, conventional magnetically coupled systems required a gap of less than a centimeter. This is not practical for vehicles of the future.

The question of whether increasing the fuel economy of light-duty natural gas fueled vehicles can improve their economic competitiveness in the U.S. market, and help the US Department of Energy meet stated goals for such vehicles is explored. Key trade-offs concerning costs, exhaust emissions and other issues are presented for a number of possible advanced engine designs. Projections of fuel economy improvements for a wide range of lean-burn engine technologies have been developed. It appears that compression ignition technologies can give the best potential fuel economy, but are less competitive for light-duty vehicles due to high engine cost. Lean-burn spark ignition technologies are more applicable to light-duty vehicles due to lower overall cost. Meeting Ultra-Low Emission Vehicle standards with efficient lean-burn natural gas engines is a key challenge.

Shadowgraph/schlieren imaging techniques have often been used for flow visualization of reacting and non-reacting systems. In this paper we show that high-speed shadowgraph visualization in a high-pressure chamber can also be used to identify cool-flame and high-temperature combustion regions of diesel sprays, thereby providing insight into the time sequence of diesel ignition and combustion. When coupled to simultaneous high-speed Mie-scatter imaging, chemiluminescence imaging, pressure measurement, and spatially-integrated jet luminosity measurements by photodiode, the shadowgraph visualization provides further information about spray penetration after vaporization, spatial location of ignition and high-temperature combustion, and inactive combustion regions where problematic unburned hydrocarbons exist. Examples of the joint application of high-speed diagnostics include transient non-reacting and reacting injections, as well as multiple injections.

Aerodynamic performance testing of heavy duty fans involve complicated test setups with specialized equipment and measurement systems as summarized in ANSI 210-07 standard. This paper describes virtual testing and simulation methods to obtain the fan aerodynamic performance data using commercial Computational Fluid Dynamics (CFD) software. Two different virtual test environments were used during the analysis. The first one is a virtual test chamber which is constructed based on the actual fan system installation. The second one is a virtual flow tube which approximates a fan flow test set-up as outlined in ANSI 210-07. The virtual fan is created from (i) the laser scan of the actual fan and (ii) the design specifications of the fan. The virtual test conditions simulate the actual test arrangement by imposing free boundary at flow inlet/outlet and proper fan rotation. The aerodynamic flow rate is controlled by a variable orifice located at the virtual test chamber outlet.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

A major driving force for change in light-duty vehicle design and technology is the National Highway Traffic Safety Administration (NHTSA) and the U.S. Environmental Protection Agency (EPA) joint final rules concerning Corporate Average Fuel Economy (CAFE) and greenhouse gas (GHG) emissions for model years 2017 (MY17) through 2025 (MY25) passenger cars and light trucks. The chief goal of this current study is to compare the already rapid pace of fuel economy improvement and technological change over the previous decade to the required rate of change to meet regulations over the next decade. EPA and NHTSA comparisons of the model year 2005 (MY05) US light-duty vehicle fleet to the model year 2015 (MY15) fleet shows improved fuel economy (FE) of approximately 26% using the same FE estimating method mandated for CAFE regulations. Future predictions by EPA and NHTSA concerning ensemble fleet fuel economy are examined as an indicator of required vehicle rate-of-change.

A generalized re-normalization group (RNG) turbulence model based on the local "dimensionality" of the flow field is proposed. In this modeling approach the model coefficients C₁, C₂, and C₃ are all constructed as functions of flow strain rate. In order to further validate the proposed turbulence model, the generalized RNG closure model was applied to model the backward facing step flow (a classic test case for turbulence models). The results indicated that the modeling of C₂ in the generalized RNG closure model is reasonable, and furthermore, the predictions of the generalized RNG model were in better agreement with experimental data than the standard RNG turbulence model. As a second step, the performance of the generalized RNG closure was investigated for a complex engine flow.

Diesel engine combustion is simulated using Large Eddy Simulation (LES) with a multi-mode combustion (MMC) model. The MMC model is based on the combination of chemical kinetics, chemical equilibrium, and quasi-steady flamelet calculations in different local combustion regimes. The local combustion regime is identified by two combustion indices based on the local temperature and the extent of mixture homogeneity. The LES turbulence model uses the dynamic structure model (DSM) for sub-grid stresses. A new spray model in the LES context is used, and the Reynolds-averaged Navier-Stokes (RANS) based wall model is retained with the LES derived scales. These models are incorporated in the KIVA3V-ERC-Release 2 code for engine combustion simulations. A wide range of diesel engine operating conditions were chosen to validate the combustion model.

While machine learning in autonomous vehicles development has increased significantly in the past few years, the use of reinforcement learning (RL) methods has only recently been applied. Convolutional Neural Networks (CNNs) became common for their powerful object detection and identification and even provided end-to-end control of an autonomous vehicle. However, one of the requirements of a CNN is a large amount of labeled data to inform and train the neural network. While data is becoming more accessible, these networks are still sensitive to the format and collection environment which makes the use of others’ data more difficult. In contrast, RL develops solutions in a simulation environment through trial and error without labeled data. Our research expands upon previous research in RL and Proximal Policy Optimization (PPO) and the application of these algorithms to 1/18th scale cars by expanding the application of this control strategy to a full-sized passenger vehicle.

Exhaust gas recirculation (EGR) can be used to mitigate knock in SI engines. However, experiments have shown that the effectiveness of various EGR constituents to suppress knock varies with fuel type and compression ratio (CR). To understand some of the underlying mechanisms by which fuel composition, octane sensitivity (S), and CR affect the knock-mitigation effectiveness of EGR constituents, the current paper presents results from a chemical-kinetics modeling study. The numerical study was conducted with CHEMKIN, imposing experimentally acquired pressure traces on a closed reactor model. Simulated conditions include combinations of three RON-98 (Research Octane Number) fuels with two octane sensitivities and distinctive compositions, three EGR diluents, and two CRs (12:1 and 10:1). The experimental results point to the important role of thermal stratification in the end-gas to smooth peak heat-release rate (HRR) and prevent acoustic noise.

The objectives of this paper are to describe the research efforts in diesel engine combustion at Sandia National Laboratories' Combustion Research Facility and to provide recent experimental results. We have four diesel engine experiments supported by the Department of Energy, Office of Heavy Vehicle Technologies: a one-cylinder version of a Cummins heavy-duty engine, a diesel simulation facility, a one-cylinder Caterpillar engine to evaluate combustion of alternative fuels, and a homogeneous-charge, compression-ignition (HCCI) engine. Recent experimental results of diesel combustion research will be discussed and a description will be given of our HCCI experimental program and of our HCCI modeling work.

To cope with regulatory standards, minimizing tailpipe emissions with rapid catalyst light-off during cold-start is critical. This requires catalyst-heating operation with increased exhaust enthalpy, typically by using late post injections for retarded combustion and, therefore, increased exhaust temperature. However, retardability of post injection(s) is constrained by acceptable pollutant emissions such as unburned hydrocarbon (UHC). This study provides further insight into the mechanisms that control the formation of UHC under catalyst-heating operation in a medium-duty diesel engine, and based on the understanding, develops combustion strategies to simultaneously improve exhaust enthalpy and reduce harmful emissions. Experiments were performed with a full boiling-range diesel fuel (cetane number of 45) using an optimized five-injections strategy (2 pilots, 1 main, and 2 posts) as baseline condition.

Exhaust gas recirculation (EGR) cooler fouling has emerged as an important issue in diesel engine development. Uncertainty about the level of impact that fuel chemistry may have upon this issue has resulted in a need to investigate the cooler fouling process with emerging non-traditional fuel sources to gage their impact on the process. This study reports experiments using both ultra-low sulfur diesel (ULSD) and 20% biodiesel (B20) at elevated exhaust hydrocarbon conditions to investigate the EGR cooler fouling process. The results show that there is little difference between the degradation in cooler effectiveness for ULSD and B20 at identical conditions. At lower coolant temperatures, B20 exhibits elevated organic fractions in the deposits compared with ULSD, but this does not appear to lead to incremental performance degradation under the conditions studied.

Unburned hydrocarbon (UHC) and carbon monoxide (CO) emission sources are examined in an optical, light-duty diesel engine operating under low load and engine speed, while employing a highly dilute, partially premixed low-temperature combustion (LTC) strategy. The impact of engine load and charge dilution on the UHC and CO sources is also evaluated. The progression of in-cylinder mixing and combustion processes is studied using ultraviolet planar laser-induced fluorescence (UV PLIF) to measure the spatial distributions of liquid- and vapor-phase hydrocarbon. A separate, deep-UV LIF technique is used to examine the clearance volume spatial distribution and composition of late-cycle UHC and CO. Homogeneous reactor simulations, utilizing detailed chemical kinetics and constrained by the measured cylinder pressure, are used to examine the impact of charge dilution and initial stoichiometry on oxidation behavior.

The friction and wear characteristics of three commercially-available, electrolytic coatings for aluminum engine cylinder bores were compared to those of cast iron liners. A Ni/SiC electrocomposite, a hard anodized treatment, and a Plasma Electrolytic Oxidation (PEO) coating were investigated. ASTM standard test method G133-95, non-firing test method, for linearly reciprocating sliding wear was modified to use segments of piston rings and cylinder liners. Tests were conducted using Mr. Goodwrench™ 5W30 as a lubricant at room temperature. The normal force was 150N, the reciprocating frequency was 15Hz, the stroke length was 8mm, and the test duration was 60 minutes. Kinetic friction coefficients ranged from 0.1 to 0.22, typical of boundary lubrication. The Ni/SiC and cast iron samples exhibited the lowest friction. The wear resistance of the Ni/SiC coating was superior to that of cast iron.

For better understanding of soot formation and oxidation processes in a biodiesel spray flame, the morphology, microstructure and sizes of soot particles directly sampled in a spray flame fuelled with soy-methyl ester were investigated using transmission electron microscopy (TEM). The soot samples were taken at different axial locations in the spray flame, 40, 50 and 70 mm from injector nozzle, which correspond to soot formation, peak, and oxidation zones, respectively. The biodiesel spray flame was generated in a constant-volume combustion chamber under a diesel-like high pressure and temperature condition (6.7 MPa, 1000K). Density, diameter of primary particles and radius of gyration of soot aggregates reached a peak at 50 mm from the injector nozzle and was lower or smaller in the formation or oxidation zones of the spray.

For a better understanding of soot formation and oxidation processes in conventional diesel and biodiesel spray flames, the morphology, microstructure and sizes of soot particles directly sampled in spray flames fuelled with US#2 diesel and soy-methyl ester were investigated using transmission electron microscopy (TEM). The soot samples were taken at 50mm from the injector nozzle, which corresponds to the peak soot location in the spray flames. The spray flames were generated in a constant-volume combustion chamber under a diesel-like high pressure and high temperature condition (6.7MPa, 1000K). Direct sampling permits a more direct assessment of soot as it is formed and oxidized in the flame, as opposed to exhaust PM measurements. Density of sampled soot particles, diameter of primary particles, size (gyration radius) and compactness (fractal dimension) of soot aggregates were analyzed and compared. No analysis of the soot micro-structure was made.