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Testing of ducted fuel injection (DFI) in a single-cylinder engine with production-like hardware previously showed that adding a duct structure increased soot emissions at the full load, rated speed operating point [1]. The authors hypothesized that the DFI flame, which travels faster than a conventional diesel combustion (CDC) flame, and has a shorter distance to travel, was being re-entrained into the on-going fuel injection around the lift-off length (LOL), thus reducing air entrainment into the on-going injection. The engine operating condition and the engine combustion chamber geometry were duplicated in a constant pressure vessel. The experimental setup used a 3D piston section combined with a glass fire deck allowing for a comparison between a CDC flame and a DFI flame via high-speed imaging. CH* imaging of the 3D piston profile view clearly confirmed the re-entrainment hypothesis presented in the previous engine work.

Predicting the aerodynamic performance of an aircraft in icing conditions is critical as failures in an aircraft’s ice protection system can compromise flight safety. Aerodynamic effects of icing have typically relied on RANS modeling, which usually struggles to predict stall behavior, including those induced by surface roughness. Encouraged by recent studies using LES that demonstrate the ability to predict stall characteristics on full aircraft with smooth wings at an affordable cost [1], this study seeks to apply this methodology to icing conditions. Measurements of lift, drag, and pitching moments of a NACA23012 airfoil under clean and iced conditions are collected at Re = 1.8M. Using laser scanned, detailed representations of the icing geometries, LES calculations are conducted to compare integrated loads against experimental measurements in both clean and iced conditions at various angles of attack through the onset of stall [2].

Modeling of icing is important for the design of aircraft lifting surfaces and for the design of efficient propulsion systems. The computational modeling of ice accretion prediction is important to replace the expensive experimental techniques for calculating the ice shapes in Icing tunnels, and the first step toward modeling ice accretion is to accurately compute the droplet collection efficiency which acts as the input to the accretion model. In this work, we perform large-eddy simulations of supercooled droplet transport and impingement onto complex aircraft geometries using a Lagrangian particle approach. We assess the improvement in modeling droplet impingement by computing the droplet collection efficiency and by comparing with the existing experimental data.

Lithium-ion batteries (LIBs) repurposed from retired electric vehicles (EVs) for grid-scale energy storage systems (ESSs) have the potential to contribute to a sustainable, low-carbon-emissions energy future. The economic and technological value of these “second-life” LIB ESSs must be evaluated based on their operation on the electric grid, which determines their aging trajectories. The battery research community needs experimental data to understand the operation of these batteries using laboratory experiments, yet there is a lack of work on experimental evaluation of second-life batteries. Previous studies in the literature use overly-simplistic duty cycling in order to age second-life batteries, which may not produce aging trajectories that are representative of grid-scale ESS operation. This mismatch may lead to inaccurate valuation of retired EV LIBs as a grid resource.

Ducted fuel injection (DFI), a concept that utilizes fuel injection through ducts, was implemented in a constant pressure High Temperature Pressure Vessel at 60 bar ambient pressure, 800-1000 K ambient temperature, and 21 % oxygen. The ducts were 14 mm long and placed 3-4.7 mm from the orifice exit. The duct diameters ranged from 1.6-3.2 mm and had a rounded inlet and a tapered outlet. Diesel fuel was used in single-orifice fuel injectors operating at 250 MPa rail pressure. The objective of this work was to study soot reduction for various combinations of orifice and duct diameters. A complete data set was taken using the 150 μm orifice. A smaller data set was acquired for a 219 μm orifice, showing similar trends. Soot reduction peaked at an optimal duct diameter of 2-2.25 mm, corresponding to an 85-90 % spray area reduction for the 150 μm orifice. Smaller or larger duct diameters were less effective. Duct diameter had a minimal effect on ignition delay.

Gasoline direct-injection (GDI) engine technology improves vehicle fuel economy while decreasing CO2 emissions. The main drawback of GDI technology is the increase in particulate emissions compared to the commonly used port fuel injection technologies. Today’s adopted strategy to limit such emissions relies upon the use of aftertreatment gasoline particulate filters (GPFs). GPFs reduce particulates resulting from fuel combustion. Soot oxidation (also known as regeneration) is required at regular intervals to clean the filter, maintain a consistent soot trapping efficiency, and avoid the formation of soot plugs in the GPF channels. In this paper, starting from a multiphysics GPF model accounting for mass, momentum, and energy transport, a sensitivity analysis is carried out to choose the best mesh refinement, time step, and relative tolerance to ensure a stable numerical solution of the transport equations during regeneration while maintaining low computational time.

In this work, we conduct a sensitivity analysis of the mean-value internal combustion engine exergy-based model, recently developed by the authors, with respect to different driving cycles, ambient temperatures, and exhaust gas recirculation rates. Such an analysis allows to assess how driving conditions and environment affect the exergetic behavior of the engine, providing insights on the system’s inefficiency. Specifically, the work is carried out for a military series hybrid electric vehicle.

Ducted fuel injection (DFI) was tested for the first time in a heavy-duty diesel metal engine. It was implemented on a Caterpillar 2.5-liter single-cylinder heavy-duty diesel engine fitted with a common rail fuel system and a Tier 4 final production piston. Engine tests consisted of single-injection timing sweeps at A100 and C100, where rail pressure and exhaust gas recirculation (EGR) were also varied. A 6-hole fuel injector tip with 205 am orifices was used with a 130° spray angle and rail pressures up to 250 MPa. The ducts were 14 mm long, had a 2.5 mm inner diameter, and were placed 3.8 mm away from the orifice exits. The ducts were attached to a base, which in turn was attached to the cylinder head with bolts. Furthermore, alignment of the ducts and their corresponding fuel jets was accomplished.

A multi-year Power System R&D project was initiated with the objective of developing an off-road hybrid heavy-duty concept diesel engine with front end accessory drive-integrated energy storage. This off-road hybrid engine system is expected to deliver 15-20% reduction in fuel consumption over current Tier 4 Final-based diesel engines and consists of a downsized heavy-duty diesel engine containing advanced combustion technologies, capable of elevated peak cylinder pressures and thermal efficiencies, exhaust waste heat recovery via SuperTurbo™ turbocompounding, and hybrid energy recovery through both mechanical (high speed flywheel) and electrical systems. The first year of this project focused on the definition of the hybrid elements using extensive dynamic system simulation over transient work cycles, with hybrid supervisory controls development focusing on energy recovery and transient load assist, in Caterpillar’s DYNASTY™ software environment.

An improved method for studying mixing-controlled compression ignition (MCCI) flame interactions with an engine combustion chamber has been developed. It is implemented in a constant pressure vessel, which contains a portion of a piston and a portion of a cylinder head, where the cylinder head is emulated by a transparent fused silica window. This method allows for vaporizing or combusting fuel jets to be imaged from two orthogonal directions. The piston and cylinder head can be adjusted to emulate in-engine piston positions from top dead center (TDC) to approximately 15 mm away from TDC. The design allows for pistons from engine bore sizes up to approximately 175 mm to be studied, including the ability to simulate injector spray included angles from 120°-180°. In this study, the piston was made as an extruded piston bowl profile, where the length of the extrusion approximated the arc length between two neighboring jets from a 6-hole injector.

This paper details results of a numerical and experimental investigation into the relative performance of vaned and vaneless mixed flow turbines for application to medium and heavy-duty diesel engines utilizing pulse exhaust systems. Previous investigations into the impact of nozzle vanes on turbine performance considered only open turbine housings, whereas a majority of medium and heavy-duty diesel engine applications are six-cylinder engines using pulse exhaust systems with divided turbines. The two turbine stages for this investigation were carefully designed to meet the constraints of engines with pulse exhaust systems and to control confounding factors that would undermine the vaned vs vaneless performance comparison. Detailed CFD analysis and turbine dynamometer test results confirm a significant efficiency advantage for the vaned turbine stage under both full and partial admission conditions.

This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc. This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices. After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results.

The Society of Automotive Engineers Fatigue Design & Evaluation Committee [SAE FD&E] is actively working on a total life project for weldments, in which the welding residual stress is a key contributor to an accurate assessment of fatigue life. Physics-based welding process simulation and various types of residual stress measurements were pursued to provide a representation of the residual stress field at the failure location in the fatigue samples. A well-controlled and documented robotic welding process was used for all sample fabrications to provide accurate inputs for the welding simulations. One destructive (contour method) residual stress measurement and several non-destructive residual stress measurements-surface X-ray diffraction (XRD), energy dispersive X-ray diffraction (EDXRD), and neutron diffraction (ND)-were performed on the same or similarly welded samples.

Gasoline particulate filters (GPFs) are devices used to filter soot emitted by gasoline direct injection (GDI) engines. A numerical model for a ceria-coated GPF presented in a previous paper by H. Arunachalam et al. in 2017 was developed to predict internal temperature and soot amount combusted during regeneration events. Being that both the internal temperature and the accumulated soot cannot be directly measured during real-time operation and owing to their critical importance for GPF health monitoring as well as regeneration scheduling, the above model turns out to be a valuable tool for OBD applications. In this paper, we first conduct a stochastic analysis to understand the relation between the model parameters and the initial value of the ceria (IV) oxide volume fraction, as a deterministic value for such a state is not known.

The increase in computational power in recent times has led to multidimensional computational fluid dynamics (CFD) modeling tools being used extensively for optimizing the diesel engine piston design. However, it is still common practice in engine CFD modeling to use constant uniform boundary temperatures. This is either due to the difficulty in experimentally measuring the component temperatures or the lack of measurements when simulation is being used predictively. This assumption introduces uncertainty in heat flux predictions. Conjugate heat transfer (CHT) modeling is an approach used to predict the component temperatures by simultaneously modeling the heat transfer in the fluid and the solid phase. However, CHT simulations are computationally expensive as they require more than one engine cycle to be simulated to converge to a steady cycle-averaged component temperature.

Ducted fuel injection (DFI) has been shown to be an effective method to significantly reduce soot formation in mixing controlled compression ignition (MCCI) diesel combustion. This reduction has been demonstrated in both combustion vessels and in an optical engine. The mechanisms driving the soot reduction are to date not fully understood. Optimal duct configurations are also not immediately evident. The objective of this study is to show the effects of two geometric variables, namely distance from fuel injector orifice exit to duct inlet (0.1-6 mm) for a 2x14 mm duct, and duct length variation (8-14 mm) at a given stand-off distance of 0.1 mm. A 138 μm on-axis single-orifice injector operated at 100-250 MPa was used in a heated, continuous flow, constant pressure vessel with optical access.

Single-cylinder engine experiments and computational fluid dynamics (CFD) modeling were used in this study to conduct a comprehensive evaluation of the accuracy of the modeling approach, with a focus on soot emissions. A semi-empirical soot model, the classic two-step Hiroyasu model with Nagle and Strickland-Constable oxidation, was used. A broad range of direct-injected (DI) combustion systems were investigated to assess the predictive accuracy of the soot model as a design tool for modern DI diesel engines. Experiments were conducted on a 2.5 liter single-cylinder engine. Combustion system combinations included three unique piston bowl shapes and seven variants of a common rail fuel injector. The pistons included a baseline “Mexican hat” piston, a reentrant piston, and a non-axisymmetric piston similar to the Volvo WAVE design. The injectors featured six or seven holes and systematically varied included angles from 120 to 150 degrees and hole sizes from 170 to 273 μm.

Ducted fuel injection (DFI) is a developing technology for reducing in-cylinder soot formed during mixing-controlled combustion in diesel compression ignition engines. Fuel injection through a small duct has the effect of extending the lift-off length (LOL) and reducing the equivalence ratio at ignition. In this work, the feasibility of DFI to reduce soot and to enable leaner lifted-flame combustion (LLFC) is investigated for a single diesel jet injected from a 138 μm orifice into engine-like (60-120 bar, 800-950 K) quiescent conditions. High-speed imaging and natural luminosity (NL) measurements of combusting sprays were used to quantify duct effects on jet penetration, ignition delay, LOL, and soot emission in a constant pressure high-temperature-pressure vessel (HTPV). At the highest ambient pressure and temperatures tested, soot luminosity was reduced by as much as 50%.

The new concept of “human-machine shared control” provides an amazing thinking to enhance driving safety, which has been attracted a great deal of research effort in recent years. However, little attention has been paid to the nonlinearity of the shared control system brought by the tire, which significantly influences the control performance under extreme driving conditions. This paper presents a novel shared steering torque control scheme to model the human-machine steering torque interaction near the vehicle’s handling limit, where both driver and driver assistance system (DAS) are exerting steering torque to maneuver the vehicle. A six-order driver-vehicle dynamic system is presented to elaborate the relationship between steering torque input and vehicle lateral motion response. Particularly, we use a piecewise affine (PWA) method to approximate the tire nonlinearity.

With the rapid development of artificial intelligence, autonomous driving technology will finally reshape an automotive industry. Although fully autonomous cars are not commercially available to common consumers at this stage, partially autonomous vehicles, which are defined as level 2 and level 3 autonomous vehicles by SAE J3016 standard, are widely tested by automakers and researchers. A typical Human-Machine-Interface (HMI) for a vehicle takes a form to support a human domination role. Although modern driving assistance systems allow vehicles to take over control at certain scenarios, the typical human-machine-interface has not changed dramatically for a long time. With deep learning neural network technologies penetrating into automotive applications, multi-modal communications between a driver and a vehicle can be enabled by a cost-effective solution.