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A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

The regulatory requirements to lower both greenhouse gases and criteria pollutants from heavy duty engines are driving new perspectives on the interaction between fuels and engines. Fuels that lower the burden on engine manufacturers to reach these goals may be of particular interest. Naphtha, a fuel with a higher volatility than diesel, but with the ability to be burned under traditional mixing-controlled combustion conditions is one such fuel. The higher volatility promotes fuel-air mixing and when combined with its typically lower aromatic content, leads to reduced soot emissions when compared directly to diesel. Naphtha also has potential to be less energy-intensive at the refinery level, and its use in transportation applications can potentially reduce CO2 emissions on a well-to-wheels basis.

Achieving robust ignitability for compression ignition of diesel engines at cold conditions is traditionally challenging due to insufficient fuel vaporization, heavy wall impingement, and thick wall films. Gasoline compression ignition (GCI) has shown the potential to offer an enhanced NOx-particulate matter tradeoff with diesel-like fuel efficiency, but it is unknown how the volatility and reactivity of the fuel will affect ignition under very cold conditions. Therefore, it is important to investigate the impact of fuel physical and chemical properties on ignition under pressures and temperatures relevant to practical engine operating conditions during cold weather. In this paper, 0-D and 3-D computational fluid dynamics (CFD) simulations of GCI combustion at cold conditions were performed.

In this study a detailed 1-D engine system model coupled with 3-D computational fluid dynamics (CFD) analysis was used to investigate the air system design requirements for a heavy duty diesel engine operating with low reactivity gasoline-like fuel (RON70) under partially premixed combustion (PPC) conditions. The production engine used as the baseline has a geometric compression ratio (CR) of 17.3 and the air system hardware consists of a 1-stage variable geometry turbine (VGT) with a high pressure exhaust gas recirculation (HP-EGR) loop. The analysis was conducted at six engine operating points selected from the heavy-duty supplemental emissions test (SET) cycle, i.e., A75, A100, B25, B50, B75, and C100. The engine-out NOx target was set at 1 g/hp-hr (1.34 g/kWh) to address a future hypothetical tailpipe NOx limit of 0.02 g/hp-hr (0.027 g/kWh) while an engine-out particulate matter (PM) target of 0.01 g/hp-hr (0.013 g/kWh) was selected to comply with existing EPA 2010 regulations.

The popularity of new Human-Machine-Interfaces (HMIs) comes with growing concerns for driver distraction. In part, this concern stems from a rising challenge to design systems that can make functions accessible to drivers while maintaining drivers’ ability to cope with the complex driving task. Therefore, engineers need assessment methods which can evaluate how well a user interface achieves the dual-goal of making secondary tasks accessible, while allowing safe driving. Most prior methods have emphasized measuring off-road glances during HMI use. An alternative to this is to consider both on-road and off-road glances, as done in Kircher and Ahlstrom’s AttenD algorithm [1]. In this study, we compared two types of prevalent visual-manual user interfaces based on AttenD. The two HMIs of interest were a touchscreen-based interface (already in production) and a remote-rotary-controller-based interface (a high-fidelity prototype).

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

This research investigates the potential of gasoline compression ignition (GCI) to achieve low engine-out NOx emissions with high fuel efficiency in a heavy-duty diesel engine. The experimental work was conducted in a model year (MY) 2013 Cummins ISX15 heavy-duty diesel engine, covering a load range of 5 to 15 bar BMEP at 1375 rpm. The engine compression ratio (CR) was reduced from the production level of 18.9 to 15.7 without altering the combustion bowl design. In this work, four gasolines with research octane number (RON) ranging from 58 to 93 were studied. Overall, GCI operation resulted in enhanced premixed combustion, improved NOx-soot tradeoffs, and similar or moderately improved fuel efficiency compared to diesel combustion. A split fuel injection strategy was employed for the two lower reactivity gasolines (RON80 and RON93), while the RON60 and RON70 gasolines used a single fuel injection strategy.

It has been the core problem in the automotive industry to realize vehicle lightweight, fuel economy and environmental protection. Weight reduction of body structure is playing a rather important role in lightening of a full vehicle. This paper presents the study, from the point of view of safety, of the structural lightweight of the front side rail and radiator support of a new multi-purpose vehicle (MPV). Frontal crash simulation is performed and comparison is shown with original crashworthiness performance of full vehicle, in deformed shape of parts, force response, original absorbed energy of full vehicle and deceleration of a key point in A-pillar. The study verifies the feasibility of this lightweight scheme, which is an exploration in the study on structural lightweight of auto-body and may offer a referential experience for the future work. Achieved weight reductions of the front side rail and radiator support are 9.11% and 22.88%, respectively.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.

Fuel stratification effects on the combustion and emissions behaviors for partially premixed compression ignition (PPCI) combustion of a high reactivity gasoline (research octane number of 80) was investigated using the third generation Gasoline Direct-Injection Compression Ignition (Gen3 GDCI) multi-cylinder engine. The PPCI combustion mode was achieved through a double injection strategy. The extent of in-cylinder fuel stratification was tailored by varying the start of second fuel injection timing (SOIsecond) while the first fuel injection event was held constant and occurred during the intake stroke. Based on the experimental results, three combustion characteristic zones were identified in terms of the SOIsecond - CA50 (crank angle at 50% cumulative heat release) relationship: (I) no response zone (HCCI-like combustion); (II) negative CA50 slope zone: (early PPCI mode); and (III) positive CA50 slope zone (late PPCI mode).

There is a strong evidence that the overrepresentation of teen drivers in motor vehicle crashes is mainly due to their poor hazard perception skills, i.e., they are unskilled at appropriately detecting and responding to roadway hazards. This study evaluates two cuing systems designed to help teens better understand their driving environment. Both systems use directional color-coding to represent different levels of proximity between one’s vehicle and outside agents. The first system provides an overview of the location of adjacent objects in a head-up display in front of the driver and relies on drivers’ focal vision (focal cuing system). The second system presents similar information, but in the drivers’ peripheral vision, by using ambient lights (peripheral cuing system). Both systems were retrofitted into a test vehicle (2014 Toyota Camry). A within-subject experiment was conducted at the University of Michigan Mcity test-track facility.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NOx within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions.