Search Results

A PPCI-diffusion combustion strategy has shown the potential to achieve high efficiency, clean gasoline compression ignition (GCI) combustion across the full engine operating range. By conducting a 3-D CFD-led combustion system design campaign, this investigation was focused on developing a next generation (NextGen), step-lipped piston design concept in a 2.6L advanced light-duty GCI engine. Key geometric features of the NextGen piston bowl were parametrized and studied with customized spray targeting. A low lip positioning design with 128° spray targeting was found to provide the best performance. Fuel injection strategy optimization was performed at a full-load operating point (OP), 2000 rpm/24 bar closed-cycle IMEP (IMEPcc).

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

Automobile industry is facing the great challenge of energy conservation and emission reduction. It's necessary to do some researches on some surface components of a car body to find out which of them may affect aerodynamic drag remarkably. This will help an aerodynamic engineer modify an initial car model more clearly. We also hope to reduce the cost during the process, including time and resources. In this paper, with the purpose of developing an aerodynamic shape optimization process and realizing its automation, a MIRA reference car model was studied and three commercial softwares were integrated-Altair HyperStudy, HyperMesh and CD-adapco STAR-CCM+. The optimization strategy in this paper was: firstly, a DOE (design of experiment) matrix, which contained four design factors and thirty levels was created. The baseline model was morphed according to the DOE matrix. Then the morphed model's aerodynamic drag coefficient (Cd) and lift coefficient (Cl) were calculated via CFD software.

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.

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.

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.

Low temperature combustion through in-cylinder blending of gasoline and diesel offers the potential to improve engine efficiency while yielding low engine-out soot and NOx emissions. This investigation utilized 3-D KIVA combustion simulation to guide the development of viable dual-fuel low temperature combustion strategies for heavy-duty applications. Model-based combustion optimization was performed at 1531rpm and 11 bar BMEP for a 12.4 L heavy-duty truck engine. Various engine operating parameters were explored through design of experiments (DoE). The parameters involved in the optimization process included compression ratio, air-fuel ratio, EGR rate, gasoline-to-diesel ratio, and diesel injection strategy (i.e., single-diesel injection vs. two-diesel injections, diesel injection timings, and the split ratio between two-diesel injections). Optimal cases showed near zero soot emissions and very low NOx emissions.

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.

To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers.

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.

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.

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).

Effectively obtaining physical parameters for vehicle dynamic model is the key to successfully performing any computer-based dynamic analysis, control strategy development or optimization. For a spring and lump mass vehicle model, which is a type of vehicle model widely used, its physical parameters include sprung mass, unsprung mass, inertial properties of the sprung mass, stiffness and damping coefficient of suspension and tire, etc. To minimize error, the paper proposes a method to estimate these parameters from vehicle modal parameters which are in turn obtained through full-car dynamic testing. To verify its effectiveness, a visual vehicle with a set of given parameters, build in the Adams(Automatic Dynamic Analysis of Mechanical Systems)/Car environment, is used to perform the dynamic testing and provide the testing data for the parameter estimation.

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.