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The rollover threshold for a partially filled tanker truck carrying fluid cargo is of great importance due to the catastrophic nature of accidents involving such vehicles, particularly when payloads are toxic and flammable. In this paper, a method for determining the threshold of rollover stability of a specific tanker truck is presented using finite element analysis methods. This approach allows the consideration of many variables which had not been fully incorporated in past models, including nonlinear spring behavior and tank flexibility. The program uses simple mechanical pendulums to simulate the fluid sloshing affects, beam elements to match the torsional and bending stiffness of the tank, and spring damper elements to simulate the suspension. The finite element model of the tanker truck has been validated using data taken by the U.S. Army Aberdeen Test Center (ATC) on a M916A1 tractor/ Etnyre model 60PRS 6000 gallon trailer combination.

The objective of this project, which is supported by the U.S. Department of Energy (DOE) through the National Renewable Energy Laboratory (NREL), is to provide a comprehensive comparison of heavy-duty trucks operating on alternative fuels and diesel fuel. Data collection from up to eight sites is planned. This paper summarizes the design of the project and early results from the first two sites. Data collection is planned for operations, maintenance, truck system descriptions, emissions, duty cycle, safety incidents, and capital costs and operating costs associated with the use of alternative fuels in trucking.

Gasoline-powered vehicles compose the vast majority of all light-duty vehicles in the United States. Improving fuel economy is currently a topic of great interest due to the rapid rise in gasoline costs as well as new fuel-economy and greenhouse-gas emissions standards. The Chevrolet Silverado is currently one of the top selling trucks in the U.S. and has been previously modeled using the commercial finite element code LS-DYNA by the National Crash Analysis Center (NCAC). This state-of the art model was employed to examine alternative weight saving configurations using material alternatives and replacement of traditional steel with composite panels. Detailed mass distribution analysis demonstrated the chassis assembly to be an ideal candidate for weight reduction and was redesigned using Aluminum 7075-T6 Alloy and Magnesium Alloy HM41A-F.

Internal combustion engines with optical access (a.k.a. optical engines) provide additional information in the quest for understanding the fundamental in-cylinder combustion phenomena. However, most optical engines have flat bowl-in-piston combustion chamber to optimize the visualization process, which is different, for example, from the traditional re-entrant bowl in compression ignition engines. A conventional heavy-duty direct-injection compression ignition engine was converted to spark ignition operation by replacing the fuel injector with a spark plug in both optical and metal setups to investigate the effect of the bowl geometry on flame propagation. Experimental data from steady-state lean-burn conditions was used to develop and validate a 3D CFD model of the engine. Numerical simulation results show that flame propagation in the radial direction was similar for both combustion chambers despite their different geometries.

Internal combustion diesel engines with optical access (a.k.a. optical engines) increase the fundamental understanding of combustion phenomena. However, optical access requirements result in most optical engines having a different in-cylinder geometry compared with the conventional diesel engine, such as a flat bowl-in-piston combustion chamber. This study investigated the effect of the bowl geometry on the flow motion and emissions inside a conventional heavy-duty direct-injection diesel engine that can operate in both metal and optical-access configurations. This engine was converted to natural-gas spark-ignition operation by replacing the fuel injector with a spark plug and adding a low-pressure gas injector in the intake manifold for fuel delivery, then operated at steady-state lean-burn conditions. A 3D CFD model based on the experimental data predicted that the different bowl geometry did not significantly affect in-cylinder emissions distribution.

Emissions from 10 refuse trucks equipped with Caterpillar C-10 engines were measured on West Virginia University's (WVU) Transportable Emissions Laboratory in Riverside, California. The engines all used a commercially available Dual-Fuel™ natural gas (DFNG) system supplied by Clean Air Partners Inc. (CAP), and some were also equipped with catalyzed particulate filters (CPFs), also from CAP. The DFNG system introduces natural gas with the intake air and then ignites the gas with a small injection of diesel fuel directly into the cylinder to initiate combustion. Emissions were measured over a modified version of a test cycle (the William H. Martin cycle) previously developed by WVU. The cycle attempts to duplicate a typical curbside refuse collection truck and includes three modes: highway-to-landfill delivery, curbside collection, and compaction. Emissions were compared to similar trucks that used Caterpillar C-10 diesels equipped with Engelhard's DPX catalyzed particulate filters.

Recent development in hydraulic fracking made natural gas (NG) to be a promising alternative gaseous fuel for heavy-duty diesel engines. The existing compression ignition (CI) engine can be retrofitted to NG spark ignition (SI) operation by replacing the diesel injector with a spark plug and fumigating NG into the intake manifold. However, the original diesel piston geometry (flat head and bowl-in-piston chamber) was usually retained to reduce modification cost. The goal of this study was to increase the understanding of the NG lean-burn characteristics in a diesel-like, fast-burn SI combustion chamber. The experimental platform can operate in conventional (i.e., all engine parts are metal) or in optical configuration (i.e., the stock piston and cylinder block are replaced with a see-through piston and an extended cylinder block). The optical data indicated a fast-propagated flame inside the piston bowl.

Machine learning algorithms are effective tools to reduce the number of engine dynamometer tests during internal combustion engine development and/or optimization. This paper provides a case study of using such a statistical algorithm to characterize the heat transfer from the combustion chamber to the environment during combustion and during the entire engine cycle. The data for building the machine learning model came from a single cylinder compression ignition engine (13.3 compression ratio) that was converted to natural-gas port fuel injection spark-ignition operation. Engine dynamometer tests investigated several spark timings, equivalence ratios, and engine speeds, which were also used as model inputs. While building the model it was found that adding the intake pressure as another model input improved model efficiency.

The conversion of existing diesel engines to natural-gas operation can reduce the dependence on petroleum imports and curtail engine-out emissions. A convenient way to perform such conversion is by adding a gas injector in the intake manifold and replacing the diesel fuel injector with a spark plug to initiate and control the combustion process. However, challenges may appear with respect to engine’s efficiency and emissions as natural-gas spark-ignition combustion inside a diesel combustion chamber is different to that in conventional spark ignition engines. For example, major difference is the phasing and duration of the fast burn, defined as the period in which the rate of heat release increases linearly with crank angle. This study presents a methodology to investigate the fast burn inside a diesel geometry using heat release data.

When performing a transient test on a heavy-duty engine as outlined in the Code of Federal Regulations (CFR), defined regression values of engine speed, torque and power must meet specific tolerances for the test to be considered valid. Regression of actual engine feedback data against target points from a schedule defined from an engine map is performed using the method of least squares to determine the slope, intercept, coefficient of regression and standard error of the estimate. To minimize the biasing effects of time lag between actual and schedule data, shifting of the data in the time domain prior to analysis and certain point deletions are permitted. There are presently no regression criteria available for heavy duty chassis testing. This leaves facilities performing these chassis tests with no suitable guidelines to validate individual tests. This study applies the regression analysis used in engine testing to chassis testing and examines the difficulties encountered.