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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.

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.

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 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.

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.

Machine learning models were shown to provide faster results but with similar accuracy to multidimensional computational fluid dynamics or in-depth experiments. This study used a support-vector machine (SVM), a set of related supervised learning methods, to predict the dynamic performance (i.e., engine power and torque) of a heavy-duty natural gas spark ignition engine. The single-cylinder four-stroke test engine was fueled with methane. The engine was operated at different spark timings, mixture equivalence ratios, and engine speeds to provide the data for training and testing the proposed SVM. The results indicated that the performance and accuracy of the built regression model were satisfactory, with correlation coefficient quantities all larger than 0.95 and root-mean-square errors close to zero for both training and validation datasets.

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.

The objective of this paper is to devise innovative lightweight design concepts for heavy vehicle structures. A 1 to 4 prototype of a trailer was built to assess the feasibility of use of polymer composites and to have first hand experience with possible bonding methods to join the design parts. In the current trailer configurations, floor assembly constitutes 70% of the overall weight. The results indicated that sandwich technology with a lightweight core that adds flexural stiffness to the overall design provides a solution to decrease the weight of heavy vehicle systems.