Search Results

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.

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.

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.

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.