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This paper describes a novel design and verification process for analytical methods used in the development of advanced combustion strategies in internal combustion engines (ICE). The objective was to improve brake thermal efficiency (BTE) as part of the US Department of Energy SuperTruck program. The tools and methods herein discussed consider spray formation and injection schedule along with piston bowl design to optimize combustion efficiency, air utilization, heat transfer, emission, and BTE. The methodology uses a suite of tools to optimize engine performance, including 1D engine simulation, high-fidelity CFD, and lab-scale fluid mechanic experiments. First, a wide range of engine operating conditions are analyzed using 1-D engine simulations in GT Power to thoroughly define a baseline for the chosen advanced engine concept; secondly, an optimization and down-select step is completed where further improvements in engine geometries and spray configurations are considered.

A fracture mechanics based failure prediction strategy for load-carrying composite structures was proposed. This strategy relies on the knowledge of failure modes and local structural details to predict failure based on coupon level test data. The methodology presented here effectively predicted structural failure of a composite hat stringer based on fracture toughness test data. In addition, ply waviness was identified as a critical factor influencing the delamination failure load. The finite element modeling (FEM) technique was used to model the skin-flange region, which included ply waviness effect. The finite element analysis results were used to calculate total strain energy release rate and its Mode I and Mode II components. The finite element analysis predicted unstable delamination growth for positive waviness angles and stable delamination growth for negative waviness angles.

With the rapid development of autonomous driving technologies, the proportion of autonomous vehicles (AVs) will increase and influence road capacity. In this study, the simulation of mixed traffic flow was studied using an improved cellular automata model. Safety inter-vehicle spacing, the length of vehicles and reaction time are introduced into the cellular automata model. We delete the acceleration, deceleration and randomization rule for ideal conditions. Numerical simulations are utilized to analyze road capacity with different proportions of AVs. Road capacity is about 2200 pcu/h/lane for pure manual vehicle (MV) traffic flow and about 3600 pcu/h/lane for pure AV traffic flow. The capacity increases by 19.2% when there are 50% AVs in the traffic flow. And the capacity increases by around 63.6% due to the pure AV traffic flow.