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In July 1997, the Pacific Northwest and Alaska Regional Bioenergy Program, in cooperation with several industrial and institutional partners initiated a long-haul 322,000 km (200,000 mile) operational demonstration using a biodiesel and diesel fuel blend in a 324 kW (435 HP), Caterpillar 3406E Engine, and a Kenworth Class 8 heavy duty truck. This project was designed to: develop definitive biodiesel performance information, collect emissions data for both regulated and non-regulated compounds including mutagenic activity, and collect heavy-duty operational engine performance and durability information. To assess long-term engine durability and wear; including injector, valve and port deposit formations; the engine was dismantled for inspection and evaluation at the conclusion of the demonstration. The fuel used was a 50% blend of biodiesel produced from used cooking oil (hydrogenated soy ethyl ester) and 50% 2-D petroleum diesel.

In a new generation of unit injector (HEUI-Hydraulically Actuated and Electronically Controlled), a thin gap of oil film exists between the armature and solenoid. At low temperatures, high pressure slows the poppet causing poor injector performance. A CFD(Computational Fluid Dynamics) study with moving boundaries/meshes was undertaken to evaluate squeeze film behavior and determine optimum venting arrangement for improved injector performance.

With the advancement of simulation software development, the efficiency of vehicle and powertrain controls research and development can be significantly improved. Traditionally, during the development of a new control algorithm, dyno or on-road testing is necessary to validate the algorithm. Physical testing is not only costly, but also time consuming. In this study, a virtual platform is developed to reduce the effort of testing. To improve the simulation accuracy, co-simulation of multiple software is suggested as each software specializes in certain area. The Platform includes Matlab Simulink, PTV Vissim, Tass Prescan and AVL Cruise. PTV Vissim is used to provide traffic environment to PreScan. PreScan is used for ego vehicle simulation and visualization. Traffic, signal and road network are synchronized in Vissim and PreScan. Powertrain system is simulated in Cruise. MATALB/Simulink serves as master of this co-simulation, and integrates the different software together.

To advance vehicle lightweighting, chopped carbon fiber sheet molding compound (SMC) is identified as a promising material to replace metals. However, there are no effective tools and methods to predict the mechanical property of the chopped carbon fiber SMC due to the high complexity in microstructure features and the anisotropic properties. In this paper, a Representative Volume Element (RVE) approach is used to model the SMC microstructure. Two modeling methods, the Voronoi diagram-based method and the chip packing method, are developed to populate the RVE. The elastic moduli of the RVE are calculated and the two methods are compared with experimental tensile test conduct using Digital Image Correlation (DIC). Furthermore, the advantages and shortcomings of these two methods are discussed in terms of the required input information and the convenience of use in the integrated processing-microstructure-property analysis.

Time-averaged temperatures at critical locations on the piston of a direct-fuel injected, two-stroke, 388 cm3, research engine were measured using an infrared telemetry device. The piston temperatures were compared to data [7] of a carbureted version of the two-stroke engine, that was operated at comparable conditions. All temperatures were obtained at wide open throttle, and varying engine speeds (2000-4500 rpm, at 500 rpm intervals). The temperatures were measured in a configuration that allowed for axial heat flux to be determined through the piston. The heat flux was compared to carbureted data [8] obtained using measured piston temperatures as boundary conditions for a computer model, and solving for the heat flux. The direct-fuel-injected piston temperatures and heat fluxes were significantly higher than the carbureted piston. On the exhaust side of the piston, the direct-fuel injected piston temperatures ranged from 33-73 °C higher than the conventional carbureted piston.

Single-cylinder engine experiments and computational fluid dynamics (CFD) modeling were used in this study to conduct a comprehensive evaluation of the accuracy of the modeling approach, with a focus on soot emissions. A semi-empirical soot model, the classic two-step Hiroyasu model with Nagle and Strickland-Constable oxidation, was used. A broad range of direct-injected (DI) combustion systems were investigated to assess the predictive accuracy of the soot model as a design tool for modern DI diesel engines. Experiments were conducted on a 2.5 liter single-cylinder engine. Combustion system combinations included three unique piston bowl shapes and seven variants of a common rail fuel injector. The pistons included a baseline “Mexican hat” piston, a reentrant piston, and a non-axisymmetric piston similar to the Volvo WAVE design. The injectors featured six or seven holes and systematically varied included angles from 120 to 150 degrees and hole sizes from 170 to 273 μm.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

Hybrid vehicle engines modified for high exhaust gas recirculation (EGR) are a good choice for high efficiency and low NOx emissions. Such operation can result in an HEV when a downsized engine is used at high load for a large fraction of its run time to recharge the battery or provide acceleration assist. However, high EGR will dilute the engine charge and may cause serious performance problems such as incomplete combustion, torque fluctuation, and engine misfire. An efficient way to overcome these drawbacks is to intensify tumble leading to increased turbulent intensity at the time of ignition. The enhancement of turbulent intensity will increase flame velocity and improve combustion quality, therefore increasing engine tolerance to higher EGR. It is accepted that the detailed experimental characterization of flow field near top dead center (TDC) in an engine environment is no longer practical and cost effective.

Today's engine and combustion process development is closely related to the intake port layout. Combustion, performance and emissions are coupled to the intensity of turbulence, the quality of mixture formation and the distribution of residual gas, all of which depend on the in-cylinder charge motion, which is mainly determined by the intake port and cylinder head design. Additionally, an increasing level of volumetric efficiency is demanded for a high power output. Most optimization efforts on typical homogeneous charge spark ignition (HCSI) engines have been at low loads because that is all that is required for a vehicle to make it through the FTP cycle. However, due to pumping losses, this is where such engines are least efficient, so it would be good to find strategies to allow the engine to operate at higher loads.

The fidelity of the hybrid electric vehicle simulation is increased with the integration of a computationally-efficient finite-element based electric machine model, in order to address optimization of component design for system level goals. In-wheel electric motors are considered because of the off-road military application which differs significantly from commercial HEV applications. Optimization framework is setup by coupling the vehicle simulation to the constrained optimization solver. Utilizing the increased design flexibility afforded by the model, the solver is able to reshape the electric machine's efficiency map to better match the vehicle operation points. As the result, the favorable design of the e-machine is selected to improve vehicle fuel economy and reduce cost, while satisfying performance constraints.

A framework to study the response of seated operators to whole-body vibration (WBV) is presented in this work. The framework consists of (i) a six-degree-of-freedom man-rated motion platform to play back ride files of typical heavy off-road machines; (ii) an optical motion capture system to collect 3D motion data of the operators and the surrounding environment (seat and platform); (iii) a computer skeletal model to embody the tested subjects in terms of their body dimensions, joint centers, and inertia properties; (iv) a marker placement protocol for seated positions that facilitates the process of collecting data of the lower thoracic and the lumbar regions of the spine regardless of the existence of the seatback; and (v) a computer human model to solve the inverse kinematics/dynamic problem for the joint profiles and joint torques. The proposed framework uses experimental data to answer critical questions regarding human response to WBV.

The components in a hybrid electric vehicle (HEV) powertrain include the battery pack, an internal combustion engine, and the electric machines such as motors and possibly a generator. These components generate a considerable amount of heat during driving cycles. A robust thermal management system with advanced controller, designed for temperature tracking, is required for vehicle safety and energy efficiency. In this study, a hybridized mid-size truck for military application is investigated. The paper examines the integration of advanced control algorithms to the cooling system featuring an electric-mechanical compressor, coolant pump and radiator fans. Mathematical models are developed to numerically describe the thermal behavior of these powertrain elements. A series of controllers are designed to effectively manage the battery pack, electric motors, and the internal combustion engine temperatures.

Vehicle structure crashworthiness design is one of the most challenging problems in product development and it has been studied for decades. Challenges still remain, which include developing a reliable and systematic approach for general crashworthiness design problems, which can be used to design an optimum vehicle structure in terms of topology, shape, and size, and for both structural layout and material layout. In this paper, an advanced and systematic approach is presented, which is called Magic Cube (MQ) approach for crashworthiness design. The proposed MQ approach consists of three major dimensions: Decomposition, Design Methodology, and General Considerations. The Decomposition dimension is related to the major approaches developed for the crashworthiness design problem, which has three layers: Time (Process) Decomposition, Space Decomposition, and Scale Decomposition.

The reach capability of drivers is currently represented in vehicle design practice in two ways. The SAE Recommended Practice J287 presents maximum reach capability surfaces for selected percentiles of a generic driving population. Driver reach is also simulated using digital human figure models. In typical applications, a family of figure models that span a large range of the target driver population with respect to body dimensions is positioned within a digital mockup of the driver's workstation. The articulated segments of the figure model are exercised to simulate reaching motions and driver capabilities are calculated from the constraints of the kinematic model. Both of these current methods for representing driver reach are substantially limited. The J287 surfaces are not configurable for population characteristics, do not provide the user with the ability to adjust accommodation percentiles, and do not provide any guidance on the difficulty of reaches that are attainable.

The performance of five positive k-factor injector tips has been assessed in this work by analyzing a comprehensive set of injected mass, momentum, and spray measurements. Using high speed shadowgraphs of the injected diesel plumes, the sensitivities of measured vapor penetration and dispersion to injection pressure (100-250MPa) and ambient density (20-52 kg/m3) have been compared with the Naber-Siebers empirical spray model to gain understanding of second order effects of orifice diameter. Varying in size from 137 to 353μm, the orifice diameters and corresponding injector tips are appropriate for a relatively wide range of engine cylinder sizes (from 0.5 to 5L). In this regime, decreasing the orifice exit diameter was found to reduce spray penetration sensitivity to differential injection pressure. The cone angle and k-factored orifice exit diameter were found to be uncorrelated.

Traditional Lagrangian spray modeling approaches for internal combustion engines are highly grid-dependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was recently developed and implemented by Senecal et al., (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against non-evaporating, evaporating, and reacting data from the literature.

Assessing the dynamic performance of multilayer plates subjected to impulsive loading is of interest for identifying configurations that either absorb energy or transmit the energy in the transverse directions, thereby mitigating the through-thickness energy propagation. A reduced-order modeling approach is presented in this paper for rapidly evaluating the structural dynamic performance of various multilayer plate designs. The new approach is based on the reverberation matrix method (RMM) with the theory of generalized rays for fast analysis of the structural dynamic characteristics of multilayer plates. In the RMM model, the waves radiated from the dynamic load are reflected and refracted at each interface between layers, and the waves within each layer are transmitted with a phase lag. These two phenomena are represented by the global scattering matrix and the global phase matrix, respectively.

A special spot weld element (SWE) is presented for simplified representation of spot joints in complex structures for structural durability evaluation using the mesh-insensitive structural stress method. The SWE is formulated using rigorous linear four-node Mindlin shell elements with consideration of weld region kinematic constraints and force/moments equilibrium conditions. The SWEs are capable of capturing all major deformation modes around weld region such that rather coarse finite element mesh can be used in durability modeling of complex vehicle structures without losing any accuracy. With the SWEs, all relevant traction structural stress components around a spot weld nugget can be fully captured in a mesh-insensitive manner for evaluation of multiaxial fatigue failure.

In applications of the Energy Finite Element Analysis (EFEA) there is an increasing need for developing comprehensive models with a large number of elements which include both structural and interior fluid elements, while certain parts of the structure are considered to be exposed to an external fluid loading. In order to accommodate efficient computations when using simulation models with a large number of elements, joints, and domains, a substructuring computational capability has been developed. The new algorithm is based on dividing the EFEA model into substructures with internal and interface degrees of freedom. The system of equations for each substructure is assembled and solved separately and the information is condensed to the interface degrees of freedom. The condensed systems of equations from each substructure are assembled in a reduced global system of equations. Once the global system of equations has been solved the solution for each substructure is pursued.

Engineering components and systems are usually subjected to mixed-mode and multiaxial fatigue loadings, and these conditions should be considered in product durability and reliability design and the maintenance of aging equipment, especially mission-critical components and systems. However, modeling the damage and degradation processes under these complex loading conditions is difficult and challenging task because not only the concepts, such as range, mean, peak, valley etc., developed for uniaxial loading usually cannot be directly transferred to mixed-mode and multiaxial loadings, but also some very unique phenomena related to these complex loading conditions. One such a phenomenon is the loading path effect that can be simply described as: out-of-phase loading is more damaging than in-phase loading for some ductile materials.