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A Lagrangian random vortex-boundary element method has been developed for the simulation of unsteady incompressible flow inside three-dimensional domains with time-dependent boundaries, similar to IC engines. The solution method is entirely grid-free in the fluid domain and eliminates the difficult task of volumetric meshing of the complex engine geometry. Furthermore, due to the Lagrangian evaluation of the convective processes, numerical viscosity is virtually removed; thus permitting the direct simulation of flow at high Reynolds numbers. In this paper, a brief description of the numerical methodology is given, followed by an example of induction flow in an off-centered port-cylinder assembly with a harmonically driven piston and a valve seat situated directly below the port. The predicted flow is shown to resemble the flow visualization results of a laboratory experiment, despite the crude approximation used to represent the geometry.

A few years ago, the automobile industry agreed to adopt standards for a new voltage for the production and use of electrical power. The perception was near universal that 14 Volts was at the limits of its capability, and that 42 Volts would be adopted in a rush. The universal perception was wrong. Since then, much of the auto industry has encountered hard financial times. In a totally separate development, parts suppliers introduced innovations at 14 Volts, some of which a few years ago were thought to require 42 Volts. Today, there are 42-Volt cars and trucks for sale, but only at numbers far lower than necessary to begin to achieve economies of scale. But the factor which caused the industry to develop the 42 Volt standard, the growth of electricity use on motor vehicles, continues with no sign of letup. Further, the true technical obstacles to adoption of 42 Volts have been discovered and at least provisionally solved.

A novel system for the forming of three dimensional sheet metal parts is described that can form a variety of part shapes without the need for fixed tooling, and given only geometry (CAD) information about the desired part. The central elements of this system are a tooling concept based on a programmable discrete die surface and closed-loop shape control. The former give the process the degrees of freedom to change shape rapidly, and the latter is used to insure that the correct shape is formed with a minimum of forming trials. A 540 kN (60 ton) lab press has been constructed with a 0.3 m (12 in) square pair of discrete tools that can be rapidly re-shaped between forming trials. The shape control system uses measured part shapes to determine a shape error and to correct the tooling shape. This correction is based on a unique “Deformation Transfer Function” approach using a spatial frequency decomposition of the surface.

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

Comparisons have been made between dual-fuel (80% port-injection gasoline and 20% direct-injection diesel by mass) Highly Premixed Charge Combustion (HPCC) and blended-fuel (80% gasoline and 20% diesel) Low Temperature Combustion (LTC) modes on a 1-L single-cylinder test engine. In the HPCC mode, both early-injection (E-HPCC) and late-injection (L-HPCC) of diesel have been used. The comparisons have been conducted with a fixed fuel injection rate of 50 mg/cycle at 1500 rpm, and with the combustion phasing fixed (by adjusting the injection timing) so that the 50% heat release point (CA50) is at 8° ATDC. The rapid heat release process of LTC leads to the highest maximum pressure rise rate (MPRR). A two-peak heat release process is observed in L-HPCC, resulting in a lower MPRR. The heat release rate and MPRR values for the E-HPCC are comparable to the L-HPCC values. The EHPCC mode provides the lowest NOX emission. The soot emissions for all three modes are low.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

Piston rings are large contributors to friction losses in internal combustion engines. To achieve higher engine efficiency, low friction ring packs that can maintain good sealing performance must be designed. To support this effort, simulation tools have been developed to model the performance of piston rings during engine operation. However, the challenge of predicting oil consumption, blow by, and ring pack friction with sufficient accuracy remains. This is mostly due to the complexity of this system. Ring dynamics, deformation, interaction with liner and piston, gas and lubricant flow must all be studied together to make relevant predictions. In this paper, a new curved beam finite element model of piston rings is proposed. Ring structural deformation and contact with the liner are treated on two separate grids. A comparison with ring models in the literature and analytical solutions shows that it can provide accurate results efficiently.

The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.

Advances in avionics and display technology are significantly changing the cockpit environment in current transport aircraft. The MIT Aeronautical Systems Lab (ASL) has developed a part-task flight simulator specifically to study the effects of these new technologies on flight crew situational awareness and performance. The simulator is based on a commercially-available graphics workstation, and can be rapidly reconfigured to meet the varying demands of experimental studies. The simulator has been successfully used to evaluate graphical microburst alerting displays, electronic instrument approach plates, terrain awareness and alerting displays, and ATC routing amendment delivery through digital datalinks.

The adoption of turbine engines for automotive power plants has been hampered by the high cost, high leakage and high wear rate of present designs of ceramic-matrix regenerators. Proposals are made and analyzed here for design directions to achieve substantial improvements in all three areas. These include lower-cost extruded and pressed matrices; and clamping seals coupled with incremental movement of the rotary-regenerator matrix.

The ability to make accurate decisions concerning early body-in-white architectures is critical to an automaker since these decisions often have long term cost and weight impacts. We address this need with a methodology which can be used to assist in body architecture decisions using process-based technical cost modeling (TCM) as a filter to evaluate alternate designs. Despite the data limitations of early design concepts, TCM can be used to identify key trends for cost-effectiveness between design variants. A compact body-in-white architecture will be used as a case study to illustrate this technique. The baseline steel structure will be compared to several alternate aluminum intensive structures in the context of production volume.

A model has been developed for estimating the oil vaporization rate from the cylinder liner of a reciprocating engine. The model uses input from an external cycle simulator and an external liner oil film thickness model. It allows for the change in oil composition and the change in oil film thickness due to vaporization. It also estimates how the passage of the compression and scraper rings combine with the vaporization to influence the steady-state composition of the oil layer in the upper ring pack. Computer model results are presented for a compression-ignition engine using a range of liner temperatures, several engine speeds, and two different oils. Vaporization is found to be highly dependent on liner temperature and steady-state oil composition. The steady-state oil composition near the top of the cylinder is found to be significantly different than the composition of the oil near the bottom of the cylinder.

Analytical models (math or computer simulation models) are typically built on the basis of many assumptions and simplifications and hence model prediction could be inaccurate in intended applications. Model validation is thus critical to quantify and improve the degree of accuracy of these models. So far, little work considers model validation for various design configurations so that model prediction is accurate in the intended design space. Furthermore, there is a lack of effective approaches that can be used to quantify model accuracy considering different number of experimental data. To overcome these limitations, objective of this paper is to develop a model validation approach for various design configurations with a reference metric for model accuracy check considering different number of experimental data.

This paper uses a model which calculates the flame kernel formation and its early development in spark ignition engines to examine the causes of cycle-to-cycle combustion variations. The model takes into account the primary physical factors influencing flame development. The spark-generated flame kernel size and temperature required to initialize the computation are completely determined by the breakdown energy and the heat conduction from burned region to unburned region. In order to verify the model, the computation results are compared with high-speed Schlieren photography flame development data from an operating spark-ignition engine; they match remarkably well with each other at all test conditions. For the application of this model to the study of cycle-to-cycle variation of the early stage of combustion, additional input is required.

A model for calculating the residual gas fraction in spark ignition engines has been formulated. The model accounts explicitly for the contribution due to the back flow of exhaust gas to the cylinder during the valve overlap period. The model has been calibrated with in-cylinder hydrocarbon measurements at different values of intake pressure, engine speed, and valve overlap timings.

An aerodynamic model of the entrainment of the head wall quench layer during blowdown and exhaust of an internal combustion engine has been developed. The model may be used to calculate the time resolved concentration and mass flowrate of hydrocarbons (HC) in the exhaust, from a knowledge of engine geometry and operating conditions. It predicts that the area As from which HC are swept will be proportional to the cube root of the ratio of the quench layer thickness δq to the thickness of the viscous boundary layer δv. Since the mass of HC emitted is proportional to the product of the HC density ρHC, the area As and the thickness δq, the HC emissions will be proportional to the product ρHC δq4/3 and this is the most important factor determining the emissions.

Homogenous Charge Compression Ignition (HCCI) engine operation has been demonstrated using both residual trapping and residual re-induction. A number of production valve train technologies can accomplish either of these HCCI modes of operation. Wide-scale testing of the many valve timing and duration options for an HCCI engine is both time and cost prohibitive, thus a modeling study was pursued to investigate optimal HCCI valve-train designs using the geometry of a conventional gasoline Port-Fuel-Injected (PFI) Spark-Ignition (SI) engine. A commercially available engine simulation program (WAVE), as well as chemical kinetic combustion modeling tools were used to predict the best approaches to achieving combustion across a wide variety of valve event durations and timings. The results of this study are consistent with experimental results reported in the literature: both residual trapping and residual re-induction are possible strategies for HCCI combustion.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.