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Fuel injection during negative valve overlap offers a promising method of controlling HCCI combustion, but sorting out the thermal and chemical effects of NVO fueling requires knowledge of temperatures throughout the cycle. Computing bulk temperatures throughout closed portions of the cycle is relatively straightforward using an equation of state, once a temperature at one crank angle is established. Unfortunately, computing charge temperatures at intake valve closing for NVO operation is complicated by a large, unknown fraction of residual gases at unknown temperature. To address the problem, we model blowdown and recompression during exhaust valve opening and closing events, allowing us to estimate in-cylinder charge temperatures based on exhaust-port measurements. This algorithm permits subsequent calculation of crank-angle-resolved bulk temperatures and residual gas fraction over a wide range of NVO operation.

As the piston pin works under significant mechanical load, it is susceptible to wear, seizure, and structural failure, especially in heavy duty internal combustion engines. It has been found that the friction loss associated with the pin is comparable to that of the piston, and can be reduced when the interface geometry is properly modified. However, the mechanism that leads to such friction reduction, as well as the approaches towards further improvement, remain unknown. This work develops a piston pin lubrication model capable of simulating the interaction between the pin, the piston, and the connecting rod. The model integrates dynamics, solid contact, oil transport, and lubrication theory, and applies an efficient numerical scheme with second order accuracy to solve the highly stiff equations. As a first approach, the current model assumes every component to be rigid.

Accurate estimation of the State of Charge (SOC), maximum capacity (Qmax) and internal resistance (R0) are essential for efficient battery monitoring, which is an important part of the battery management system. SOC provides information regarding the instantaneous status of the battery system, while Qmax is a key indicator of the long-term State of Health (SOH) of the cell, which represents the abilities of a battery to store energy and retain charge over extended periods. In addition, the internal resistance is also required to predict the peak available power. Traditional methods use complex models and look-up tables that have high computation requirements and are thus unsuitable for online applications. In this paper, we propose a simple method for simultaneous SOC, Qmax and internal resistance estimation based on a second-order equivalent circuit model (ECM).

The existing models and algorithms for predicting human reachable workspaces do not allow users to specify important performer and task parameters, such as body weight and muscular strengths of reach performer and hand-held object's weight. This makes it difficult to consider individuals with unique physical characteristics (e.g., obesity, muscle strength deficiencies and injuries) and many common tasks involving hand-held objects during reach analyses. To address this, this study presents a novel, biomechanically based workspace generation algorithm. Given a set of input data specified in terms of body dimensions, joint ranges of motion, body joints muscular strengths, gender, body weight of a reach performer and a hand-held load weight, the algorithm generates the corresponding reachable workspace. The algorithm combines the existing human figure based modeling approach with empirically obtained biomechanical data and established biomechanical models and constraints.

Direct injection (DI) has proven to be a promising option in Diesel and low temperature combustion engines. In conventional Diesel and homogeneous charge compression ignition (HCCI) applications, DI lowers soot and NOx production and improves fuel economy. In hydrogen fueled engines, DI provides the appropriate energy density required for high efficiency and low NOx emissions. To realize the full benefit of DI, however, the effect of various injection parameters, such as injection timing, duration, pressure, and dilution, must be investigated and optimized under a range of engine operating conditions. In this work, we have developed a model for high-fidelity calculations of DI processes using the Large Eddy Simulation (LES) technique and an advanced property evaluation scheme. Calculations were performed using an idealized domain to establish a baseline level of validation.

An extravehicular activity (EVA) path-planning and navigation tool, called the Mission Planner, has been developed to assist with pre-mission planning, scenario simulation, real-time navigation, and contingency replanning during astronaut EVAs, The Mission Planner calculates the most efficient path between user-specified waypoints. Efficiency is based on an exploration cost algorithm, which is a function of the estimated astronaut metabolic rate. Selection of waypoints and visualization of the generated path are realized within a 3D mapping interface through terrain elevation models. The Mission Planner is also capable of computing the most efficient path back home from any point along the path.

A comparison of scanning mobility particle sizer (SMPS) and laser-induced incandescence (LII) measurements of diesel particulate matter (PM) was performed. The results reveal the significance of the aggregate nature of diesel PM on interpretation of size and volume fraction measurements obtained with an SMPS, and the accuracy of primary particle size measurements by LII. Volume fraction calculations based on the mobility diameter measured by the SMPS substantially over-predict the space-filling volume fraction of the PM. Correction algorithms for the SMPS measurements, to account for the fractal nature of the aggregate morphology, result in a substantial reduction in the reported volume. The behavior of the particulate volume fraction, mean and standard deviation of the mobility diameter, and primary particle size are studied as a function of the EGR for a range of steady-state engine speeds and loads for a turbocharged direct-injection diesel engine.

While spark-ignition (SI) engine technology is aggressively moving towards challenging (dilute and boosted) combustion regimes, advanced ignition technologies generating non-equilibrium types of plasma are being considered by the automotive industry as a potential replacement for the conventional spark-plug technology. However, there are currently no models that can describe the low-temperature plasma (LTP) ignition process in computational fluid dynamics (CFD) codes that are typically used in the multi-dimensional engine modeling community. A key question for the engine modelers that are trying to describe the non-equilibrium ignition physics concerns the plasma characteristics. A key challenge is also represented by the plasma formation timescale (nanoseconds) that can hardly be resolved within a full engine cycle simulation.

A Computational Fluid Dynamics (CFD) study was conducted on four-vehicle platoons, and the aerodynamic data is then coupled with a high-fidelity truck simulation software (TruckSim) to determine fuel efficiency. Previous studies typically have focused on identical two vehicle platoons, whereas this study accounted for more complex platoon configurations. Heavy duty vehicles (HDVs), both military and commercial, make up a significant percentage of fuel consumption. This study aimed to quantify fuel savings of a platoon consisting of dissimilar trucks and trailers, thus reducing vehicle operational cost. The vehicle platoon featured two M915 trucks and two Peterbilt 579 trucks with dissimilar trailer configurations. An unloaded flatbed trailer, a centered 20 ft shipping container, two 20 ft shipping containers, and a 53 ft box trailer configurations were utilized.

A Multi-Objective Optimization (MOO) problem concerning the thermal control problem of Multifunctional Structures (MFSs) is here addressed. In particular the use of Multi-Objective algorithms from an optimization tool and Self-Organizing Maps (SOM) is proposed for the identification of the optimal topological distribution of the heating components for a multifunctional test panel, the Advanced Bread Board (ABB). MFSs are components that conduct many functions within a single piece of hardware, shading the clearly defined boundaries that identify traditional subsystems. Generally speaking, MFSs have already proved to be a disrupting technology, especially in aeronautics and space application fields. The case study exploited in this paper refers to a demonstrator breadboard called ABB. ABB belongs to a particular subset of an extensive family of MFS, that is, of thermo-structural panels with distributed electronics and a health monitoring network.

Autonomous Navigation for military unmanned ground vehicles requires the ability to act without human intervention in increasingly difficult environments such as off-road terrain and military operations in urban terrain (MOUT). One method to accomplish this is through the combination of a local perception sensor suite with an a priori route planner and a local guidance algorithm that provides an automated obstacle detection, classification, and avoidance capability. This paper will detail the feasibility of this approach based on preliminary test results.

Human explorers of planetary surfaces would benefit greatly from a spacesuit design that facilitates locomotion. To aid in the development of such an extravehicular activity suit, a design effort incorporating the concept of mechanical counter pressure (MCP) was undertaken. Three-dimensional laser scanning of the human body was used to identify the main effects of knee flexion angle on the size and shape of the leg. This laser scanning quantified the changes in shape that must be supported by an MCP garment and the tension that must be developed to produce even MCP. Evaluation of a hybrid-MCP concept using inextensible materials demonstrated strong agreement between experimental data and a mathematical model with rigid cylinder geometry. Testing of a form-fitting garment on the right lower leg of a subject demonstrated successful pressure production. Further research is required to evaluate how evenly pressure can be distributed using the hybrid-MCP concept.

As technical and non-technical constraints make testing of complex physical systems more restrictive, an increased reliance on modeling and simulation of large systems has arisen. Time and/or frequency domain analyzes of finite element models are performed on massively parallel computers. Due to the high consequence of these analyses, there is a need to quantitatively assess the predictive accuracy of finite element models used to simulate these complex physical systems relative to any available experimental results. In this paper we outline our understanding of model validation and the process followed to make a determination of the validity of a model. An example will be used to demonstrate the process.

This paper will describe the development of a load estimation algorithm that is used to estimate the load parameters necessary to detect a vehicle’s proximity to rollover. When operating a vehicle near its handling limits or with large loads, vehicle rollover must be considered for safe operation. Vehicle mass and center of gravity (CG) height play a large role in a vehicle’s rollover propensity. Cargo and passenger vehicles operate under a range of load configurations; therefore, changes in load should be estimated. Researchers have often developed load estimation and rollover detection algorithms separately. This paper will develop a load estimation algorithm and use the load estimates and vehicle states to detect rollover. The load estimation algorithm uses total least squares and is broken into two parts. First, mass is estimated based on a “full-car” dynamic ride model. Next, the CG height and inertia are estimated using the previously estimated mass and a dynamic roll model.

This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.

Topology of liner finish is critical to the performance of internal combustion engines. Proper liner finish simulation processes lead to efficient engine design and research. Fourier methods have been well studied to numerically generate liner topology. However, three major issues wait to be addressed to make the generation processes feasible and reliable. First, in order to simulate real plateau honed liners, approaches should be developed to calculate accurate liner geometric parameters. These parameters are served as the input of the generation algorithm. Material ratio curve, the common geometry calculation method, should be modified so that accurate root mean square of plateau height distribution could be obtained. Second, the set of geometric parameters used in generating liner finish (ISO 13565-2) is different from the set of parameters used in manufacturing industry (ISO 13565-3). Quantitative relations between these two sets should be studied.

Three-piece oil control rings (TPOCR) are widely used in the majority of modern gasoline engines and they are critical for lubricant regulation and friction reduction. Despite their omnipresence, the TPOCRs’ motion and sealing mechanisms are not well studied. With stricter emission standards, gasoline engines are required to maintain lower oil consumption limits, since particulate emissions are strongly correlated with lubricant oil emissions. This piqued our interest in building a numerical model coupling TPOCR dynamics and oil transport to explain the physical mechanisms. In this work, a 2D dynamics model of all three pieces of the ring is built as the main frame. Oil transport in different zones are coupled into the dynamics model. Specifically, two mass-conserved fluid sub-models predict the oil movement between rail liner interface and rail groove clearance to capture the potential oil leakage through TPOCR. The model is applied on a 2D laser induced fluorescence (2D-LIF) engine.

Appropriate spray modeling in multidimensional simulations of diesel engines is well known to affect the overall accuracy of the results. More and more accurate models are being developed to deal with drop dynamics, breakup, collisions, and vaporization/multiphase processes; the latter ones being the most computationally demanding. In fact, in parallel calculations, the droplets occupy a physical region of the in-cylinder domain, which is generally very different than the topology-driven finite-volume mesh decomposition. This makes the CPU decomposition of the spray cloud severely uneven when many CPUs are employed, yielding poor parallel performance of the spray computation. Furthermore, mesh-independent models such as collision calculations require checking of each possible droplet pair, which leads to a practically intractable O(np2/2) computational cost, np being the total number of droplets in the spray cloud, and additional overhead for parallel communications.

An adaptive air/fuel ratio controller for SI engine throttle transient was developed. The scheme is based on an event- based, single- parameter fuel dynamics model. A least- square- error algorithm with an active forgetting factor was used for parameter identifications. A one- step- look- ahead controller was designed to maintain the desired air/fuel ratio by canceling the fuel dynamics with the controller setting updated adaptively according to the identified parameters. When implemented on a Ford Ztech engine and tested under a set of throttle- transient operations, the adaptive controller learned quickly and performed well.

For 2D surface temperature monitoring applications, a variant of Electrical Impedance Tomography (EIT) was evaluated computationally in this study. Literature examples of poor sensor performance in the center of the 2D domains away from the side electrodes motivated these efforts which seek to overcome some of the previously noted shortcomings. In particular, the use of ‘sensing skins’ with novel tailored baseline conductivities was examined using the EIDORS package for EIT. It was found that the best approach for detecting a temperature hot spot depends on several factors such as the current injection (stimulation) patterns, the measurement patterns, and the reconstruction algorithms. For well-performing combinations of these factors, customized baseline conductivities were assessed and compared to the baseline uniform conductivity.