Search Results

Predicting the aerodynamic performance of an aircraft in icing conditions is critical as failures in an aircraft’s ice protection system can compromise flight safety. Aerodynamic effects of icing have typically relied on RANS modeling, which usually struggles to predict stall behavior, including those induced by surface roughness. Encouraged by recent studies using LES that demonstrate the ability to predict stall characteristics on full aircraft with smooth wings at an affordable cost [1], this study seeks to apply this methodology to icing conditions. Measurements of lift, drag, and pitching moments of a NACA23012 airfoil under clean and iced conditions are collected at Re = 1.8M. Using laser scanned, detailed representations of the icing geometries, LES calculations are conducted to compare integrated loads against experimental measurements in both clean and iced conditions at various angles of attack through the onset of stall [2].

Modeling of icing is important for the design of aircraft lifting surfaces and for the design of efficient propulsion systems. The computational modeling of ice accretion prediction is important to replace the expensive experimental techniques for calculating the ice shapes in Icing tunnels, and the first step toward modeling ice accretion is to accurately compute the droplet collection efficiency which acts as the input to the accretion model. In this work, we perform large-eddy simulations of supercooled droplet transport and impingement onto complex aircraft geometries using a Lagrangian particle approach. We assess the improvement in modeling droplet impingement by computing the droplet collection efficiency and by comparing with the existing experimental data.

Lithium-ion batteries (LIBs) repurposed from retired electric vehicles (EVs) for grid-scale energy storage systems (ESSs) have the potential to contribute to a sustainable, low-carbon-emissions energy future. The economic and technological value of these “second-life” LIB ESSs must be evaluated based on their operation on the electric grid, which determines their aging trajectories. The battery research community needs experimental data to understand the operation of these batteries using laboratory experiments, yet there is a lack of work on experimental evaluation of second-life batteries. Previous studies in the literature use overly-simplistic duty cycling in order to age second-life batteries, which may not produce aging trajectories that are representative of grid-scale ESS operation. This mismatch may lead to inaccurate valuation of retired EV LIBs as a grid resource.

Gasoline direct-injection (GDI) engine technology improves vehicle fuel economy while decreasing CO2 emissions. The main drawback of GDI technology is the increase in particulate emissions compared to the commonly used port fuel injection technologies. Today’s adopted strategy to limit such emissions relies upon the use of aftertreatment gasoline particulate filters (GPFs). GPFs reduce particulates resulting from fuel combustion. Soot oxidation (also known as regeneration) is required at regular intervals to clean the filter, maintain a consistent soot trapping efficiency, and avoid the formation of soot plugs in the GPF channels. In this paper, starting from a multiphysics GPF model accounting for mass, momentum, and energy transport, a sensitivity analysis is carried out to choose the best mesh refinement, time step, and relative tolerance to ensure a stable numerical solution of the transport equations during regeneration while maintaining low computational time.

In this work, we conduct a sensitivity analysis of the mean-value internal combustion engine exergy-based model, recently developed by the authors, with respect to different driving cycles, ambient temperatures, and exhaust gas recirculation rates. Such an analysis allows to assess how driving conditions and environment affect the exergetic behavior of the engine, providing insights on the system’s inefficiency. Specifically, the work is carried out for a military series hybrid electric vehicle.

Engine and vehicle tailpipe emissions can be measured in laboratories equipped with engine dynamometers and chassis dynamometers, respectively. In addition to laboratory testing, there is an increase in interest to measure on-road vehicle emissions using portable emissions measurement systems in order to determine real-driving emissions. Current methods to quantify engine, vehicle tailpipe, and real-driving emissions include the raw continuous, dilute continuous, and dilute bag measurement methods. Although the dilute bag measurement method is robust, recent improvements to the raw and dilute continuous measurement methods can account for the time delay between the probe tip and analyzer in addition to gas transport dynamics in order to reliably recover the tailpipe concentration signals. These improvements significantly increase the reliability of results using the raw and dilute continuous measurement methods, making them possible alternatives to the bag method.

This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc. This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices. After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results.

Current recommendations for restraining child occupants are based on biomechanical testing and data from national and international field studies primarily conducted prior to 2011. We hypothesized that analysis to identify factors associated with pediatric injury in motor-vehicle crashes using a national database of more recent police-reported crashes in the United States involving children under age 13 where type of child restraint system (CRS) is recorded would support previous recommendations. Weighted data were extracted from the National Automotive Sampling System General Estimates System (NASS-GES) for crash years 2010 to 2015. Injury outcomes were grouped as CO (possible and no injury) or KAB (killed, incapacitating injury, non-incapacitating injury). Restraint was characterized as optimal, suboptimal, or unrestrained based on current best practice recommendations. Analysis used survey methods to identify factors associated with injury.

Gasoline particulate filters (GPFs) are devices used to filter soot emitted by gasoline direct injection (GDI) engines. A numerical model for a ceria-coated GPF presented in a previous paper by H. Arunachalam et al. in 2017 was developed to predict internal temperature and soot amount combusted during regeneration events. Being that both the internal temperature and the accumulated soot cannot be directly measured during real-time operation and owing to their critical importance for GPF health monitoring as well as regeneration scheduling, the above model turns out to be a valuable tool for OBD applications. In this paper, we first conduct a stochastic analysis to understand the relation between the model parameters and the initial value of the ceria (IV) oxide volume fraction, as a deterministic value for such a state is not known.

For high-load applications, natural gas represents a clean burning, readily available, and relatively inexpensive alternative to number 2 Diesel fuel. However, the fuel’s poor ignitability has previously limited implementation to spark ignited and dual-fueled engines. These approaches suffer from reduced peak load and high engine-out particulate emissions, respectively, requiring lean operation and expensive aftertreatment to meet regulatory standards. A high-temperature combustion strategy can overcome the difficult ignitibility, allowing for true Diesel-style combustion of pure methane-the least ignitable and least sooting component of natural gas. In order to achieve this result, a compression system was designed to supply fuel at pressures suitably high to achieve good mixing and short injection durations, and a solenoid-actuated Diesel fuel injector was modified to function at these pressures with a gaseous fuel.

Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

Occupant stature and body shape may have significant effects on injury risks in motor vehicle crashes, but the current finite element (FE) human body models (HBMs) only represent occupants with a few sizes and shapes. Our recent studies have demonstrated that, by using a mesh morphing method, parametric FE HBMs can be rapidly developed for representing a diverse population. However, the biofidelity of those models across a wide range of human attributes has not been established. Therefore, the objectives of this study are 1) to evaluate the accuracy of HBMs considering subject-specific geometry information, and 2) to apply the parametric HBMs in a sensitivity analysis for identifying the specific parameters affecting body responses in side impact conditions. Four side-impact tests with two male post-mortem human subjects (PMHSs) were selected to evaluate the accuracy of the geometry and impact responses of the morphed HBMs.

This study investigates using driver prediction to anticipate energy usage over a 160-meter look-ahead distance for a series, plug-in, hybrid-electric vehicle to improve conventional thermostatic powertrain control. Driver prediction algorithms utilize a hidden Markov model to predict route and a regression tree to predict speed over the route. Anticipated energy consumption is calculated by integrating force vectors over the look-ahead distance using the predicted incline slope and vehicle speed. Thermostatic powertrain control is improved by supplementing energy produced by the series generator with regenerative braking during events where anticipated energy consumption is negative, typically associated with declines or decelerations.

Law enforcement officers (LEO) make extensive use of vehicles to perform their jobs, often spending large portions of a shift behind the wheel. Few LEO vehicles are purpose-built; the vast majority are modified civilian vehicles. Data from the field indicate that LEO suffer from relatively high levels musculoskeletal injury that may be due in part to poor accommodation provided by their vehicles. LEO are also exposed to elevated crash injury risk, which may be exacerbated by a compromise in the performance of the occupant restraint systems due to body-borne equipment. A pilot study was conducted to demonstrate the application of three-dimensional anthropometric scanning and measurement technology to address critical concerns related to vehicle design. Detailed posture and belt fit data were gathered from five law enforcement officers as they sat in the patrol vehicles that they regularly used and in a mockup of a mid-sized vehicle.

Age and experience influence driver ability to cope with transitions between automated and manual driving, especially when drivers are engaged in media use. This study evaluated three age cohorts (young/new drivers, adults, and seniors) on their performance in transitions from automated driving to manual vehicle control in a laboratory driving simulator. Drivers were given three tasks to perform during the automated driving segments: to watch a movie on a tablet, to read a story on a tablet, or to supervise the car's driving. We did not find significant differences in people's driving performance following the different tasks. We also did not find significant differences in driving performance between the people in each age group who successfully completed the study; however, the rejection rate of the senior age group was over 30% because many of the people in this age group had difficulty hearing instructions, understanding tasks, or remembering what to do.

Autonomous vehicles represent a new class of transportation that may be qualitatively different from existing cars. Two online experiments assessed lay perceptions of moral norms and responsibility for traffic accidents involving autonomous vehicles. In Experiment 1, 120 US adults read a narrative describing a traffic incident between a pedestrian and a motorist. In different experimental conditions, the pedestrian, the motorist, or both parties were at fault. Participants assigned less responsibility to a self-driving car that was at fault than to a human driver who was at fault. Participants confronted with a self-driving car at fault allocated greater responsibility to the manufacturer and the government than participants who were confronted with a human driver at fault did.

The objective of this study is to develop a method that uses a combination of field data analysis, naturalistic driving data analysis, and computational simulations to explore the potential injury reduction capabilities of integrating passive and active safety systems in frontal impact conditions. For the purposes of this study, the active safety system is actually a driver assist (DA) feature that has the potential to reduce delta-V prior to a crash, in frontal or other crash scenarios. A field data analysis was first conducted to estimate the delta-V distribution change based on an assumption of 20% crash avoidance resulting from a pre-crash braking DA feature. Analysis of changes in driver head location during 470 hard braking events in a naturalistic driving study found that drivers’ head positions were mostly in the center position before the braking onset, while the percentage of time drivers leaning forward or backward increased significantly after the braking onset.

The aging population is a growing concern as the increased fragility and frailty of the elderly results in an elevated incidence of injury as well as an increased risk of mortality and morbidity. To assess elderly injury risk, age-specific computational models can be developed to directly calculate biomechanical metrics for injury. The first objective was to develop an older occupant Global Human Body Models Consortium (GHBMC) average male model (M50) representative of a 65 year old (YO) and to perform regional validation tests to investigate predicted fractures and injury severity with age. Development of the GHBMC M50 65 YO model involved implementing geometric, cortical thickness, and material property changes with age. Regional validation tests included a chest impact, a lateral impact, a shoulder impact, a thoracoabdominal impact, an abdominal bar impact, a pelvic impact, and a lateral sled test.

This paper investigates experimental uncertainties associated with gaseous and particulate emissions measurements in a partial flow emissions sampling system developed and built at the Larson Transportation Institute of the Pennsylvania State University. A small fraction of the tail pipe exhaust is diluted with dilution air and passed through a cyclone to eliminate particles bigger than 2.5 microns. The diluted exhaust is then passed through a 47 mm Teflon filter for gravimetric measurement of Particulate Matter (PM). Mass flow controllers operating at 5Hz are used to control the flow rates of dilution air, diluted exhaust, and proportional flow of diluted exhaust into a Tedlar bag. An ultrasonic flow meter is used to measure flow rate of tail pipe exhaust. At the end of a test, the concentration of gaseous emissions in the bag, namely CO2, CO, HC, and NOx are measured using a bag emissions analyzer.