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Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

ADAS (Advanced Driver Assistance System) functions can help the driver avoid accidents or mitigate their effect when they occur, and are pre-cursors to full autonomous driving (SAE defined as Level 4+). The main goal of this work is to develop a Model-Based system to actuate the Evasive Maneuver Assist (EMA) function. A typical scenario is the situation in which longitudinal Autonomous Emergency Braking (AEB) is too late and the driver has to adopt an evasive maneuver to avoid an object suddenly appearing on the road ahead. At this time, EMA can help improve the driver’s steering and braking operation in a coordinated way. The vehicle maneuverability and response performance will be enhanced when the driver is facing the collision. The function will additionally let the vehicle steer in a predetermined optimized trajectory based on a yaw rate set point and stabilize the vehicle. The EMA function is introduced with some analysis of benchmarking data.

The goal of this interview analysis was to explore and document the perceptions of two unique lane centering systems (S90’s Pilot Assist and CT6’s Super Cruise). Both systems offer a similar type of functionality (adaptive cruise control and lane centering), but have significantly different design philosophies and HMI (Human-Machine Interface) implementations. Twenty-four drivers drove one of the two vehicle models for a month as part of a field operational test (FOT) study. Upon vehicle return, drivers took part in a 60-minute semi-structured interview covering their perceptions of the vehicle’s various advanced driver-assistance systems (ADAS). Transcripts of the interviews were coded by two researchers, who tagged each statement with relevant system and perception code labels. For analysis, the perception codes were grouped into larger thematic bins of safety, comfort, driver attention, and system performance.

This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

To obtain realistic noise characteristics from CAA studies of subsonic fans, it is important to prescribe properly constructed turbulent inflow statistics. This is frequently omitted; instead it is assumed that the stochastic characteristics of turbulence, absent at the initial stage, progressively develops as the rotor inflicts the flow field over time and hence that the sound generating mechanism governed by surface pressure fluctuations are asymptotically accounted for. That assumption violates the actual interplay taking place between an ingested flow field and the surface pressure fluctuations exerted by the blades producing noise. The aim of the present study is to examine the coupling effect between synthetically ingested turbulence to sound produced from a subsonic ducted fan. The steady state inflow parameters are mapped from a precursor RANS simulation onto the inflow boundaries of a reduced domain to limit the computational cost.

This paper presents the results of a study of how people interacted with a production voice-command based interface while driving on public roadways. Tasks included phone contact calling, full address destination entry, and point-of-interest (POI) selection. Baseline driving and driving while engaging in multiple-levels of an auditory-vocal cognitive reference task and manual radio tuning were used as comparison points. Measures included self-reported workload, task performance, physiological arousal, glance behavior, and vehicle control for an analysis sample of 48 participants (gender balanced across ages 21-68). Task analysis and glance measures confirm earlier findings that voice-command interfaces do not always allow the driver to keep their hands on the wheel and eyes on the road, as some assume.

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.

Advanced driver assistance systems (ADAS) are an increasingly common feature of modern vehicles. The influence of such systems on driver behavior, particularly in regards to the effects of intermittent warning systems, is sparsely studied to date. This paper examines dynamic changes in physiological and operational behavior during lane departure warnings (LDW) in two commercial automotive systems utilizing on-road data. Alerts from the systems, one using auditory and the other haptic LDWs, were monitored during highway driving conditions. LDW events were monitored during periods of single-task driving and dual-task driving. Dual-task periods consisted of the driver interacting with the vehicle’s factory infotainment system or a smartphone to perform secondary visual-manual (e.g., radio tuning, contact dialing, etc.) or auditory-vocal (e.g. destination address entry, contact dialing, etc.) tasks.

The combustion process after auto-ignition is investigated. Depending on the non-uniformity of the end gas, auto-ignition could initiate a flame, produce pressure waves that excite the engine structure (acoustic knock), or result in detonation (normal or developing). For the “acoustic knock” mode, a knock intensity (KI) is defined as the pressure oscillation amplitude. The KI values over different cycles under a fixed operating condition are observed to have a log-normal distribution. When the operating condition is changed (over different values of λ, EGR, and spark timing), the mean (μ) of log (KI/GIMEP) decreases linearly with the correlation-based ignition delay calculated using the knock-point end gas condition of the mean cycle. The standard deviation σ of log(KI/GIMEP) is approximately a constant, at 0.63. The values of μ and σ thus allow a statistical description of knock from the deterministic calculation of the ignition delay using the mean cycle properties

Vehicle dynamics development relies on subjective assessments (SA), which is a resource-intensive procedure requiring both expert drivers and vehicles. Furthermore, development projects becoming shorter and more complex, and increasing demands on quality require higher efficiency. Most research in this area has focused on moving from physical to virtual testing. However, SA remains the central method. Less attention has been given to provide better tools for the SA process itself. One promising approach is to introduce computer-tablets to aid data collection, which has proven to be useful in medical studies. Simple software solutions can eliminate the need to transcribe data and generate more flexible and better maintainable questionnaires. Tablets’ technical features envision promising enhancements of SA, which also enable better correlations to objective metrics, a requirement to improve CAE evaluations.

The rotary engine provides high power density compared to piston engine, but one of its downside is higher oil consumption. In order to better understand oil transport, a laser induced fluorescence technique is used to visualize oil motion on the side of the rotor during engine operation. Oil transport from both metered oil and internal oil is observed. Starting from inside, oil accumulates in the rotor land during inward motion of the rotor created by its eccentric motion. Oil seals are then scraping the oil outward due to seal-housing clearance asymmetry between inward and outward motion. Cut-off seal does not provide an additional barrier to internal oil consumption. Internal oil then mixes with metered oil brought to the side of the rotor by gas leakage. Oil is finally pushed outward by centrifugal force, passes the side seals, and is thrown off in the combustion chamber.

The fuel energy consumption of subsonic air transportation is examined. The focus is on identification and quantification of fundamental engineering design tradeoffs which drive the design of subsonic tube and wing transport aircraft. The sensitivities of energy efficiency to recent and forecast technology developments are also examined.

The aim of this study was to explore if fingernail delamination injury following EMU glove use may be caused by compression-induced blood flow occlusion in the finger. During compression tests, finger blood flow decreased more than 60%, however this occurred more rapidly for finger pad compression (4 N) than for fingertips (10 N). A pressure bulb compression test resulted in 50% and 45% decreased blood flow at 100 mmHg and 200 mmHg, respectively. These results indicate that the finger pad pressure required to articulate stiff gloves is more likely to contribute to injury than the fingertip pressure associated with tight fitting gloves.

The Closed-Loop Air REvitalisation System ARES is a regenerative life support system for closed habitats. With regenerative processes the ARES covers the life support functions: 1. Removal of carbon dioxide from the spacecraft atmosphere via a regenerative adsorption/desorption process, 2. Supply of breathable oxygen via electrolysis of water, 3. Catalytic conversion of carbon dioxide with hydrogen to water and methane. ARES will be accommodated in a double ISPR Rack which will contain all main and support functions like power and data handling and process water management. It is foreseen to be installed onboard the International Space Station (ISS) in the Columbus Module in 2013. After an initial technology demonstration phase ARES shall continue to operate thus enhancing the capabilities of the ISS Life Support System as acknowledged by NASA [5]. Due to its regenerative processes ARES will allow a significant reduction of water upload to the ISS.

The nearly 77 million baby boomers, born between 1946 and 1964, can say that they are the automobile generation. Now turning 60 one every seven seconds, what are the new safety challenges and opportunities posed by the next generation of older adults? This paper presents a modified Haddon matrix to identify key product development, design and liability issues confronting the automobile industry and related stakeholders. The industry is now at a critical juncture to address the development of key technological innovations as well as the changing policy and liability environments being reshaped by an aging population.

The National Space Biomedical Research Institute (NSBRI), established in 1997, is a twelve-university consortium dedicated to research that will impact mankind's next exploratory steps. The NSBRI's Education and Public Outreach Program (EPOP), is supporting NASA's education mission to, “Inspire the next generations…as only NASA can,” through a comprehensive Kindergarten through post-doctoral education program. The goals of the EPOP are to: communicate space exploration biology to schools; support undergraduate and graduate space-based courses and degrees; fund postdoctoral fellows to pursue space life sciences research; and engage national and international audiences to promote understanding of how space exploration benefits people on Earth. NSBRI EPOP presents its accomplishments as an educational strategy for supporting science education reform, workforce development, and public outreach.

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.

The Columbus ECS PFM Test was intended as the final verification of the Module Thermal Design after a series of successful tests at subsystems level (e.g. the Active Thermal Control Subsystem and the Environmental Control and Life Support System) The test campaign has been articulated as a sequence of several test cases to investigate the main thermal aspects, to prove the Module thermal design in the extreme operative conditions and to correlate the thermal mathematical model (TMM). The interpretation of test results and the correlation confirmed that the thermal design of the module is adequate, but some areas of concern remain, mainly for the difficulty to translate to 0-g the results of a complex test in 1-g environment, and for some aspects of the air and cabin loops.

Given the constraints of the current launchers, manned exploration beyond LEO implies long time missions, a high mass of metabolic consumables and consequently regenerative life support technologies developments. To validate their efficiency, as well as their reliability, these technologies need to be tested in the most analog conditions (i.e. isolation, limited spare part, …). A large number of these conditions are met in the new permanent French-Italian settlement called Concordia, currently being built in the Antarctic continent. Over the last 15 years, ESA developed regenerative life support technologies. Two of these technologies: a Grey Water Treatment Unit and a Black Water Treatment Unit are currently assembled at the size of 15 to 70 persons to fulfill the Concordia crew needs The first technology is a multi step filtration system and will recycle the shower, washing machine, dish washer and cleaning water.

Cranking and startup fuel control has become increasingly important due to ever tightening emission requirements. Additionally, engine-off strategies during idle will require substantially more engine startup events with the associated need for very clean starts. Thus, knowledge of an engine's Air-Fuel Ratio (AFR) during its early cycles is necessary in order to optimize cranking and startup fueling. This paper examines and compares two methods of measuring an engine's AFR during engine startup (approximately the first second of operation); an in-cylinder technique using a Fast Flame Ionization Detector (FFID) and the conventional exhaust based Universal Exhaust Gas Oxygen (UEGO) sensor method. Engine starts using a Ford Zetec engine were performed at three different temperatures (0, 20 and 90 C) as well as different initial engine starting positions.