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The design of transportation vehicles, whether passenger or commercial, typically involves a lengthy process from concept to prototype and eventual manufacture. To improve competitiveness, original equipment manufacturers are continually exploring ways to shorten the design process. The application of digital tools such as computer-aided-design and computer-aided-engineering, as well as model-based computer simulation enable team members to virtually design and evaluate ideas within realistic operating environments. Recent advances in machine learning (ML)/artificial intelligence (AI) can be integrated into this paradigm to shorten the initial design sequence through the creation of digital agents. A digital agent can intelligently explore the design space to identify promising component features which can be collectively assessed within a virtual vehicle simulation.

Shorter development times in the automotive industry are leading to the increased use of computer simulation in the vehicle design cycle to pre-optimize vehicle concepts. The focus of the work presented in this study is vehicle dynamic performance in different driving maneuvers. More specifically this paper presents a methodology for simulation-based parameter design of vehicles for excellent performance in multiple maneuvers. The model used in the study consists of eight degrees-of-freedom and has been validated previously. The vehicle data used is for a commercially available vehicle. A number of different driving scenarios (maneuvers) based on ISO standards for transient dynamic behavior are implemented and performance indices are calculated for each individual maneuver considered. Vehicle performance is assessed based on the performance indices.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

This paper argues the importance of engineering heuristics and introduces an educational data-driven tool to help novice engineers develop their engineering heuristics more effectively. The main objective in engineering practice is to identify opportunities for improvement and apply methods to effect change. Engineers do so by applying ‘how to’ knowledge to make decisions and take actions. This ‘how to’ knowledge is encoded in engineering heuristics. In this paper, we describe a tool that aims to provide heuristic knowledge to users by giving them insight into heuristics applied by experts in similar situations. A repository of automotive data is transformed into a tool with powerful search and data visualization functionalities. The tool can be used to educate novice automotive engineers alongside the current resource intensive practices of teaching engineering heuristics through social methods such as an apprenticeship.

Novice drivers are often ill-equipped to safely operate a motor vehicle due to their limited repertoire of skills and experiences. However, automotive simulation tools can be applied to better educate young drivers for a number of common driving scenarios. In this paper, the Clemson Automotive Training System (CATS) will be presented to educate and train novice drivers to safely operate four wheel passenger vehicles on paved roadways. A portable automotive simulator can be programmed to emulate a variety of high-crash rate scenarios and roadway geometries. Drivers receive instructions regarding proper driving techniques and behaviors with an opportunity to practice the given vehicle maneuver. An on-line evaluation methodology has been designed to analyze the drivers' capabilities at handling these roadway events. First, a pre-simulation questionnaire evaluates their basic understanding of everyday driving situations.

The Vision for Space Exploration outlines NASA's goals to return to the Moon, and travel on to Mars. The exploration activities associated with these endeavors will include both space and surface extravehicular activities (EVAs). This paper describes the plans for education outreach activities and products related to the technological developments and challenges similar to those being addressed by the Advanced EVA (AEVA) team. Efforts to involve and coordinate educational research projects with the AEVA team will also be discussed. The proposed activities and products will provide hands-on, interactive exercises through workshops, presentations, and demonstrations to allow students of all levels to learn about and experience the design challenges similar to what NASA deals with everyday in developing EVA systems.

The Advanced Integration Matrix (AIM) will design a ground-based test facility for developing revolutionary integrated systems for joint human-robotic missions in order to study and solve systems-level integration issues for exploration missions beyond Low Earth Orbit (LEO). This paper describes development plans for educational outreach activities related to technological and operational integration scenarios similar to the challenges that will be encountered through this project. The education outreach activities will provide hands-on, interactive exercises to allow students of all levels to experience design and operational challenges similar to what NASA deals with everyday in performing the integration of complex missions. These experiences will relate to and impact students' everyday lives by demonstrating how their interests in science and engineering can develop into future careers, and reinforcing the concepts of teamwork and conflict resolution.

In planning for Exploration missions and developing the required suite of environmental monitors, the difficulty lies in down-selecting a multitude of technology options to a few candidates with exceptional potential. Technology selection criteria include conventional analytical parameters (e.g., range, sensitivity, selectivity), operational factors (degree of automation, portability, required level of crew training, maintenance), logistical factors (size, mass, power, consumables, waste generation) and engineering factors such as complexity and reliability. Other more subtle considerations include crew interfaces, data readout and degree of autonomy from the ground control center. We anticipate that technology demonstrations designed toward these goals will be carried out on the International Space Station, the end result of which is a suite of techniques well positioned for deployment during Exploration missions.

The deployment of autonomous vehicles in real-world scenarios requires thorough testing to ensure sufficient safety levels. Driving simulators have proven to be useful testbeds for assisted and autonomous driving functionalities but may fail to capture all the nuances of real-world conditions. In this paper, we present a snapshot of the design and evaluation using a Cooperative Adaptive Cruise Control application of virtual reality platform currently in development at our institution. The platform is designed so to: allow for incorporating live real-world driving data into the simulation, enabling Vehicle-in-the-Loop testing of autonomous driving behaviors and providing us with a useful mean to evaluate the human factor in the autonomous vehicle context.

A safe driver program has been established through a public-private partnership. This program targets novice drivers and uses a combination of classroom and in-vehicle training exercises to address critical driver errors known to lead to crashes. Students participate in four modules: braking to learn proper stopping technique, obstacle avoidance / reaction time to facilitate proper lane selection and collision avoidance, tailgating to learn about following distances, and loss of control to react appropriately when a vehicle is about to become laterally unstable. Knowledge pre and posttests are also administered at the start and end of the program. Students' in-vehicle driving performance are evaluated by instructors as well as recorded by onboard data acquisition units. The data has been evaluated with objective and subjective grading rubrics. The 70 participants in three classes used as a case study achieved an average skill score of 83.93/100.

Automotive simulators offer an immersive environment to operate vehicle systems in a safe and repeatable manner. A fundamental question exists regarding their effectiveness for an identified task. For instance, driving simulators can play a significant role in evaluating vehicle designs, developing safety regulations, supporting human factors engineering research, administering driver training and education, and offering individual entertainment. Some of the driving simulator technology users include automotive manufacturers and suppliers, research laboratories at universities and government agencies, driver education and training programs, and motorsports and racing entertainment venues. In each case, the simulator capabilities and functionality must encompass the expectations of the driver to permit their perception of realistic scenarios for evaluation. This paper investigates three driving simulators in terms of their hardware and software, as well as their applications.

Integrated modeling of vehicle, tire and terrain is a fundamental challenge to be addressed for off-road autonomous navigation. The complexities arise due to lack of tools and techniques to predict the continuously varying terrain and environmental conditions and the resultant non-linearities. The solution to this challenge can now be found in the plethora of data driven modeling and control techniques that have gained traction in the last decade. Data driven modeling and control techniques rely on the system’s repeated interaction with the environment to generate a lot of data and then use a function approximator to fit a model for the physical system with the data. Getting good quality and quantity of data may involve extensive experimentation with the physical system impacting developer’s resource. The process is computationally expensive, and the overhead time required is high.

Tradespace analysis is used to define the characteristics of the solution space for a vehicle design problem enabling decision-makers (DMs) to evaluate the risk-benefit posture of a vehicle design program. The tradespace itself is defined by a set of functional objectives defined by vehicle simulations and evaluating the performance of individual design solutions that are modeled by a set of input variables. Of special interest are efficient design solutions because their perfomance is Pareto meaning that none of their functional objective values can be improved without decaying the value of another objective. The functional objectives are derived from a combination of simulations to determine vehicle performance metrics and direct calculations using vehicle characteristics. The vehicle characteristics represent vendor specifications of vehicle subsystems representing various technologies.

Passenger comfort is a critical factor in user acceptance of autonomous vehicles (AVs). Despite existing methods for passenger comfort assessment, new approaches to assessing passenger comfort in AVs may be valuable to the automotive industry. In this paper, continuous pressing-based and discrete smartphone-based approaches for comfort assessment were designed and implemented in a user study. Participants used the two approaches to evaluate their comfort levels in an experimental study based on a high-fidelity autonomous driving simulator. Performances of the two approaches in assessing comfort levels were analyzed and compared. In general, the discrete approach showed better measurement repeatability and lower measurement bias than the continuous approach. The performance gap of the continuous approach could be reduced with proper post-processing measures. Discussions on the potential uses of the approaches were also raised.

This paper is a review of turbulence models and computational methods that have been produced at Clemson University's Advanced Computational Research Laboratory. The goal of the turbulence model development has been to create physics-based models that are economically feasible and can be used in a competitive environment, where turnaround time is a critical factor. Given this goal, all of the work has been focused on Reynolds-Averaged Navier-Stokes (RANS) simulations in the eddy-viscosity framework with the majority of the turbulence models having three transport equations in addition to mass, momentum, and energy. Several areas have been targeted for improvement in turbulence modeling for complex flows such as those found in motorsports aerodynamics: the effects of streamline curvature and rotation on the turbulence field, laminar-turbulent transition, and separated shear layer rollup and breakdown.

Development of new air revitalization system (ARS) technology can initially be performed in a subscale laboratory environment, but in order to advance the maturity level, the technology must be tested in an end-to-end integrated environment. The Air Revitalization Technology Evaluation Facility (ARTEF) at the NASA Johnson Space Center (JSC) serves as a ground test bed for evaluating emerging ARS technologies in an environment representative of spacecraft atmospheres. At the center of the ARTEF is a hypobaric chamber which serves as a sealed atmospheric chamber for closed loop testing. A Human Metabolic Simulator (HMS) was custom-built to simulate the consumption of oxygen, and production of carbon dioxide, moisture and heat by up to eight persons. A variety of gas analyzers and dew point sensors are used to monitor the chamber atmosphere and the process flow upstream and downstream of a test article. A robust vacuum system is needed to simulate the vacuum of space.

NASA's Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and webcasting. As part of NASA's Strategic Plan to reach the next generation of space explorers, the DLN develops and delivers educational programs that reinforce principles in the areas of science, technology, engineering and mathematics. The DLN has created a series of live education videoconferences connecting the Desert Research and Technology Studies (RATS) field test to students across the United States. The programs are also extended to students around the world via live webcasting. The primary focus of the events is the Vision for Space Exploration. During the programs, Desert RATS engineers and scientists inform and inspire students about the importance of exploration and share the importance of the field test as it correlates with plans to return to the Moon and explore Mars. This paper describes the events that took place in September 2006.

NASA's Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and webcasting. The DLN has created a series of live education videoconferences connecting NASA's Extreme Environment Missions Operations (NEEMO) team to students across the United States. Programs are also extended to students around the world via live webcasting. The primary focus of the events is the vision for space exploration. During the programs, NEEMO crewmembers, including NASA astronauts, engineers and scientists, inform and inspire students about the importance of exploration and share the importance of the project as it correlates with plans to return to the moon and explore the planet Mars. These events highlight interactivity. Students talk live with the aquanauts in Aquarius, the National Oceanic and Atmospheric Administration's underwater laboratory located 4.5 kilometers off Key Largo in the Florida Keys National Marine Sanctuary.

Among the many firsts which will occur on STS-49, the maiden voyage of the Space Shuttle Endeavour, a Space Station Freedom (SSF) experiment entitled Assembly of Station by Extravehicular Activity (EVA) Methods (ASEM) promises to test the boundaries of EVA operational capabilities. Should the results be favorable, station and other major users of EVA stand to benefit from increased capabilities. Even the preparation for the ASEM experiment is serving as a pathfinder for complex SSF operations. This paper reviews the major tasks planned for ASEM and discusses the operational analogies investigators are attempting to draw between ASEM and SSF. How these findings may be applied to simplify station assembly and maintenance will also be discussed.

The wonder of space exploration is a sure way to catch the attention of students of all ages, and space biology is one of many sciences critical to understanding the spaceflight environment. Many systems used in the past for space-to-classroom biology activities have required extensive crew time and material resources, making space-linked education logistically and financially difficult. The new Education Payload Operations Kit C (EPO Kit C) aims to overcome obstacles to space-linked education and outreach by dramatically reducing the resources required for educational activities in plant space biology that have a true spaceflight component. EPO Kit C is expected to be flown from STS-118 to the International Space Station in June 2007. NASA and several other organizations are currently planning an outreach program to complement the flight of EPO Kit C.