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This paper explores the current status of error management strategies and human factors efforts within regional airlines. It briefly addresses the potential needs of the environment from a perspective of the market’s accident and incident history as well as anecdotal reports received from members of the regional airline community. It also raises questions concerning the applicability of human factors and error management strategies developed in other segments of aviation to the problems faced within regional airline environments.

The present work was directed towards the design of a sideline microphone array specifically adapted to the visualization of automotive noise sources in the 500 Hz to 2000 Hz range. The particular design philosophy followed here involved the minimization of the array redundancy: i.e., the minimization of the number of pairs of microphones that are separated by the same distance in the same directions. The performance of sixty-four element microphone arrays designed according to this principle will be illustrated through the use of simulated motor vehicle passbys. In addition, their performance will be compared with more conventional array designs: e.g., elliptical, and spiral arrays.

An overview of model development for seated occupants is presented. Two approaches have been investigated for modeling the vertical response of a seated dummy: finite element and simplified mass-spring-damper methods. The construction and implementation of these models are described, and the various successes and drawbacks of each modeling approach are discussed. To evaluate the performance of the models, emphasis was also placed on producing accurate, repeatable measurements of the static and dynamic characteristics of a seated dummy.

A procedure for simulating the dynamics of agricultural and forestry machines using mechanical system simulation software is presented. A soil/track interface model including rubber-track and steel-track was introduced as well as equations that can be used to model mechanical and hydraulic power trains commonly found in tracked vehicles. Two rubber-tracked vehicles (agricultural tractors) and two steel-tracked machines (forestry vehicles) were simulated to illustrate the technique, and some analysis results are presented. The examples given in this paper are based on the author’s research over the past several years.

Throughout the industry, organizations struggle with the task of implementing effective human factors programs aimed at reducing maintenance errors. Almost universally, many barriers have frustrated these efforts. In 1998 and 1999, the National Transportation Safety Board sponsored two workshops designed at identifying barriers to the implementation of human factors programs and to explore what was working and what was not working among the many industry efforts. This paper explores the findings of these workshops. In addition, it will report findings of Purdue University studies that reveal a rapid deterioration of even the most successful human factors programs. The research findings disclose several “disconnects” within most organizations which rapidly negate the positive effects of successful human factors and error management training and nullify many proactive human factors programs.

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

This work introduces a new design algorithm to optimize progressively folding thin-walled structures and in order to improve automotive crashworthiness. The proposed design algorithm is composed of three stages: conceptual thickness distribution, design parameterization, and multi-objective design optimization. The conceptual thickness distribution stage generates an innovative design using a novel one-iteration compliant mechanism approach that triggers progressive folding even on irregular structures under oblique impact. The design parameterization stage optimally segments the conceptual design into a reduced number of clusters using a machine learning K-means algorithm. Finally, the multi-objective design optimization stage finds non-dominated designs of maximum specific energy absorption and minimum peak crushing force.

Electrical connectors and terminals are widely used in the automotive industry. It is desirable to mate the electrical connections using materials or coatings with low friction force to improve the ergonomics of the assembly process while maintaining good electrical conduction over the lifetime of the vehicle. We have previously shown that plasma-enhanced chemical vapor deposition (PECVD) of graphene on gold (Au) and silver (Ag) terminals can significantly reduce the insertion force (friction force during the terminal insertion process). However, the cost of this deposition method is rather high, and its high temperature process (> 400 oC) makes it impractical for materials with low melting temperatures. For example, tin (Sn) coating with a melting temperature of 232 oC is commonly used in electrical connectors, which cannot sustain the high temperature process. In this study, reduced graphene oxide was prepared using a low-cost solution process and applied onto metallic terminals.

The design of better active materials for lithium-ion batteries (LIBs) is crucial to satisfy the increasing demand of high performance batteries for portable electronics and electric vehicles. Currently, the development of new active materials is driven by physical experimentation and the designer’s intuition and expertise. During the development process, the designer interprets the experimental data to decide the next composition of the active material to be tested. After several trial-and-error iterations of data analysis and testing, promising active materials are discovered but after long development times (months or even years) and the evaluation of a large number of experiments. Bayesian global optimization (BGO) is an appealing alternative for the design of active materials for LIBs. BGO is a gradient-free optimization methodology to solve design problems that involve expensive black-box functions. An example of a black-box function is the prediction of the cycle life of LIBs.

This paper explains the basic principles behind two platform technologies that my research team has developed in the field of optical metrology and optical information processing: 1) high-speed 3D optical sensing; and 2) real-time 3D video compression and streaming. This paper will discuss how such platform technologies could benefit the automotive industry including in-situ quality control for additive manufacturing and autonomous vehicle systems. We will also discuss some of other applications that we have been working on such as crime scene capture in forensics.

Inside an automobile, hundreds of connectors and electrical terminals in various locations experience different corrosive environments. These connectors and electrical terminals need to be corrosion-proof and provide a good electrical contact for a vehicle’s lifetime. Saltwater and sulfuric acid are some of the main corrosion concerns for these electrical terminals. Currently, various thin metallic layers such as gold (Au), silver (Ag), or tin (Sn) are plated with a nickel (Ni) layer on copper alloy (Cu) terminals to ensure reliable electrical conduction during service. Graphene due to its excellent chemical stability can serve as a corrosion protective layer and prevent electrochemical oxidation of metallic terminals. In this work, effects of thin graphene layers grown by plasma-enhanced chemical vapor deposition (PECVD) on Au and Ag terminals and thin-film devices were investigated. Various mechanical, thermal/humidity, and electrical tests were performed.

The cost and complexity of aircraft power systems limit the number of integrated system evaluations that can be performed in hardware. As a result, evaluations are often performed using emulators to mimic components or subsystems. As an example, aircraft generation systems are often tested using an emulator that consists of a bank of resistors that are switched to represent the power draw of one or more actuators. In this research, consideration is given to modern wide bandwidth emulators (WBEs) that use power electronics and digital controls to obtain wide bandwidth control of power, current, or voltage. Specifically, this paper first looks at how well a WBE can emulate the impedance of a load when coupled to a real-time model. Capturing the impedance of loads and sources is important for accurately assessing the small-signal stability of a system.

A common scenario in engineering design is the evaluation of expensive black-box functions: simulation codes or physical experiments that require long evaluation times and/or significant resources, which results in lengthy and costly design cycles. In the last years, Bayesian optimization has emerged as an efficient alternative to solve expensive black-box function design problems. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition functions that drives the design process. Successful Bayesian optimization strategies are characterized by accurate surrogate models and well-balanced acquisition functions. The Gaussian process (GP) regression model is arguably the most popular surrogate model in Bayesian optimization due to its flexibility and mathematical tractability. GP regression models are defined by two elements: the mean and covariance functions.