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Abstract Introduction: This study evaluates driving ability in those with Charcot Marie Tooth Disease Type 1A, a hereditary peripheral neuropathy. Methods: Individuals with Charcot Marie Tooth Disease Type 1A (n = 18, age = 42 ± 7) and controls (n = 19; age = 35 ± 10) were evaluated in a driving simulator. The Charcot Marie Tooth Neuropathy Score version 2 was obtained for individuals. Rank Sum test and Spearman rank correlations were used for statistical analysis. Results: A 74% higher rate of lane departures and an 89% higher rate of lane deviations were seen in those with Charcot Marie Tooth Disease Type 1A than for controls (p = 0.005 and p < 0.001, respectively). Lane control variability was 10% higher for the individual group and correlated with the neuropathy score (rS = 0.518, p = 0.040), specifically sensory loss (rS = 0.710, p = 0.002) and pinprick sensation loss in the leg (rS = 0.490, p = 0.054).

Abstract The use of simulation is a long-standing industry standard at every level of flight training. Historically, given the acquisition and maintenance costs associated with such equipment, full-motion devices have been reserved for advanced corporate and airline training programs. The Motion Cueing Seat (MCS) is a relatively inexpensive alternative to full-motion flight simulators and has the potential to enhance the fixed-base flight simulation in primary flight training. In this article, we discuss the results of an evaluation of the effect of motion cueing on pilot workload and performance during primary instrument training. Twenty flight students and instructors from a collegiate flight training program participated in the study. Each participant performed three runs of a basic circuit using a fixed-base Advanced Aviation Training Device (AATD) and an MCS.

Abstract A two-dimensional model of three elements, high-lift airfoil, was designed at a Reynolds number of ?????? using computational fluid dynamics (CFD) to generate downforce with good lift-to-drag efficiency for a formula student open-wheel race car basing on the nominal track speeds. The numerical solver uses the Reynolds-averaged Navier-Stokes (RANS) equation model coupled with the Langtry-Menter four-equation transition shear stress transport (SST) turbulence model. Such model adds two further equations to the ?? − ?? SST model resulting in an accurate prediction for the amount of flow separation due to adverse pressure gradient in low Reynolds number flow. The ?? − ?? SST model includes the transport effects into the eddy-viscosity formulation, whereas the two equations of transition momentum thickness Reynolds number and intermittency should further consider transition effects at low Reynolds number.

Abstract Today’s explosive condition of the market is compelling the manufacturing organizations to switch from traditional manufacturing (TM) to lean manufacturing (LM) to create a footprint in this competitive era. In this article, 16 critical success factors (CSFs) for LM implementation are identified through a vast literature review, the opinion of academicians and industry experts and interpretive structural modeling (ISM) is used to create interrelationships among the identified CSFs, and interpretive ranking process (IRP) rank these CSFs based on dominance with respect to performance dimensions. Leadership and management made the foundation of an ISM model while the training and people development have secured the first rank in the IRP model. Implementation of such ISM- and IRP-based models of CSF would give a clear understanding of these CSFs so that LM researchers, decision-makers, managers, and practitioners of LM will use their resources more efficiently.

Abstract The ability of senior citizens as well as other members of the general population to engage in an effective manner with technology is of increasing importance as new and innovative technologies become available. While recognizing the challenges that technologies can have on different populations, the ability to interact successfully with new technologies will, for seniors, have important consequences that can affect their quality of life and those of their families in numerous and important ways. This study, building upon previous research, examines the major dimensions of decision-making regarding attitudes toward autonomous vehicle technologies (ATVs) and their use. The study utilized data from a study of senior citizens in the Dallas-Fort Worth (DFW) area and compared the results with a sample of graduate students from a local university.

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Abstract To solve the problem that it is difficult for drivers to control the vehicle at low speed, a new setting scheme of pedal map is proposed to ensure that the vehicle has the speed controllability in the full speed range. In this scheme, based on obtaining the maximum and minimum driving characteristics of the vehicle and the driving resistance characteristics of the vehicle, the pedal map is divided into a sensitive area and insensitive area. In the insensitive area, acceleration hysteresis is formed, which ensures that the throttle is slightly fluctuated and has good speed stability. At the same time, the sensitive area of the accelerator pedal is formed far away from the driving resistance curve to ensure that the vehicle has a great acceleration ability. To verify the effectiveness of the proposed scheme, the data of a commercial vehicle is selected for the design of the pedal map, and the driver-vehicle closed-loop test based on the driving simulator is conducted.

Abstract A new method that allows data-enabled (empirical) models, commonly used for automotive engine calibration, to extrapolate beyond the range of training data has been developed. This method used a physics-based system-level one-dimensional model to improve interpolation and allow extrapolation for three data-based algorithms, by modifying the model input (feature) space. Neural network, regression, and k-nearest neighbor predictions of engine emissions and volumetric efficiency were greatly improved by generating 736,281 artificial feature spaces and then performing feature selection to choose feature spaces (feature selection) so that extrapolations in the original feature space were interpolations in the new feature space. A novel feature selection method was developed that used a two-stage search process to uniquely select the best feature spaces for every prediction.

Abstract This work focuses on the effects of the laminar flame speed (LFS) and flame stretch on the phenomenological modeling of the combustion process in spark ignition engines. The study is carried out using a 1D model of a small-size naturally aspirated SI engine, equipped with an external EGR circuit. The model, developed in GT-Power™ environment, includes advanced sub-models of the in-cylinder processes. The combustion is modeled using a fractal approach, where the burning rate is directly related to the laminar flame speed. A novel LFS correlation based on 1D chemical kinetics computations is presented and assessed with the experimentally derived Metghalchi and Keck correlation. Moreover, the effects of the flame stretch, evaluated according to an asymptotic theory, are properly considered in the combustion model.

Abstract Federal Aviation Administration (FAA)’s 20 years of research and development with 200 unleaded blends and full-scale engine tests on 45 high-octane unleaded blends has not found a “drop-in” unleaded replacement for aviation gasoline (AVGAS) 100 low lead (100LL) fuel. In this study, analysis of compatibility via optimization of Lycoming O-320 engine fuelled with RON 97, RON 98, RON 100, and AVGAS was conducted using the Response Surface Methodology (RSM). Test fuels were compositionally characterized based on Gas Chromatography (GC) analysis and were categorized based on types of Hydrocarbon (HC). Basic fuel properties of fuels in this research were analyzed and recorded. For optimization analysis, engine speed and fuel were considered as the input parameters.

Abstract The heat transfer phenomena in Internal Combustion Engines (ICEs) are one of the main research topics that need to be addressed to enhance the performance in terms of power, efficiency, emissions and reliability. The present study is focused on the evaluation of the in-cylinder heat fluxes through the use of Computational Fluid Dynamic (CFD) simulations, with a wall function approach. In particular, the aim of this work is to present a new fully non-isothermal wall function obtained from the one-dimensional (1-D) energy balance equation for turbulent flows in the boundary layers, specifying all the steps and assumptions which have carried to the final fully compressible formulation. The new proposed wall function has been validated against experimental data of the General Motors (GM) Pancake Engine, representative of low Brake Mean Effective Pressure (bmep) operating point, comparing the results with other existing wall functions.

Abstract Higher water evaporation and proper water vapor distribution in the cylinder are very vital for improving emission and performance characteristics of water-injected engines. The concentration of water vapor should be higher and uniform near the walls of the combustion chamber and nil at the spark plug location. In direct water-injected engines, water evaporation, vapor distribution, and spray impingement are highly dependent on injector parameters, viz., water injector orientation (WIO), location, and spray angle. Therefore, in this article, a computational fluid dynamics (CFD) investigation is conducted to study the effects of water injector spray angle (WISA), and WIO on the water evaporation, emission, and performance characteristics of a four-stroke, wall-guided gasoline direct injection (GDI) engine. The WISA is varied from 10° to 35°, whereas the WIO is varied from 15° to 35° in steps of 5°.

Abstract In recent years, lean-burn gasoline Spark-Ignition (SI) engines have been a major subject of investigations. With this solution, in fact, it is possible to simultaneously reduce NOx raw emissions and fuel consumption due to decreased heat losses, higher thermodynamic efficiency, and enhanced knock resistance. However, the real applicability of this technique is strongly limited by the increase in cyclic variation and the occurrence of misfire, which are typical for the combustion of homogeneous lean air/fuel mixtures. The employment of a Pre-Chamber (PC), in which the combustion begins before proceeding in the main combustion chamber, has already shown the capability of significantly extending the lean-burn limit. In this work, the potential of an ultralean PC SI engine for a decisive improvement of the thermal efficiency is presented by means of numerical and experimental analyses.

Abstract The development and control of spark-ignition engines with increased efficiency and reduced engine-out emissions requires tools and methods capable of providing insight and eventually predicting Cycle-to-Cycle Variations (CCV). To this end, Large-Eddy Simulations (LES) can improve the understanding of stochastic in-cylinder phenomena during the engine design process. However, available LES methods are typically not able to reproduce the full extent of cyclic variability observed in experiments, and computational costs are higher compared to established simulation approaches. In this work, an engine flowbench configuration suitable for validation of LES methods and intake flow assessment is considered. To supplement an existing validation database, Particle Image Velocimetry (PIV) and wall pressure measurements have been conducted in a reference optical engine geometry, which is available through the Darmstadt Engine Workshop.

Abstract Identifying and eliminating failure scenarios is critical in the development of autonomous vehicle (AV) systems. However, finding such failures through real-world vehicle-level testing is a difficult task as system disengagements and accidents are rare occurrences. Simulation approaches have been proposed to supplement vehicle-level testing and reduce the costs associated with operating large fleets of autonomous test vehicles. While one can run more vehicles in simulation than in the real world, applying traditional Monte Carlo sampling techniques to find failures still yields an unguided search and a large waste of computing resources. A more directed method than random sampling is needed to identify failure scenarios in a computationally efficient manner. Adaptive Stress Testing (AST) is a method that uses reinforcement learning (RL) paradigms to efficiently find failure scenarios in stochastic sequential decision-making systems.

Abstract Autonomous vehicles (AVs) are being rapidly introduced into our lives. However, public misunderstanding and mistrust have become prominent issues hindering the acceptance of these driverless technologies. The primary objective of this study is to evaluate the effectiveness of a driving simulator to help the public gain an understanding of AVs and build trust in them. To achieve this aim, we built an integrated simulation platform, designed various driving scenarios, and recruited 28 participants for the experiment. The study results indicate that a driving simulator effectively decreases the participants’ perceived risk of AVs and increases perceived usefulness. The proposed methodologies and findings of this study can be further explored by auto manufacturers and policymakers to provide user-friendly AV design.

Abstract Connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications.

Abstract Contemporary research has found differences between demographic groups in their stated instrument cluster component design preferences. For instance, elderly drivers prefer large icons and textual displays of information, while younger drivers preferred gauges to display information. The purpose of this study was to evaluate whether instrument clusters, designed for specific demographic groups, would facilitate safe driving behavior and solicit higher evaluation scores in their targeted demographics. Fifty participants, consisting of 30 elderly and 20 younger drivers (gender-balanced), completed a series of tasks to retrieve information from the instrument cluster while driving a high-fidelity simulator. Participants’ driving behavior, response time, subjective ratings, and a semi-structured post-experimental interview on different cluster designs were collected to evaluate each instrument cluster design.

Abstract In stationary natural gas engines, lean-burn combustion offers higher engine efficiencies with simultaneous compliance with emission regulations. A prominent problem that one encounters with lean operation is cyclic variations. Advanced ignition systems offer a potential solution as they suppress cyclic variations in addition to extending the lean ignition limit. In this article, the performance of three ignition systems-conventional spark ignition (SI), single-point laser ignition (LI), and prechamber equipped laser ignition (PCLI)-in a single-cylinder natural gas engine is presented. First, a thorough discussion regarding the efficacy of several metrics, in addition to coefficient of variation of indicated mean effective pressure (COV_IMEP), in representing combustion instability is presented. This is followed by a discussion about the performance of the three ignition systems at a single operational condition, that is, same excess air ratio (λ) and ignition timing (IT).

Abstract This article presents data mining methodologies designed to support data-driven, long-term, and large-scale research in the areas of in-vehicle monitoring, learning, and assessment of older adults’ driving behavior and physiological signatures under a set of well-defined driving scenarios. The major components presented in the article include the instrumentation of an easily transportable vehicle data acquisition system (VDAS) designed to collect multimodal sensor data during naturalistic driving, an ontology that enables the study of driver behaviors at different levels of integration of semantic heterogeneity into the driving context, and a driving trip segmentation algorithm for automatically partitioning a recorded real-world driving trip into segments representing different types of roadways and traffic conditions.