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Abstract After a severe lane change, a wind gust, or another disturbance, the driver might be unable to recover the intended motion. Even though this fact is known by any driver, the scientific investigation and testing on this phenomenon is just at its very beginning, as a literature review, focusing on SAE Mobilus® database, reveals. We have used different mathematical models of car and driver for the basic description of car motion after a disturbance. Theoretical topics such as nonlinear dynamics, bifurcations, and global stability analysis had to be tackled. Since accurate mathematical models of drivers are still unavailable, a couple of driving simulators have been used to assess human driving action. Classic unstable motions such as Hopf bifurcations were found. Such bifurcations seem almost disregarded by automotive engineers, but they are very well-known by mathematicians. Other classic unstable motions that have been found are “unstable limit cycles.”

Abstract The use of appropriate loads and regulations is of great importance in weld fatigue assessment of rail on-track maintenance equipment and similar vehicles for optimized design. The regulations and available loads, however, are often generalized for several categories, which proves to be overly conservative for some specific categories of machines. EN (European Norm) and AAR (Association of American Railroads) regulations play a pivotal role in determining the applicable loads and acceptance criteria within this study. The availability of track-induced fatigue load data for the cumulative damage approach in track maintenance machines is often limited. Consequently, the FEA-based validation of rail track maintenance equipment often resorts to the infinite life approach rather than cumulative damage approach for track-induced travel loads, resulting in overly conservative designs.

Abstract The exponentially growing electrification market is driving demand for lithium-ion batteries (LIBs) with high performance. However, LIB thermal runaway events are one of the unresolved safety concerns. Thermal runaway of an individual LIB can cause a chain reaction of runaway events in nearby cells, or thermal propagation, potentially causing significant battery fires and explosions. Such a safety issue of LIBs raises a huge concern for a variety of applications including electric vehicles (EVs). With increasingly higher energy-density battery technologies being implemented in EVs to enable a longer driving mileage per charge, LIB safety enhancement is becoming critical for customers. This comprehensive review offers an encompassing overview of prevalent abuse conditions, the thermal event processes and mechanisms associated with LIBs, and various strategies for suppression, prevention, and mitigation.

Abstract The cooperative platoon of multiple trucks with definite proximity has the potential to enhance traffic safety, improve roadway capacity, and reduce fuel consumption of the platoon. To investigate the truck platooning performance in a real-world environment, two Peterbilt class-8 trucks equipped with cooperative truck platooning systems (CTPS) were deployed to conduct the first-of-its-kind on-road commercial trial in Canada. A total of 41 CTPS trips were carried out on Alberta Highway 2 between Calgary and Edmonton during the winter season in 2022, 25 of which were platooning trips with 3 to 5 sec time gaps. The platooning trips were performed at ambient temperatures from −24 to 8°C, and the total truck weights ranged from 16 to 39 tons. The experimental results show that the average time gap error was 0.8 sec for all the platooning trips, and the trips with the commanded time gap of 5 sec generally had the highest variations.

Abstract Neutron diffraction is a powerful tool for noninvasive and nondestructive characterization of materials and can be applied even in large devices such as internal combustion engines thanks to neutrons’ exceptional ability to penetrate many materials. While proof-of-concept experiments have shown the ability to measure spatially and temporally resolved lattice strains in a small aluminum engine on a timescale of minutes over a limited spatial region, extending this capability to timescales on the order of a crank angle degree over the full volume of the combustion chamber requires careful design and optimization of the engine structure to minimize attenuation of the incident and diffracted neutrons to maximize count rates.

Abstract The electrically interconnected suspension (EIS) is a novel suspension system that has gained attention due to its potential to improve vehicle vibration control. This article provides a comprehensive review of EIS and related technologies. It starts with an overview of the research on hydraulic interconnected suspension (HIS) and its limitations. Then, it discusses the development of the electromagnetic suspension (EMS) and its advantages in adjusting mechanical characteristics. The article focuses on the electrical network and decoupling control characteristics of EIS, demonstrating the principle of synchronous decoupling control of multiple vibration modes. A comparison of the structure and control characteristics of EIS and HIS highlights the advantages of EIS in vehicle vibration control.

Abstract Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm.

Abstract The development of predictive maintenance has become one of the most important drivers of innovation, not only in the maritime industry. The proliferation of on-board and remote sensing and diagnostic systems is creating many new opportunities to reduce maintenance costs and increase operational stability. By predicting impending system faults and failures, proactive maintenance can be initiated to prevent loss of seaworthiness or operability. The motivation of this study is to optimize predictive maintenance in the maritime industry by determining the minimum useful remaining lead-acid battery capacity measurement frequency required to achieve cost-efficiency and desired prognostic performance in a remaining battery capacity indication system. The research seeks to balance operational stability and cost-effectiveness, providing valuable insight into the practical considerations and potential benefits of predictive maintenance.

Abstract This article addresses the design, testing, and evaluation of rigorous and verifiable prognostic and health management (PHM) functions applied to autonomous aircraft systems. These PHM functions—many deployed as algorithms—are integrated into a holistic framework for integrity management of aircraft components and systems that are subject to both operational degradation and incipient failure modes. The designer of a comprehensive and verifiable prognostics system is faced with significant challenges. Data (both baseline and faulted) that are correlated, time stamped, and appropriately sampled are not always readily available. Quantifying uncertainty, and its propagation and management, which are inherent in prognosis, can be difficult. High-fidelity modeling of critical components/systems can consume precious resources. Data mining tools for feature extraction and selection are not easy to develop and maintain.

Abstract The shot-to-shot variations in common rail injection systems are primarily caused by pressure wave oscillations in the rail, pipes, and injector body. These oscillations are influenced by fuel physical properties, injector needle movement, and pressure and suction control valve activations. The pressure waves are generated by pump actuation and injector needle movement, and their frequency and amplitude are determined by fluid properties and flow path geometry. These variations can result in cycle-to-cycle engine fluctuations. In multi-injection and split-injection strategies, the pressure oscillation from the first shot can impact the hydraulic characteristics of subsequent shots, resulting in variations in injection rate and amount. This is particularly significant when using alternative fuels such as biodiesel, which aim to reduce emissions while maintaining fuel atomization quality.

Abstract A rapid compression and expansion machine (RCEM) was used to experimentally investigate the ignition phenomena of dielectric-barrier discharge (DBD) in engine conditions. The effect of elevated pressure and temperature on ignition phenomena of a methane/air premixed mixture was investigated using a DBD igniter. The equivalence ratio was changed to elucidate the impact of DBD on flame kernel development. High-speed imaging of natural light and OH* chemiluminescence enabled visualization of discharges and flame kernel. According to experimental findings, the discharges become concentrated and the intensity increases as the pressure and temperature rise. Under different equivalence ratios, the spark ignition (SI) system has a shorter flame development time (FDT) as compared with the DBD ignition system.

Abstract This article explores the value of simulation for autonomous-vehicle research and development. There is ample research that details the effectiveness of simulation for training humans to fly and drive. Unfortunately, the same is not true for simulations used to train and test artificial intelligence (AI) that enables autonomous vehicles to fly and drive without humans. Research has shown that simulation “fidelity” is the most influential factor affecting training yield, but psychological fidelity is a widely accepted definition that does not apply to AI because it describes how well simulations engage various cognitive functions of human operators. Therefore, this investigation reviewed the literature that was published between January 2010 and May 2022 on the topic of simulation fidelity to understand how researchers are defining and measuring simulation fidelity as applied to training AI.

Abstract Prediction of the surface finish of hardened bearing steels was estimated in machining with ceramic uncoated cutting tools under various process parameters using two statistical approaches. A second-order (quadratic) regression model (MQR, multiple quantile regression) for the surface finish was developed and then compared with the artificial neural network (ANN) method based on the coefficient determination (R 2), root mean square error (RMSE), and percentage error (PE). The experimental results exhibited that cutting speed was the dominant parameter, but feed rate and depth of cut were insignificant in terms of the Pareto chart and analysis of variance (ANOVA). The optimum surface finish in machining bearing steel was achieved at 100 m/min speed, 0.1 mm/revolution (rev) feed rate, and 0.6 mm depth of cut.

Abstract ERRATUM

Abstract A comprehensive literature review of the optimization techniques used for the process parameter optimization of Abrasive Jet Machining (AJM), Ultrasonic Machining (USM), Laser Beam Machining (LBM), Electrochemical Machining (ECM), and Plasma Arc Machining (PAM) are presented in this review article. This review article is an extension of the review work carried out by previous researchers for the process parameter optimization of non-traditional machining processes using various advanced optimization algorithms. The review period considered for the same is from 2012 to 2022. The prime motive of this review article is to find out the sanguine effects of various optimization techniques used for the optimization of various considered objectives of selected non-traditional machining processes in addition to deemed materials and foremost process parameters.

Abstract Personal watercraft (PWC) users and other high-speed watersports participants have sustained rectal and vaginal injuries during falls into the water, herein referred to as water intrusion injuries (WIIs). WIIs result from the rapid introduction of water into these lower body cavities causing injury to the soft tissues of the perineum, rectum, and vagina. While case studies of injured water-skiers and PWC users are reported in the literature, there is little information related to passenger kinematics and pressure exposure during a rearward fall from a PWC. The results of an experimental study of passenger falls from two “high-performance” PWC are presented herein. A human passenger was caused to fall rearward as the PWC was accelerated at maximum throttle starting from idle speed (≈3–4 mph) and planing speeds of ≈20–30 mph. The subject passenger fell from the aft seat position and while standing on the rear platform.

Abstract Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated.

Abstract This article reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults in different components, aiming toward simulated data-driven machine learning. We have employed lumped and one-dimensional analytical models of the subcomponents for better insight into the complex dynamic response. The framework addresses some of the challenges encountered in analyzing and optimizing fault detection and identification schemes for health monitoring of aeroengines and other rotating machinery. We model the bladed disks and shafts by combining lumped elements and one-dimensional finite elements, leading to a coupled system. The simulation results are in good agreement with previously published data. We model and analyze the cracks in a blade with their effective reduced stiffness approximation.

Abstract Laminated composites are extensively used in the aerospace industry. However, structures made from laminated composites are highly susceptible to delamination failures. It is therefore imperative to consider a structure tolerance to delamination during design and operation. Hybrid composites with laminas containing different fibers were used earlier in laminates to achieve certain benefits in strength, stiffness, and buckling. However, the concept of mixing laminas with different fibers was not explored by researchers to enhance delamination tolerance levels. This article examines the above aspect of hybridization by employing machine learning algorithms and proposes a reliable method of analysis to study delamination, which is crucial to ensure the safety of airframe composite panels.

Abstract Statistical machine learning classification methods have been widely used in the fault detection analysis in several engineering domains. This motivates us to provide in this article an overview on the application of these methods in the fault diagnosis strategies and also their successful use in unmanned aerial vehicles (UAVs) systems. Different existing aspects including the implementation conditions, offline design, and online computation algorithms as well as computation complexity and detection time are discussed in detail. Evaluation and validation of these aspects have been ensured by a simple demonstration of the basic classification methods and neural network techniques in solving the fault detection and diagnosis problem of the propulsion system failure of a multirotor UAV. A testing platform of an Hexarotor UAV is completely realized.