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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 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 This study implements Flame Image Velocimetry (FIV), a diagnostic technique based on post-processing of high-speed soot luminosity images, to show the in-flame flow field development impacted by after-injection in a single-cylinder, small-bore optical diesel engine. Two after-injection cases with different dwell times between the main injection and after-injection, namely, close-coupled and long-dwell, as well as a main-injection-only case are compared regarding flow fields, flow vector magnitude, and turbulence intensity distribution. For each case, high-speed soot luminosity movies from 100 individual combustion cycles are recorded at a high frame rate of 45 kHz for FIV processing. The Reynolds decomposition using a spatial filtering method is applied to the obtained flow vectors so that bulk flow structures and turbulence intensity distributions can be discussed.

Abstract We survey the state of the art in off-board diagnostics for vehicles, their occupants, and environments, with particular focus on vibroacoustic (VA) approaches. We identify promising application areas including data-driven management for shared mobility and automated fleets, usage-based insurance, and vehicle, occupant, and environmental state and condition monitoring. We close by exploring the particular application of VA monitoring to vehicle diagnostics and prognostics and propose the introduction of automated vehicle- and context-specific model selection as a means of improving algorithm performance, e.g., to enable smartphone-resident diagnostics. Towards this vision, four strong-performing, interdependent classifiers are presented as a proof of concept for identifying vehicle configuration from acoustic signatures. The described approach may serve as the first step in developing “universal diagnostics,” with applicability extending beyond the automotive domain.

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.

Abstract The civil aircraft nosewheel is clamped, lifted, and retained through the pick-up and holding system of the towbarless towing vehicle (TLTV), and the aircraft may be moved from the parking position to an adjacent one, the taxiway, a maintenance hangar, a location near the active runway, or conversely only with the power of the TLTV. The TLTV interfacing with the nose-landing gear of civil transport aircraft for the long-distance towing operations at a high speed could be defined as a towbarless aircraft taxiing system (TLATS). The dynamic loads induced by the system vibration may cause damage or reduce the certified safe-life limit of the nose-landing gear or the TLTV when the towing speed increases up to 40 km/h during the towing operations due to the maximum ramp weight of a heavy aircraft.

Abstract To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicles (EVs).

Abstract Previous studies have shown that injector aging adversely affects the diesel engine spray formation and combustion. It has also been shown that the oxygenated fuel additive tripropylene glycol monomethyl ether (TPGME) can lower soot emissions. In this study, the effects of injector aging and TPGME on the late-cycle oxidation of soot were investigated using laser diagnostic techniques in a light-duty optical diesel engine at two load conditions. The engine was equipped with a quartz piston with the same complex piston geometry as a production engine. Planar laser-induced incandescence (LII) was used to obtain semiquantitative in-cylinder two-dimensional (2D) soot volume fraction (fv ) distributions using extinction measurements. The soot oxidation rate was estimated from the decay rate of the in-cylinder soot concentration for differently aged injectors and for cases with and without TPGME in the fuel.

Abstract Load-time histories can be used to predict vehicle durability by calculating the pseudo damage (PD) through one or more load paths for a vehicle. When the dynamics of each load path are taken into account, a PD density (damage per distance traveled) can be expressed for each load path for any given road input to a vehicle. When damage is expressed as a PD density for a segment of road, separable damaging events can be identified using the PD density in all load paths of interest for a vehicle. However, it would be beneficial if events with similar damage characteristics can be identified and grouped together to provide an additional level of durability information. The objective of this work is to develop a similarity test for identifying the similarity/dissimilarity between multiple damaging events using the damage characteristics in multiple load paths. The damage characteristics for events are defined using the distribution of PD density samples for all known load paths.

Abstract In this study, active-passive filling friction stir repairing (A-PFFSR) process was employed to repair the volume defects in friction stir welding (FSW) joints of Al-Cu alloy. The volume defects with varied geometries were first machined into taper holes, which are similar to keyhole defect by a rotational tool with a threaded pin. Then, the keyhole defect was effectively filled with the materials around the keyhole and an additional filler using a number of nonconsumable pinless tools with the shoulders having six spiral flutes. The macro/microstructures, microhardness, and tensile properties of the repaired joints were investigated. The influences of plunge speed on macro/microstructures and mechanical properties of the repaired joints have been analyzed too. It was noticed that decreasing plunge speed was effective to improve the frictional heat and material flow, which increased joint surface integrity avoiding interfacial drawbacks.

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 Numerous operational procedures regulate aerodrome ground traffic. Detailed solutions in these procedures often come from preventive recommendations formulated as a result of accident cause analysis. With time, the conclusions drawn based on incidents, i.e., events that did not result in material damage or casualties, are becoming increasingly significant. In this article, we propose a new method for determining the probability of an incident turning into an air accident, based on the example of aerodrome traffic operations. Premises conducive to an accident in the considered class of events depend on both human and physical factors. Thus a hybrid approach was applied. We used a fuzzy inference system to analyze the premises dependent on vehicle operators, while the simulation method was selected to examine the premises dependent on physical factors. Both were integrated using the technique of event trees with fuzzy probabilities (ETFP).

Abstract Electrolytic hydrogen production equipment has numerous hydrogen pipelines and high-pressure hydrogen storage tanks which may leak hydrogen which can lead to explosions causing damage to the nearby personnel and equipment. The present work modeled hydrogen explosions in a skid-mounted electrolytic hydrogen production unit. The model was first used to predict the area affected by an explosion without protective walls. The effects of protective walls on the flame and overpressure were then studied by modeling explosions with various protective walls at various distances from the opening on the side of the unit. The results show that the protective walls effectively reduced the damage behind the wall. However, the reflected shock waves may cause secondary damage in front of the wall if the protective wall is too close to the opening. Moreover, the protective wall blocks the hydrogen diffusion which increases the flammable gas mass.

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 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 Conventional fusion joining techniques pervasive in the automotive industry are unable to effectively join aluminum and steel. To solve this problem, a technique termed friction element welding (FEW) has been developed, which is able to join any nonferrous top sheet material to a base steel layer, independent of the base layer strength. FEW works on the same principles as friction welding, as a steel element is pushed and rotated against a nonferrous top sheet to create frictional energy which softens and flows the material around the fastener shaft and under the fastener head, exposing the steel below. The element then contacts the steel and bonds through traditional friction welding. FEW is a four-step process (penetration, cleaning, welding, compression), with two to four parameters (endload, spindle speed, displacement transition, time transition) controlling each step.

Abstract Every year the demands on the electric grid increase, but the ability to deliver power where needed remains problematic because of transmission and distribution losses, vulnerabilities to natural disasters, and struggles to meet peak load requirements in an increasing number of regions. To meet these increasing demands, especially with emerging electric vehicles, it becomes ever more important to develop integrated demand and response systems. One such promising technology is the use of a micro Combined Heat and Power (mCHP)-based distributed energy system that addresses both electricity and thermal demands (i.e., electricity, hot water, and space heating demands) by using a single unit. However, one major problem with this technology at the residential level is the optimal sizing and maximizing the operational time of mCHP systems in meeting electrical and thermal demands.

Abstract The proposed approach present the development of a computer tool that allows, in the first phase, the modeling of the electric vehicle power chain. This phase is based on a library developed under the Matlab-Simulink simulation environment. This library contains all the components of the power chain; it offers the selection of the desired configuration of each component. In the second phase, the tool solves the autonomy optimization problem. This problem is resolved by a program based on genetic algorithms. This program permits to optimize the configuration parameters maximizing the vehicle autonomy of the chosen chain. This tool is based on a graphical interface developed under the Matlab simulation environment.

Abstract This article addresses the issue of a design to provide remote vehicle communication services sustainably. These services include new features such as remote repair of Electronic Control Unit (ECU)’s software errors and feature on demand, to mention just a few key objectives. With the usual implementations of the Modular Vehicle Communication Interface (MVCI) runtime server [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] many difficulties remain [15]. They are not sustainable and require dedicated hardware. The Dictionary Server discussed here provides necessary data to diagnostic applications in general, without putting at risk Original Equipment Manufacturer (OEM)’s expertise. It also provides data to the road infrastructure for V2V- and Vehicle-to-Infrastructure (V2X)-based services. This crucial diagnostic data contains ECUs’ communication parameters, memory programming data, and other available functions. They are kept confidentially by OEMs.

Abstract The main focus of this article is the online estimation of the terminal airspace sector capacity from the Air Traffic Controller 0ATC) dynamical neural model using Neural Partial Differentiation (NPD) with permissible safe separation and affordable workload. For this purpose, a primarily neural model of a multi-input-single-output (MISO) ATC dynamical system is established, and the NPD method is used to estimate the model parameters from the experimental data. These estimated parameters have a less relative standard deviation, and hence the model validation results show that the predicted neural model response is well matched with the intervention of the ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters, which are unknown in practice.