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Abstract Noise, vibration, and harshness (NVH) is a key aspect in the vehicle development. Reducing noise and vibration to create a comfortable environment is one of the main objectives in vehicle design. In the literature, many theoretical and experimental methods have been presented for improving the NVH performances of vehicles. However, in the great majority of situations, physical prototypes are still required as NVH is highly dependent on subjective human perception and a pure computational approach often does not suffice. In this article, driving simulators are discussed as a tool to reduce the need of physical prototypes allowing a reduction in development time while providing a deep understanding of vehicle NVH characteristics. The present article provides a review of the current development of driving simulator focused on problems, challenges, and solutions for NVH applications.

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 Various internal combustion (IC) engine condition monitoring techniques exist for early fault detection and diagnosis to ensure smooth operation, increased durability, low emissions, and prevent breakdowns. A fault, such as piston slap, can damage critical components like the piston, piston rings, and cylinder liner and is among those faults that may lead to such consequences. This research has been conducted to monitor piston slap conditions by analyzing the engine vibration and acoustic emission (AE) signals. An experimental setup has been established for acquiring vibration and AE sensor signatures for various piston slap severity conditions. Time-domain features are extracted from vibration and AE sensor signatures, and among them, the best features are selected using one-way analysis of variance (ANOVA) to create machine learning (ML) models. Apart from individual sensor feature classification, the feature fusion method increases the prediction accuracy.

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 In the following, a multiphysics simulation approach for the calculation of the noise, vibration, and harshness (NVH) behavior of a three-phase permanent magnet synchronous machine is presented. Based on a defined operating point, the electromagnetic force densities in the air gap between the rotor and stator are determined on the basis of the flowing currents using the finite element method (FEM). In addition to the electromagnetic force densities, the structural modes with natural frequency and natural mode shapes are also determined by modal analysis. The electromagnetic forces and structural modes can then be reduced to the most important contributions in the modal space to significantly reduce the computation time. Using a frequency-dependent damping model, a full motor run-up is simulated and the resulting velocities at the surface of the machine are evaluated. The simulation results are then compared with a measurement and validated.

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.

Abstract The notable increase in combustion noise in the 7–10 kHz band has become an issue in the development of pre-chamber jet ignition combustion gasoline engines that aim for enhanced thermal efficiency. Combustion noise in such a high-frequency band is often an issue in diesel engine development and is known to be due to resonance in the combustion chamber. However, there are few cases of it becoming a serious issue in gasoline engines, and effective countermeasures have not been established. The authors therefore decided to elucidate the mechanism of high-frequency combustion noise generation specific to this engine, and to investigate effective countermeasures. As the first step, in order to analyze the combustion chamber resonance modes of this engine in detail, calculation analysis using a finite element model and experimental modal analysis using an acoustic excitation speaker were conducted.

Abstract In an automated machining process, monitoring the conditions of the tool is essential for deciding to replace or repair the tool without any manual intervention. Intelligent models built with sensor information and machine learning techniques are predicting the condition of the tool with good accuracy. In this study, statistical models are developed to identify the conditions of the abrasive grinding wheel using the Acoustic Emission (AE) signature acquired during the surface grinding operation. Abrasive grinding wheel conditions are identified using the abrasive wheel wear plot established by conducting experiments. The piezoelectric sensor is used to capture the AE from the grinding process, and statistical features of the abrasive wheel conditions are extracted in time and wavelet domains of the signature. Machine learning algorithms, namely, Classification and Regression Trees (CART) and Support Vector Classifiers (SVC), are used to build statistical models.

Abstract Internal combustion (IC) engines incorporating the conventional slider-crank mechanism are subjected to high frictional power losses mainly due to the piston-rod assembly. Due to its simplicity, IC engines have utilized this mechanism almost unchanged since its introduction. This study introduces the hypocycloid gear mechanism (HGM) as an alternative to the conventional slider-crank mechanism for IC engine systems. The HGM provides several advantages that allow for enhancing both the thermal and mechanical efficiencies of IC engines. In this study, the kinematic and dynamic performances of the HGM engine are analyzed in detail. The geometric relations of the HGM are used to derive the kinematic equations that describe the piston motion. These equations are then used to derive the dynamics equations considering gas and inertia forces acting on the HGM.

Abstract Automotive vehicle includes various systems like engine, transmission, exhaust, air intake, cooling and many more systems. No doubt the performance of individual system depends upon their core design. But for performance, the system needs to be fastened properly. In automotive, most of the joints used fasteners which helps in serviceability of the components. There are more than thousands of fasteners used in the vehicle. At various locations, we found issue of bolt loosening and because of this design intent performance has not met by the system. During product development of ECS (Engine cooling system), various issues reported to loosening the bolt. The pre-mature failure of bolt loosening, increases the interest in young engineers for understanding the behavior of fastener in vehicle running conditions. This paper focuses on the design of wedge shape of washer to avoid bolt loosening.

Abstract There are significant differences in sound and vibration between combustion engine vehicles (CV) and electric vehicles (EV), which may affect occupants’ experiences of overall ride comfort. There have been few studies on human perception of the overall ride comfort in EVs. The purpose of this study is to identify how sound and vibration influence perceived overall ride comfort in an EV under different driving scenarios and to study differences between an EV and a CV in terms of the influences of sound and vibration on the perceived ride comfort. The user study compared the experiences of ten participants’ riding in a CV and an EV through eight typical driving scenarios. The subjective judgment and objective measurements showed that in the EV, dynamic discomfort was dominated by high-frequency tones from electric components. The influence of sound on dynamic discomfort was more pronounced in the EV, and the causes of sound annoyance differed between the EV and the CV.

Abstract Stiffener ribs are widely used to increase the stiffness of engine blocks, shifting the vibration modes to higher frequencies where excitation is weaker so that radiated noise can be reduced. The effect of different rib design parameters on the radiated noise emission of a diesel engine has been investigated considering its impact on block weight. A heavy-duty engine block was modeled using finite element method, multi-body dynamics approach was used to determine the excitation forces acting due to combustion pressure and inertias, and boundary element method was used to find the acoustic transfer vectors which give the relationship between engine surface velocities and sound pressure levels at predetermined microphone locations. Initially, the baseline analytical sound pressure level and surface velocity results for the engine without ribs were obtained. Two prototype engines, with and without stiffened ribs, were tested in an acoustic dynamometer in complete speed range.