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Abstract Transient temperature analysis is involved in the thermal simulation of the heat treatment process, in which the hot metal temperature changes with respect to time from an initial state to the final state. The critical part of the simulation is to determine the heat transfer coefficient (HTC) between the hot part and the quenching medium or quenchant. In liquid quenching, the heat transfer between the hot metal part and water becomes complicated and it is difficult to determine HTC. In the current experimentation a medium carbon steel EN 9 rod with a diameter of 50 mm and length 100 mm was quenched in water and ethylene glycol mixture with different concentrations. A part model was created; meshed and actual boundary conditions were applied to conduct computational fluid dynamics (CFD) analysis. In order to validate CFD analysis the experimental trials were conducted.

Abstract This article introduces an innovative method for predicting tire–road interaction forces by exclusively utilizing longitudinal and lateral acceleration measurements. Given that sensors directly measuring these forces are either expensive or challenging to implement in a vehicle, this approach fills a crucial gap by leveraging readily available sensor data. Through the application of a multi-output neural network architecture, the study focuses on simultaneously predicting the longitudinal, lateral, and vertical interaction forces exerted by the rear wheels, specifically those involved in traction. Experimental validation demonstrates the efficacy of the methodology in accurately forecasting tire–road interaction forces. Additionally, a thorough analysis of the input–output relationships elucidates the intricate dynamics characterizing tire–road interactions.

Abstract In this article, we investigated the effects of material parameters on the clinching joint geometry using finite element model (FEM) simulation and machine learning-based metamodels. The FEM described in this study was first developed to reproduce the shape of clinching joints between two AA5052 aluminum alloy sheets. Neural network metamodels were then used to investigate the relation between material parameters and joint geometry as predicted by FEM. By interpreting the data-driven metamodels using explainable machine learning techniques, the effects of the hard-to-measure material parameters during the clinching are studied. It is demonstrated that the friction between the two metal sheets and the flow stress of the material at high (up to 100%) plastic strain are the most influential factors on the interlock and the neck thickness of the clinching joints. However, their dependence on the material parameters is found to be opposite.

Abstract Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NOx–soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO–soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot–main and a pilot–main–post-fuel injection strategy.

Abstract This article presents the design and the analysis of a control logic capable of optimizing vehicle’s energy consumption during a braking maneuver. The idea arose with the purpose of enhancing regeneration and health management in electric vehicles with electro-actuated brakes. Regenerative braking improves energy efficiency and allows a considerable reduction in secondary emissions, but its efficiency is strongly dependent on the state of charge (SoC) of the battery. In the analyzed case, a vehicle equipped with four in-wheel motors (one for each wheel), four electro-actuated brakes, and a battery was considered. The proposed control system can manage and optimize electrical and energy exchanges between the driveline’s components according to the working conditions, monitoring parameters such as SoC of the battery, brake temperature, battery temperature, motor temperature, and acts to optimize the total energy consumption.

Abstract Measurements of air–fuel ratio (AFR) and λ (AFRactual/AFRstoich) are crucial for understanding internal combustion engine (ICE) performance. However, current λ sensors suffer from long light-off times (on the order of seconds following a cold start) and limited time resolution. In this study, a four-color mid-infrared laser absorption spectroscopy (LAS) sensor was developed to provide 5 kHz measurements of temperature, CO, CO2, and NO in engine-out exhaust. This LAS sensor was then combined with 1 kHz hydrocarbon (HC) measurements from a flame ionization detector (FID), and the Spindt exhaust gas analysis method to provide 1 kHz measurements of λ. To the authors’ knowledge, this is the first time-resolved measurement of λ during engine cold starts using the full Spindt method. Three tests with various engine AFR calibrations were conducted and analyzed: (1) 10% lean, (2) stoichiometric, and (3) 10% rich.

Abstract This article presents a strategy for the virtual calibration of a large-scale model representing a self-piercing rivet (SPR) connection. The connection is formed between a stack of three AA6016-T4 aluminum sheets and one SPR. The calibration process involves material characterization, a detailed riveting process simulation, virtual joint unit tests, and the final large-scale model calibration. The virtual tests were simulated by detailed solid element FE models of the joint unit. These detailed models were validated using experimental tests, namely peeling, single-lap joint, and cross-tests. The virtual parameter calibration was compared to the experimental calibration and finally applied to component test simulations. The article contains both experiments and numerical models to characterize the mechanical behavior of the SPR connection under large deformation and failure.

Abstract AISI H13 hot work tool steel is commonly used for applications such as hot forging and hot extrusion in mechanical working operations that face thermal and mechanical stress fluctuations, leading to premature failures. Cryogenic treatment was applied for AISI H13 steel to improve the surface hardness and thereby fatigue resistance. This work involves failure analysis of H13 steel specimens subjected to cryogenic treatment and gas nitriding. The specimens were heated to 1020°C, oil quenched followed by double tempering at 550°C for 2 h, and subsequently, deep cryogenically treated at −185°C in the cryochamber. Gas nitriding was carried out for 24 h at 500°C for 200 μm case depth in NH3 surroundings. The specimens were subjected to rotating bending fatigue at constant amplitude loading at room temperature.

Abstract This study aims to explore the wear characteristics of fused deposition modeling (FDM) printed automotive parts and techniques to improve wear performance. The surface roughness of the parts printed from this widely used additive manufacturing technology requires more attention to reduce surface roughness further and subsequently the mechanical strength of the printed geometries. The main aspect of this study is to examine the effect of process parameters and annealing on the surface roughness and the wear rate of FDM printed acrylonitrile butadiene styrene (ABS) parts to diminish the issue mentioned above. American Society for Testing and Materials (ASTM) G99 specified test specimens were fabricated for the investigations. The parameters considered in this study were nozzle temperature, infill density, printing velocity, and top/bottom pattern.

Abstract In today’s landscape, environmental protection and nature conservation have become paramount across industries, spurring the ever-increasing aspect of decarbonization. Regulatory measures in transportation have shifted focus away from combustion engines, making way for electric mobility, particularly in smaller engines. However, larger applications like ships and stationary power generation face limitations, not enabling an analogous shift to electrification. Instead, the emphasis shifted to zero-carbon fuel alternatives such as hydrogen and ammonia. In addition to minimal carbon-containing emissions due to incineration of lubricating oil, hydrogen combustion with air results in nitrogen oxide emissions, still necessitating quantification for engine operation compliance with legal regulations.

Abstract This study demonstrates the defossilized operation of a heavy-duty port-fuel-injected dual-fuel engine and highlights its potential benefits with minimal retrofitting effort. The investigation focuses on the optical characterization of the in-cylinder processes, ranging from mixture formation, ignition, and combustion, on a fully optically accessible single-cylinder research engine. The article revisits selected operating conditions in a thermodynamic configuration combined with Fourier transform infrared spectroscopy. One approach is to quickly diminish fossil fuel use by retrofitting present engines with decarbonized or defossilized alternatives. As both fuels are oxygenated, a considerable change in the overall ignition limits, air–fuel equivalence ratio, burning rate, and resistance against undesired pre-ignition or knocking is expected, with dire need of characterization.

Abstract Fossil fuel reserves are swiftly depleting when consumer demand for these fuels continues to rise. In order to meet the demand and diminish the pollution derived through conventional fuels, it is crucial to employ cleaner fuels made from substitutes such as waste biomass. Also, converting waste biomass to fuel can lower usage of landfills. There are many biomass resources that are suitable for fuel production, out of which groundnut is also a potential feedstock. Groundnut shell biomass was chosen for this study, as it is a waste leftover during shelling of groundnuts for various commercial applications. The procured groundnut shells were converted to oil using pyrolysis process and was distilled. Both the pyrolysis oil and the distilled oil were analyzed using Fourier transform infrared instrument wherein the presence of functional groups such as alcohols, amines, and carboxylic acids were identified.

Abstract This article focuses on the development of an active braking control system tailored for electric vehicles. The essence of this system lies in its ability to regulate the slip coefficient to optimize traction during braking, thereby maximizing energy recuperation. In the context of the simulation on enhancing regenerative energy capture in electric vehicles, the use of integral sliding mode control (ISMC) as an alternative for regulating braking performance can be understood through a comparison of two key output variables in braking control systems: wheel deceleration and wheel slip. Traditionally, wheel deceleration has been a controlled variable in braking systems, and it is still utilized in some anti-lock braking systems (ABS). It can be easily measured using a basic wheel encoder. However, the dynamic performance of wheel deceleration control may suffer when there are rapid changes in the road surface.

Abstract The expansion of the internet has made everyone’s personal and professional lives more transparent. There are network security issues because people like sharing resources under the right conditions. Academics have demonstrated significant interest in situation awareness, which includes situation prediction, situation appraisal, and event detection, rather than focusing on the security of a single device in the network. Multi-stage attack forecasting and security situation awareness are two significant issues for network supervisors because the future usually is unknown. Hence, this study suggests combined intuitionistic fuzzy sets and deep neural network (CIFS-DNN) for network security situation prediction. The goal is to provide network administrators with a resource they can use as a point of reference while they formulate and carry out preventive actions in the event of a network assault.

Abstract With the rapid growth of automobile ownership, traffic congestion has become a major concern at intersections. In order to alleviate the blockage of intersection traffic flow caused by signals, reduce the length of vehicle congestion and waiting time, and for improving the intersection access efficiency, therefore, this article proposes a vehicle speed guidance strategy based on the intersection signal change by combining the vehicle–road cooperative technology. The randomness of vehicle traveling speed in the road is being considered. According to the vehicle traveling speed, a speed guidance model is established under different conditions.

Abstract Various feedstocks can be employed for biodiesel production, leading to considerable variation in composition and engine fuel characteristics. Using biodiesels originating from diverse feedstocks introduces notable variations in engine characteristics. Therefore, it is imperative to scrutinize the composition and properties of biodiesel before deployment in engines, a task facilitated by predictive models. Additionally, the international commercialization of biodiesel fuel is contingent upon stringent regulations. The traditional experimental measurement of biodiesel properties is laborious and expensive, necessitating skilled personnel. Predictive models offer an alternative approach by estimating biodiesel properties without depending on experimental measurements. This research is centered on building models that correlate mid-infrared spectra of biodiesel and critical fuel properties, encompassing kinematic viscosity, cetane number, and calorific value.

Abstract A new revolution has taken place in the automobile industry in recent years, intelligent and connected vehicle (ICV) [1] has achieved a higher market share in recent years and relevant technologies have been quickly developed and widely accepted, so the auto industry needs to make regulations for automated driving system (ADS) on ICVs, mainly to assure the safety of ICV. To meet the requirements above, the definition of operational design domain (ODD) [2, 3] was put forward by the Society of Automotive Engineers (SAE) and International Organization for Standardization (ISO) a few years ago. ODD defines necessary external environment conditions for the ADS to operate, but the internal status of the vehicle is also a key part of judging whether ADS can operate safely.

Abstract The water droplet erosion (WDE) on high-speed rotating wheels appears in several engineering fields such as wind turbines, stationary steam turbines, fuel cell turbines, and turbochargers. The main reasons for this phenomenon are the high relative velocity difference between the colliding particles and the rotor, as well as the presence of inadequate material structure and surface parameters. One of the latest challenges in this area is the compressor wheels used in turbochargers, which has a speed up to 300,000 rpm and have typically been made of aluminum alloy for decades, to achieve the lowest possible rotor inertia. However, while in the past this component was only encountered with filtered air, nowadays, due to developments in compliance with tightening emission standards, various fluids also collide with the spinning blades, which can cause mechanical damage.

The initial cost of battery electric vehicles (BEVs) is higher than internal combustion engine-powered vehicles (ICEVs) due to expensive batteries. Various factors affect the total cost of ownership of a vehicle. In India, consumers are concerned with a vehicle’s initial purchase cost and prefer owning an economical vehicle. The higher cost and shorter range of BEVs compared to ICEVs severely limit their penetration in the Indian market. However, government subsidies and incentives support BEVs. The total cost of ownership assessment is used to evaluate the entire cost of a vehicle to find the most economical option among different powertrains. This study compares 2W (two-wheeler) and 4W (four-wheeler) BEV’s cost vis-à-vis equivalent ICEVs in Delhi and Mumbai. The cost analysis assesses the current and future government policies to promote BEVs. Two assumed policies were applied to estimate future scenarios.

Abstract In 2018, the state explicitly proposed to “promote the cancellation of expressway toll stations at provincial boundaries.” Electronic toll collection has become the main toll collection method on expressways. With the construction of ETC toll lanes, the proportion of ETC vehicles in the expressway traffic flow is increasing, and the rapid processing of vehicle special situations is facing challenges. At present, various provinces have adopted various methods to improve the traffic efficiency and transaction success rate of ETC from the issuance link, customer service link, and lane transaction link. According to statistical data, the average transaction success rate of ETC lane is not higher than 99% at present. As of October 2021, the number of ETC users nationwide has reached 256 million, and there are an average of 40 million ETC transactions per day across the network, that is, about 400,000 special cases need to be processed.