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Abstract Due to the increasing requirements for efficiency, the wide range of characteristics and the improved possibilities of modern development and production processes, compressors in turbochargers have become more individualized in order to adapt to the requirements of internal combustion engines. An understanding of the working mechanisms as well as an understanding of the way that losses occur in the flow allows a reduced development effort during the optimization process. This article presents three-dimensional (3D) Computational Fluid Dynamics (CFD) investigations of the loss mechanisms and quantitative calculations of individual losses. The 3D-CFD method used in this article will reduce the drawbacks of one-dimensional calculation as far as possible. For example, the twist of the blades is taken into account and the “discrete” method is used for loss calculation instead of the “average” method.

Abstract Additive manufacturing (AM) technologies can produce lighter parts; reduce manual assembly processes; reduce the number of production steps; shorten the production cycle; significantly reduce material consumption; enable the production of prostheses, implants, and artificial organs; and produce end-user products since it is used in many sectors for many reasons; it has also started to be used widely, especially in the field of aerospace. In this study, polylactic acid (PLA) was preferred for the antenna substrate because it is environmentally friendly, easy to recycle, provides convenience in production design with a three-dimensional (3D) printer, and is less expensive compared to other available materials. Copper (Cu) tape and graphene filament were employed for the antenna patch component due to their benefits.

Abstract This article discusses a multi-item planning and scheduling problem in a job-shop system with consideration of energy consumption. Planning is considered by a set of periods, each one is characterized by a demand, energy, and length. Scheduling is determined by the sequences of jobs on available resources. A Mixed-Integer Linear Programming (MILP) problem is formulated to integrate planning and scheduling, it is considered as an NP-difficult problem. A Genetic Algorithm (GA) is then developed to solve the MILP, and then a hybridized approach of simulated annealing with genetic algorithm (HGASA) is presented to optimize the results. Finally, numerical results are presented and analyzed to evaluate the effectiveness of the proposed algorithms.

Abstract Motion planning for autonomous vehicles remains challenging, especially in environments with multiple vehicles and high speeds. Autonomous racing offers an opportunity to develop algorithms that can deal with such situations and adds the requirement of following race rules. We propose a hybrid local planning approach capable of generating rule-compliant trajectories at the dynamic limits for multi-vehicle oval racing. The planning method is based on a spatiotemporal graph, which is searched in a two-step process to exploit the dynamic limits on the one hand and achieve a long planning horizon on the other. We introduce a soft-checking procedure that can handle cases where no collision-free, feasible, or rule-compliant solutions are found to restore an admissible state as quickly as possible. We also present a state machine explicitly designed for fully autonomous operation on a racetrack, acting on a higher level of the planning algorithm.

Abstract With the introduction of advanced lightweight materials with complex microstructures and behaviors, more focus is put on the accurate determination of their forming limits, and that can only be possible through experiments as the conventional theoretical models for the forming limit curve (FLC) prediction fail to perform. Despite that, CAE engineers, designers, and toolmakers still rely heavily on theoretical models due to the steep costs associated with formability testing, including mechanical setup, a large number of tests, and the cost of a stereo digital image correlation (DIC) system. The international standard ISO 12004-2:2021 recommends using a stereo DIC system for formability testing since two-dimensional (2D) DIC systems are considered incapable of producing reliable strains due to errors associated with out-of-plane motion and deformation.

Abstract Turbocharging can provide a low cost means for increasing the power output and fuel economy of an internal combustion engine. Currently, turbocharging is common in multi-cylinder engines, but due to the inconsistent nature of intake air flow, it is not commonly used in single-cylinder engines. In this article, we propose a novel method for turbocharging single-cylinder, four-stroke engines. Our method adds an air capacitor-an additional volume in series with the intake manifold, between the turbocharger compressor and the engine intake-to buffer the output from the turbocharger compressor and deliver pressurized air during the intake stroke. We analyzed the theoretical feasibility of air capacitor-based turbocharging for a single-cylinder engine, focusing on fill time, optimal volume, density gain, and thermal effects due to adiabatic compression of the intake air.

Abstract The Formula SAE (FSAE) is an international engineering competition where a Formula style race car is designed and built by students from worldwide universities. According to FSAE regulation, an air restrictor with circular cross section of 20 mm for gasoline-fuelled and 19 mm for E-85-fuelled vehicles is to be incorporated between the throttle valve and engine inlet. The sole purpose of this regulation is to limit the airflow to the engine used. The only sequence allowed is throttle valve, restrictor and engine inlet. A new approach of combining ram theory and acoustic theory methods are investigated to increase the performance of the engine by designing an optimized intake runner for a particular engine speed range and an optimized plenum volume in this range. Engine performance characteristics such as brake power, brake torque and volumetric efficiency are taken into considerations.

Abstract Optimization is essential in real-life mechanical engineering problems that mostly are nonlinear, depend on mixed decision variables, and are usually subject to constraints. However, most of the studied problems are modelled assuming continuous variables. A limited number of studies have been devoted to cases with mixed variables. Moreover, there is a lack of algorithm treating mixed variable problems properly. This article introduces a hybrid algorithm that can handle constrained problems depending on continuous or mixed variables. The proposed algorithm combines two meta-heuristics, Jaya and particle swarm optimization (PSO). PSO is one of the most popular methods to solve nonlinear problems, and Jaya is a novel parameter-free optimization algorithm. This new hybrid optimization algorithm is proposed in order to improve the convergence speed and to investigate what improvements it will bring to optimization problem solutions.

Abstract The two-wheel system design is widely used in various mobile tools, such as remote-control vehicles and robots, due to its simplicity and stability. However, the specific wheel and body models in the real world can be complex, and the control accuracy of existing algorithms may not meet practical requirements. To address this issue, we propose a double inverted pendulum on mobile device (DIPM) model to improve control performances and reduce calculations. The model is based on the kinetic and potential energy of the DIPM system, known as the Euler-Lagrange equation, and is composed of three second-order nonlinear differential equations derived by specifying Lagrange. We also propose a stable feedback control method for mobile device drive systems. Our experiments compare several mainstream reinforcement learning (RL) methods, including linear quadratic regulator (LQR) and iterative linear quadratic regulator (ILQR), as well as Q-learning, SARSA, DQN (Deep Q Network), and AC.

Abstract Maximum lifetime and minimum manufacturing cost for new products are the primary goals of companies for competitiveness. These two objectives are contradictory and the geometric dimensions of the products directly control them. In addition, the earlier design errors of new products are predicted, the easier and more inexpensive their rectification becomes. To achieve these objectives, we propose in this article a novel model that makes it possible to solve the problem of optimizing the lifespan and the manufacturing cost of new products during the phase of their design. The prediction of the life of the products is carried out by an energy damage method implemented on the finite element (FE) calculation by using the ABAQUS software. The manufacturing cost prediction is carried out by applying the ABC cost estimation analytical method. In addition, the optimization problem is solved by the method of genetic algorithms.

Abstract The demand for contactless, rapid manufacturing has increased over the years, especially during the COVID-19 pandemic. Additive manufacturing (AM), a type of rapid manufacturing, is a computer-based system that precisely manufactures products. It proves to be a faster, cheaper, and more efficient production system when integrated with cloud-based manufacturing (CBM). Similarly, the need for semiconductors has grown exponentially over the last five years. Several companies could not keep up with the increasing demand for many reasons. One of the main reasons is the lack of a workforce due to the COVID-19 protocols. This article proposes a novel technique to manufacture semiconductor chips in a fast-paced manner. An algorithm is integrated with cloud, machine vision, sensors, and email access to monitor with live feedback and correct the manufacturing in case of an anomaly.

Abstract Lightweight and high-strength high-pressure die casting (HPDC) aluminum has been widely used in automotive components such as the cylinder block, lower crankcase extension, transmission case, and drive unit. Die cast parts have good surface finishes with relatively higher material strength in the casting skin than the center core material, maintain consistent features and tolerance, and maximize metal yield, therefore making it the most cost-effective casting process for mass production of aluminum parts. However, due to the rapid filling rates, the HPDC process tends to form large porosity and oxides because of the entrapped gas and solidification shrinkage, thereby deteriorating the mechanical properties of the casting parts.

Abstract Many consider hydrogen to be the automobile fuel of the future. Indeed, it has numerous characteristics that makes it very attractive. Hydrogen has a much higher energy density than gasoline, can be produced from water, and its only emission is water. However, there are numerous challenges associated with hydrogen. In particular, the production of hydrogen is a key issue. Currently, most hydrogen is developed from methane, resulting in hydrogen having a carbon footprint. New investments into electrolysis from renewable energy sources is showing promise as an alternative for generating hydrogen. Further, the distribution of hydrogen poses many problems, requiring substantial infrastructure to support a hydrogen economy. Additionally, hydrogen storage is a key issue since most conventional storage mechanisms are overly bulky. If these three issues can be addressed, hydrogen is posed for being a key fuel as the world tries to move away from fossil fuels.

Abstract Connected autonomous vehicles that employ internet connectivity are technologically complex, which makes them vulnerable to cyberattacks. Many cybersecurity researchers, white hat hackers, and black hat hackers have discovered numerous exploitable vulnerabilities in connected vehicles. Several studies indicate consumers do not fully trust automated driving systems. This study expanded the technology acceptance model (TAM) to include cybersecurity and level of trust as determinants of technology acceptance. This study surveyed a diverse sample of 209 licensed US drivers over 18 years old. Results indicated that perceived ease of use positively influences perceived usefulness, perceived ease of usefulness negatively influences perceived cyber threats, and perceived cyber threats negatively influence the level of trust.

Abstract In this work, the refinement of a phenomenological turbulence model developed in recent years by the authors is presented in detail. As known, reliable information about the underlying turbulence intensity is a mandatory prerequisite to predict the burning rate in phenomenological combustion models. The model is embedded under the form of “user routine” in the GT-Power™ software. The main advance of the proposed approach is the potential to describe the effects on the in-cylinder turbulence of some geometrical parameters, such as the intake runner orientation, the compression ratio, the bore-to-stroke ratio, and the valve number. The model is based on three balance equations, referring to the mean flow kinetic energy, the tumble vortex momentum, and the turbulent kinetic energy (3-eq. concept). An extended formulation is also proposed, which includes a fourth equation for the dissipation rate, allowing to forecast also the integral length scale (4-eq. concept).

Abstract The Noise, Vibration, and Harshness (NVH) performance of automotive powertrain (PT) mounts involves the PT source vibration, PT mount stiffness, road input, and overall transfer path design. Like safety, performance, and durability driving dynamics, vehicle-level NVH also plays a major contributing factor for electric vehicle (EV) refinement. This article highlights the recent research on PT mounting-related NVH controls on electric cars. This work’s main contribution lies in the comparative study of the internal combustion engine (ICE)-based PT mounting and EV-based PT mounting system (PMS) with specific EV challenges. Various literature on PT mounts from the passive, semi-active, and active mounting systems are studied. The parameter optimization technique for mount stiffness and location by various research papers is summarized to understand the existing methodologies and research gap in EV application.

Abstract In view of the combustion efficiency and emission performance, various new clean combustion modes put forward higher requirements for the performance of the fuel injection system, and the cavitating two-phase flow characteristics in the injector nozzle have a significant impact on the spray atomization and combustion performance. This article comprehensively discusses and summarizes the factors that affect cavitation and the effectiveness of cavitation, and presents the research status and existent problems under each factor. Among them, viscosity factors are a hot research topic that researchers are passionate about, and physical properties factors still have the value of further in-depth research. However, the importance of material surface factors ranks last since the nozzle material was determined. Establishing a more comprehensive cavitation–atomization model considering various factors is the focus of research on cavitation phenomena.

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 Electromagnetic forming (EMF) is a high-speed impulse forming process developed during the 1950s and 1960s to acquire shapes from sheet metal that could not be obtained using conventional forming techniques. In order to attain required deformation, EMF process applies high Lorentz force for a very short duration of time. Due to the ability to form aluminum and other low-formability materials, the use of EMF of sheet metal for automobile parts has been rising in recent years. This review gives an inclusive survey of historical progress in EMF of continuum sheet metals. Also, the EMF is reviewed based on analytical approach, finite element method (FEM) simulation-based approach and experimental approach, on formability of the metals.

Abstract In the past years, the automotive industry has been integrating multiple hardware in the vehicle to enable new features and applications. In particular automotive applications, it is important to monitor the actions and behaviors of drivers and passengers to promote their safety and track abnormal situations such as social disorders or crimes. These applications rely on multiple sensors that generate real-time data to be processed, and thus, they require adequate data acquisition and analysis systems. This article proposes a prototype to enable in-vehicle data acquisition and analysis based on the middleware framework Robot Operating System (ROS). The proposed prototype features two processing devices and enables synchronized audio and video acquisition, storage, and processing. It was assessed through the implementation of a live inference system consisting of a face detection algorithm from the data gathered from the cameras and the microphone.