Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Modeling and Simulation in Engineering

The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

Effects of park energy on spark plug fault recognition in a spark ignition engine

The increasing demands for fuel economy and emission reduction have led to the development of lean/diluted combustion strategies for modern Spark Ignition (SI) engines. The new generation of SI engines requires higher spark energy and a longer discharge duration to improve efficiency and reduce the backpressure. However, the increased spark energy gives negative impacts on the ignition system which results in deterioration of the spark plug. Therefore, a numerical model was used to estimate the spark energy of the ignition system based on the breakdown voltage. The trend of spark energy is then recognized by implementing the classification method. Significant features were identified from the Information Gain (IG) scoring of the statistical analysis

Viewgraph description of Penn State's Propulsion Engineering Research Center: Activity highlights and future plans

Viewgraphs are presented that describe the progress and status of Penn State's Propulsion Engineering Research Center. The Center was established in Jul. 1988 by a grant from NASA's University Space Engineering Research Centers Program. After two and one-half years of operation, some 16 faculty are participating, and the Center is supporting 39 graduate students plus 18 undergraduates. In reviewing the Center's status, long-term plans and goals are reviewed and then the present status of the Center and the highlights and accomplishments of the past year are summarized. An overview of plans for the upcoming year are presented

New advances in vehicular technology and automotive engineering

An automobile was seen as a simple accessory of luxury in the early years of the past century. Therefore, it was an expensive asset which none of the common citizen could afford. It was necessary to pass a long period and waiting for Henry Ford to establish the first plants with the series fabrication. This new industrial paradigm makes easy to the common American to acquire an automobile, either for running away or for working purposes. Since that date, the automotive research grown exponentially to the levels observed in the actuality. Now, the automobiles are indispensable goods; saying with other words, the automobile is a first necessity article in a wide number of aspects of living: for workers to allow them to move from their homes into their workplaces, for transportation of students, for allowing the domestic women in their home tasks, for ambulances to carry people with decease to the hospitals, for transportation of materials, and so on, the list don’t ends. The new goal pursued by the automotive industry is to provide electric vehicles at low cost and with high reliability. This commitment is justified by the oil’s peak extraction on 50s of this century and also by the necessity to reduce the emissions of CO2 to the atmosphere, as well as to reduce the needs of this even more valuable natural resource. In order to achieve this task and to improve the regular cars based on oil, the automotive industry is even more concerned on doing applied research on technology and on fundamental research of new materials. The most important idea to retain from the previous introduction is to clarify the minds of the potential readers for the direct and indirect penetration of the vehicles and the vehicular industry in the today’s life. In this sequence of ideas, this book tries not only to fill a gap by presenting fresh subjects related to the vehicular technology and to the automotive engineering but to provide guidelines for future research. This book account with valuable contributions from worldwide experts of automotive’s field. The amount and type of contributions were judiciously selected to cover a broad range of research. The reader can found the most recent and cutting-edge sources of information divided in four major groups: electronics (power, communications, optics, batteries, alternators and sensors), mechanics (suspension control, torque converters, deformation analysis, structural monitoring), materials (nanotechnology, nanocomposites, lubrificants, biodegradable, composites, structural monitoring) and manufacturing (supply chains). We are sure that you will enjoy this book and will profit with the technical and scientific contents. To finish, we are thankful to all of those who contributed to this book and who made it possible.info:eu-repo/semantics/publishedVersio

Experimental Modeling of NOx and PM Generation from Combustion of Various Biodiesel Blends for Urban Transport Buses

Biodiesel has diverse sources of feedstock and the amount and composition of its emissions vary significantly depending on combustion conditions. Results of laboratory and field tests reveal that nitrogen oxides (NOx) and particulate matter (PM) emissions from biodiesel are influenced more by combustion conditions than emissions from regular diesel. Therefore, NOx and PM emissions documented through experiments and modeling studies are the primary focus of this investigation. In addition, a comprehensive analysis of the feedstock-related combustion characteristics and pollutants are investigated. Research findings verify that the oxygen contents, the degree of unsaturation, and the size of the fatty acids in biodiesel are the most important factors that determine the amounts and compositions of NOx and PM emissions

Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application