Otimização de parâmetros de suspensão veicular com algoritmo heurístico QPSO

Dentre os estudos necessários para o projeto de veículos encontra-se o dimensionamento do sistema de suspensão. Este sistema é o responsável pela redução das vibrações ocasionadas pelas irregularidades da pista e transmitidas até os ocupantes do veículo. No caso de vibrações excessivas, estas podem trazer desconforto para os passageiros, reduzir o tempo de vida útil das partes e peças do veículo, por desgaste excessivo, além induzir danos à pista em que o veículo trafega. O presente trabalho tem como objetivo a otimização do sistema de suspensão de um ônibus modelado com treze graus de liberdade, simulando a sua dinâmica vertical e lateral, em resposta às irregularidades da pista e de manobra de dupla troca de faixa (DLC). É feito um estudo e implementação de um algoritmo metaheurístico de enxame de partículas com comportamento quântico (QPSO), indicado na literatura como bastante robusto, para a minimização dos efeitos de vibração nos passageiros do ônibus, melhorando o conforto. O algoritmo QPSO é testado em problemas benchmarks e comparado com outros algoritmos, para verificação da sua acurácia e robustez. A otimização do sistema de suspensão veicular é realizada tratando o problema de duas formas. A primeira abordagem é realizada com o problema na forma multiobjetiva através do NSGA-II, um algoritmo bastante difundido nesse tipo de análise, permitindo a obtenção do conjunto de possíveis soluções para o problema. A segunda abordagem, já com o QPSO, trata o problema na forma mono-objetiva através da soma ponderada das funções objetivas. Parâmetros utilizados na modelagem do veículo foram retirados de estudos e normas. Comparativos entre os algoritmos de otimização, os históricos de aceleração no tempo assim como dos valores RMS das acelerações resultantes são feitos entre os resultados obtidos. Ao final, são obtidos parâmetros da suspensão que reduzem a aceleração resultante do motorista e dos passageiros.Among the studies required for vehicle design is the sizing of the suspension system. This system is responsible for attenuating the vibrations caused by road irregularities and transmitted to the occupants of the vehicle. In the event of excessive vibration, these can cause discomfort to passengers, shorten the service life of the vehicle parts, excessive wear, and damage the road where the vehicle is traveling. The present work has the objective of optimizing the suspension system of a bus modelled with thirteen degrees of freedom, simulating its vertical and lateral dynamics, in response to the irregularities of the runway and double track change (DLC) manoeuvre. It is made a study and implementation of a metaheuristic particle swarm algorithm with quantum behaviour, reported in the literature as quite robust, to minimize the effects of vibration on the passengers of the bus, improving comfort. The QPSO algorithm is tested in benchmark problems and compared with other algorithms to verify its accuracy and robustness. The optimization of the vehicle suspension system is performed by treating the problem in two ways. The first approach is performed with the problem in multiobjective form through the NSGA-II, a rather widespread algorithm in this type of analysis that allows to obtain the set of possible solutions to the problem. The second approach, already with the QPSO, treats the problem in the mono-objective form through the weighted sum of the objective functions. Parameters used in vehicle modeling were taken from studies and standards. Comparisons between optimization algorithms, the acceleration time histories as well as the RMS values of the resulting accelerations are made between the obtained results. At the end, suspension parameters that reduce the resulting acceleration of the driver and the passengers are achieved

A novel multi-objective quantum particle swarm algorithm for suspension optimization

In this paper, a novel multi-objective archive-based Quantum Particle Optimizer (MOQPSO) is proposed for solving suspension optimization problems. The algorithm has been adapted from the well-knownsingle objectiveQPSOby substantialmodifications in the core equations and implementation of new multi-objectivemechanisms. The novel algorithmMOQPSO and the long-establishedNSGA-II andCOGA-II (Compressed-ObjectiveGenetic Algorithm with Convergence Detection) are compared. Two situations are considered in this paper: a simple half-car suspension model and a bus suspension model. The numerical model of the bus allows complex dynamic interactions not considered in previous studies. The suitability of the solution is evaluated based on vibration-related ISO Standards, and the efficiency of the proposed algorithm is tested by dominance comparison. For a specifically chosen Pareto front solution found by MOQPSO in the second case, the passengers and driver accelerations attenuated about 50% and 33%, respectively, regarding non-optimal suspension parameters. All solutions found by NSGA-II are dominated by those found byMOQPSO,which presented a Pareto front noticeably wider for the same number of objective function calls

Otimização de parâmetros de suspensão veicular com algoritmo heurístico QPSO

Dentre os estudos necessários para o projeto de veículos encontra-se o dimensionamento do sistema de suspensão. Este sistema é o responsável pela redução das vibrações ocasionadas pelas irregularidades da pista e transmitidas até os ocupantes do veículo. No caso de vibrações excessivas, estas podem trazer desconforto para os passageiros, reduzir o tempo de vida útil das partes e peças do veículo, por desgaste excessivo, além induzir danos à pista em que o veículo trafega. O presente trabalho tem como objetivo a otimização do sistema de suspensão de um ônibus modelado com treze graus de liberdade, simulando a sua dinâmica vertical e lateral, em resposta às irregularidades da pista e de manobra de dupla troca de faixa (DLC). É feito um estudo e implementação de um algoritmo metaheurístico de enxame de partículas com comportamento quântico (QPSO), indicado na literatura como bastante robusto, para a minimização dos efeitos de vibração nos passageiros do ônibus, melhorando o conforto. O algoritmo QPSO é testado em problemas benchmarks e comparado com outros algoritmos, para verificação da sua acurácia e robustez. A otimização do sistema de suspensão veicular é realizada tratando o problema de duas formas. A primeira abordagem é realizada com o problema na forma multiobjetiva através do NSGA-II, um algoritmo bastante difundido nesse tipo de análise, permitindo a obtenção do conjunto de possíveis soluções para o problema. A segunda abordagem, já com o QPSO, trata o problema na forma mono-objetiva através da soma ponderada das funções objetivas. Parâmetros utilizados na modelagem do veículo foram retirados de estudos e normas. Comparativos entre os algoritmos de otimização, os históricos de aceleração no tempo assim como dos valores RMS das acelerações resultantes são feitos entre os resultados obtidos. Ao final, são obtidos parâmetros da suspensão que reduzem a aceleração resultante do motorista e dos passageiros.Among the studies required for vehicle design is the sizing of the suspension system. This system is responsible for attenuating the vibrations caused by road irregularities and transmitted to the occupants of the vehicle. In the event of excessive vibration, these can cause discomfort to passengers, shorten the service life of the vehicle parts, excessive wear, and damage the road where the vehicle is traveling. The present work has the objective of optimizing the suspension system of a bus modelled with thirteen degrees of freedom, simulating its vertical and lateral dynamics, in response to the irregularities of the runway and double track change (DLC) manoeuvre. It is made a study and implementation of a metaheuristic particle swarm algorithm with quantum behaviour, reported in the literature as quite robust, to minimize the effects of vibration on the passengers of the bus, improving comfort. The QPSO algorithm is tested in benchmark problems and compared with other algorithms to verify its accuracy and robustness. The optimization of the vehicle suspension system is performed by treating the problem in two ways. The first approach is performed with the problem in multiobjective form through the NSGA-II, a rather widespread algorithm in this type of analysis that allows to obtain the set of possible solutions to the problem. The second approach, already with the QPSO, treats the problem in the mono-objective form through the weighted sum of the objective functions. Parameters used in vehicle modeling were taken from studies and standards. Comparisons between optimization algorithms, the acceleration time histories as well as the RMS values of the resulting accelerations are made between the obtained results. At the end, suspension parameters that reduce the resulting acceleration of the driver and the passengers are achieved

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present