Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

A Few-Shot Learning-Based Crashworthiness Analysis and Optimization for Multi-Cell Structure of High-Speed Train

Due to the requirement of significant manpower and material resources for the crashworthiness tests, various modelling approaches are utilized to reduce these costs. Despite being informative, finite element models still have the disadvantage of being time-consuming. A data-driven model has recently demonstrated potential in terms of computational efficiency, but it is also accompanied by challenges in collecting an amount of data. Few-shot learning is a perspective approach in addressing the problem of insufficient data in engineering. In this paper, using a novel hybrid data augmentation method, we investigate a deep-learning-based few-shot learning approach to evaluate and optimize the crashworthiness of multi-cell structures. Innovatively, we employ wide and deep neural networks to develop a surrogate model for multi-objective optimization. In comparison with the original results, the optimized result of the multi-cell structure demonstrates that the mean crushing force (Fm) and specific energy absorption (SEA) are increased by 17.1% and 30.1%, respectively, the mass decreases by 4.0%, and the optimized structure offers a significant improvement in design space. Overall, this proposed method exhibits great potential in relation to the crashworthiness analysis and optimization for multi-cell structures of the high-speed trai

Real-coded chemical reaction optimization

Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain. © 2012 IEEE.published_or_final_versio

Lightweight energy absorbing structures for crashworthy design

PhD ThesisThe application of lightweight composite materials into the rail industry requires a stepwise approach to ensure rail vehicle designs can make optimal use of the inherent properties of each material. Traditionally, materials such as steel and aluminium have been used in railway rolling stock to achieve the energy absorption and structural resistance demanded by European rail standards. Adopting composite materials in primary structural roles requires an innovative design approach which makes the best use of the available space within the rolling stock design such that impact energies and loads are accommodated in a managed and predictable manner. This thesis describes the innovative design of a rail driver’s cab to meet crashworthiness and structural requirements using lightweight, cost-effective composite materials. This takes the application of composite materials in the rail industry beyond the current state-of-the-art and delivers design solutions which are readily applicable across rolling stock categories. An overview of crashworthiness with respect to the rail industry is presented, suitable composite materials for incorporation into rolling stock designs are identified and a methodology to reconfigure and enhance the space available within rail vehicles to meet energy absorption requirements is provided. To realise the application of composite materials, this body of work describes the pioneering application of aluminium honeycomb to deliver unique solutions for rail vehicle energy absorbers, as well as detailing the use of lightweight composite materials to react the structural loads into the cab and carbody. To prove the capability of the design it is supported by finite element analysis and the construction of a full-scale prototype cab which culminated in the successful filing of two patents to protect the intellectual property of the resulting design.The European Commission whose Framework 6 funded project “De-Light” (Contract Number 031483) forms the basis of this work

Mechanical metamaterials for sports helmets: structural mechanics, design optimisation, and performance

Sports concussions are a public health concern. Improving helmet performance to reduce concussion risk is a key part of the research and development community response. Head impacts with compliant surfaces that cause long duration moderate or high linear and rotational accelerations are associated with a high rate of clinical diagnoses of concussion. As engineered structures with unusual combinations of properties, mechanical metamaterials are being applied to sports helmets, with the goal of improving impact performance and reducing brain injury risk. Replacing established helmet material (i.e., foam) selection with a metamaterials design approach (structuring material to obtain desired properties) allows development of near optimal properties. Objective functions based on up to date understanding of concussion could be applied to topology optimisation regimes, when designing mechanical metamaterials for helmets. Such regimes balance computational efficiency with predictive accuracy, both of which could be improved under high strains and strain rates to allow helmet modifications as knowledge of concussion develops. Researchers could also share mechanical metamaterial data, topologies and computational models in open, homogenised repositories, to improve the efficiency of their development

Investigation of an Adaptable Crash Energy Management System to Enhance Vehicle Crashworthiness

The crashworthiness enhancement of vehicle structures is a very challenging task during the vehicle design process due to complicated nature of vehicle design structures that need to comply with different conflicting design task requirements. Although different safety agencies have issued and modified standardized crash tests to guarantee structural integrity and occupant survivability, there is continued rise of fatalities in vehicle crashes especially the passenger cars. This dissertation research explores the applicability of a crash energy management system of providing variable energy absorbing properties as a function of the impact speed to achieve enhanced occupant safety. The study employs an optimal crash pulse to seek designs of effective energy absorption mechanisms for reducing the occupant impact severity. The study is conducted in four different phases, where the performance potentials of different concepts in add-on energy absorbing/dissipating elements are investigated in the initial phase using a simple lumped-parameter model. For this purpose, a number of performance measures related to crash safety are defined, particular those directly related to occupant deceleration and compartment intrusion. Moreover, the effects of the linear, quadratic and cubic damping properties of the add-on elements are investigated in view of structure deformation and occupant`s Head Injury Criteria (HIC). In the second phase of this study, optimal design parameters of the proposed add-on energy absorber concept are identified through solutions of single- and weighted multi-objective minimization functions using different methods, namely sequential quadratic programming (SQP), genetic algorithms (GA) and hybrid genetic algorithms. The solutions obtained suggest that conducting multiobjective optimization of conflicting functions via genetic algorithms could yield an improved design compromise over a wider range of impact speeds. The effectiveness of the optimal add-on energy absorber configurations are subsequently investigated through its integration to a full-scale vehicle model in the third phase. The elasto-plastic stress-strain and force-deflection properties of different substructures are incorporated in the full-scale vehicle model integrating the absorber concept. A scaling method is further proposed to adapt the vehicle model to sizes of current automobile models. The influences of different design parameters on the crash energy management safety performance measures are studied through a comprehensive sensitivity analysis. In the final phase, the proposed add-on absorber concept is implemented in a high fidelity nonlinear finite element (FE) model of a small passenger car in the LS-DYNA platform. The simulation results of the model with add-on system, obtained at different impact speeds, are compared with those of the baseline model to illustrate the crashworthiness enhancement and energy management properties of the proposed concept. The results show that vehicle crashworthiness can be greatly enhanced using the proposed add-on crash energy management system, which can be implemented in conjunction with the crush elements

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

Aural stealth for night vision portable imagers

Modern tactics for carrying out military and antiterrorist operations calls for the development of a new generation of enhanced portable infrared imagers. The high performance of these imagers relies on the focal plane arrays, which are maintained at cryogenic temperatures using rotary Stirling cryogenic engines. These engines are known as powerful sources of wideband vibration export. For the sake of weight and compactness, the enclosure of the above imager is usually designed in the form of a light metal thin-walled shell, accommodating a directly mounted Infrared Detector Dewar Cooler Assembly. The operation of the device typically leads to an excitation of the inherently lightly damped structural resonances and therefore, to a radiation of the specific acoustic signature capable of compromising the aural stealth of the IR imager. Such a noisy IR imager may be detected from quite a long distance using enhanced sniper detection equipment or even aurally spotted when used in a close proximity to the target. Numerous efforts were taken towards achieving the desired inaudibility level, apparently becoming one of a crucial figure of merit characterizing the portable IR imager. However, even the best examples of modern should-be silent imagers are quite audible from as far as 50 meters. The presented research intends to improve the aural stealth of the portable IR imager by using three different approaches: First, by compliantly mounting the Infrared Detector Dewar Cooler Assembly where the stiffness and damping of the vibration protective pad are optimized for the best acoustical performance without developing excessive line of sight jitter. Secondly, by using the concept of the weak radiator to reshape the enclosure mode shapes, and finally developing a multi-modal distributed dynamic absorber (MMDA) to enhance the absorption of the vibrating structure. The multi-modal characteristic of such a dynamic absorber makes it highly dynamically reactive through a wide frequency range (20 kHz) of excitation. It will be shown that incorporating a MMDA into the vibrating structure will result in ultra range vibration attenuation, making the IR aurally silent