Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation

In the UK, in 2014 almost fifty thousand motorists made claims about vehicle damages caused by potholes. Pothole damage mitigation has become so important that a number of car manufacturers have officially designated it as one of their priorities. The objective is to improve suspension shock performance without degrading road holding and ride comfort. In this study, it is shown that significant improvement in performance is achieved if a clipped quadratic parameter varying suspension is employed. Optimal design of the proposed system is challenging because of the multiple local minima causing global optimisation algorithms to get trapped at local minima, located far from the optimum solution. To this end an enhanced Fruit Fly Optimisation Algorithm − based on a recent study on how well a fruit fly’s tiny brain finds food − was developed. The new algorithm is first evaluated using standard and nonstandard benchmark tests and then applied to the computationally expensive suspension design problem. The proposed algorithm is simple to use, robust and well suited for the solution of highly nonlinear problems. For the suspension design problem new insight is gained, leading to optimum damping profiles as a function of excitation level and rattle space velocity

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

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

Proportional-integral state-feedback controller optimization for a full-car active suspension setup using a genetic algorithm

The use of active car suspensions to maximize driver comfort has been of growing interest in the last decades. Various active car suspension control technologies have been developed. In this work, an optimal control for a full-car electromechanical active suspension is presented. Therefore, a scaled-down lab setup model of this full-car active suspension is established, capable of emulating a car driving over a road surface with a much simpler approach in comparison with a classical full-car setup. A kinematic analysis is performed to assure system behaviour which matches typical full-car dynamics. A state-space model is deducted, in order to accurately simulate the behaviour of a car driving over an actual road prole, in agreement with the ISO 8608 norm. The active suspension control makes use of a Multiple-Input-Multiple- Output (MIMO) state-feedback controller with proportional and integral actions. The optimal controller tuning parameters are determined using a Genetic Algorithm, with respect to actuator constraints and without the need of any further manual fine-tuning