International audienceIn the context of global energy demand increase, working on energy efficiency is essential. This paper deals with energy harvesting on car suspensions. In order to have a real added value, some criteria must be considered: the need to design a system that would be easily integrated into cars, the possibility to locally use the recovered energy to add new functionalities that can improve the security or the comfort of the car, and the necessity to not degrade and, if possible, to improve (semi-active or active dampers) the performances of the suspension. From the mechanical point of view, the functional analysis is used to define and to characterize the main suspension parts, to investigate the connexions and the energy flows and to identify the key elements for energy recovery. Then, quarter car and half car models implemented with Matlab/Simulink software are presented in order to evaluate the quantity of energy that could be recovered. Three locations are presented and evaluated. Simulations results will finally give an overview on the implementation opportunities
This paper presents the development of a new scavenging model to optimize cylinder design. The developed model explicitly integrates some of the cylinder's design and environmental variables to describe flows during the scavenging process. Then, based on the reduced model, an optimization phase was carried out in order to determine the optimal values of cylinder variables. From computational fluid dynamics (CFD) models to optimal cylinder design, all method steps used are described in this paper. Adaptable to any type of engine, here it is applied to the particular two-stroke diesel engine with ports only. In order to fully understand fluid flow, the methodology integrates a number of CFD calculations with different cylinder configurations to provide data. The CFD results are used as neural network outputs during the training phase, whereas the cylinder variables plus the crankshaft angle are the inputs. The trained network provides the analytical reduced model for gas composition transiting through ports, which characterize the scavenging process. Thanks to these, genetic algorithms are run to define the most suitable values of cylinder variables in order to improve scavenging. The entire process for establishing the reduced model and the optimal design of the chamber is presented in this paper.
Abstract. In an optimization process, models are applied to simulate different design behaviors in order to determine the most suitable one. However, this requires the use of a structured methodology to correctly explore the design space and truly converge to the best solution. It is therefore necessary to test and validate the optimal design. For engines, two ways are essentially used: building and testing a real cylinder, or simulating the new design with Computational-Fluid-Dynamics (CFD) models. These two techniques are both expensive and time consuming. An alternative way is proposed to test new designs with a fast simulation based on a kriging method. The exploration of the design space is based on 27 cylinder configurations and the results of their CFD models. It converged to an optimal design depending on the objective function. A kriging method was used to interpolate the behavior of the optimal design just found. In this paper we present the b-NTF model reduction (to define the data set used by the kriging method) and the principle of the kriging technique. We then briefly discuss the results. The results underline the method's advantages despite the small gap between the expected results and those for kriging.
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