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4 results on '"Stéphanie Cagin"'

2. β-NTF reduction and fast kriging simulation of optimal engine configurations

Author

Daniel Coutelier, Céline Morin, Sylvain Loumé, Xavier Fischer, Nachida Bourabaa, Eric Delacourt, Bertrand Carré, Stéphanie Cagin, ESTIA Recherche, Ecole Supérieure des Technologies Industrielles Avancées (ESTIA), ENSIAME, Université de Valenciennes et du Hainaut-Cambrésis (UVHC), Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), and AKIRA-Technologies, ZA Saint Frédéric, rue de la Galupe, 64100 Bayonne

Subjects
Optimal design, 010407 polymers, Engineering, Mathematical optimization, 2-stroke engine optimization, design space, Computational fluid dynamics, 01 natural sciences, 7. Clean energy, Industrial and Manufacturing Engineering, Cylinder (engine), law.invention, Reduction (complexity), 010104 statistics & probability, [SPI]Engineering Sciences [physics], Surrogate model, Kriging, law, General Materials Science, kriging, 0101 mathematics, business.industry, Mechanical Engineering, Process (computing), [SPI.MECA]Engineering Sciences [physics]/Mechanics [physics.med-ph], 0104 chemical sciences, Data set, β-NTF reduction, fast simulation, business
Abstract

International audience; 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 β-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.

Published
2017

3. Neuro-separated meta-model of the scavenging process in 2-Stroke Diesel engine

Author

Xavier Fischer, Stéphanie Cagin, Institut de Mécanique et d'Ingénierie de Bordeaux (I2M), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), ESTIA Recherche, Ecole Supérieure des Technologies Industrielles Avancées (ESTIA), École Nationale Supérieure d'Arts et Métiers (ENSAM), and HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Institut National de la Recherche Agronomique (INRA)

Subjects
Engine configuration, Artificial neural network, NTF variables separation, Computer science, neural network, Process (computing), Neuro-separated meta-model, 020101 civil engineering, 02 engineering and technology, scavenging, Diesel engine, 0201 civil engineering, Cylinder (engine), law.invention, 020303 mechanical engineering & transports, [PHYS.MECA.STRU]Physics [physics]/Mechanics [physics]/Structural mechanics [physics.class-ph], 0203 mechanical engineering, law, Control theory, Engine efficiency, [PHYS.MECA.STRU]Physics [physics]/Mechanics [physics]/Mechanics of the structures [physics.class-ph], model reduction, 2-stroke engine ports, Reduction (mathematics), Two-stroke engine
Abstract

International audience; The complexity of flow inside cylinder leads to develop new accurate and specific models. Influencing the 2-stroke engine efficiency, the scavenging process is particularly dependent to the cylinder design. To improve the engine performances, the enhancement of the chamber geometry is necessary. The development of a new neuro-separated meta-model is required to represent the scavenging process depending on the cylinder configuration. Two general approaches were used to establish the meta-model: neural networks and NTF (Non-negative Tensor Factorization) separation of variables. To fully describe the scavenging process, the meta-model is composed by four static neural models (representing the Heywood parameters), two dynamic neural models (representing the evolution of gases composition through the ports) and one separated model (the mapping of the flow path during the process). With low reduction errors, these two methods ensure the accuracy and the relevance of the meta-model results. The establishment of this new meta-model is presented step by step in this article.

Published
2016

4. From functional analysis to energy harvesting system design: application to car suspension

Author

Octavian Curea, Amélie Hacala Perret, Stéphanie Cagin, B. Lafarge, Institut d’Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520 (IEMN), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Université Polytechnique Hauts-de-France (UPHF)-Ecole Centrale de Lille-Université Polytechnique Hauts-de-France (UPHF)-Institut supérieur de l'électronique et du numérique (ISEN), ESTIA Recherche, Ecole Supérieure des Technologies Industrielles Avancées (ESTIA), Institut de Mécanique et d'Ingénierie de Bordeaux (I2M), École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB), Curea, Octavian, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Institut National de la Recherche Agronomique (INRA), ESTIA INSTITUTE OF TECHNOLOGY, Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Arts et Métiers Sciences et Technologies, and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)

Subjects
energy harvesting, Energy recovery, Engineering, car suspension, business.industry, 020209 energy, [SPI.NRJ]Engineering Sciences [physics]/Electric power, Context (language use), 02 engineering and technology, [SPI.MECA]Engineering Sciences [physics]/Mechanics [physics.med-ph], 7. Clean energy, Industrial and Manufacturing Engineering, Automotive engineering, functional analysis, Modeling and Simulation, 0202 electrical engineering, electronic engineering, information engineering, Systems design, Engineering design process, Suspension (vehicle), business, Energy harvesting, Simulation, Energy (signal processing), [SPI.NRJ] Engineering Sciences [physics]/Electric power, Efficient energy use
Abstract

International audience; In 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.

Published
2015

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