75 research outputs found
A combined numerical and experimental study of the 3D tumble structure and piston boundary layer development during the intake stroke of a gasoline engine
Due to its positive effect on flame propagation in the case of a well-defined breakdown, the formation of a large-scale tumble motion is an important goal in engine development. Cycle-to-cycle variations (CCV) in the tumble position and strength however lead to a fluctuating tumble breakdown in space and time and therefore to combustion variations, indicated by CCV of the peak pressure. This work aims at a detailed investigation of the large-scale tumble motion and its interaction with the piston boundary layer during the intake stroke in a state-of-the-art gasoline engine. To allow the validation of the flow near the piston surface obtained by simulation, a new measurement technique called “Flying PIV” is applied. A detailed comparison between experimental and simulation results is carried out as well as an analysis of the obtained flow field. The large-scale tumble motion is investigated based on numerical data of multiple highly resolved intake strokes obtained using scale-resolving simulations. A method to detect the tumble center position within a 3D flow field, as an extension of previously developed 2D and 3D algorithms, is presented and applied. It is then used to investigate the phase-averaged tumble structure, its characteristics in terms of angular velocity and the CCV between the individual intake strokes. Finally, an analysis is presented of the piston boundary layer and how it is influenced by the tumble motion during the final phase of the intake stroke
Understanding the unsteady pressure field inside combustion chambers of compression-ignited engines using a computational fluid dynamics approach
[EN] In this article, a numerical methodology for assessing combustion noise in compression ignition engines is described with the specific purpose of analysing the unsteady pressure field inside the combustion chamber. The numerical results show consistent agreement with experimental measurements in both the time and frequency domains. Nonetheless, an exhaustive analysis of the calculation convergence is needed to guarantee an independent solution. These results contribute to the understanding of in-cylinder unsteady processes, especially of those related to combustion chamber resonances, and their effects on the radiated noise levels. The method was applied to different combustion system configurations by modifying the spray angle of the injector, evidencing that controlling the ignition location through this design parameter, it is possible to decrease the combustion noise by minimizing the resonance contribution. Important efficiency losses were, however, observed due to the injector/bowl matching worsening which compromises the performance and emissions levels.The authors want to express their gratitude to CONVERGENT SCIENCE Inc. and Convergent Science GmbH for their kind support for performing
the CFD calculations using CONVERGE software.Torregrosa, AJ.; Broatch, A.; Margot, X.; Gómez-Soriano, J. (2018). Understanding the unsteady pressure field inside combustion chambers of compression-ignited engines using a computational fluid dynamics approach. International Journal of Engine Research. 1-13. https://doi.org/10.1177/1468087418803030S113Benajes, J., Novella, R., De Lima, D., & Tribotté, P. (2014). Analysis of combustion concepts in a newly designed two-stroke high-speed direct injection compression ignition engine. International Journal of Engine Research, 16(1), 52-67. doi:10.1177/1468087414562867Costa, M., Bianchi, G. M., Forte, C., & Cazzoli, G. (2014). A Numerical Methodology for the Multi-objective Optimization of the DI Diesel Engine Combustion. Energy Procedia, 45, 711-720. doi:10.1016/j.egypro.2014.01.076Navid, A., Khalilarya, S., & Taghavifar, H. (2016). 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Simulation aux grandes échelles: instabilités thermo-acoustiques, combustion diphasique et couplages multi-physiques
La combustion turbulente, que ce soit dans des configurations de laboratoire ou dans des configurations réelles industrielles, met en oeuvre un nombre important de physiques fortement couplées: chimie, turbulence, multi-phasique, thermique, etc. Pour répondre aux demandes de plus en plus exigeantes des concepteurs, qui doivent proposer des solutions concurrentielles tout en respectant les contraintes environnementales de bruit et d'émission de polluants, la simulation numérique est devenue incontournable. Plus précisément, la simulation maintenant utilisée comme outil de conception, doit être fiable et précise. Dans le domaine de la combustion turbulente, à fort caractère instationnaire, la Simulation aux Grandes Echelles (SGE) s'est récemment imposée. Cette technique s'est en effet avérée capable de prédire finement le comportement des brûleurs dans des environnements complexes, et permet aujourd'hui d'aborder des problématiques encore mal maîtrisées telles que les instabilités thermo-acoustiques ou la combustion diphasique. On donne ici quelques exemples de problèmes encore ouverts dans ce domaine
Bandit strategies in social search: the case of the DARPA red balloon challenge
Collective search for people and information has tremendously benefited from emerging communication technologies that leverage the wisdom of the crowds, and has been increasingly influential in solving time-critical tasks such as the DARPA Network Challenge (DNC, also known as the Red Balloon Challenge). However, while collective search often invests significant resources in encouraging the crowd to contribute new information, the effort invested in verifying this information is comparable, yet often neglected in crowdsourcing models. This paper studies how the exploration-verification trade-off displayed by the teams modulated their success in the DNC, as teams had limited human resources that they had to divide between recruitment (exploration) and verification (exploitation). Our analysis suggests that team performance in the DNC can be modelled as a modified multi-armed bandit (MAB) problem, where information arrives to the team originating from sources of different levels of veracity that need to be assessed in real time. We use these insights to build a data-driven agent-based model, based on the DNC’s data, to simulate team performance. The simulation results match the observed teams’ behavior and demonstrate how to achieve the best balance between exploration and exploitation for general time-critical collective search tasks.</p
European Space Agency experiments on thermodiffusion of fluid mixtures in space
Abstract.: This paper describes the European Space Agency (ESA) experiments devoted to study thermodiffusion of fluid mixtures in microgravity environment, where sedimentation and convection do not affect the mass flow induced by the Soret effect. First, the experiments performed on binary mixtures in the IVIDIL and GRADFLEX experiments are described. Then, further experiments on ternary mixtures and complex fluids performed in DCMIX and planned to be performed in the context of the NEUF-DIX project are presented. Finally, multi-component mixtures studied in the SCCO project are detailed
Chromoblastomycosis murine model and in vitro test to evaluate the sensitivity of Fonsecaea pedrosoi to ketoconazole, itraconazole and saperconazole
Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions
A fluid formalism for low-temperature plasma flows dedicated to space propulsion in an unstructured high performance computing solver
Abstract
With the increased interest in electric propulsion for space applications, a wide variety of electric thrusters have emerged. For many years, Hall effect thrusters have been the selected technology to sustain observation and telecommunication satellites thanks to their advantageous service lifetime, their high specific impulse and high power to thrust ratio. Despite several studies on the topic, the Hall thruster electric discharge remains still poorly understood. With the increase of available computing resources, numerical simulation becomes an interesting tool in order to explain some complex plasma phenomena. In this paper, a fluid model for plasma flows is presented for the numerical simulation of space thrusters. Fluid solvers often exhibit strong hypotheses on electron dynamics via the drift-diffusion approximation. Some of them use a quasi-neutral assumption for the electric field which is not adapted near walls due to the presence of sheaths. In the present model, all these simplifications are removed and the full set of plasma equations is considered for the simulation of low-temperature plasma flows inside a Hall thruster chamber. This model is implemented in the unstructured industrial solver AVIP, efficient on large clusters and adapted to complex geometries. Electrical sheaths are taken into account as well as magnetic field and majors collision processes. A particular attention is paid on a precise expression of the different source terms for elastic an inelastic processes. The whole system of equations with adapted boundary conditions is challenged with a simulation of a realistic 2D r–z Hall thruster configuration. The full-fluid simulation exhibits a correct behavior of plasma characteristics inside a Hall effect thruster. Comparisons with results from the literature exhibit a good ability of AVIP to model the plasma inside the ionization chamber. Finally a specific attention was brought to the analysis of the thruster performances.</jats:p
Generalized Bayesian pursuit: A novel scheme for multi-armed Bernoulli bandit problems
In the last decades, a myriad of approaches to the multi-armed bandit problem have appeared in several different fields. The current top performing algorithms from the field of Learning Automata reside in the Pursuit family, while UCB-Tuned and the ε -greedy class of algorithms can be seen as state-of-the-art regret minimizing algorithms. Recently, however, the Bayesian Learning Automaton (BLA) outperformed all of these, and other schemes, in a wide range of experiments. Although seemingly incompatible, in this paper we integrate the foundational learning principles motivating the design of the BLA, with the principles of the so-called Generalized Pursuit algorithm (GPST), leading to the Generalized Bayesian Pursuit algorithm (GBPST). As in the BLA, the estimates are truly Bayesian in nature, however, instead of basing exploration upon direct sampling from the estimates, GBPST explores by means of the arm selection probability vector of GPST. Further, as in the GPST, in the interest of higher rates of learning, a set of arms that are currently perceived as being optimal is pursued to minimize the probability of pursuing a wrong arm. It turns out that GBPST is superior to GPST and that it even performs better than the BLA by controlling the learning speed of GBPST. We thus believe that GBPST constitutes a new avenue of research, in which the performance benefits of the GPST and the BLA are mutually augmented, opening up for improved performance in a number of applications, currently being tested
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