On the impact of DPF downsizing and cellular geometry on filtration efficiency in pre- and post-turbine placement

[EN] the current work a computational study to evaluate the effect of the DPF downsizing on filtration efficiency is performed. The DPF is conventionally placed downstream of the turbine. However, its placement upstream of the turbine is growing in interest because of the benefits in specific fuel consumption, passive regeneration and aptitude to downsizing. Hence both pre- and post-turbine placement are considered in presence of clean and soot loaded substrates. An in-house 1D wall-flow DPF model for unsteady compressible flow is used. Volume reduction is approached considering diameter and length variation. In parallel, the cell density is also varied modifying the meso-geometry, i.e. cell size and porous wall thickness, imposing constant thermal integrity factor. The sensitivity to this last parameter is also analysed resulting its influence of second order in comparison to volume and cellular geometry effects. The lower Peclet number in the pre-turbine placement leads to higher filtration efficiency than post-turbine location comparing at the same DPF volume. Diameter based volume reduction provides slightly better results in filtration efficiency than length based reduction because of the way the filtration velocity field is varied. This general behaviour involves additional advantages to the potential for volume reduction of pre-turbine DPFs. Thus, different strategies with boundaries defined by volume reduction at constant filtration area or at constant specific filtration area can be approached looking for the best balance between fuel economy reduction and filtration efficiency increase provided by pre-turbine DPF placement.This work has been partially funded by FEDER and Government of Spain through Project No. TRA2016-79185-R. Additionally, the Ph.D. student E. Angiolini has been funded by a grant from Conselleria de Educacio, Cultura i Esport of the Generalitat Valenciana with reference GRISOLIA/2013/036. These supports are gratefully acknowledged by the authors.Serrano, J.; Bermúdez, V.; Piqueras, P.; Angiolini, E. (2017). On the impact of DPF downsizing and cellular geometry on filtration efficiency in pre- and post-turbine placement. Journal of Aerosol Science. 113:20-35. https://doi.org/10.1016/j.jaerosci.2017.07.014S203511

On potential cognitive abilities in the machine kingdom

The final publication is available at Springer via http://dx.doi.org/10.1007/s11023-012-9299-6Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different for the ‘machine kingdom’. While machines can be characterised by a set of cognitive abilities, and measuring them is already a big challenge, known as ‘universal psychometrics’, a more informative, and yet more challenging, goal would be to also determine the potential cognitive abilities of a machine. In this paper we investigate the notion of potential cognitive ability for machines, focussing especially on universality and intelligence. We consider several machine characterisations (non-interactive and interactive) and give definitions for each case, considering permanent and temporal potentials. From these definitions, we analyse the relation between some potential abilities, we bring out the dependency on the environment distribution and we suggest some ideas about how potential abilities can be measured. Finally, we also analyse the potential of environments at different levels and briefly discuss whether machines should be designed to be intelligent or potentially intelligent.We thank the anonymous reviewers for their comments, which have helped to significantly improve this paper. This work was supported by the MEC-MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT. Finally, we thank three pioneers ahead of their time(s). We thank Ray Solomonoff (1926-2009) and Chris Wallace (1933-2004) for all that they taught us, directly and indirectly. And, in his centenary year, we thank Alan Turing (1912-1954), with whom it perhaps all began.Hernández-Orallo, J.; Dowe, DL. (2013). On potential cognitive abilities in the machine kingdom. Minds and Machines. 23(2):179-210. https://doi.org/10.1007/s11023-012-9299-6S179210232Amari, S., Fujita, N., Shinomoto, S. (1992). Four types of learning curves. Neural Computation 4(4), 605–618.Aristotle (Translation, Introduction, and Commentary by Ross, W.D.) (1924). Aristotle’s Metaphysics. Oxford: Clarendon Press.Barmpalias, G. & Dowe, D. L. (2012). 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Analysis of the Flow in a Typified USBR II Stilling Basin through a Numerical and Physical Modeling Approach

[EN] Adaptation of stilling basins to higher discharges than those considered for their design implies deep knowledge of the flow developed in these structures. To this end, the hydraulic jump occurring in a typified United States Bureau of Reclamation Type II (USBR II) stilling basin was analyzed using a numerical and experimental modeling approach. A reduced-scale physical model to conduct an experimental campaign was built and a numerical computational fluid dynamics (CFD) model was prepared to carry out the corresponding simulations. Both models were able to successfully reproduce the case study in terms of hydraulic jump shape, velocity profiles, and pressure distributions. The analysis revealed not only similarities to the flow in classical hydraulic jumps but also the influence of the energy dissipation devices existing in the stilling basin, all in good agreement with bibliographical information, despite some slight differences. Furthermore, the void fraction distribution was analyzed, showing satisfactory performance of the physical model, although the numerical approach presented some limitations to adequately represent the flow aeration mechanisms, which are discussed herein. Overall, the presented modeling approach can be considered as a useful tool to address the analysis of free surface flows occurring in stilling basins.This research was funded by 'Generalitat Valenciana predoctoral grants (Grant number [2015/7521])', in collaboration with the European Social Funds and by the research project: 'La aireacion del flujo y su implementacion en prototipo para la mejora de la disipacion de energia de la lamina vertiente por resalto hidraulico en distintos tipos de presas' (BIA2017-85412-C2-1-R), funded by the Spanish Ministry of Economy.Macián Pérez, JF.; García-Bartual, R.; Huber, B.; Bayón, A.; Vallés-Morán, FJ. (2020). Analysis of the Flow in a Typified USBR II Stilling Basin through a Numerical and Physical Modeling Approach. Water. 12(1):1-20. https://doi.org/10.3390/w12010227S120121Bayon, A., Valero, D., García-Bartual, R., Vallés-Morán, F. ​José, & López-Jiménez, P. A. (2016). Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump. Environmental Modelling & Software, 80, 322-335. doi:10.1016/j.envsoft.2016.02.018Chanson, H. (2008). Turbulent air–water flows in hydraulic structures: dynamic similarity and scale effects. Environmental Fluid Mechanics, 9(2), 125-142. doi:10.1007/s10652-008-9078-3Heller, V. (2011). Scale effects in physical hydraulic engineering models. Journal of Hydraulic Research, 49(3), 293-306. doi:10.1080/00221686.2011.578914Chanson, H. (2013). Hydraulics of aerated flows:qui pro quo? Journal of Hydraulic Research, 51(3), 223-243. doi:10.1080/00221686.2013.795917Blocken, B., & Gualtieri, C. (2012). Ten iterative steps for model development and evaluation applied to Computational Fluid Dynamics for Environmental Fluid Mechanics. Environmental Modelling & Software, 33, 1-22. doi:10.1016/j.envsoft.2012.02.001Wang, H., & Chanson, H. (2015). Experimental Study of Turbulent Fluctuations in Hydraulic Jumps. Journal of Hydraulic Engineering, 141(7), 04015010. doi:10.1061/(asce)hy.1943-7900.0001010Valero, D., Viti, N., & Gualtieri, C. (2018). Numerical Simulation of Hydraulic Jumps. Part 1: Experimental Data for Modelling Performance Assessment. Water, 11(1), 36. doi:10.3390/w11010036Viti, N., Valero, D., & Gualtieri, C. (2018). Numerical Simulation of Hydraulic Jumps. Part 2: Recent Results and Future Outlook. Water, 11(1), 28. doi:10.3390/w11010028Bayon-Barrachina, A., & Lopez-Jimenez, P. A. (2015). Numerical analysis of hydraulic jumps using OpenFOAM. 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Direct simulation of liquid-gas-solid flow with a free surface lattice Boltzmann method

Direct numerical simulation of liquid-gas-solid flows is uncommon due to the considerable computational cost. As the grid spacing is determined by the smallest involved length scale, large grid sizes become necessary -- in particular if the bubble-particle aspect ratio is on the order of 10 or larger. Hence, it arises the question of both feasibility and reasonability. In this paper, we present a fully parallel, scalable method for direct numerical simulation of bubble-particle interaction at a size ratio of 1-2 orders of magnitude that makes simulations feasible on currently available super-computing resources. With the presented approach, simulations of bubbles in suspension columns consisting of more than $100\,000$ fully resolved particles become possible. Furthermore, we demonstrate the significance of particle-resolved simulations by comparison to previous unresolved solutions. The results indicate that fully-resolved direct numerical simulation is indeed necessary to predict the flow structure of bubble-particle interaction problems correctly.Comment: submitted to International Journal of Computational Fluid Dynamic

Design and Analysis of a Task-based Parallelization over a Runtime System of an Explicit Finite-Volume CFD Code with Adaptive Time Stepping

FLUSEPA (Registered trademark in France No. 134009261) is an advanced simulation tool which performs a large panel of aerodynamic studies. It is the unstructured finite-volume solver developed by Airbus Safran Launchers company to calculate compressible, multidimensional, unsteady, viscous and reactive flows around bodies in relative motion. The time integration in FLUSEPA is done using an explicit temporal adaptive method. The current production version of the code is based on MPI and OpenMP. This implementation leads to important synchronizations that must be reduced. To tackle this problem, we present the study of a task-based parallelization of the aerodynamic solver of FLUSEPA using the runtime system StarPU and combining up to three levels of parallelism. We validate our solution by the simulation (using a finite-volume mesh with 80 million cells) of a take-off blast wave propagation for Ariane 5 launcher.Comment: Accepted manuscript of a paper in Journal of Computational Scienc

Theoretical and numerical comparison of hyperelastic and hypoelastic formulations for Eulerian non-linear elastoplasticity

The aim of this paper is to compare a hyperelastic with a hypoelastic model describing the Eulerian dynamics of solids in the context of non-linear elastoplastic deformations. Specifically, we consider the well-known hypoelastic Wilkins model, which is compared against a hyperelastic model based on the work of Godunov and Romenski. First, we discuss some general conceptual differences between the two approaches. Second, a detailed study of both models is proposed, where differences are made evident at the aid of deriving a hypoelastic-type model corresponding to the hyperelastic model and a particular equation of state used in this paper. Third, using the same high order ADER Finite Volume and Discontinuous Galerkin methods on fixed and moving unstructured meshes for both models, a wide range of numerical benchmark test problems has been solved. The numerical solutions obtained for the two different models are directly compared with each other. For small elastic deformations, the two models produce very similar solutions that are close to each other. However, if large elastic or elastoplastic deformations occur, the solutions present larger differences.Comment: 14 figure