531,020 research outputs found
A Study of the Transient Response of Duct Junctions: Measurements and Gas-Dynamic Modeling with a Staggered Mesh Finite Volume Approach
[EN] Duct junctions play a major role in the operation and design of most piping systems. The objective of this paper is to establish the potential of a staggered mesh finite volume model as a way to improve the description of the effect of simple duct junctions on an otherwise one-dimensional flow system, such as the intake or exhaust of an internal combustion engine. Specific experiments have been performed in which different junctions have been characterized as a multi-port, and that have provided precise and reliable results on the propagation of pressure pulses across junctions. The results obtained have been compared to simulations performed with a staggered mesh finite volume method with different flux limiters and different meshes and, as a reference, have also been compared with the results of a more conventional pressure loss- based model. The results indicate that the staggered mesh finite volume model provides a closer description of wave dynamics, even if further work is needed to establish the optimal calculation settings.Manuel Hernandez is partially supported through contract FPI-S2-2015-1064 of Programa de Apoyo para la Investigacin y Desarrollo (PAID) of Universitat Politecnica de Valencia.Torregrosa, AJ.; Broatch, A.; García-Cuevas González, LM.; Hernández-Marco, M. (2017). A Study of the Transient Response of Duct Junctions: Measurements and Gas-Dynamic Modeling with a Staggered Mesh Finite Volume Approach. Applied Sciences. 7(5):1-25. https://doi.org/10.3390/app7050480S12575Payri, F., Reyes, E., & Galindo, J. (2000). Analysis and Modeling of the Fluid-Dynamic Effects in Branched Exhaust Junctions of ICE. Journal of Engineering for Gas Turbines and Power, 123(1), 197-203. doi:10.1115/1.1339988Tang, S. K. (2004). Sound transmission characteristics of Tee-junctions and the associated length corrections. The Journal of the Acoustical Society of America, 115(1), 218-227. doi:10.1121/1.1631830Harrison, M. F., De Soto, I., & Rubio Unzueta, P. L. (2004). A linear acoustic model for multi-cylinder IC engine intake manifolds including the effects of the intake throttle. Journal of Sound and Vibration, 278(4-5), 975-1011. doi:10.1016/j.jsv.2003.12.009Karlsson, M., & Åbom, M. (2011). Quasi-steady model of the acoustic scattering properties of a T-junction. Journal of Sound and Vibration, 330(21), 5131-5137. doi:10.1016/j.jsv.2011.05.012Karlsson, M., & Åbom, M. (2010). Aeroacoustics of T-junctions—An experimental investigation. Journal of Sound and Vibration, 329(10), 1793-1808. doi:10.1016/j.jsv.2009.11.024Corberán, J. M. (1992). A New Constant Pressure Model for N-Branch Junctions. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 206(2), 117-123. doi:10.1243/pime_proc_1992_206_167_02Schmandt, B., & Herwig, H. (2015). The head change coefficient for branched flows: Why «losses» due to junctions can be negative. International Journal of Heat and Fluid Flow, 54, 268-275. doi:10.1016/j.ijheatfluidflow.2015.06.004Shaw, C. T., Lee, D. J., Richardson, S. H., & Pierson, S. (2000). Modelling the Effect of Plenum-Runner Interface Geometry on the Flow Through an Inlet System. SAE Technical Paper Series. doi:10.4271/2000-01-0569Pérez-García, J., Sanmiguel-Rojas, E., Hernández-Grau, J., & Viedma, A. (2006). Numerical and experimental investigations on internal compressible flow at T-type junctions. Experimental Thermal and Fluid Science, 31(1), 61-74. doi:10.1016/j.expthermflusci.2006.02.001Naeimi, H., Domiry, G., Gorji, M., Javadirad, G., & Keshavarz, M. (2011). A parametric design of compact exhaust manifold junction in heavy duty diesel engine using CFD. Thermal Science, 15(4), 1023-1033. doi:10.2298/tsci100417041nSakowitz, A., Mihaescu, M., & Fuchs, L. (2014). Turbulent flow mechanisms in mixing T-junctions by Large Eddy Simulations. International Journal of Heat and Fluid Flow, 45, 135-146. doi:10.1016/j.ijheatfluidflow.2013.06.014Bassett, M. D., Winterbone, D. E., & Pearson, R. J. (2001). Calculation of steady flow pressure loss coefficients for pipe junctions. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 215(8), 861-881. doi:10.1177/095440620121500801Hager, W. H. (1984). An Approximate Treatment of Flow in Branches and Bends. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 198(1), 63-69. doi:10.1243/pime_proc_1984_198_088_02Paul, J., Selamet, A., Miazgowicz, K. D., & Tallio, K. V. (2007). Combining Flow Losses at Circular T-Junctions Representative of Intake Plenum and Primary Runner Interface. SAE Technical Paper Series. doi:10.4271/2007-01-0649Pérez-García, J., Sanmiguel-Rojas, E., & Viedma, A. (2010). New coefficient to characterize energy losses in compressible flow at T-junctions. Applied Mathematical Modelling, 34(12), 4289-4305. doi:10.1016/j.apm.2010.05.005Wang, W., Lu, Z., Deng, K., & Qu, S. (2014). An experimental study of compressible combining flow at 45° T-junctions. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 229(9), 1600-1610. doi:10.1177/0954406214546678Peters, B., & Gosman, A. D. (1993). Numerical Simulation of Unsteady Flow in Engine Intake Manifolds. SAE Technical Paper Series. doi:10.4271/930609Bingham, J. F., & Blair, G. P. (1985). An Improved Branched Pipe Model for Multi-Cylinder Automotive Engine Calculations. Proceedings of the Institution of Mechanical Engineers, Part D: Transport Engineering, 199(1), 65-77. doi:10.1243/pime_proc_1985_199_140_01William-Louis, M. J. P., Ould-El-Hadrami, A., & Tournier, C. (1998). On the calculation of the unsteady compressible flow through an N-branch junction. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 212(1), 49-56. doi:10.1243/0954406981521033Bassett, M. D., Pearson, R. J., Fleming, N. P., & Winterbone, D. E. (2003). A Multi-Pipe Junction Model for One-Dimensional Gas-Dynamic Simulations. SAE Technical Paper Series. doi:10.4271/2003-01-0370Pearson, R. J., Bassett, M. D., Batten, P., Winterbone, D. E., & Weaver, N. W. E. (1999). Multi-Dimensional Wave Propagation in Pipe Junctions. SAE Technical Paper Series. doi:10.4271/1999-01-1186Bassett, M. D., Winterbone, D. E., & Pearson, R. J. (2000). Modelling Engines with Pulse Converted Exhaust Manifolds Using One-Dimensional Techniques. SAE Technical Paper Series. doi:10.4271/2000-01-0290Montenegro, G., Onorati, A., Piscaglia, F., & D’Errico, G. (2007). Integrated 1D-MultiD Fluid Dynamic Models for the Simulation of I.C.E. Intake and Exhaust Systems. SAE Technical Paper Series. doi:10.4271/2007-01-0495Onorati, A., Montenegro, G., D’Errico, G., & Piscaglia, F. (2010). Integrated 1D-3D Fluid Dynamic Simulation of a Turbocharged Diesel Engine with Complete Intake and Exhaust Systems. SAE Technical Paper Series. doi:10.4271/2010-01-1194Montenegro, G., Onorati, A., & Della Torre, A. (2013). The prediction of silencer acoustical performances by 1D, 1D–3D and quasi-3D non-linear approaches. Computers & Fluids, 71, 208-223. doi:10.1016/j.compfluid.2012.10.016Morel, T., Silvestri, J., Goerg, K.-A., & Jebasinski, R. (1999). Modeling of Engine Exhaust Acoustics. SAE Technical Paper Series. doi:10.4271/1999-01-1665Sapsford, S. M., Richards, V. C. M., Amlee, D. R., Morel, T., & Chappell, M. T. (1992). Exhaust System Evaluation and Design by Non-Linear Modeling. SAE Technical Paper Series. doi:10.4271/920686Montenegro, G., Della Torre, A., Onorati, A., Fairbrother, R., & Dolinar, A. (2011). Development and Application of 3D Generic Cells to the Acoustic Modelling of Exhaust Systems. SAE Technical Paper Series. doi:10.4271/2011-01-1526Payri, F., Desantes, J. M., & Broatch, A. (2000). Modified impulse method for the measurement of the frequency response of acoustic filters to weakly nonlinear transient excitations. The Journal of the Acoustical Society of America, 107(2), 731-738. doi:10.1121/1.428256Torregrosa, A. J., Broatch, A., Fernández, T., & Denia, F. D. (2006). Description and measurement of the acoustic characteristics of two-tailpipe mufflers. The Journal of the Acoustical Society of America, 119(2), 723. doi:10.1121/1.2159228Torregrosa, A. J., Broatch, A., Arnau, F. J., & Hernández, M. (2016). A non-linear quasi-3D model with Flux-Corrected-Transport for engine gas-exchange modelling. Journal of Computational and Applied Mathematics, 291, 103-111. doi:10.1016/j.cam.2015.03.034Montenegro, G., Della Torre, A., Onorati, A., & Fairbrother, R. (2013). A Nonlinear Quasi-3D Approach for the Modeling of Mufflers with Perforated Elements and Sound-Absorbing Material. Advances in Acoustics and Vibration, 2013, 1-10. doi:10.1155/2013/546120CMT—Motores Térmicos, Universitat Politècnica de Valènciahttp://www.openwam.org/Ikeda, T., & Nakagawa, T. (1979). On the SHASTA FCT Algorithm for the Equation ∂ρ ∂t + ∂ ∂x (υ(ρ)ρ) = 0. Mathematics of Computation, 33(148), 1157. doi:10.2307/2006453Toro, E. F., Spruce, M., & Speares, W. (1994). Restoration of the contact surface in the HLL-Riemann solver. Shock Waves, 4(1), 25-34. doi:10.1007/bf01414629Van Leer, B. (1979). Towards the ultimate conservative difference scheme. V. A second-order sequel to Godunov’s method. Journal of Computational Physics, 32(1), 101-136. doi:10.1016/0021-9991(79)90145-
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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(Eds.), Proceedings of 3rd international conference on artificial general intelligence (pp. 25–30). New York: Atlantis Press.Hernández-Orallo, J., & Dowe, D. L. (2010). Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence, 174(18), 1508–1539.Hernández-Orallo, J. & Dowe, D. L. (2011, April). Mammals, machines and mind games. Who’s the smartest?. The conversation, http://theconversation.edu.au/mammals-machines-and-mind-games-whos-the-smartest-566 .Hernández-Orallo J., Dowe D. L., España-Cubillo S., Hernández-Lloreda M. V., & Insa-Cabrera J. (2011). On more realistic environment distributions for defining, evaluating and developing intelligence. In: J. Schmidhuber, K. R. Thórisson, & M. Looks (Eds.), Artificial general intelligence 2011, volume 6830, LNAI series, pp. 82–91. New York: Springer.Hernández-Orallo, J., Dowe, D. L., & Hernández-Lloreda, M. V. (2012a, March). 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Multiscale computational homogenization: review and proposal of a new enhanced-first-order method
This is a copy of the author 's final draft version of an article published in the Archives of computational methods in engineering. The final publication is available at Springer via http://dx.doi.org/10.1007/s11831-016-9205-0The continuous increase of computational capacity has encouraged the extensive use of multiscale techniques to simulate the material behaviour on several fields of knowledge. In solid mechanics, the multiscale approaches which consider the macro-scale deformation gradient to obtain the homogenized material behaviour from the micro-scale are called first-order computational homogenization. Following this idea, the second-order FE2 methods incorporate high-order gradients to improve the simulation accuracy. However, to capture the full advantages of these high-order framework the classical boundary value problem (BVP) at the macro-scale must be upgraded to high-order level, which complicates their numerical solution. With the purpose of obtaining the best of both methods i.e. first-order and second-order, in this work an enhanced-first-order computational homogenization is presented. The proposed approach preserves a classical BVP at the macro-scale level but taking into account the high-order gradient of the macro-scale in the micro-scale solution. The developed numerical examples show how the proposed method obtains the expected stress distribution at the micro-scale for states of structural bending loads. Nevertheless, the macro-scale results achieved are the same than the ones obtained with a first-order framework because both approaches share the same macro-scale BVP.Peer ReviewedPostprint (author's final draft
Homogenization of plain weave composites with imperfect microstructure: Part II--Analysis of real-world materials
A two-layer statistically equivalent periodic unit cell is offered to predict
a macroscopic response of plain weave multilayer carbon-carbon textile
composites. Falling-short in describing the most typical geometrical
imperfections of these material systems the original formulation presented in
(Zeman and \v{S}ejnoha, International Journal of Solids and Structures, 41
(2004), pp. 6549--6571) is substantially modified, now allowing for nesting and
mutual shift of individual layers of textile fabric in all three directions.
Yet, the most valuable asset of the present formulation is seen in the
possibility of reflecting the influence of negligible meso-scale porosity
through a system of oblate spheroidal voids introduced in between the two
layers of the unit cell. Numerical predictions of both the effective thermal
conductivities and elastic stiffnesses and their comparison with available
laboratory data and the results derived using the Mori-Tanaka averaging scheme
support credibility of the present approach, about as much as the reliability
of local mechanical properties found from nanoindentation tests performed
directly on the analyzed composite samples.Comment: 28 pages, 14 figure
- …