Multi-qubit doilies: enumeration for all ranks and classification for ranks four and five

For $N \geq 2$, an $N$-qubit doily is a doily living in the $N$-qubit symplectic polar space. These doilies are related to operator-based proofs of quantum contextuality. Following and extending the strategy of Saniga et al. (Mathematics 9 (2021) 2272) that focused exclusively on three-qubit doilies, we first bring forth several formulas giving the number of both linear and quadratic doilies for any $N > 2$. Then we present an effective algorithm for the generation of all $N$-qubit doilies. Using this algorithm for $N=4$ and $N=5$, we provide a classification of $N$-qubit doilies in terms of types of observables they feature and number of negative lines they are endowed with. We also list several distinguished findings about $N$-qubit doilies that are absent in the three-qubit case, point out a couple of specific features exhibited by linear doilies and outline some prospective extensions of our approach.Comment: Minor revisions and corrections. Published in Journal of Computational Science, Volume 64, 2022, 101853, ISSN 1877-7503, https://doi.org/10.1016/j.jocs.2022.10185

Three-Dimensional Lattice Boltzmann Simulation of Two-Phase Flow Containing a Deformable Body with a Viscoelastic Membrane

First published in Communications in Commun. Comput. Phys. in No. 5, 9 (2011), published by Global Science PressThe lattice Boltzmann method (LBM) with an elastic model is applied to the simulation of two-phase flows containing a deformable body with a viscoelastic membrane. The numerical method is based on the LBM for incompressible two-phase fluid flows with the same density. The body has an internal fluid covered by a viscoelastic membrane of a finite thickness. An elastic model is introduced to the LBM in order to determine the elastic forces acting on the viscoelastic membrane of the body. In the present method, we take account of changes in surface area of the membrane and in total volume of the body as well as shear deformation of the membrane. By using this method, we calculate two problems, the behavior of an initially spherical body under shear flow and the motion of a body with initially spherical or biconcave discoidal shape in square pipe flow. Calculated deformations of the body (the Taylor shape parameter) for various shear rates are in good agreement with other numerical results. Moreover, tank-treading motion, which is a characteristic motion of viscoelastic bodies in shear flows, is simulated by the present method.ArticleCommunications in Computational Physics. 9(5):1397-1413 (2011)journal articl

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-

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. Journal of Hydroinformatics, 17(4), 662-678. doi:10.2166/hydro.2015.041Teuber, K., Broecker, T., Bayón, A., Nützmann, G., & Hinkelmann, R. (2019). CFD-modelling of free surface flows in closed conduits. Progress in Computational Fluid Dynamics, An International Journal, 19(6), 368. doi:10.1504/pcfd.2019.103266Chachereau, Y., & Chanson, H. (2011). Free-surface fluctuations and turbulence in hydraulic jumps. Experimental Thermal and Fluid Science, 35(6), 896-909. doi:10.1016/j.expthermflusci.2011.01.009Zhang, G., Wang, H., & Chanson, H. (2012). Turbulence and aeration in hydraulic jumps: free-surface fluctuation and integral turbulent scale measurements. Environmental Fluid Mechanics, 13(2), 189-204. doi:10.1007/s10652-012-9254-3Mossa, M. (1999). On the oscillating characteristics of hydraulic jumps. Journal of Hydraulic Research, 37(4), 541-558. doi:10.1080/00221686.1999.9628267Chanson, H., & Brattberg, T. (2000). Experimental study of the air–water shear flow in a hydraulic jump. International Journal of Multiphase Flow, 26(4), 583-607. doi:10.1016/s0301-9322(99)00016-6Murzyn, F., Mouaze, D., & Chaplin, J. R. (2005). Optical fibre probe measurements of bubbly flow in hydraulic jumps. International Journal of Multiphase Flow, 31(1), 141-154. doi:10.1016/j.ijmultiphaseflow.2004.09.004Gualtieri, C., & Chanson, H. (2007). Experimental analysis of Froude number effect on air entrainment in the hydraulic jump. Environmental Fluid Mechanics, 7(3), 217-238. doi:10.1007/s10652-006-9016-1Chanson, H., & Gualtieri, C. (2008). Similitude and scale effects of air entrainment in hydraulic jumps. Journal of Hydraulic Research, 46(1), 35-44. doi:10.1080/00221686.2008.9521841Ho, D. K. H., & Riddette, K. M. (2010). Application of computational fluid dynamics to evaluate hydraulic performance of spillways in australia. Australian Journal of Civil Engineering, 6(1), 81-104. doi:10.1080/14488353.2010.11463946Dong, Wang, Vetsch, Boes, & Tan. (2019). Numerical Simulation of Air–Water Two-Phase Flow on Stepped Spillways Behind X-Shaped Flaring Gate Piers under Very High Unit Discharge. Water, 11(10), 1956. doi:10.3390/w11101956Toso, J. W., & Bowers, C. E. (1988). Extreme Pressures in Hydraulic‐Jump Stilling Basins. Journal of Hydraulic Engineering, 114(8), 829-843. doi:10.1061/(asce)0733-9429(1988)114:8(829)Houichi, L., Ibrahim, G., & Achour, B. (2006). Experiments for the Discharge Capacity of the Siphon Spillway Having the Creager-Ofitserov Profile. International Journal of Fluid Mechanics Research, 33(5), 395-406. doi:10.1615/interjfluidmechres.v33.i5.10Padulano, R., Fecarotta, O., Del Giudice, G., & Carravetta, A. (2017). Hydraulic Design of a USBR Type II Stilling Basin. Journal of Irrigation and Drainage Engineering, 143(5), 04017001. doi:10.1061/(asce)ir.1943-4774.0001150Hirt, C. ., & Nichols, B. . (1981). Volume of fluid (VOF) method for the dynamics of free boundaries. Journal of Computational Physics, 39(1), 201-225. doi:10.1016/0021-9991(81)90145-5Bombardelli, F. A., Meireles, I., & Matos, J. (2010). Laboratory measurements and multi-block numerical simulations of the mean flow and turbulence in the non-aerated skimming flow region of steep stepped spillways. Environmental Fluid Mechanics, 11(3), 263-288. doi:10.1007/s10652-010-9188-6Pope, S. B. (2001). Turbulent Flows. Measurement Science and Technology, 12(11), 2020-2021. doi:10.1088/0957-0233/12/11/705Harlow, F. H. (1967). Turbulence Transport Equations. Physics of Fluids, 10(11), 2323. doi:10.1063/1.1762039Launder, B. E., & Sharma, B. I. (1974). Application of the energy-dissipation model of turbulence to the calculation of flow near a spinning disc. Letters in Heat and Mass Transfer, 1(2), 131-137. doi:10.1016/0094-4548(74)90150-7Yakhot, V., Orszag, S. A., Thangam, S., Gatski, T. B., & Speziale, C. G. (1992). Development of turbulence models for shear flows by a double expansion technique. Physics of Fluids A: Fluid Dynamics, 4(7), 1510-1520. doi:10.1063/1.858424Li, S., & Zhang, J. (2018). Numerical Investigation on the Hydraulic Properties of the Skimming Flow over Pooled Stepped Spillway. Water, 10(10), 1478. doi:10.3390/w10101478Zhang, W., Wang, J., Zhou, C., Dong, Z., & Zhou, Z. (2018). Numerical Simulation of Hydraulic Characteristics in A Vortex Drop Shaft. Water, 10(10), 1393. doi:10.3390/w10101393Xiang, M., Cheung, S. C. P., Tu, J. Y., & Zhang, W. H. (2014). A multi-fluid modelling approach for the air entrainment and internal bubbly flow region in hydraulic jumps. Ocean Engineering, 91, 51-63. doi:10.1016/j.oceaneng.2014.08.016Procedure for Estimation and Reporting of Uncertainty Due to Discretization in CFD Applications. (2008). Journal of Fluids Engineering, 130(7), 078001. doi:10.1115/1.2960953Cartellier, A., & Achard, J. L. (1991). Local phase detection probes in fluid/fluid two‐phase flows. Review of Scientific Instruments, 62(2), 279-303. doi:10.1063/1.1142117Cartellier, A., & Barrau, E. (1998). Monofiber optical probes for gas detection and gas velocity measurements: conical probes. International Journal of Multiphase Flow, 24(8), 1265-1294. doi:10.1016/s0301-9322(98)00032-9Boyer, C., Duquenne, A.-M., & Wild, G. (2002). Measuring techniques in gas–liquid and gas–liquid–solid reactors. Chemical Engineering Science, 57(16), 3185-3215. doi:10.1016/s0009-2509(02)00193-8Hager, W. H., & Bremen, R. (1989). Classical hydraulic jump: sequent depths. Journal of Hydraulic Research, 27(5), 565-585. doi:10.1080/00221688909499111Hager, W. H., & Li, D. (1992). Sill-controlled energy dissipator. Journal of Hydraulic Research, 30(2), 165-181. doi:10.1080/00221689209498932Bakhmeteff, B. A., & Matzke, A. E. (1936). The Hydraulic Jump in Terms of Dynamic Similarity. Transactions of the American Society of Civil Engineers, 101(1), 630-647. doi:10.1061/taceat.0004708Hager, W. H., Bremen, R., & Kawagoshi, N. (1990). Classical hydraulic jump: length of roller. Journal of Hydraulic Research, 28(5), 591-608. doi:10.1080/00221689009499048Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., … Andreassian, V. (2013). Characterising performance of environmental models. Environmental Modelling & Software, 40, 1-20. doi:10.1016/j.envsoft.2012.09.011McCorquodale, J. A., & Khalifa, A. (1983). Internal Flow in Hydraulic Jumps. Journal of Hydraulic Engineering, 109(5), 684-701. doi:10.1061/(asce)0733-9429(1983)109:5(684)Kirkgöz, M. S., & Ardiçlioğlu, M. (1997). Velocity Profiles of Developing and Developed Open Channel Flow. Journal of Hydraulic Engineering, 123(12), 1099-1105. doi:10.1061/(asce)0733-9429(1997)123:12(1099

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). Universality probability of a prefix-free machine. Philosophical transactions of the Royal Society A [Mathematical, Physical and Engineering Sciences] (Phil Trans A), Theme Issue ‘The foundations of computation, physics and mentality: The Turing legacy’ compiled and edited by Barry Cooper and Samson Abramsky, 370, pp 3488–3511.Chaitin, G. J. (1966). On the length of programs for computing finite sequences. Journal of the Association for Computing Machinery, 13, 547–569.Chaitin, G. J. (1975). A theory of program size formally identical to information theory. Journal of the ACM (JACM), 22(3), 329–340.Dowe, D. L. (2008, September). Foreword re C. S. Wallace. Computer Journal, 51(5):523–560, Christopher Stewart WALLACE (1933–2004) memorial special issue.Dowe, D. L. (2011). MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness. In: P. S. Bandyopadhyay, M. R. Forster (Eds), Handbook of the philosophy of science—Volume 7: Philosophy of statistics (pp. 901–982). Amsterdam: Elsevier.Dowe, D. L. & Hajek, A. R. (1997a). A computational extension to the turing test. Technical report #97/322, Dept Computer Science, Monash University, Melbourne, Australia, 9 pp, http://www.csse.monash.edu.au/publications/1997/tr-cs97-322-abs.html .Dowe, D. L. & Hajek, A. R. (1997b, September). A computational extension to the Turing Test. in Proceedings of the 4th conference of the Australasian Cognitive Science Society, University of Newcastle, NSW, Australia, 9 pp.Dowe, D. L. & Hajek, A. R. (1998, February). A non-behavioural, computational extension to the Turing Test. In: International conference on computational intelligence and multimedia applications (ICCIMA’98), Gippsland, Australia, pp 101–106.Dowe, D. L., Hernández-Orallo, J. (2012). IQ tests are not for machines, yet. Intelligence, 40(2), 77–81.Gallistel, C. R., Fairhurst, S., & Balsam, P. (2004). The learning curve: Implications of a quantitative analysis. In Proceedings of the National Academy of Sciences of the United States of America, 101(36), 13124–13131.Gardner, M. (1970). Mathematical games: The fantastic combinations of John Conway’s new solitaire game “life”. Scientific American, 223(4), 120–123.Goertzel, B. & Bugaj, S. V. (2009). AGI preschool: A framework for evaluating early-stage human-like AGIs. In Proceedings of the second international conference on artificial general intelligence (AGI-09), pp 31–36.Hernández-Orallo, J. (2000a). Beyond the Turing Test. Journal of Logic, Language & Information, 9(4), 447–466.Hernández-Orallo, J. (2000b). On the computational measurement of intelligence factors. In A. Meystel (Ed), Performance metrics for intelligent systems workshop (pp 1–8). Gaithersburg, MD: National Institute of Standards and Technology.Hernández-Orallo, J. (2010). On evaluating agent performance in a fixed period of time. In M. Hutter et al. (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). Measuring cognitive abilities of machines, humans and non-human animals in a unified way: towards universal psychometrics. Technical report 2012/267, Faculty of Information Technology, Clayton School of I.T., Monash University, Australia.Hernández-Orallo, J., Insa, J., Dowe, D. L., & Hibbard, B. (2012b). Turing tests with Turing machines. In A. Voronkov (Ed.), The Alan Turing centenary conference, Turing-100, Manchester, volume 10 of EPiC Series, pp 140–156.Hernández-Orallo, J., & Minaya-Collado, N. (1998). A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In Proceedings of the international symposium of engineering of intelligent systems (EIS’98) (pp 146–163). Switzerland: ICSC Press.Herrmann, E., Call, J., Hernández-Lloreda, M. V., Hare, B., & Tomasello, M. (2007). Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science, 317(5843), 1360–1366.Herrmann, E., Hernández-Lloreda, M. V., Call, J., Hare, B., & Tomasello, M. (2010). The structure of individual differences in the cognitive abilities of children and chimpanzees. Psychological Science, 21(1), 102–110.Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallized general intelligences. Journal of educational psychology, 57(5), 253.Hutter, M. (2005). Universal artificial intelligence: Sequential decisions based on algorithmic probability. New York: Springer.Insa-Cabrera, J., Dowe, D. L., España, S., Hernández-Lloreda, M. V., & Hernández-Orallo, J. (2011a). Comparing humans and AI agents. In AGI: 4th conference on artificial general intelligence—Lecture Notes in Artificial Intelligence (LNAI), volume 6830, pp 122–132. Springer, New York.Insa-Cabrera, J., Dowe, D. L., & Hernández-Orallo, J. (2011b). Evaluating a reinforcement learning algorithm with a general intelligence test. In CAEPIA—Lecture Notes in Artificial Intelligence (LNAI), volume 7023, pages 1–11. Springer, New York.Kearns, M. & Singh, S. (2002). Near-optimal reinforcement learning in polynomial time. Machine Learning, 49(2), 209–232.Kolmogorov, A. N. (1965). Three approaches to the quantitative definition of information. Problems of Information Transmission, 1, 4–7.Legg, S. (2008, June). Machine super intelligence. Department of Informatics, University of Lugano.Legg, S. & Hutter, M. (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines, 17(4), 391–444.Legg, S., & Veness, J. (2012). An approximation of the universal intelligence measure. In Proceedings of Solomonoff 85th memorial conference. New York: Springer.Levin, L. A. (1973). Universal sequential search problems. Problems of Information Transmission, 9(3), 265–266.Li, M., Vitányi, P. (2008). An introduction to Kolmogorov complexity and its applications (3rd ed). New York: Springer.Little, V. L., & Bailey, K. G. (1972). Potential intelligence or intelligence test potential? A question of empirical validity. Journal of Consulting and Clinical Psychology, 39(1), 168.Mahoney, M. V. (1999). Text compression as a test for artificial intelligence. In Proceedings of the national conference on artificial intelligence, AAAI (pp. 486–502). New Jersey: Wiley.Mahrer, A. R. (1958). Potential intelligence: A learning theory approach to description and clinical implication. The Journal of General Psychology, 59(1), 59–71.Oppy, G., & Dowe, D. L. (2011). The Turing Test. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Stanford University. http://plato.stanford.edu/entries/turing-test/ .Orseau, L. & Ring, M. (2011). Self-modification and mortality in artificial agents. In AGI: 4th conference on artificial general intelligence—Lecture Notes in Artificial Intelligence (LNAI), volume 6830, pages 1–10. Springer, New York.Ring, M. & Orseau, L. (2011). Delusion, survival, and intelligent agents. In AGI: 4th conference on artificial general intelligence—Lecture Notes in Artificial Intelligence (LNAI), volume 6830, pp. 11–20. Springer, New York.Schaeffer, J., Burch, N., Bjornsson, Y., Kishimoto, A., Muller, M., Lake, R., et al. (2007). Checkers is solved. Science, 317(5844), 1518.Solomonoff, R. J. (1962). Training sequences for mechanized induction. In M. Yovits, G. Jacobi, & G. Goldsteins (Eds.), Self-Organizing Systems, 7, 425–434.Solomonoff, R. J. (1964). A formal theory of inductive inference. Information and Control, 7(1–22), 224–254.Solomonoff, R. J. (1967). Inductive inference research: Status, Spring 1967. RTB 154, Rockford Research, Inc., 140 1/2 Mt. Auburn St., Cambridge, Mass. 02138, July 1967.Solomonoff, R. J. (1978). Complexity-based induction systems: comparisons and convergence theorems. IEEE Transactions on Information Theory, 24(4), 422–432.Solomonoff, R. J. (1984). Perfect training sequences and the costs of corruption—A progress report on induction inference research. Oxbridge research.Solomonoff, R. J. (1985). The time scale of artificial intelligence: Reflections on social effects. Human Systems Management, 5, 149–153.Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge: The MIT press.Thorp, T. R., & Mahrer, A. R. (1959). Predicting potential intelligence. Journal of Clinical Psychology, 15(3), 286–288.Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.Veness, J., Ng, K. S., Hutter, M., & Silver, D. (2011). A Monte Carlo AIXI approximation. Journal of Artificial Intelligence Research, JAIR, 40, 95–142.Wallace, C. S. (2005). Statistical and inductive inference by minimum message length. New York: Springer.Wallace, C. S., & Boulton, D. M. (1968). An information measure for classification. Computer Journal, 11, 185–194.Wallace, C. S., & Dowe, D. L. (1999a). Minimum message length and Kolmogorov complexity. Computer Journal 42(4), 270–283.Wallace, C. S., & Dowe, D. L. (1999b). Refinements of MDL and MML coding. Computer Journal, 42(4), 330–337.Woergoetter, F., & Porr, B. (2008). Reinforcement learning. Scholarpedia, 3(3), 1448.Zvonkin, A. K., & Levin, L. A. (1970). The complexity of finite objects and the development of the concepts of information and randomness by means of the theory of algorithms. Russian Mathematical Surveys, 25, 83–124

Characterization of Structural Properties in High Reynolds Hydraulic Jump Based on CFD and Physical Modeling Approaches

[EN] A classical hydraulic jump with Froude number (Fr1=6) and Reynolds number (Re1=210,000) was characterized using the computational fluid dynamics (CFD) codes OpenFOAM and FLOW-3D, whose performance was assessed. The results were compared with experimental data from a physical model designed for this purpose. The most relevant hydraulic jump characteristics were investigated, including hydraulic jump efficiency, roller length, free surface profile, distributions of velocity and pressure, and fluctuating variables. The model outcome was also compared with previous results from the literature. Both CFD codes were found to represent with high accuracy the hydraulic jump surface profile, roller length, efficiency, and sequent depths ratio, consistently with previous research. Some significant differences were found between both CFD codes regarding velocity distributions and pressure fluctuations, although in general the results agree well with experimental and bibliographical observations. This finding makes models with these characteristics suitable for engineering applications involving the design and optimization of energy dissipation devices.The research presented herein was possible thanks to the Generalitat Valenciana predoctoral grants [Ref. (2015/7521)], in collaboration with the European Social Funds and to 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.; Bayón, A.; García-Bartual, R.; López Jiménez, PA.; Vallés-Morán, FJ. (2020). Characterization of Structural Properties in High Reynolds Hydraulic Jump Based on CFD and Physical Modeling Approaches. Journal of Hydraulic Engineering. 146(12):1-13. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001820S11314612Abdul Khader, M. H., & Elango, K. (1974). TURBULENT PRESSURE FIELD BENEATH A HYDRAULIC JUMP. Journal of Hydraulic Research, 12(4), 469-489. doi:10.1080/00221687409499725Bakhmeteff B. A. and A. E. Matzke. 1936. “The hydraulic jump in terms of dynamic similarity.” In Vol. 101 of Proc. American Society of Civil Engineers 630–647. Reston VA: ASCE.Bayon A. 2017. “Numerical analysis of air-water flows in hydraulic structures using computational fluid dynamics (CFD).” Ph.D. thesis Research Institute of Water and Environmental Engineering Universitat Politècnica de València.Bayon-Barrachina, A., & Lopez-Jimenez, P. A. (2015). Numerical analysis of hydraulic jumps using OpenFOAM. Journal of Hydroinformatics, 17(4), 662-678. doi:10.2166/hydro.2015.041Bayon A. J. F. Macián-Pérez F. J. Vallés-Morán and P. A. López-Jiménez. 2019. “Effect of RANS turbulence model in hydraulic jump CFD simulations.” In E-proc. 38th IAHR World Congress. Panama City Panama: Spanish Ministry of Economy.Bayon, A., Toro, J. P., Bombardelli, F. A., Matos, J., & López-Jiménez, P. A. (2018). Influence of VOF technique, turbulence model and discretization scheme on the numerical simulation of the non-aerated, skimming flow in stepped spillways. Journal of Hydro-environment Research, 19, 137-149. doi:10.1016/j.jher.2017.10.002Bayon, 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.2016.02.018Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., … Andreassian, V. (2013). Characterising performance of environmental models. Environmental Modelling & Software, 40, 1-20. doi:10.1016/j.envsoft.2012.09.011Biswas, R., & Strawn, R. C. (1998). Tetrahedral and hexahedral mesh adaptation for CFD problems. Applied Numerical Mathematics, 26(1-2), 135-151. doi:10.1016/s0168-9274(97)00092-5Blocken, 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.2012.02.001Bombardelli, F. A., Meireles, I., & Matos, J. (2010). Laboratory measurements and multi-block numerical simulations of the mean flow and turbulence in the non-aerated skimming flow region of steep stepped spillways. Environmental Fluid Mechanics, 11(3), 263-288. doi:10.1007/s10652-010-9188-6Bradshaw, P. (1997). Understanding and prediction of turbulent flow—1996. International Journal of Heat and Fluid Flow, 18(1), 45-54. doi:10.1016/s0142-727x(96)00134-8Caishui, H. (2012). Three-dimensional Numerical Analysis of Flow Pattern in Pressure Forebay of Hydropower Station. Procedia Engineering, 28, 128-135. doi:10.1016/j.proeng.2012.01.694Castillo L. G. J. M. Carrillo J. T. García and A. Vigueras-Rodríguez. 2014. “Numerical simulations and laboratory measurements in hydraulic jumps.” In Proc. 11th Int. Conf. of Hydroinformatics. New York: Spanish Ministry of Economy.Castro-Orgaz, O., & Hager, W. H. (2009). Classical hydraulic jump: basic flow features. Journal of Hydraulic Research, 47(6), 744-754. doi:10.3826/jhr.2009.3610Procedure for Estimation and Reporting of Uncertainty Due to Discretization in CFD Applications. (2008). Journal of Fluids Engineering, 130(7), 078001. doi:10.1115/1.2960953Chachereau, Y., & Chanson, H. (2011). Free-surface fluctuations and turbulence in hydraulic jumps. Experimental Thermal and Fluid Science, 35(6), 896-909. doi:10.1016/j.expthermflusci.2011.01.009Chanson, H. (2006). Bubble entrainment, spray and splashing at hydraulic jumps. Journal of Zhejiang University-SCIENCE A, 7(8), 1396-1405. doi:10.1631/jzus.2006.a1396Chanson, H. (2009). Current knowledge in hydraulic jumps and related phenomena. A survey of experimental results. European Journal of Mechanics - B/Fluids, 28(2), 191-210. doi:10.1016/j.euromechflu.2008.06.004Chanson, H. (2013). Hydraulics of aerated flows:qui pro quo? Journal of Hydraulic Research, 51(3), 223-243. doi:10.1080/00221686.2013.795917Chanson, H., & Brattberg, T. (2000). Experimental study of the air–water shear flow in a hydraulic jump. International Journal of Multiphase Flow, 26(4), 583-607. doi:10.1016/s0301-9322(99)00016-6Chanson, H., & Gualtieri, C. (2008). Similitude and scale effects of air entrainment in hydraulic jumps. Journal of Hydraulic Research, 46(1), 35-44. doi:10.1080/00221686.2008.9521841Chanson, H., & Montes, J. S. (1995). Characteristics of Undular Hydraulic Jumps: Experimental Apparatus and Flow Patterns. Journal of Hydraulic Engineering, 121(2), 129-144. doi:10.1061/(asce)0733-9429(1995)121:2(129)Cheng, C.-K., Tai, Y.-C., & Jin, Y.-C. (2017). Particle Image Velocity Measurement and Mesh-Free Method Modeling Study of Forced Hydraulic Jumps. Journal of Hydraulic Engineering, 143(9), 04017028. doi:10.1061/(asce)hy.1943-7900.0001325Dong, Wang, Vetsch, Boes, & Tan. (2019). Numerical Simulation of Air–Water Two-Phase Flow on Stepped Spillways Behind X-Shaped Flaring Gate Piers under Very High Unit Discharge. Water, 11(10), 1956. doi:10.3390/w11101956Fuentes-Pérez, J. F., Silva, A. T., Tuhtan, J. A., García-Vega, A., Carbonell-Baeza, R., Musall, M., & Kruusmaa, M. (2018). 3D modelling of non-uniform and turbulent flow in vertical slot fishways. Environmental Modelling & Software, 99, 156-169. doi:10.1016/j.envsoft.2017.09.011Gualtieri, C., & Chanson, H. (2007). Experimental analysis of Froude number effect on air entrainment in the hydraulic jump. Environmental Fluid Mechanics, 7(3), 217-238. doi:10.1007/s10652-006-9016-1Hager, W. H. (1992). Energy Dissipators and Hydraulic Jump. Water Science and Technology Library. doi:10.1007/978-94-015-8048-9Hager, W. H., & Bremen, R. (1989). Classical hydraulic jump: sequent depths. Journal of Hydraulic Research, 27(5), 565-585. doi:10.1080/00221688909499111Hager, W. H., Bremen, R., & Kawagoshi, N. (1990). Classical hydraulic jump: length of roller. Journal of Hydraulic Research, 28(5), 591-608. doi:10.1080/00221689009499048Heller, V. (2011). Scale effects in physical hydraulic engineering models. Journal of Hydraulic Research, 49(3), 293-306. doi:10.1080/00221686.2011.578914Hirt, C. ., & Nichols, B. . (1981). Volume of fluid (VOF) method for the dynamics of free boundaries. Journal of Computational Physics, 39(1), 201-225. doi:10.1016/0021-9991(81)90145-5Ho, D. K. H., & Riddette, K. M. (2010). Application of computational fluid dynamics to evaluate hydraulic performance of spillways in australia. Australian Journal of Civil Engineering, 6(1), 81-104. doi:10.1080/14488353.2010.11463946Jesudhas, V., Balachandar, R., Roussinova, V., & Barron, R. (2018). Turbulence Characteristics of Classical Hydraulic Jump Using DES. Journal of Hydraulic Engineering, 144(6), 04018022. doi:10.1061/(asce)hy.1943-7900.0001427Jesudhas, V., Roussinova, V., Balachandar, R., & Barron, R. (2017). Submerged Hydraulic Jump Study Using DES. Journal of Hydraulic Engineering, 143(3), 04016091. doi:10.1061/(asce)hy.1943-7900.0001231KIM, J. (2004). A numerical study of the effects of ambient wind direction on flow and dispersion in urban street canyons using the RNG k?? turbulence model. Atmospheric Environment, 38(19), 3039-3048. doi:10.1016/j.atmosenv.2004.02.047Kim, S.-E., & Boysan, F. (1999). Application of CFD to environmental flows. Journal of Wind Engineering and Industrial Aerodynamics, 81(1-3), 145-158. doi:10.1016/s0167-6105(99)00013-6Kirkgöz, M. S., & Ardiçlioğlu, M. (1997). Velocity Profiles of Developing and Developed Open Channel Flow. Journal of Hydraulic Engineering, 123(12), 1099-1105. doi:10.1061/(asce)0733-9429(1997)123:12(1099)Langhi, M., & Hosoda, T. (2018). Three-dimensional unsteady RANS model for hydraulic jumps. ISH Journal of Hydraulic Engineering, 1-8. doi:10.1080/09715010.2018.1555775Liu, M., Rajaratnam, N., & Zhu, D. Z. (2004). Turbulence Structure of Hydraulic Jumps of Low Froude Numbers. Journal of Hydraulic Engineering, 130(6), 511-520. doi:10.1061/(asce)0733-9429(2004)130:6(511)Liu, T., Song, L., Fu, W., Wang, G., Lin, Q., Zhao, D., & Yi, B. (2018). Experimental Study on Single-Hole Injection of Kerosene into Pressurized Quiescent Environments. Journal of Energy Engineering, 144(3), 04018014. doi:10.1061/(asce)ey.1943-7897.0000536Ma, J., Oberai, A. A., Lahey, R. T., & Drew, D. A. (2011). Modeling air entrainment and transport in a hydraulic jump using two-fluid RANS and DES turbulence models. Heat and Mass Transfer, 47(8), 911-919. doi:10.1007/s00231-011-0867-8McCorquodale, J. A., & Khalifa, A. (1983). Internal Flow in Hydraulic Jumps. Journal of Hydraulic Engineering, 109(5), 684-701. doi:10.1061/(asce)0733-9429(1983)109:5(684)McDonald P. W. 1971. “The computation of transonic flow through two-dimensional gas turbine cascades.” In Proc. ASME 1971 Int. Gas Turbine Conf. and Products Show. Houston: International Gas Turbine Institute.Mossa, M. (1999). On the oscillating characteristics of hydraulic jumps. Journal of Hydraulic Research, 37(4), 541-558. doi:10.1080/00221686.1999.9628267Padulano, R., Fecarotta, O., Del Giudice, G., & Carravetta, A. (2017). Hydraulic Design of a USBR Type II Stilling Basin. Journal of Irrigation and Drainage Engineering, 143(5), 04017001. doi:10.1061/(asce)ir.1943-4774.0001150Resch, F. J., & Leutheusser, H. J. (1972). Le ressaut hydraulique : mesures de turbulence dans la région diphasique. La Houille Blanche, 58(4), 279-293. doi:10.1051/lhb/1972021Sarfaraz M. and J. Attari. 2011. “Numerical simulation of uniform flow region over a steeply sloping stepped spillway.” In Proc. 6th National Congress on Civil Engineering. Semnan Iran: Iran Water and Power Development Company.Spalart, P. . (2000). Strategies for turbulence modelling and simulations. International Journal of Heat and Fluid Flow, 21(3), 252-263. doi:10.1016/s0142-727x(00)00007-2Speziale, C. G., & Thangam, S. (1992). Analysis of an RNG based turbulence model for separated flows. International Journal of Engineering Science, 30(10), 1379-IN4. doi:10.1016/0020-7225(92)90148-aSpoljaric A. 1984. “Dynamic characteristics of the load on the bottom plate under hydraulic jump.” In Proc. Int. Conf. Hydrosoft’84: Hydraulic Engineering Software. New York: Elsevier.Teuber, K., Broecker, T., Bayón, A., Nützmann, G., & Hinkelmann, R. (2019). CFD-modelling of free surface flows in closed conduits. Progress in Computational Fluid Dynamics, An International Journal, 19(6), 368. doi:10.1504/pcfd.2019.103266Toso, J. W., & Bowers, C. E. (1988). Extreme Pressures in Hydraulic‐Jump Stilling Basins. Journal of Hydraulic Engineering, 114(8), 829-843. doi:10.1061/(asce)0733-9429(1988)114:8(829)Valero D. and D. B. Bung. 2015. “Hybrid investigations of air transport processes in moderately sloped stepped spillway flows.” In Vol. 28 of E-proc. 36th IAHR World Congress 1–10. The Hague Netherlands: IHE Delft.Valero, D., & Bung, D. B. (2016). Sensitivity of turbulent Schmidt number and turbulence model to simulations of jets in crossflow. Environmental Modelling & Software, 82, 218-228. doi:10.1016/j.envsoft.2016.04.030Valero, 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/w11010028von Kármán T. 1930. “Mechanische Ähnlichkeit und Turbulenz.” In Proc. 3rd Int. Congress on Applied Mechanics. New York: Springer.Wang H. 2014. “Turbulence and air entrainment in hydraulic jumps.” Ph.D. thesis Dept. of Civil Engineering Univ. of Queensland.Wang, H., & Chanson, H. (2013). Air entrainment and turbulent fluctuations in hydraulic jumps. Urban Water Journal, 12(6), 502-518. doi:10.1080/1573062x.2013.847464Wang, H., & Chanson, H. (2015). Experimental Study of Turbulent Fluctuations in Hydraulic Jumps. Journal of Hydraulic Engineering, 141(7), 04015010. doi:10.1061/(asce)hy.1943-7900.0001010Weller, H. G., Tabor, G., Jasak, H., & Fureby, C. (1998). A tensorial approach to computational continuum mechanics using object-oriented techniques. Computers in Physics, 12(6), 620. doi:10.1063/1.168744Witt, A., Gulliver, J., & Shen, L. (2015). Simulating air entrainment and vortex dynamics in a hydraulic jump. International Journal of Multiphase Flow, 72, 165-180. doi:10.1016/j.ijmultiphaseflow.2015.02.012Wu, J., Zhou, Y., & Ma, F. (2018). Air entrainment of hydraulic jump aeration basin. Journal of Hydrodynamics, 30(5), 962-965. doi:10.1007/s42241-018-0088-4Xiang, M., Cheung, S. C. P., Tu, J. Y., & Zhang, W. H. (2014). A multi-fluid modelling approach for the air entrainment and internal bubbly flow region in hydraulic jumps. Ocean Engineering, 91, 51-63. doi:10.1016/j.oceaneng.2014.08.016Yakhot, V., Orszag, S. A., Thangam, S., Gatski, T. B., & Speziale, C. G. (1992). Development of turbulence models for shear flows by a double expansion technique. Physics of Fluids A: Fluid Dynamics, 4(7), 1510-1520. doi:10.1063/1.858424Zhang, G., Wang, H., & Chanson, H. (2012). Turbulence and aeration in hydraulic jumps: free-surface fluctuation and integral turbulent scale measurements. Environmental Fluid Mechanics, 13(2), 189-204. doi:10.1007/s10652-012-9254-

Evaluation of different heat transfer conditions on an automotive turbocharger

This paper presents a combination of theoretical and experimental investigations for determining the main heat fluxes within a turbocharger. These investigations consider several engine speeds and loads as well as different methods of conduction, convection, and radiation heat transfer on the turbocharger. A one-dimensional heat transfer model of the turbocharger has been developed in combination with simulation of a turbocharged engine that includes the heat transfer of the turbocharger. Both the heat transfer model and the simulation were validated against experimental measurements. Various methods were compared for calculating heat transfer from the external surfaces of the turbocharger, and one new method was suggested. The effects of different heat transfer conditions were studied on the heat fluxes of the turbocharger using experimental techniques. The different heat transfer conditions on the turbocharger created dissimilar temperature gradients across the turbocharger. The results show that changing the convection heat transfer condition around the turbocharger affects the heat fluxes more noticeably than changing the radiation and conduction heat transfer conditions. Moreover, the internal heat transfers from the turbine to the bearing housing and from the bearing housing to the compressor are significant, but there is an order of magnitude difference between these heat transfer rates.The Swedish Energy Agency and KTH Royal Institute of Technology sponsored this work within the Competence Centre for Gas Exchange (CCGEx).Aghaali, H.; Angström, H.; Serrano Cruz, JR. (2015). Evaluation of different heat transfer conditions on an automotive turbocharger. International Journal of Engine Research. 16(2):137-151. doi:10.1177/1468087414524755S137151162Romagnoli, A., & Martinez-Botas, R. (2012). Heat transfer analysis in a turbocharger turbine: An experimental and computational evaluation. Applied Thermal Engineering, 38, 58-77. doi:10.1016/j.applthermaleng.2011.12.022Romagnoli, A., & Martinez-Botas, R. (2009). Heat Transfer on a Turbocharger Under Constant Load Points. Volume 5: Microturbines and Small Turbomachinery; Oil and Gas Applications. doi:10.1115/gt2009-59618Baines, N., Wygant, K. D., & Dris, A. (2010). The Analysis of Heat Transfer in Automotive Turbochargers. Journal of Engineering for Gas Turbines and Power, 132(4). doi:10.1115/1.3204586Serrano, J. R., Olmeda, P., Páez, A., & Vidal, F. (2010). An experimental procedure to determine heat transfer properties of turbochargers. Measurement Science and Technology, 21(3), 035109. doi:10.1088/0957-0233/21/3/035109Bohn, D., Heuer, T., & Kusterer, K. (2005). Conjugate Flow and Heat Transfer Investigation of a Turbo Charger. Journal of Engineering for Gas Turbines and Power, 127(3), 663-669. doi:10.1115/1.1839919Galindo, J., Luján, J. M., Serrano, J. R., Dolz, V., & Guilain, S. (2006). Description of a heat transfer model suitable to calculate transient processes of turbocharged diesel engines with one-dimensional gas-dynamic codes. Applied Thermal Engineering, 26(1), 66-76. doi:10.1016/j.applthermaleng.2005.04.010Sirakov, B., & Casey, M. (2012). Evaluation of Heat Transfer Effects on Turbocharger Performance. Journal of Turbomachinery, 135(2). doi:10.1115/1.4006608Serrano, J., Olmeda, P., Arnau, F., Reyes-Belmonte, M., & Lefebvre, A. (2013). Importance of Heat Transfer Phenomena in Small Turbochargers for Passenger Car Applications. SAE International Journal of Engines, 6(2), 716-728. doi:10.4271/2013-01-0576Larsson, P.-I., Westin, F., Andersen, J., Vetter, J., & Zumeta, A. (2009). Efficient turbo charger testing. MTZ worldwide, 70(7-8), 16-21. doi:10.1007/bf03226965Aghaali, H., & Ångström, H.-E. (2012). Turbocharged SI-Engine Simulation With Cold and Hot-Measured Turbocharger Performance Maps. Volume 5: Manufacturing Materials and Metallurgy; Marine; Microturbines and Small Turbomachinery; Supercritical CO2 Power Cycles. doi:10.1115/gt2012-68758Leufven, O., & Eriksson, L. (2012). Investigation of compressor correction quantities for automotive applications. International Journal of Engine Research, 13(6), 588-606. doi:10.1177/146808741243901

Computational fluid dynamics assessment of subcooled flow boiling in internal-combustion engine-like conditions at low flow velocities with a volume-of-fluid model and a two-fluid model

The use of subcooled flow boiling is a convenient option for the thermal management of downsized engines, but proper control of the phenomenon requires the accurate prediction of heat transfer at the coolant side, for which the use of computational fluid dynamics is a suitable alternative. While in most of the applications found to engine cooling a single-fluid equivalent method is used, in this paper the performance of a twofluid method is evaluated in engine-like conditions with special interest in the low velocity range. The results indicate that the description of the process at low velocities provided by the two-fluid method is better than that of a single-fluid model, while model calibration is simpler and more robust and the computational cost is substantially reduced.The equipment used in this work was partially supported by FEDER project funds 'Dotacion de infraestructuras cientifico tecnicas para el Centro Integral de Mejora Energetica y Medioambiental de Sistemas de Transporte' (grant number FEDER-ICTS-2012-06), framed in the operational program of the unique scientific and technical infrastructure of the Ministry of Science and Innovation of Spain. This work was partially supported by Senacyt Panama (Omar Cornejo, grant 797-7-2)Torregrosa, AJ.; Olmeda González, PC.; Gil Megías, A.; Cornejo, O. (2015). Computational fluid dynamics assessment of subcooled flow boiling in internal-combustion engine-like conditions at low flow velocities with a volume-of-fluid model and a two-fluid model. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 229(13):1830-1839. https://doi.org/10.1177/0954407015571674S1830183922913Pang, H. H., & Brace, C. J. (2004). Review of engine cooling technologies for modern engines. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 218(11), 1209-1215. doi:10.1243/0954407042580110Burke, R. D., Brace, C. J., Hawley, J. G., & Pegg, I. (2010). Review of the systems analysis of interactions between the thermal, lubricant, and combustion processes of diesel engines. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 224(5), 681-704. doi:10.1243/09544070jauto1301Steiner, H., Brenn, G., Ramstorfer, F., & Breitschadel, B. (2011). Increased Cooling Power with Nucleate Boiling Flow in Automotive Engine Applications. New Trends and Developments in Automotive System Engineering. doi:10.5772/13489Li, Z., Huang, R.-H., & Wang, Z.-W. (2011). Subcooled boiling heat transfer modelling for internal combustion engine applications. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 226(3), 301-311. doi:10.1177/0954407011417349Hawley, J. G., Wilson, M., Campbell, N. A. F., Hammond, G. P., & Leathard, M. J. (2004). Predicting boiling heat transfer using computational fluid dynamics. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 218(5), 509-520. doi:10.1243/095440704774061165Li, G., Fu, S., Liu, Y., Liu, Y., Bai, S., & Cheng, L. (2009). A homogeneous flow model for boiling heat transfer calculation based on single phase flow. Energy Conversion and Management, 50(7), 1862-1868. doi:10.1016/j.enconman.2008.12.029Chen, J. C. (1966). Correlation for Boiling Heat Transfer to Saturated Fluids in Convective Flow. Industrial & Engineering Chemistry Process Design and Development, 5(3), 322-329. doi:10.1021/i260019a023Torregrosa, A. J., Broatch, A., Olmeda, P., & Cornejo, O. (2014). Experiments on subcooled flow boiling in I.C. engine-like conditions at low flow velocities. Experimental Thermal and Fluid Science, 52, 347-354. doi:10.1016/j.expthermflusci.2013.10.004Robinson, K., Hawley, J. G., & Campbell, N. A. F. (2003). Experimental and modelling aspects of flow boiling heat transfer for application to internal combustion engines. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 217(10), 877-889. doi:10.1243/095440703769683289Lee, H. S., & O’Neill, A. T. (2009). Forced convection and nucleate boiling on a small flat heater in a rectangular duct: Experiments with two working fluids, a 50–50 ethylene glycol—water mixture, and water. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 223(2), 203-219. doi:10.1243/09544070jauto1008Biswas, R., & Strawn, R. C. (1998). Tetrahedral and hexahedral mesh adaptation for CFD problems. Applied Numerical Mathematics, 26(1-2), 135-151. doi:10.1016/s0168-9274(97)00092-5Hernandez-Perez, V., Abdulkadir, M., & Azzopardi, B. J. (2011). Grid Generation Issues in the CFD Modelling of Two-Phase Flow in a Pipe. The Journal of Computational Multiphase Flows, 3(1), 13-26. doi:10.1260/1757-482x.3.1.13Pioro, I. L., Rohsenow, W., & Doerffer, S. S. (2004). Nucleate pool-boiling heat transfer. II: assessment of prediction methods. International Journal of Heat and Mass Transfer, 47(23), 5045-5057. doi:10.1016/j.ijheatmasstransfer.2004.06.020Saiz Jabardo, J. M. (2010). An Overview of Surface Roughness Effects on Nucleate Boiling Heat Transfer~!2009-10-31~!2010-01-01~!2010-04-16~! The Open Transport Phenomena Journal, 2(1), 24-34. doi:10.2174/1877729501002010024Podowski, M. Z. (2012). TOWARD MECHANISTIC MODELING OF BOILING HEAT TRANSFER. Nuclear Engineering and Technology, 44(8), 889-896. doi:10.5516/net.02.2012.720Lo, S., & Osman, J. (2012). CFD Modeling of Boiling Flow in PSBT 5×5 Bundle. Science and Technology of Nuclear Installations, 2012, 1-8. doi:10.1155/2012/795935Del Valle, V. H., & Kenning, D. B. R. (1985). Subcooled flow boiling at high heat flux. International Journal of Heat and Mass Transfer, 28(10), 1907-1920. doi:10.1016/0017-9310(85)90213-3Cole, R. (1960). A photographic study of pool boiling in the region of the critical heat flux. AIChE Journal, 6(4), 533-538. doi:10.1002/aic.69006040

Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks

[EN] In this paper, we present an approach to Spanish talk shows summarization. Our approach is based on the use of Siamese Neural Networks on the transcription of the show audios. Specifically, we propose to use Hierarchical Attention Networks to select the most relevant sentences for each speaker about a given topic in the show, in order to summarize his opinion about the topic. We train these networks in a siamese way to determine whether a summary is appropriate or not. Previous evaluation of this approach on summarization task of English newspapers achieved performances similar to other state-of-the-art systems. In the absence of enough transcribed or recognized speech data to train our system for talk show summarization in Spanish, we acquire a large corpus of document-summary pairs from Spanish newspapers and we use it to train our system. We choose this newspapers domain due to its high similarity with the topics addressed in talk shows. A preliminary evaluation of our summarization system on Spanish TV programs shows the adequacy of the proposal.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R). Work of Jose-Angel Gonzalez is financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Hurtado Oliver, LF.; Segarra Soriano, E.; García-Granada, F.; Sanchís Arnal, E. (2019). Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks. Applied Sciences. 9(18):1-13. https://doi.org/10.3390/app9183836S113918Carbonell, J., & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’98. doi:10.1145/290941.291025Erkan, G., & Radev, D. R. (2004). LexRank: Graph-based Lexical Centrality as Salience in Text Summarization. Journal of Artificial Intelligence Research, 22, 457-479. doi:10.1613/jair.1523Lloret, E., & Palomar, M. (2011). Text summarisation in progress: a literature review. Artificial Intelligence Review, 37(1), 1-41. doi:10.1007/s10462-011-9216-zSee, A., Liu, P. J., & Manning, C. D. (2017). Get To The Point: Summarization with Pointer-Generator Networks. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). doi:10.18653/v1/p17-1099Narayan, S., Cohen, S. B., & Lapata, M. (2018). Ranking Sentences for Extractive Summarization with Reinforcement Learning. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). doi:10.18653/v1/n18-1158González, J.-Á., Segarra, E., García-Granada, F., Sanchis, E., & Hurtado, L.-F. (2019). Siamese hierarchical attention networks for extractive summarization. Journal of Intelligent & Fuzzy Systems, 36(5), 4599-4607. doi:10.3233/jifs-179011Furui, S., Kikuchi, T., Shinnaka, Y., & Hori, C. (2004). Speech-to-Text and Speech-to-Speech Summarization of Spontaneous Speech. IEEE Transactions on Speech and Audio Processing, 12(4), 401-408. doi:10.1109/tsa.2004.828699Shih-Hung Liu, Kuan-Yu Chen, Chen, B., Hsin-Min Wang, Hsu-Chun Yen, & Wen-Lian Hsu. (2015). Combining Relevance Language Modeling and Clarity Measure for Extractive Speech Summarization. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(6), 957-969. doi:10.1109/taslp.2015.2414820Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical Attention Networks for Document Classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. doi:10.18653/v1/n16-1174Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017). Supervised Learning of Universal Sentence Representations from Natural Language Inference Data. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. doi:10.18653/v1/d17-1070Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407. doi:10.1002/(sici)1097-4571(199009)41:63.0.co;2-

Semi-automatic assessment of unrestrained Java code: a Library, a DSL, and a workbench to assess exams and exercises

© ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in http://dx.doi.org/10.1145/2729094.2742615Automated marking of multiple-choice exams is of great interest in university courses with a large number of students. For this reason, it has been systematically implanted in almost all universities. Automatic assessment of source code is however less extended. There are several reasons for that. One reason is that almost all existing systems are based on output comparison with a gold standard. If the output is the expected, the code is correct. Otherwise, it is reported as wrong, even if there is only one typo in the code. Moreover, why it is wrong remains a mystery. In general, assessment tools treat the code as a black box, and they only assess the externally observable behavior. In this work we introduce a new code assessment method that also verifies properties of the code, thus allowing to mark the code even if it is only partially correct. We also report about the use of this system in a real university context, showing that the system automatically assesses around 50% of the work.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economíay Competitividad (Secretaría de Estado de Investigación, Desarrollo e Innovación) under grant TIN2013-44742-C4-1-R and by the Generalitat Valenciana under grant PROMETEOII2015/013. David Insa was partially supported by the Spanish Ministerio de Educación under FPU grant AP2010-4415.Insa Cabrera, D.; Silva, J. (2015). Semi-automatic assessment of unrestrained Java code: a Library, a DSL, and a workbench to assess exams and exercises. ACM. https://doi.org/10.1145/2729094.2742615SK. A Rahman and M. Jan Nordin. A review on the static analysis approach in the automated programming assessment systems. In National Conference on Programming 07, 2007.K. Ala-Mutka. A survey of automated assessment approaches for programming assignments. In Computer Science Education, volume 15, pages 83--102, 2005.C. Beierle, M. Kula, and M. Widera. Automatic analysis of programming assignments. In Proc. der 1. E-Learning Fachtagung Informatik (DeLFI '03), volume P-37, pages 144--153, 2003.J. Biggs and C. Tang. Teaching for Quality Learning at University : What the Student Does (3rd Edition). In Open University Press, 2007.P. Denny, A. Luxton-Reilly, E. Tempero, and J. Hendrickx. CodeWrite: Supporting student-driven practice of java. In Proceedings of the 42nd ACM technical symposium on Computer science education, pages 09--12, 2011.R. Hendriks. Automatic exam correction. 2012.P. Ihantola, T. Ahoniemi, V. Karavirta, and O. Seppala. Review of recent systems for automatic assessment of programming assignments. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, pages 86--93, 2010.H. Kitaya and U. Inoue. An online automated scoring system for Java programming assignments. In International Journal of Information and Education Technology, volume 6, pages 275--279, 2014.M.-J. Laakso, T. Salakoski, A. Korhonen, and L. Malmi. Automatic assessment of exercises for algorithms and data structures - a case study with TRAKLA2. In Proceedings of Kolin Kolistelut/Koli Calling - Fourth Finnish/Baltic Sea Conference on Computer Science Education, pages 28--36, 2004.Y. Liang, Q. Liu, J. Xu, and D. Wang. The recent development of automated programming assessment. In Computational Intelligence and Software Engineering, pages 1--5, 2009.K. A. Naudé, J. H. Greyling, and D. Vogts. Marking student programs using graph similarity. In Computers & Education, volume 54, pages 545--561, 2010.A. Pears, S. Seidman, C. Eney, P. Kinnunen, and L. Malmi. Constructing a core literature for computing education research. In SIGCSE Bulletin, volume 37, pages 152--161, 2005.F. Prados, I. Boada, J. Soler, and J. Poch. Automatic generation and correction of technical exercices. In International Conference on Engineering and Computer Education (ICECE 2005), 2005.M. Supic, K. Brkic, T. Hrkac, Z. Mihajlovic, and Z. Kalafatic. Automatic recognition of handwritten corrections for multiple-choice exam answer sheets. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1136--1141, 2014.S. Tung, T. Lin, and Y. Lin. An exercise management system for teaching programming. In Journal of Software, 2013.T. Wang, X. Su, Y. Wang, and P. Ma. Semantic similarity-based grading of student programs. In Information and Software Technology, volume 49, pages 99--107, 2007