Combustion 1, CD-ROM (1986).  B. Karlsson and J. Quintiere, Enclosure Fire Dynamics , CRC Press, London, 2000.  G. Hadijsophocleous and C. McCartney, “Guidelines for the use of cfd simulations for fire and smoke modeling”, ASHRAE Trans. 111, CD-ROM (2005).  H.Y. Guan and K.Y. Kwok, Computational Fluid Dynamics in Fire Engineering: Theory, Modelling and Practice , Elsevier, Oxford, 2009.  G. Sztarbała, “Computational fluid dynamics as a tool of fire engineers - good practice”, Proc
Petr Beneš and Róbert Kollárik
References Ansys, Inc. ANSYS FLUENT 12.0/12.1 Documentation Ansys, Inc. (2009): Introductory Fluent Training BANAVAN, A. A.; AHMED, Y. M. (2006): Use of Computational Fluid Dynamics for the Calculation of Ship Resistance and its Variations with the Ship Hull Form Parameters, AEJ, Vol.45, Faculty of Engineering Alexandria University, Alexandria JACHOWSKI, J. (2008): Assessment of Ship Squat in Shallow Water Using CFD, Archive of Civil and Mechanical
Tae-Hwan Joung, Karl Sammut, Fangpo He and Seung-Keon Lee
Performance of an Underwater Vehicle Estimated by a CFD Method and Experiment, ISOPE '07, Lisbon, Spain. Phillips, A., Furlong, M. and Turnock, S.R., 2007. The Use of Computational Fluid Dynamics to Access the Hull Resistance of Concept Autonomous Underwater Vehicles, OCEAN '07 IEEE Aberdeen. Wilcox, D.C., 1998. Turbulence Modeling for CFD, DCW Industries
M. Lamas, J. Rodríguez, C. Rodríguez and P. González
References Shen, L., Zhang, X., Yue, D. K. P., (2003), Turbulent flow over a flexible wall undergoing a streamwise travelling wave motion, Journal of Fluid Mechanics , vol. 484, pp. 197-221. Barret, D. S., Triantafyllou, M. S., Yue, D. K. P., Grosenbaugh, M. A., and Wolfgang, M. J. (1999) Drag reduction in fish-like locomotion. Journal of Fluid Mechanics 392, 183-212. Liu, H., Kawachi, K. (1999) A numerical study of undulatory swimming, Journal of Computational Physics 155 , pp
Tae-Hwan Joung, Hyeung-Sik Choi, Sang-Ki Jung, Karl Sammut and Fangpo He
References ANSYS Inc., 2011. ANSYS CFX-solver theory guide: release 14.0. Canonsburg: ANSYS Ltd.. Bellingham, J.G., Zhang, Y., Kerwin, J.E., Erikson, J., Hobson, B., Kieft, B., Godin, M., McEwen, R., Hoover, T., Paul, J., Hamilton, A., Franklin, J. and Banka A., 2010. Efficient propulsion for the Tethys long-range autonomous underwater vehicle. Autonomous Underwater Vehicles (AUV), 2010 IEEE/OES, pp.1-7. CFX-TASCow, 2002. Computational fluid dynamics software theory documentation (Version 2.12). Pittsburgh: AEA
Adam Sieradzki, Adam Dziubiński and Cezary Galiński
The joined wing concept is an unconventional airplane configuration, known since the mid-twenties of the last century. It has several possible advantages, like reduction of the induced drag and weight due to the closed wing concept. The inverted joined wing variant is its rarely considered version, with the front wing being situated above the aft wing. The following paper presents a performance prediction of the recently optimized configuration of this airplane. Flight characteristics obtained numerically were compared with the performance of two classical configuration airplanes of similar category. Their computational fluid dynamics (CFD) models were created basing on available documentation, photographs and some inverse engineering methods. The analysis included simulations performed for a scale of 3-meter wingspan inverted joined wing demonstrator and also for real-scale manned airplanes. Therefore, the results of CFD calculations allowed us to assess the competitiveness of the presented concept, as compared to the most technologically advanced airplanes designed and manufactured to date. At the end of the paper, the areas where the inverted joined wing is better than conventional airplane were predicted and new research possibilities were described.
M. I. Lamas and C. G. Rodríguez
.A.; Heywood, J.B.; Keck, J.C.: Experimental and theoretical investigation of nitric oxide formation in internal combustion engines . Combustion Science Technology 1, pp. 313-326, 1970. 15. Versteeg H.K., Malalasekera W.: An introduction to computational fluid dynamics: the finite volume method. 2nd Edition. Harlow: Pearson Education, 2007. 16. Taylor, C.F.: The internal combustion engine in theory and practice . 2nd Edition. MIT Press, 1985.
REFERENCES Abramowski, T., and Sugalski, K. (2017). Energy saving procedures for fishing vessels by means of numerical optimization of hull resistance. Scientific Journals of the Maritime University of Szczecin 121, pp. 19-27. Blazek, J. (2005). Computational Fluid Dynamics: Principles and applications. Elsevier. Ferziger, J.H., and Perić, M. (2002). Computational Methods for Fluid Dynamics. Berlin: Springer-Verlag. Kim, G.H., and Park, S. (2017). Development of a numerical tool for efficient and robust prediction of ship resistance
Tomasz Janoszek, Krzysztof Stańczyk and Adam Smoliński
-2201. Chui E.H., Majeski A.J., Lu D.Y., Hughes R., Gao H., McCalden D.J., Anthony E.J., 2009. Simulation of entrained flow coal gasification. Energy Procedia, 1, 503-509. Daggupati S., Ramesh N., Manadapati R.N., Mahajani S.M., Ganesh A., Chapru R.K., 2011. Laboratory studies on cavity growth and product gas composition in the context of underground coal gasification. Energy, 36, p. 1776. Jaworski Z., 2005. Computational Fluid Dynamics in Chemical and Process Engineering (in Polish). EXIT, Warsaw. Janoszek T., Łączny J
M. Shabani and A. Mazahery
Computational Fluid Dynamics (CFD) Simulation of Liquid-Liquid Mixing in Mixer Settler
Mixer-settlers are widely used inmetallurgical, mineral and chemical process. One of the greatest challenges in the area of hydrometallurgy process simulation is agitation made by impeller inside mixer-settler which yet presents one of the most common operations. Computational fluid dynamics (CFD) model has been developed to predict the effect of different physical parameters including temperature and density on the mixing characteristics of the system. It is noted that non-isotropic nature of flow in a mixer-settler, the complex geometry of rotating impellers and the large disparity in geometric scales present are some of the factors which contribute to the simulation difficulty. The experimental data for different velocity outlet was also used in order to validate the model.