Multiscale Computational Fluid Dynamics
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Multiphase Turbulence in Bubbly Flows: RANS Simulations
This is a repository copy of Multiphase turbulence in bubbly flows: RANS simulations. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/90676/ Version: Accepted Version Article: Colombo, M and Fairweather, M (2015) Multiphase turbulence in bubbly flows: RANS simulations. International Journal of Multiphase Flow, 77. 222 - 243. ISSN 0301-9322 https://doi.org/10.1016/j.ijmultiphaseflow.2015.09.003 © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ 1 Multiphase turbulence in bubbly flows: RANS simulations 2 3 Marco Colombo* and Michael Fairweather 4 Institute of Particle Science and Engineering, School of Chemical and Process Engineering, 5 University of Leeds, Leeds LS2 9JT, United Kingdom 6 E-mail addresses: [email protected] (Marco Colombo); [email protected] 7 (Michael Fairweather) 8 *Corresponding Author: +44 (0) 113 343 2351 9 10 Abstract 11 12 The ability of a two-fluid Eulerian-Eulerian computational multiphase fluid dynamic model to 13 predict bubbly air-water flows is studied. -
Convective Boiling Heat Transfer and Two-Phase Flow Characteristics
International Journal of Heat and Mass Transfer 46 (2003) 4779–4788 www.elsevier.com/locate/ijhmt Convective boiling heat transfer and two-phase flow characteristics inside a small horizontal helically coiled tubing once-through steam generator Liang Zhao, Liejin Guo *, Bofeng Bai, Yucheng Hou, Ximin Zhang State Key Laboratory of Multiphase Flow in Power Engineering, XiÕan Jiaotong University, Xi’an Shaanxi 710049, China Received 15 February 2003; received in revised form 8 June 2003 Abstract The pressure drop and boiling heat transfer characteristics of steam-water two-phase flow were studied in a small horizontal helically coiled tubing once-through steam generator. The generator was constructed of a 9-mm ID 1Cr18Ni9Ti stainless steel tube with 292-mm coil diameter and 30-mm pitch. Experiments were performed in a range of steam qualities up to 0.95, system pressure 0.5–3.5 MPa, mass flux 236–943 kg/m2s and heat flux 0–900 kW/m2. A new two-phase frictional pressure drop correlation was obtained from the experimental data using ChisholmÕs B-coefficient method. The boiling heat transfer was found to be dependent on both of mass flux and heat flux. This implies that both the nucleation mechanism and the convection mechanism have the same importance to forced convective boiling heat transfer in a small horizontal helically coiled tube over the full range of steam qualities (pre-critical heat flux qualities of 0.1–0.9), which is different from the situations in larger helically coiled tube where the convection mechanism dominates at qualities typically >0.1. Traditional single parameter Lockhart–Martinelli type correlations failed to satisfactorily correlate present experimental data, and in this paper a new flow boiling heat transfer correlation was proposed to better correlate the experimental data. -
Deep Learning Models for Turbulent Shear Flow
DEGREE PROJECT IN MATHEMATICS, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Deep Learning models for turbulent shear flow PREM ANAND ALATHUR SRINIVASAN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES Deep Learning models for turbulent shear flow PREM ANAND ALATHUR SRINIVASAN Degree Projects in Scientific Computing (30 ECTS credits) Degree Programme in Computer Simulations for Science and Engineering (120 credits) KTH Royal Institute of Technology year 2018 Supervisors at KTH: Ricardo Vinuesa, Philipp Schlatter, Hossein Azizpour Examiner at KTH: Michael Hanke TRITA-SCI-GRU 2018:236 MAT-E 2018:44 Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci iii Abstract Deep neural networks trained with spatio-temporal evolution of a dy- namical system may be regarded as an empirical alternative to conven- tional models using differential equations. In this thesis, such deep learning models are constructed for the problem of turbulent shear flow. However, as a first step, this modeling is restricted to a simpli- fied low-dimensional representation of turbulence physics. The train- ing datasets for the neural networks are obtained from a 9-dimensional model using Fourier modes proposed by Moehlis, Faisst, and Eckhardt [29] for sinusoidal shear flow. These modes were appropriately chosen to capture the turbulent structures in the near-wall region. The time series of the amplitudes of these modes fully describe the evolution of flow. Trained deep learning models are employed to predict these time series based on a short input seed. Two fundamentally different neural network architectures, namely multilayer perceptrons (MLP) and long short-term memory (LSTM) networks are quantitatively compared in this work. -
Development of a Wake Model for Wind Farms Based on an Open Source CFD Solver
Development of a wake model for wind farms based on an open source CFD solver. Strategies on parabolization and turbulence modeling PhD Thesis Daniel Cabezón Martínez Departamento de Ingeniería Energética y Fluidomecánica, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid (UPM), Madrid, Spain Abstract Wake effect represents one of the most important aspects to be analyzed at the engineering phase of every wind farm since it supposes an important power deficit and an increase of turbulence levels with the consequent decrease of the lifetime. It depends on the wind farm design, wind turbine type and the atmospheric conditions prevailing at the site. Traditionally industry has used analytical models, quick and robust, which allow carry out at the preliminary stages wind farm engineering in a flexible way. However, new models based on Computational Fluid Dynamics (CFD) are needed. These models must increase the accuracy of the output variables avoiding at the same time an increase in the computational time. Among them, the elliptic models based on the actuator disk technique have reached an extended use during the last years. These models present three important problems in case of being used by default for the solution of large wind farms: the estimation of the reference wind speed upstream of each rotor disk, turbulence modeling and computational time. In order to minimize the consequence of these problems, this PhD Thesis proposes solutions implemented under the open source CFD solver OpenFOAM and adapted for each type of site: a correction on the reference wind speed for the general elliptic models, the semi-parabollic model for large offshore wind farms and the hybrid model for wind farms in complex terrain. -
Simulation of Turbulent Flows
Simulation of Turbulent Flows • From the Navier-Stokes to the RANS equations • Turbulence modeling • k-ε model(s) • Near-wall turbulence modeling • Examples and guidelines ME469B/3/GI 1 Navier-Stokes equations The Navier-Stokes equations (for an incompressible fluid) in an adimensional form contain one parameter: the Reynolds number: Re = ρ Vref Lref / µ it measures the relative importance of convection and diffusion mechanisms What happens when we increase the Reynolds number? ME469B/3/GI 2 Reynolds Number Effect 350K < Re Turbulent Separation Chaotic 200 < Re < 350K Laminar Separation/Turbulent Wake Periodic 40 < Re < 200 Laminar Separated Periodic 5 < Re < 40 Laminar Separated Steady Re < 5 Laminar Attached Steady Re Experimental ME469B/3/GI Observations 3 Laminar vs. Turbulent Flow Laminar Flow Turbulent Flow The flow is dominated by the The flow is dominated by the object shape and dimension object shape and dimension (large scale) (large scale) and by the motion and evolution of small eddies (small scales) Easy to compute Challenging to compute ME469B/3/GI 4 Why turbulent flows are challenging? Unsteady aperiodic motion Fluid properties exhibit random spatial variations (3D) Strong dependence from initial conditions Contain a wide range of scales (eddies) The implication is that the turbulent simulation MUST be always three-dimensional, time accurate with extremely fine grids ME469B/3/GI 5 Direct Numerical Simulation The objective is to solve the time-dependent NS equations resolving ALL the scale (eddies) for a sufficient time -
Experimental Research of Multiphase Flow with Cavitation in the Nozzle
EPJ Web of Conferences 114, 02 058 (2016 ) DOI: 10.1051/epjconf/201611004 2 58 C Owned by the authors, published by EDP Sciences, 2016 Experimental research of multiphase flow with cavitation in the nozzle Milada Kozubkova1, Marian Bojko1, Jana Jablonska1, Dorota Homa2,a and Jií T\ma3 1Department of Hydromechanics and Hydraulic Equipment, Faculty of Mechanical Engineering, VSB – Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic 2Institute of Power Engineering and Turbomachinery, Faculty of Energy and Environmental Engineering, Silesian University of Technology, Konarskiego Street 18, 44 100 Gliwice, Poland 3Department of Controls Systems and Instrumentations, Faculty of Mechanical Engineering, VSB – Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic Abstract. The paper deals with the problems of cavitation in water flow in the nozzle. The area of research is divided into two directions (experimental and numerical research). During the experimental research the equipment with the nozzle is under the measurement and basic physical quantities such as pressure and volume flow rate are recorded. In the following phase measuring of noise which is generated during flow through the nozzle in the area of cavitation is measured at various operating conditions of the pump. In the second part the appropriate multiphase mathematical model including the consideration of cavitation is defined. Boundary conditions for numerical simulation are defined on the basis of experimental measurements. Undissolved air in the flow is taken into account to obtain pressure distribution in accordance to measured one. Results of the numerical simulation are presented by means of basic current quantities such as pressure, velocity and volume fractions of each phase. -
An Introduction to Turbulence Modeling for CFD
An Introduction to Turbulence Modeling for CFD Gerald Recktenwald∗ February 19, 2020 ∗Mechanical and Materials Engineering Department Portland State University, Portland, Oregon, [email protected] Turbulence is a Hard Problem • Unsteady • Many length scales • Energy transfer between scales: ! Large eddies break up into small eddies • Steep gradients near the wall ME 4/548: Turbulence Modeling page 1 Engineering Model: Flow is \Steady-in-the-Mean" (1) 2 1.8 1.6 1.4 1.2 u 1 0.8 0.6 0.4 0.2 0 0 0.5 1 1.5 2 t ME 4/548: Turbulence Modeling page 2 Engineering Model: Flow is \Steady-in-the-Mean" (2) Reality: Turbulent flows are unsteady: fluctuations at a point are caused by convection of eddies of many sizes. As eddies move through the flow the velocity field changes in complex ways at a fixed point in space. Turbulent flows have structures { blobs of fluid that move and then break up. ME 4/548: Turbulence Modeling page 3 Engineering Model: Flow is \Steady-in-the-Mean" (3) Engineering Model: When measured with a \slow" sensor (e.g. Pitot tube) the velocity at a point is apparently steady. For basic engineering analysis we treat flow variables (velocity components, pressure, temperature) as time averages (or ensemble averages). These averages are steady (ignorning ensemble averaging of periodic flows). ME 4/548: Turbulence Modeling page 4 Engineering Model: Enhanced Transport (1) Turbulent eddies enhance mixing. • Transport in turbulent flow is much greater than in laminar flow: e.g. pollutants spread more rapidly in a turbulent flow than a laminar flow • As a result of enhanced local transport, mean profiles tend to be more uniform except near walls. -
Modeling and Simulation of High-Speed Wake Flows
Modeling and Simulation of High-Speed Wake Flows A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Michael Daniel Barnhardt IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Graham V. Candler, Adviser August 2009 c Michael Daniel Barnhardt August 2009 Acknowledgments The very existence of this dissertation is a testament to the support and en- couragement I’ve received from so many individuals. Foremost among these is my adviser, Professor Graham Candler, for motivating this work, and for his consider- able patience, enthusiasm, and guidance throughout the process. Similarly, I must thank Professor Ellen Longmire for allowing me to tinker around late nights in her lab as an undergraduate; without those experiences, I would’ve never been stimulated to attend graduate school in the first place. I owe a particularly large debt to Drs. Pramod Subbareddy, Michael Wright, and Ioannis Nompelis for never cutting me any slack when I did something stupid and always pushing me to think bigger and better. Our countless hours of discussions provided a great deal of insight into all aspects of CFD and buoyed me through the many difficult times when codes wouldn’t compile, simulations crashed, and everything generally seemed to be broken. I also have to thank Travis Drayna and all of my other friends and colleagues at the University of Minnesota and NASA Ames. Dr. Matt MacLean of CUBRC and Dr. Anita Sengupta at JPL provided much of the experimental results and were very helpful throughout the evolution of the project. -
Introduction
On the use of Darcy's law and invasion‐percolation approaches for modeling large‐scale geologic carbon sequestration Curtis M. Oldenburg Sumit Mukhopadhyay Abdullah Cihan First published: 01 December 2015 https://doi.org/10.1002/ghg.1564 Cited by: 4 UC-eLinks Abstract Most large‐scale flow and transport simulations for geologic carbon sequestration (GCS) applications are carried out using simulators that solve flow equations arising from Darcy's law. Recently, the computational advantages of invasion‐percolation (IP) modeling approaches have been presented. We show that both the Darcy's‐law‐ and the gravity‐capillary balance solved by IP approaches can be derived from the same multiphase continuum momentum equation. More specifically, Darcy's law arises from assuming creeping flow with no viscous momentum transfer to stationary solid grains, while it is assumed in the IP approach that gravity and capillarity are the dominant driving forces in a quasi‐static two‐phase (or more) system. There is a long history of use of Darcy's law for large‐scale GCS simulation. However, simulations based on Darcy's law commonly include significant numerical dispersion as users employ large grid blocks to keep run times practical. In contrast, the computational simplicity of IP approaches allows large‐scale models to honor fine‐scale hydrostratigraphic details of the storage formation which makes these IP models suitable for analyzing the impact of small‐scale heterogeneities on flow. However, the lack of time‐dependence in the IP models is a significant disadvantage, while the ability of Darcy's law to simulate a range of flows from single‐phase‐ and pressure‐gradient‐driven flows to buoyant multiphase gravity‐capillary flow is a significant advantage. -
Multiphase Turbulent Flow
Multiphase Turbulent Flow Ken Kiger - UMCP Overview • Multiphase Flow Basics – General Features and Challenges – Characteristics and definitions • Conservation Equations and Modeling Approaches – Fully Resolved – Eulerian-Lagrangian – Eulerian-Eulerian • Averaging & closure – When to use what approach? • Preferential concentration • Examples • Modified instability of a Shear Layer • Sediment suspension in a turbulent channel flow • Numerical simulation example: Mesh-free methods in multiphase flow What is a multiphase flow? • In the broadest sense, it is a flow in which two or more phases of matter are dynamically interacting – Distinguish multiphase and/or multicomponent • Liquid/Gas or Gas/Liquid • Gas/Solid • Liquid/Liquid – Technically, two immiscible liquids are “multi-fluid”, but are often referred to as a “multiphase” flow due to their similarity in behavior Single component Multi-component Water Air Single phase Pure nitrogen H20+oil emulsions Steam bubble in H 0 Coal particles in air Multi-phase 2 Ice slurry Sand particle in H20 Dispersed/Interfacial • Flows are also generally categorized by distribution of the components – “separated” or “interfacial” • both fluids are more or less contiguous throughout the domain – “dispersed” • One of the fluids is dispersed as non- contiguous isolated regions within the other (continuous) phase. • The former is the “dispersed” phase, while the latter is the “carrier” phase. • One can now describe/classify the geometry of the dispersion: • Size & geometry • Volume fraction Gas-Liquid Flow -
Prediction of Multiphase Flow in Pipelines: Literature Review
Ingeniería y Ciencia ISSN:1794-9165 | ISSN-e: 2256-4314 ing. cienc., vol. 11, no. 22, pp. 213–233, julio-diciembre. 2015. http://www.eafit.edu.co/ingciencia This article is licensed under a Creative Commons Attribution 4.0 by Prediction of Multiphase Flow in Pipelines: Literature Review M. Jerez-Carrizales1, J. E. Jaramillo2 and D. Fuentes3 Received: 06-05-2015 | Accepted: 02-07-2015 | Online: 31-07-2015 MSC:76T10 | PACS:47.55.Ca doi:10.17230/ingciencia.11.22.10 Abstract In this work a review about the most relevant methods found in the lite- rature to model the multiphase flow in pipelines is presented. It includes the traditional simplified and mechanistic models, moreover, principles of the drift flux model and the two fluid model are explained. Even though, it is possible to find several models in the literature, no one is able to re- produce all flow conditions presented in the oil industry. Therefore, some issues reported by different authors related to model validation are here also discussed. Key words: drift flux; two fluid; mechanistic; vertical lift performance; pressure drop 1 Universidad Industrial de Santander, Bucaramanga, Colombia, [email protected]. 2 Universidad Industrial de Santander, Bucaramanga,Colombia, [email protected]. 3 Universidad Industrial de Santander, Bucaramanga, Colombia, [email protected]. Universidad EAFIT 213| Prediction of Multiphase Flow in Pipelines: Literature Review Predicción del flujo multifásico en tuberías: artículo de revisión Resumen En este trabajo se presenta una revisión de los métodos más relevantes en la industria del petróleo para modelar el flujo multifásico en tuberías. -
Enhancement of Turbulent Convective Heat Transfer Using a Microparticle Multiphase Flow
energies Article Enhancement of Turbulent Convective Heat Transfer using a Microparticle Multiphase Flow Tao Wang 1,2,*, Zengliang Gao 1,* and Weiya Jin 1 1 Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310032, China; [email protected] 2 Institute of Mechanical Engineering, Quzhou University, Quzhou 324000, China * Correspondence: [email protected] (T.W.); [email protected] (Z.G.) Received: 12 February 2020; Accepted: 9 March 2020; Published: 10 March 2020 Abstract: The turbulent heat transfer enhancement of microfluid as a heat transfer medium in a tube was investigated. Within the Reynolds number ranging from 7000 to 23,000, heat transfer, friction loss and thermal performance characteristics of graphite, Al2O3 and CuO microfluid with the particle volume fraction of 0.25%–1.0% and particle size of 5 µm have been respectively tested. The results showed that the thermal performance of microfluids was better than water. In addition, the graphite microfluid had the best turbulent convective heat transfer effect among several microfluids. To further investigate the effect of graphite particle size on thermal performance, the heat transfer characteristics of the graphite microfluid with the size of 1 µm was also tested. The results showed that the thermal performance of the particle size of 1 µm was better than that of 5 µm. Within the investigated range, the maximum value of the thermal performance of graphite microfluid was found at a 1.0% volume fraction, a Reynolds number around 7500 and a size of 1 µm. In addition, the simulation results showed that the increase of equivalent thermal conductivity of the microfluid and the turbulent kinetic energy near the tube wall, by adding the microparticles, caused the enhancement of heat transfer; therefore, the microfluid can be potentially used to enhance turbulent convective heat transfer.