Simulation of transport phenomena in porous membrane evaporators using computational fluid dynamics

2016 ◽  
Vol 7 (2) ◽  
pp. 87-100 ◽  
Author(s):  
Mehrnoush Mohammadi ◽  
Azam Marjani ◽  
Mehdi Asadollahzadeh ◽  
Alireza Hemmati ◽  
Seyyed Masoud Kazemi
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Transport Phenomena ◽  
Porous Membrane

scholarly journals NUMERICAL SIMULATIONS OF FLOW BEHAVIOR IN DRIVEN CAVITY AT HIGH REYNOLDS NUMBERS

2012 ◽  
Vol 12 (6) ◽  
Author(s):  
Fudhail Bin Abdul Munir
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Flow Behavior ◽  
Transport Phenomena ◽  
Reynolds Numbers ◽  
Driven Cavity ◽  
Boltzmann Method ◽  
Lattice Boltzmann Scheme

In recent years, due to rapidly increasing computational power, computational methods have become the essential tools to conduct researches in various engineering fields.  In parallel to the development of ultra high speed digital computers, computational fluid dynamics (CFD) has become the new third approach apart from theory and experiment in the philosophical study and development of fluid dynamics.  Lattice Boltzmann method (LBM) is an alternative method to conventional CFD.  LBM is relatively new approach that uses simple microscopic models to simulate complicated microscopic behavior of transport phenomena.  In this paper, fluid flow behaviors of steady incompressible flow inside lid driven square cavity are studied.  Numerical calculations are conducted for different Reynolds numbers by using Lattice Boltzmann scheme.  The objective of the paper is to demonstrate the capability of this lattice Boltzmann scheme for engineering applications particularly in fluid transport phenomena. Keywords-component; lattice Boltzmann method, lid driven cavity, computational fluid dynamics.

Enhancing the Teaching of Fluid Mechanics and Transport Phenomena via FlowLab: A Computational Fluid Dynamics Tool

Volume 1 ◽  
2004 ◽  
Author(s):  
Jennifer Sinclair Curtis ◽  
Kimberly Henthorn ◽  
Shane Moeykens ◽  
Murali Krishnan
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Fluid Mechanics ◽  
Engineering Students ◽  
Chemical Engineering ◽  
Transport Phenomena ◽  
Operating Conditions ◽  
Structured Learning ◽  
Post Processing ◽  
Engineering Problems

Introducing Computational Fluid Dynamics (CFD) to engineering students at the undergraduate level has become more common in recent years, although there are significant barriers for doing so using a generalized CFD solver. A common constraint is the quantity of material to be covered in a fixed amount of time in a given course, which leaves little time left for learning the use of a generalized CFD package. With this consideration in mind, FlowLab (www.flowlab.fluent.com) was introduced by Fluent Inc. FlowLab may be described as a virtual fluids laboratory—a computer based analysis and visualization package. Using FlowLab, students solve predefined CFD exercises. These predefined exercises facilitate teaching and provide students with hands-on CFD experience. Through the design of each FlowLab exercise, students are introduced to engineering problems and concepts as well as CFD via a structured learning process. In the fall 2003 semester at Purdue University, FlowLab was used in CHE 540, a transport phenomena course offered within the School of Chemical Engineering. This course is open to advanced undergraduate engineering students and graduate students. Students were exposed to eight separate FlowLab exercises in this course. This paper gives a detailed summary of one of these specific exercises, developing flow in a pipe with and without heat transfer. The paper emphasizes how the use of CFD via FlowLab enhanced the teaching of specific concepts in transport phenomena as well as concepts in CFD such as creating a parametric geometry, discretizing the geometry, specifying boundary conditions, material properties and operating conditions, numerical solution techniques and post-processing. Experiences from this course are that FlowLab is a positive force for creating student interest and excitement in the area of fluid mechanics and transport phenomena. Using FlowLab’s post-processing capabilities, students were able to visualize complex flow fields and make direct comparison to analytical theory and experimental correlation. In addition, FlowLab provided a structured learning experience which reinforced proper pedagogy for applying CFD to engineering problems. Upon completion of the course, a student survey was performed in CHE 540 focusing on FlowLab integration and usage, and survey responses are summarized in this paper.

Predicting Hydrodynamic and Heat Transfer Effects of Sparger Geometry and Placement Within a Column Photobioreactor Using Computational Fluid Dynamics

2014 ◽  
Vol 11 (3) ◽  
Author(s):  
Ghazi S. Bari ◽  
Taylor N. Suess ◽  
Gary A. Anderson ◽  
Stephen P. Gent
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Porous Membrane ◽  
Flow Patterns ◽  
Transfer Effects ◽  
Eulerian Approach ◽  
Cfd Models ◽  
Computational Fluid Dynamics Cfd ◽  
Heat Transfer Effects

This research investigates the effects of the sparger on flow patterns and heat transfer within a column photobioreactor (PBR) using computational fluid dynamics (CFD). This study compares two types of spargers: a porous membrane, which occupies the entire floor of the reactor, and a single sparger, which is located along the centerline of the PBR floor. The PBR is modeled using the Lagrangian–Eulerian approach. The objective of this research is to predict the performance of PBRs using CFD models, which can be used to improve the design of PBRs used to grow microalgae that are used to produce biofuels and bioproducts.

Status of Computational Fluid Dynamics and Its Application to Materials Manufacturing

MRS Bulletin ◽  
1994 ◽  
Vol 19 (1) ◽  
pp. 14-19
Author(s):  
Ikuo Sawada ◽  
Hiroyuki Tanaka ◽  
Masahiro Tanaka
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Fluid Flow ◽  
Chemical Reaction ◽  
Transport Phenomena ◽  
Scale Up ◽  
List Type ◽  
Experimental Conditions ◽  
Analytical Approaches

Computational fluid dynamics was born principally in the aerospace field as a method for fluid flow and heat transfer research methods following experimental and analytical approaches. Along with progress in the cost performance of computers, computational fluid dynamics is now establishing itself as a tool to improve production processes and product quality in the steel, nonferrous metals, glass, plastics, and composite materials industries.Materials manufacturers use computational fluid dynamics for diverse purposes:1. Reduction in experimental conditions and costs;2. Detailed analysis of mechanisms with multifaceted information unobtainable through experimentation;3. Universal tool for scale-up; and4. Evaluation of novel processes.It can be readily imagined that accuracy, flexibility, and other requirements of computational fluid dynamics should vary with specific applications.Fluids generally observed in materials manufacturing processes are molten materials such as metal, glass, and plastics, and gases for stirring and refining. In the flow of such fluids, materials quality and process characteristics are governed by the following:1. Transport phenomena in the bulk region (where fluid flow is normally turbulent);2. Chemical reaction at interfaces;3. Transport phenomena in boundary layers near the interfaces; and4. Complex coupled phenomena (heat transfer, diffusion, chemical reaction, phase transformation like solidification, free surface, electromagnetic force, and bubble flow).

Hydrodynamic and Heat Transfer Effects of Different Sparger Spacings Within a Column Photobioreactor Using Computational Fluid Dynamics

2012 ◽  
Author(s):  
Ghazi S. Bari ◽  
Stephen P. Gent ◽  
Taylor N. Suess ◽  
Gary A. Anderson
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Interfacial Area ◽  
Porous Membrane ◽  
Flow Patterns ◽  
Transfer Effects ◽  
Drag Forces ◽  
The Media ◽  
Heat Transfer Effects

An important factor in designing photobioreactors is appropriate selection of sparger geometry and placement. The sparger governs the bubble size distribution and gas hold-up. These factors in turn influence flow pattern, effective interfacial area, rates of mass transfer, heat transfer, and mixing. This project investigates the effects of sparger geometry and placement on bubble and fluid flow patterns and convective heat transfer within a column photobioreactor (PBR) using Computational Fluid Dynamics (CFD). Experimental and computational studies have been completed that focused on the hydrodynamics and heat transfer within a rectangular column photobioreactor (34.29 cm long × 15.25 cm wide × 34.29 cm tall) with a single sparger located at the center of its base (33.02 cm × 1.27 cm) running lengthwise. Similar studies have also been completed analyzing a full width sparger on the bottom of the PBR similar to a porous membrane sparger. This study extends previous work by investigating the flow patterns and heat transfer effects due to multiple rows of spargers at different spacings running perpendicular to the length of the PBR. Comparison of hydrodynamic and heat transfer parameters are made for the different types of spargers at different volumetric flow rates. The gas bubbles and the water-based media within the photobioreactor are modeled using the Lagrangian-Eulerian approach. A low Reynolds k-Epsilon turbulence model is used to predict near-wall flow patterns. The main interaction forces between the bubbles and the media, including drag forces, added mass forces, and lift forces, are considered. The overarching goal of this research is to improve PBR designs, thus enhancing microalgae production for biofuel and bioproducts production. It is hypothesized that changing the spacing of the PBR spargers will alter the bubble flow patterns. Despite its importance, optimizing the sparger geometry and placement in PBRs for microalgae production is still largely not understood. In this study, simulation results are presented for various sparger spacings, which can be helpful in designing sparger geometry and placement for maximized microalgae production.

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