AUSTRALIA SINGAPORE
SUSPENSION MIXING TANK - DESIGN HEURISTIC
The design of suspension mixing tanks is often heavily reliant on empirical methods due to an incomplete understanding of the hydrodynamics involved in dense solid-liquid interactions. This limitation is particularly significant when designing mixing tanks or batch reactors for producing high-quality expensive products in the fine chemicals, biological and pharmaceutical industries. In this paper, a computational fluid dynamics (CFD)-assisted design approach is employed to examine the effectiveness of various mixing tank geometries in suspending particles. Ten key variables were analysed, and the impact of altering these variables was thoroughly documented. Additionally, a multivariable study was conducted to determine the most influential variables among them. Based on the findings, a design heuristic was developed, offering a practical guideline applicable across the process industry.
VALIDATION OF CFD RESULTS VIA PEER-REVIEWED JOURNAL
As the leading CFD consulting company in Australia, Singapore and the Asia Pacific region, it is important that we demonstrate the accuracy of our simulation results. There are two computational fluid dynamics (CFD) journals in this folder. These CFD simulation - related journals can be downloaded simply by clicking the icon on the left, namely:
Lea J [2009], Suspension mixing tank-design heuristic, Chemical product and process modelling, Volume 4, Issue 1, Article 17, The Berkeley Electronic Press
Fort I [2009], Comments on Lea J [2009] “Suspension mixing tank-design heuristic” Manuscript 1419, Chemical product and process modelling, The Berkeley Electronic Press
In Lea [2009], a computational fluid dynamics (CFD)-assisted design approach has been employed to study the effectiveness of mixing tank geometrical configurations to suspend particles. In contrast, the paper Fort [2009] deals with the analysis, via physical experimentation, of the process characteristics of agitated system with a pitched blade impeller and radial baffles (impeller power input and impeller pumping capacity) under turbulent regime of flow of agitated batch. Original experimental data are compared with results of CFD simulation in a pilot plant mixing system published in literature. These two papers concluded that the CFD results from Lea [2009] and experimental results from Fort [2009] are in good agreement. Download the article by clicking the icon on the left.
INTRODUCTION TO COMPUTATIONAL FLUID DYNAMICS
Computational fluid dynamics (CFD) was born during World War II, when scientists at Los Alamos National Laboratory were not only developing the atomic bomb, but also the numerical tools to describe the violent flow created by such a device. Among them was mathematician J. von Neumann, who contributed the keystone method of artificial viscosity for “capturing” shocks in numerical solutions and is regarded as the father of CFD. In the 65 years of its existence, CFD has earned itself a respectable place alongside the established disciplines of theoretical and experimental fluid dynamics. It is a branch of science that attracts mathematicians, physicists, and engineers alike, possibly because of the strong sense of empowerment it confers on its practitioners. Developers of CFD methods create a virtual reality that users may populate with anything that flows, regardless of scale. It could be a trickle of molecules finding their way through a micro-electro-mechanical system microchannel or air that lifts an entire airplane; it could be the flame moving through a combustion chamber or the melting core of a nuclear reactor gone unstable; it could be the earth’s restless oceans and atmosphere or the gaseous disk of a spiral galaxy. Moreover, the concept of flowing is broad: traffic flows over multilane highways, and an ensemble of stars flows through phase space; both may be treated with CFD. The bottom line: CFD methods allow the user virtual experiments that in the words of P.L Roe, would be “expensive, difficult, or dangerous, [or] impossible” in the real world.
CFD ANALYSIS SERVICES
As a leading CFD analysis services firm, we specialise in advanced CFD analysis, with a particular focus on multiphase and multiphysics CFD simulations. Our expertise spans a wide range of industries, including chemical and petrochemical, hydrogen, manufacturing, marine and offshore, mining and mineral processing, oil and gas, pharmaceutical, renewable energy, semiconductor, and water and wastewater sectors.
The contents in the publications on this page were based on the industry knowledge we have gained whilst working on commercial projects as a top-tier CFD simulation company. Checking CFD calculations can be tedious whereas conducting physical validations to determine the accuracy of CFD results may be impractical and/or economically prohibitive. The peer reviewed papers on this page aim to explain how CFD validation can be conducted in a laboratory. We have also provided a paper that shows how design heuristic for a mixing tank can be developed using CFD simulation technology.
VALIDATION OF CFD RESULTS VIA PARTICLE IMAGE VELOCIMETRY (PIV)
The manufacture of nitrocellulose, which is the basis of most artillery, rocket and missile propellant, is intrinsically risky due to the energetic nature of the product and the sensitivity of the process. To optimise the performance of the nitration unit, computational fluid dynamics (CFD) was employed to characterise the nitration unit and to provide detailed, relevant process data emanating from a new perspective. The actual nitration unit was scaled-down in order to use independent measurements from particle image velocimetry (PIV) to validate the CFD results. It was only after the model and simulation results were successfully validated that the characterisation of the actual unit was carried out. With the nitration unit characterised, its optimisation was carried out by performing modelling on different geometrical configurations with a view to selecting one which gives an optimum performance.