Computational Fluids Dynamics (CFD) is a widely used technique for modelling and understanding the behaviour of fluids. For example observing the flow of air over an aircraft wing. It is very computationally demanding but with the growing power of computers it is increasingly used as a more cost effective route than experimental rigs. In the process industries research in CFD is growing to model unit processes such as batch mixing, polymerisation reactions, polymer extrusion and processing and crystallization. However these fields are extremely demanding and in many cases reliable, full models remain elusive.
The many variables changing during a process (concentration and turbulence) and the different length scales make CFD very challenging for many processes.
While the mathematical techniques and tools for CFD are advancing rapidly the Achilles’ heel is always the validation of the computer model against the real behaviour and determining the “constants” and initial conditions of the CFD model.
Good process measurements can help CFD developers optimise their models. Data can also be used to validate whether a model is reliable
Please Note: The image above is showing a comparison between CFD and Process Tomography in order to help validate the data / model.
Process tomography provides concentration measurements through a process volume. This data can be used to test and validate CFD models.
In applying tomography to CFD challenges, a key aspect is the accuracy of the tomography data and the confidence levels in the CFD model at different positions through the mesh.
The example on the right shows the CFD prediction of flow through a modified fluidized bed. A model reactor was instrumented and the flow monitored through the vessel. Cross correlation techniques where applied to determine the velocity profile through the vessel.
Process tomography has been used to test and validate a wide range of CFD models, including:
Depending on the nature of the process, a variety of other offline and online different reconstruction methods can be used, including SCG (Sensitivity Conjugate Gradients) and parametric reconstruction.
CFD simulated velocity map of
model reactor at experimental conditions
Key benefits include:
- identify regional changes in process conditions over time for comparison with CFD predictions.
- provide process information to improve CFD models.
- determine repeatability of process conditions to characterise inherent process variability.
"CFD validation and improvement to models helped research group develop sludge thickener design improvements and realise significant savings"
Publications:
Bolton GT, Hooper C, Mann R and Stitt EH(2004) Flow distribution and velocity measurement in a radial flow fixed bed reactor using electrical resistance Tomography, Chemical Engineering Science, Vol. 59, No. 10, pp 1989-1997
G.T. Bolton, K.M. Primrose, C. Qiu, M. Castillo del Rio, M. Wang, H.I. Schlaberg, D. Brown, J. Brown, K.C. Low, G. Padron (2009) Comparison between electrical resistance tomography, CFD and other measurement techniques, 3rd International Workshop on Process Tomography (IWPT-3), 17-19 April 2009, Tokyo, Japan
Pakzad, L., Ein-Mozzafari, F. and Chan. P. (2008) Using electrical resistance tomography and computational fluid dynamics modelling to study the formation of cavern in the mixing of pseudoplastic fluids possessing yield stress, Chemical Engineering Science, Vol. 63, pp 2508-2522
In the press:
- Engineering Talk - January 2003 - Process Tomography validates Synetix calculations
- Chemical Engineering - March 2003 - Process Tomography validates CFD model
- Process Engineering - March 2003 - Validate CFD with tomography
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Register to access our "Radial flow packed bed reactor CFD validation" case study available from the Download section on the right.
