Abstract

A literature screening on computational fluid dynamics (CFD) modelling in water treatment applications showed a vast range of validation ranging from no validation at all, over residence time distribution (RTD) and tracer testing, to velocity field, species concentration and, finally, turbulence properties measurements. The validation level also differs depending on process scale (laboratory, pilot, full) and type of system (rheology, single phase vs. multiphase). Given the fact that CFD is in more widespread use, a discussion on the extent and need of validation needs to be initiated. This paper serves as a discussion starter on the topic.

INTRODUCTION

Computational fluid dynamics (CFD) has become a mature modelling framework in the water sector and is still gaining ground. Where it used to be a tool for troubleshooting, its application spans further to be used for model-based reactor design and virtual piloting for scale-up. In view of such decisions, the validation step as mentioned in the Good Modelling Practice guidelines for CFD modelling for water applications (Wicklein et al. 2016) becomes important with respect to trust in the model and, hence, the decisions based on it. However, there is not really a detailed description on how this validation needs to be performed and to what extent.

In the literature, the validation of CFD models spans a wide variety with respect to the level of detail (Table 1). Through screening 28 papers (no full review was envisioned), measurement variables used included velocity profiles (48%), gas holdup (13%) concentrations of solutes including oxygen (13%) and shear stress (6%). Only a few papers use multiple variables to validate and full-scale validation examples in literature are nearly non-existent. Direct, quantitative comparison to measurement data is scarce, and often (dis)agreement between model prediction and measurement data is assessed by visually comparing trends (e.g. using colour maps). Also, within a certain validation measurement ‘category’ (e.g. ‘velocity’), a multitude of different sensors and methods are used (e.g. velocity can be measured using a wide array of acoustic and other methods). No clear quantitative level is recommended for a CFD model to be perceived as adequate.

Table 1

A summary of CFD validation approaches and measurement methodologies used in 28 papers reviewed

Type of measurementMeasurement methodType of fluidType of technologyVolume of systemScaleHow data were collectedHow validity was assessedReference
velocity electromagnetic wastewater activated sludge bioreactor n/a full scale 4 points generally mentioned in text (no graphical comparison) Elshaw et al. (2016)  
velocity acoustic wastewater activated sludge bioreactor (aeration) 10 L lab scale 5 points vector maps compared Karpinska & Bridgeman (2017)  
laser n/a 
dissolved oxygen portable dual channel multimeter n/a n/a 
digital luminescent DO probes n/a 
RTD (mixing time) radiotracer (BuOH) water bubble column reactor 10 L lab scale n/a CFD and experimental results compared on diagrams (in every point) Pant et al. (2004)  
NaCl tracer (additional experiments) n/a 
velocity laser Doppler velocimetry wastewater activated sludge bioreactor n/a lab scale 2 planes – 3 heights each CFD and experimental results compared on diagrams (in every point) Le Moullec et al. (2008)  
liquid phase residence time distribution tracer (NaCl) 1 point 
velocity/turbulence laser Doppler velocimetry wastewater stirred tank 0.5 m i.d. vessel lab scale 13 points CFD and experimental results compared on diagrams (in every point) Sahu et al. (1999)  
velocity/turbulent kinetic energy dissipation rate laser Doppler anemometry wastewater stirred tank approx. 1 L lab scale 8 points CFD and experimental results compared on diagrams (in every point) Yeoh et al. (2004)  
instantaneous velocity acoustic Doppler velocimetry wastewater storm-water tank 547 L lab scale n/a colour maps compared Dufresne et al. (2017)  
mean velocity particle image velocimetry lab scale n/a 
velocity laser Doppler anemometry water flocculator 1.725 L lab scale 23 points at different depth CFD and experimental results compared on diagrams (in every point) Bridgeman et al. (2010)  
power dissipation power input measurements lab scale n/a n/a 
velocity acoustic Doppler velocimetry water n/a n/a full scale 5 points CFD and experimental results compared on diagrams (in every point) Andersson et al. (2013)  
10 velocity acoustic velocimetry water n/a ok 6 m³ lab scale 12 CFD and experimental results compared on diagrams (in every point) and table Baghalian et al. (2012)  
11 velocity acoustic Doppler velocimetry water n/a 13.4 m³ lab scale 10 CFD and experimental results compared on diagrams (in every point) Baranya et al. (2012)  
12 velocity propeller flowmeter water n/a approx. 10 m³ lab scale CFD and experimental results compared on diagrams and in the table (in every point) Erduran et al. (2012)  
13 velocity ultrasonic Doppler velocimeter river water n/a n/a (10 m long) full scale 40 results not compared Greco et al. (2004)  
14 velocity horizontal acoustic Doppler current profiler (H-ADCP) river water n/a n/a full scale 1 point experimental and numerical methods compared on diagrams Nihei & Kimizu (2008)  
15 velocity horizontal acoustic Doppler current profiler (H-ADCP) river water n/a n/a full scale H-ADCP validated with ADCP in summary and on diagram Sassi et al. (2011)  
16 velocity acoustic Doppler profiler (aDp) river water n/a n/a full scale 1 point no validation Szupiany et al. (2007)  
17 velocity mono-directional flow-meter wastewater activated sludge bioreactor 200 dm3 – 142 m³ lab scale and full scale 20 points CFD and experimental results compared on diagrams (in every point) Fayolle et al. (2007)  
gas hold-up optical probe 15 
oxygen transfer measurement probe 
18  concentration of benzoic acid UV spectrophotometer water annular reactor 2 × 0,2 L lab scale CFD and experimental results compared on diagrams (in every point) Duran et al. (2009)  
19 flow rate weighing method water water film n food contact surface (not water treatment) 5m2 – testing surface lab scale n/a no validation Suthanarak & Nunak (2018)  
thickness of water film (shear) n/a 10 CFD and experimental results compared on diagrams (in every point) 
20 velocity particle image velocimetry water ozonation column 350 m³ full scale ? (some measurements were taken in lab scale) experimental data not shown; no grpahic comparison of calculated and measured results Cockx et al. (1999)  
gas fraction (hold up) n/a 
mass transfer n/a 
21 velocity particle image velocimetry wastewater airlift reactor (activated sludge) 700 L lab scale 1 points CFD and experimental results compared on diagrams and in the table (in every point) Cockx et al. (2001)  
gas hold up n/a 
mass transfer n/a 
axial dispersion n/a 
22 velocity particle image velocimetry wastewater activated sludge bioreactor 587,5 L lab scale 11 points CFD and experimental results compared on diagrams (in every point) Do-Quang et al. (1998)  
 average gas retention (gas hold up) n/a 9 points 
23 velocity laser Doppler velocimetry (shear calculated based on LDV results) glycerine & Carbopol polymer mixing tank 70 L lab scale n/a CFD and experimental results compared on diagrams (in every point) Kelly & Gigas (2003)  
shear rates 
24  local gas holdup double-sensor conductivity probe air and water internal loop reactors 385 L lab scale 5 points CFD and experimental results compared on diagrams (in every point) Lu et al. (2009)  
25  velocity pH probes (H2SO4 tracers) air and water internal loop air lift reactor 50 L lab scale 4 points CFD and experimental results compared on diagrams (in every point) Šimčík et al. (2011)  
gas holdup U-tube manometers 4 points 
26 velocity laser Doppler anemometry air and water bubble column 12 L lab scale 8 points CFD and experimental compared on graph (not clear) Buwa & Ranade (2004)  
average bubble size high-speed digital imaging system n/a n/a 
wall pressure fluctuations pressure transducers n/a n/a 
voidage fluctuation measurements electrical conduction n/a n/a 
time-averaged gasholdup high-speed imaging n/a n/a 
27 nitrate concentration ion chromatography wastewater activated sludge bioreactor 130 L lab scale 13 points CFD and experimental results compared on diagrams (in every point) Le Moullec et al. (2010)  
ammonium concentration and soluble COD standard HACH protocols 
oxygen concentration standard oxygen probe 
velocity/turbulent kinetic energy laser Doppler velocimetry colour maps compared 
28 shear rates electrodiffusion measurement method wastewater stirred-tank reactor n/a lab scale n/a n/a Vlaev et al. (2007)  
Type of measurementMeasurement methodType of fluidType of technologyVolume of systemScaleHow data were collectedHow validity was assessedReference
velocity electromagnetic wastewater activated sludge bioreactor n/a full scale 4 points generally mentioned in text (no graphical comparison) Elshaw et al. (2016)  
velocity acoustic wastewater activated sludge bioreactor (aeration) 10 L lab scale 5 points vector maps compared Karpinska & Bridgeman (2017)  
laser n/a 
dissolved oxygen portable dual channel multimeter n/a n/a 
digital luminescent DO probes n/a 
RTD (mixing time) radiotracer (BuOH) water bubble column reactor 10 L lab scale n/a CFD and experimental results compared on diagrams (in every point) Pant et al. (2004)  
NaCl tracer (additional experiments) n/a 
velocity laser Doppler velocimetry wastewater activated sludge bioreactor n/a lab scale 2 planes – 3 heights each CFD and experimental results compared on diagrams (in every point) Le Moullec et al. (2008)  
liquid phase residence time distribution tracer (NaCl) 1 point 
velocity/turbulence laser Doppler velocimetry wastewater stirred tank 0.5 m i.d. vessel lab scale 13 points CFD and experimental results compared on diagrams (in every point) Sahu et al. (1999)  
velocity/turbulent kinetic energy dissipation rate laser Doppler anemometry wastewater stirred tank approx. 1 L lab scale 8 points CFD and experimental results compared on diagrams (in every point) Yeoh et al. (2004)  
instantaneous velocity acoustic Doppler velocimetry wastewater storm-water tank 547 L lab scale n/a colour maps compared Dufresne et al. (2017)  
mean velocity particle image velocimetry lab scale n/a 
velocity laser Doppler anemometry water flocculator 1.725 L lab scale 23 points at different depth CFD and experimental results compared on diagrams (in every point) Bridgeman et al. (2010)  
power dissipation power input measurements lab scale n/a n/a 
velocity acoustic Doppler velocimetry water n/a n/a full scale 5 points CFD and experimental results compared on diagrams (in every point) Andersson et al. (2013)  
10 velocity acoustic velocimetry water n/a ok 6 m³ lab scale 12 CFD and experimental results compared on diagrams (in every point) and table Baghalian et al. (2012)  
11 velocity acoustic Doppler velocimetry water n/a 13.4 m³ lab scale 10 CFD and experimental results compared on diagrams (in every point) Baranya et al. (2012)  
12 velocity propeller flowmeter water n/a approx. 10 m³ lab scale CFD and experimental results compared on diagrams and in the table (in every point) Erduran et al. (2012)  
13 velocity ultrasonic Doppler velocimeter river water n/a n/a (10 m long) full scale 40 results not compared Greco et al. (2004)  
14 velocity horizontal acoustic Doppler current profiler (H-ADCP) river water n/a n/a full scale 1 point experimental and numerical methods compared on diagrams Nihei & Kimizu (2008)  
15 velocity horizontal acoustic Doppler current profiler (H-ADCP) river water n/a n/a full scale H-ADCP validated with ADCP in summary and on diagram Sassi et al. (2011)  
16 velocity acoustic Doppler profiler (aDp) river water n/a n/a full scale 1 point no validation Szupiany et al. (2007)  
17 velocity mono-directional flow-meter wastewater activated sludge bioreactor 200 dm3 – 142 m³ lab scale and full scale 20 points CFD and experimental results compared on diagrams (in every point) Fayolle et al. (2007)  
gas hold-up optical probe 15 
oxygen transfer measurement probe 
18  concentration of benzoic acid UV spectrophotometer water annular reactor 2 × 0,2 L lab scale CFD and experimental results compared on diagrams (in every point) Duran et al. (2009)  
19 flow rate weighing method water water film n food contact surface (not water treatment) 5m2 – testing surface lab scale n/a no validation Suthanarak & Nunak (2018)  
thickness of water film (shear) n/a 10 CFD and experimental results compared on diagrams (in every point) 
20 velocity particle image velocimetry water ozonation column 350 m³ full scale ? (some measurements were taken in lab scale) experimental data not shown; no grpahic comparison of calculated and measured results Cockx et al. (1999)  
gas fraction (hold up) n/a 
mass transfer n/a 
21 velocity particle image velocimetry wastewater airlift reactor (activated sludge) 700 L lab scale 1 points CFD and experimental results compared on diagrams and in the table (in every point) Cockx et al. (2001)  
gas hold up n/a 
mass transfer n/a 
axial dispersion n/a 
22 velocity particle image velocimetry wastewater activated sludge bioreactor 587,5 L lab scale 11 points CFD and experimental results compared on diagrams (in every point) Do-Quang et al. (1998)  
 average gas retention (gas hold up) n/a 9 points 
23 velocity laser Doppler velocimetry (shear calculated based on LDV results) glycerine & Carbopol polymer mixing tank 70 L lab scale n/a CFD and experimental results compared on diagrams (in every point) Kelly & Gigas (2003)  
shear rates 
24  local gas holdup double-sensor conductivity probe air and water internal loop reactors 385 L lab scale 5 points CFD and experimental results compared on diagrams (in every point) Lu et al. (2009)  
25  velocity pH probes (H2SO4 tracers) air and water internal loop air lift reactor 50 L lab scale 4 points CFD and experimental results compared on diagrams (in every point) Šimčík et al. (2011)  
gas holdup U-tube manometers 4 points 
26 velocity laser Doppler anemometry air and water bubble column 12 L lab scale 8 points CFD and experimental compared on graph (not clear) Buwa & Ranade (2004)  
average bubble size high-speed digital imaging system n/a n/a 
wall pressure fluctuations pressure transducers n/a n/a 
voidage fluctuation measurements electrical conduction n/a n/a 
time-averaged gasholdup high-speed imaging n/a n/a 
27 nitrate concentration ion chromatography wastewater activated sludge bioreactor 130 L lab scale 13 points CFD and experimental results compared on diagrams (in every point) Le Moullec et al. (2010)  
ammonium concentration and soluble COD standard HACH protocols 
oxygen concentration standard oxygen probe 
velocity/turbulent kinetic energy laser Doppler velocimetry colour maps compared 
28 shear rates electrodiffusion measurement method wastewater stirred-tank reactor n/a lab scale n/a n/a Vlaev et al. (2007)  

CFD models are based on first principles and come in different levels of complexity depending on the required ingredients. For more simple cases (e.g. single phase/laminar) there are not really a suite of parameters that can be calibrated. However, in more complex cases (e.g. multiphase, occurrence of turbulence) models already come with certain calibration of models to provide closure. These are based on detailed studies at laboratory-scale and sufficient data collection at full-scale would be cumbersome. That is why in a CFD model development project there is not such a thing as calibration. Indeed, when validation is insufficient, the modeller needs to look back to the different steps in the development in order to adapt the model structure, the mesh, the solver settings, etc. In many cases, wrong assumptions lead to models with insufficient predictive power (i.e. prediction capability under different conditions). This can be sitting in details that are often thought to be of no influence. The objective of this paper is to initiate a discussion on the need and extent of validation required for CFD models. In what follows, we do so by providing some examples to illustrate the different level of validation detail required/available/possible to assess the accuracy of CFD models.

CASE STUDIES HIGHLIGHTING THE DIFFERENT LEVELS OF VALIDATION FOR CFD MODELS

Case of high detailed velocity measurements (mechanical stirrer) at laboratory scale

This case illustrates the setup to gather high detailed velocity measurements for a scaled-down mechanical stirrer at laboratory scale (Fernandes del Pozo et al. 2020; Figure 1). The high quality dataset allows derivation of time-dependent highly detailed velocity measurements as well as derived quantities for a complete CFD validation study (local turbulent kinetic energy, local shear rate, local viscosity, etc.; Figure 2).

Figure 1

Particle image velocimetry (PIV) setup for an axially stirred tank with a medium-viscosity fluid mimicking the rheology of a digested sludge (Carbopol).

Figure 1

Particle image velocimetry (PIV) setup for an axially stirred tank with a medium-viscosity fluid mimicking the rheology of a digested sludge (Carbopol).

Figure 2

Contour plot of shear rate below the impeller (left) and normalised axial, radial and tangential velocity profiles (right) at 26 mm below the impeller. R = 0.075 m.

Figure 2

Contour plot of shear rate below the impeller (left) and normalised axial, radial and tangential velocity profiles (right) at 26 mm below the impeller. R = 0.075 m.

Mechanical stirrers mixing viscous fluids such as the ones encountered in anaerobic digesters (AD) are often considered black-boxes due to the difficulty of obtaining any data from inside the reactor. In this line, the use of rheologically mimicking fluids (surrogates) such as Carbopol provide a promising technique to study the mixing mechanism of sludges in high level of detail and can provide sufficient metrics for tedious validation of CFD stirrer models. It is noted that a high level of detail is required for anaerobic digesters due to the complexity in modelling non-Newtonian flows (Dapelo & Bridgeman 2018).

Case of WWTP Eindhoven (Waterboard De Dommel, The Netherlands)

This case simulates the outer ring of a concentric bioreactor which is partly aerated. Different levels of validation were pursued here. First, a plain hydrodynamic model was validated using velocity measurements obtained by an acoustic Doppler current profiler (ADCP). The limitation is that such a measurement is only possible in a non-aerated zone. A first approach used the density of water and resulted in a large offset with the data (Figure 3, left). Accounting for sludge density led to a vast improvement (Figure 3, middle). Further improvement was achieved by including the swirl boundary condition of propellers present (Figure 3, right). At first, this was not accounted for as it complicated the modelling effort quite a bit (instead of defining a boundary condition, the motion of multiple impellers needs to be accounted for, requiring a moving reference frame and finer mesh) and it was assumed that this was likely not going to influence the macroscopic flow behaviour. This assumption proved to be too harsh leading to a deterioration in predictive power.

Figure 3

Improved validation of velocity along the depth of the bioreactor by accounting for more ingredients in the CFD model: density of water (left), density of sludge (middle) and account for swirling motion of propeller (right).

Figure 3

Improved validation of velocity along the depth of the bioreactor by accounting for more ingredients in the CFD model: density of water (left), density of sludge (middle) and account for swirling motion of propeller (right).

A next level of validation was by means of dissolved oxygen (DO) measurements at 99 locations conducted by a construction crane (Figure 4). This qualitatively shows that model predictions of the integrated CFD-biokinetic model (Rehman et al. 2017) were in line with measured patterns.

Figure 4

Validation of predicted DO with interpolated DO measurements (grid of 9 points) at two locations (‘A and B’) in the outer ring.

Figure 4

Validation of predicted DO with interpolated DO measurements (grid of 9 points) at two locations (‘A and B’) in the outer ring.

Case of large drinking water storage basin (PWN, The Netherlands)

This case concerns the CFD modelling of a large surface water storage basin for drinking water treatment (6Mm³ volume; operated by the Dutch drinking water utility PWN). Figure 5 shows the transport of a virtual tracer introduced at the inlet (a), a satellite image (b) and a drone image (c). The basin contains one coarse bubble aerator (white spot in Figure 5(b)). Also different levels of validation were pursued here. Detailed velocity measurements across the basin depth were performed using ADCP (the ten measurement points are indicated in Figure 5(b)). Further, the visible transport of the ‘white plume’ of precipitated calcium carbonate was compared to the virtual tracer transport.

Figure 5

The storage basin modelled: CFD simulation (a), satellite image (b) and drone image (c).

Figure 5

The storage basin modelled: CFD simulation (a), satellite image (b) and drone image (c).

Initially, the basin was modelled using one-phase CFD, not accounting for the coarse bubble aeration. Large deviations between predicted and measured velocities were observed. The model was extended to a simple two-phase aeration model with improved outcomes. However, after further refinement of the aeration model, good predictive power was observed (Figure 6). It is worth mentioning that the model also incorporated the impact of wind speed and direction by introducing momentum source on the top of the basin.

Figure 6

Comparison of measured and predicted velocity profiles obtained using the final two-phase CFD model (points 2, 3, 9 and 10 in Figure 5(b)).

Figure 6

Comparison of measured and predicted velocity profiles obtained using the final two-phase CFD model (points 2, 3, 9 and 10 in Figure 5(b)).

The general transport behaviour of the white precipitate was also reproduced by the model. The shape of the white front was indicated by the red line in Figure 5(c). Transport patterns in Figure 5(b) and 5(c) corresponded very well with the predicted front of the virtual tracer in 5a.

Very important to note is that in this case, the impact of aeration was significant, even though the basin had a huge volume. Validation measurements led to significant improvements of the CFD model, with high trust in the model as a result.

DISCUSSION

As a first lesson, all cases clearly illustrated that often details in the CFD modelling process (i.e. finer mesh, including (more detailed description of) phenomena, using better submodels for rheology) are the reason for insufficient predictive power. Adding certain mechanisms or more geometry detail can increase predictive power quite drastically. Another lesson learned (although this was not new) is that data collection is not straightforward, especially at full-scale. It is time and resource demanding and one can ask the question whether this effort is required for every new case.

In contrast, further generalising, one could argue that it would be more useful to gather experiences where validation has been performed and rather list recommendations for the model development (geometry details, mesh, turbulence model choice, rheological model) for future users, keeping them from performing detailed validation experiments infinitely. It would therefore be valuable to start collecting successful CFD validation cases in a database including all the data and metadata as well as all settings of the CFD model and specifically highlighting the important details leading to a high predictive power. Obviously, the level of validation currently required will depend on the system complexity and the objective. There is likely not going to be anyone still validating the parabolic profile of a laminar flow in a tube. However, it becomes more cumbersome for more complex systems, such as multiphase systems. Here, it is likely more efforts are still required to build this knowledge base. In the region of intermediate complexity, we need some guidance to nail down knowledge (just like the laminar pipe flow) to avoid continued large investments in measurement campaigns.

In the whole discussion above, one should also not lose the link with the modelling objective. Depending on the goal, validation needs might be different. Take the example of a sand filter. If one is interested in the design of the inlet and outlet structures and how they affect flow, a one-phase liquid model suffices and a tracer test could be used for validation. However, if one is interested in the detailed capturing of solids in the voids of the sand bed, a more sophisticated two-phase model would be needed, along with a filter bed autopsy to evaluate the non-homogeneity of the depositions. Noteworthy is that, apart from its value for model improvement, validation drastically increases the practitioner's trust in the model. High trust leads to increased weight of decisions people take based on model outcomes, markedly increasing its value.

Finally, it is of utmost importance that the modeller knows the limitations of the model and software being used. This is especially true for commercial software as not all equations and settings are clearly accessible. Open-source codes could be helpful here as it provides full flexibility with regards to adapting solvers. However, they present a steep learning curve for swift application given the fact that the graphical user interface is less user-friendly.

CONCLUSIONS

In view of the increasing usage of CFD, the demand for predictive power through validation pops up continuously. We argue that this demand should be put in perspective and not force people to eternally validate systems for which knowledge has been built up. We propose a knowledge base of validation cases to be developed that scrutinizes the need for explicit validation of future CFD models. The need for validation will in this way dynamically shift towards more complex modelling cases. For simple cases, this is already widely accepted. We need to bring this to practice for intermediate complexity problems. Complex cases still require further validation efforts to be performed and might also require more flexible open source software tools for accurate validation.

ACKNOWLEDGEMENT

We acknowledge Waterboard De Dommel and PWN for providing access to their systems to study the detailed hydrodynamics.

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