Modelling and measurements in the aluminium industry ‐ Where are the obstacles?

2

Proceedings of the 12

International Conference on Computational Fluid Dynamics in the Oil & Gas, Metallurgical and Process Industries

SINTEF

PROCEEDINGS

Progress in Applied CFD –

CFD2017

Editors:

Jan Erik Olsen and Stein Tore Johansen

Progress in Applied CFD – CFD2017

Proceedings of the 12

International Conference on Computational Fluid Dynamics in the Oil & Gas, Metallurgical and Process Industries

SINTEF Proceedings

SINTEF Academic Press

SINTEF Proceedings no 2

Editors: Jan Erik Olsen and Stein Tore Johansen Progress in Applied CFD – CFD2017

Selected papers from 10^th International Conference on Computational Fluid Dynamics in the Oil & Gas, Metal lurgical and Process Industries

Key words:

CFD, Flow, Modelling

Cover, illustration: Arun Kamath

ISSN 2387-4295 (online) ISBN 978-82-536-1544-8 (pdf)

© Copyright SINTEF Academic Press 2017

The material in this publication is covered by the provisions of the Norwegian Copyright Act. Without any special agreement with SINTEF Academic Press, any copying and making available of the material is only allowed to the extent that this is permitted by law or allowed through an agreement with Kopinor, the Reproduction Rights Organisation for Norway. Any use contrary to legislation or an agreement may lead to a liability for damages and confiscation, and may be punished by fines or imprisonment

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PREFACE  

This book contains all manuscripts approved by the reviewers and the organizing committee of the  12th International Conference on Computational Fluid Dynamics in the Oil & Gas, Metallurgical and  Process Industries. The conference was hosted by SINTEF in Trondheim in May/June 2017 and is also  known as CFD2017 for short. The conference series was initiated by CSIRO and Phil Schwarz in 1997. 

So far the conference has been alternating between CSIRO in Melbourne and SINTEF in Trondheim. 

The  conferences  focuses on the  application  of CFD in the oil and  gas  industries, metal production,  mineral processing, power generation, chemicals and other process industries. In addition pragmatic  modelling  concepts  and  bio‐mechanical  applications  have  become  an  important  part  of  the  conference. The papers in this book demonstrate the current progress in applied CFD.  

The conference papers undergo a review process involving two experts. Only papers accepted by the  reviewers  are  included  in  the  proceedings.  108  contributions  were  presented  at  the  conference  together  with  six  keynote  presentations.  A  majority  of  these  contributions  are  presented  by  their  manuscript in this collection (a few were granted to present without an accompanying manuscript).  

The organizing committee would like to thank everyone who has helped with review of manuscripts,  all  those  who  helped  to  promote  the  conference  and  all  authors  who  have  submitted  scientific  contributions. We are also grateful for the support from the conference sponsors: ANSYS, SFI Metal  Production and NanoSim. 

Stein Tore Johansen & Jan Erik Olsen 

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Organizing committee: 

Conference chairman: Prof. Stein Tore Johansen  Conference coordinator: Dr. Jan Erik Olsen  Dr.

Dr.Sigrid Karstad Dahl  Dr.Shahriar Amini  Dr.Ernst Meese  Dr.Josip Zoric  Dr.Jannike Solsvik  Dr.Peter Witt   

Scientific committee: 

Stein Tore Johansen, SINTEF/NTNU  Bernhard Müller, NTNU 

Phil Schwarz, CSIRO 

Akio Tomiyama, Kobe University 

Hans Kuipers, Eindhoven University of Technology  Jinghai Li, Chinese Academy of Science 

Markus Braun, Ansys  Simon Lo, CD‐adapco 

Patrick Segers, Universiteit Gent  Jiyuan Tu, RMIT 

Jos Derksen, University of Aberdeen  Dmitry Eskin, Schlumberger‐Doll Research  Pär Jönsson, KTH 

Stefan Pirker, Johannes Kepler University  Josip Zoric, SINTEF 

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CONTENTS 

PRAGMATIC MODELLING ...  9 

On pragmatism in industrial modeling. Part III: Application to operational drilling  ... 11 

CFD modeling of dynamic emulsion stability ... 23 

Modelling of interaction between turbines and terrain wakes using pragmatic approach ... 29 

  FLUIDIZED BED ... 37 

Simulation of chemical looping combustion process in a double looping fluidized bed        reactor with cu‐based oxygen carriers  ... 39 

Extremely fast simulations of heat transfer in fluidized beds  ... 47 

Mass transfer phenomena in fluidized beds with horizontally immersed membranes ... 53 

A Two‐Fluid model study of hydrogen production via water gas shift in fluidized bed   membrane reactors   ... 63 

Effect of lift force on dense gas‐fluidized beds of non‐spherical particles ... 71 

Experimental and numerical investigation of a bubbling dense gas‐solid fluidized bed  ... 81 

Direct numerical simulation of the effective drag in gas‐liquid‐solid systems ... 89 

A Lagrangian‐Eulerian hybrid model for the simulation of direct reduction of iron ore  in fluidized beds... 97 

High temperature fluidization ‐ influence of inter‐particle forces on fluidization behavior   ... 107 

Verification of filtered two fluid models for reactive gas‐solid flows  ... 115 

  BIOMECHANICS  ... 123 

A computational framework involving CFD and data mining tools for analyzing disease in       cartoid artery   ... 125 

Investigating the numerical parameter space for a stenosed patient‐specific internal  carotid artery model  ...  133 

Velocity profiles in a 2D model of the left ventricular outflow tract, pathological  case study using PIV and CFD modeling  ...  139 

Oscillatory flow and mass transport in a coronary artery  ... 147 

Patient specific numerical simulation of flow in the human upper airways for assessing       the effect of nasal surgery  ... 153 

CFD simulations of turbulent flow in the human upper airways ... 163 

  OIL & GAS APPLICATIONS  ... 169 

Estimation of flow rates and parameters in two‐phase stratified and slug flow by an   ensemble Kalman filter ... 171 

Direct numerical simulation of proppant transport in a narrow channel for hydraulic   fracturing application ... 179 

Multiphase direct numerical simulations (DNS) of oil‐water flows through   homogeneous porous rocks  ... 185 

CFD erosion modelling of blind tees ... 191 

Shape factors inclusion in a one‐dimensional, transient two‐fluid model for stratified  and slug flow simulations in pipes ... 201 

Gas‐liquid two‐phase flow behavior in terrain‐inclined pipelines for wet natural  gas transportation  ... 207 

NUMERICS, METHODS & CODE DEVELOPMENT ... 213 

Innovative computing for industrially‐relevant multiphase flows  ... 215 

Development of GPU parallel multiphase flow solver for turbulent slurry flows in cyclone  ... 223 

Immersed boundary method for the compressible Navier–Stokes equations using  high order summation‐by‐parts difference operators  ... 233 

Direct numerical simulation of coupled heat and mass transfer in fluid‐solid systems ... 243 

A simulation concept for generic simulation of multi‐material flow,   using staggered Cartesian grids  ... 253 

A cartesian cut‐cell method, based on formal volume averaging of mass,   momentum equations  ... 265 

SOFT: a framework for semantic interoperability of scientific software ... 273 

  POPULATION BALANCE ... 279 

Combined multifluid‐population balance method for polydisperse multiphase flows ... 281 

A multifluid‐PBE model for a slurry bubble column with bubble size dependent   velocity, weight fractions and temperature  ... 285 

CFD simulation of the droplet size distribution of liquid‐liquid emulsions   in stirred tank reactors  ... 295 

Towards a CFD model for boiling flows: validation of QMOM predictions with   TOPFLOW experiments ... 301 

Numerical simulations of turbulent liquid‐liquid dispersions with quadrature‐based   moment methods  ... 309 

Simulation of dispersion of immiscible fluids in a turbulent couette flow ... 317 

Simulation of gas‐liquid flows in separators ‐ a Lagrangian approach  ... 325 

CFD modelling to predict mass transfer in pulsed sieve plate extraction columns ... 335 

  BREAKUP & COALESCENCE ... 343 

Experimental and numerical study on single droplet breakage in turbulent flow ... 345 

Improved collision modelling for liquid metal droplets in a copper slag cleaning process  ... 355 

Modelling of bubble dynamics in slag during its hot stage engineering  ... 365 

Controlled coalescence with local front reconstruction method ... 373 

  BUBBLY FLOWS ... 381 

Modelling of fluid dynamics, mass transfer and chemical reaction in bubbly flows ... 383 

Stochastic DSMC model for large scale dense bubbly flows  ... 391 

On the surfacing mechanism of bubble plumes from subsea gas release  ... 399 

Bubble generated turbulence in two fluid simulation of bubbly flow  ... 405 

  HEAT TRANSFER ... 413 

CFD‐simulation of boiling in a heated pipe including flow pattern transitions   using a multi‐field concept  ... 415 

The pear‐shaped fate of an ice melting front ... 423 

Flow dynamics studies for flexible operation of continuous casters (flow flex cc)  ... 431 

An Euler‐Euler model for gas‐liquid flows in a coil wound heat exchanger  ... 441 

  NON‐NEWTONIAN FLOWS  ... 449 

Viscoelastic flow simulations in disordered porous media  ... 451 

Tire rubber extrudate swell simulation and verification with experiments ... 459 

Front‐tracking simulations of bubbles rising in non‐Newtonian fluids  ... 469 

A 2D sediment bed morphodynamics model for turbulent, non‐Newtonian,  

particle‐loaded flows  ...  479 

METALLURGICAL APPLICATIONS  ... 491 

Experimental modelling of metallurgical processes ... 493 

State of the art: macroscopic modelling approaches for the description of multiphysics   phenomena within the electroslag remelting process ... 499 

LES‐VOF simulation of turbulent interfacial flow in the continuous casting mold ... 507 

CFD‐DEM modelling of blast furnace tapping  ... 515 

Multiphase flow modelling of furnace tapholes ... 521 

Numerical predictions of the shape and size of the raceway zone in a blast furnace  ... 531 

Modelling and measurements in the aluminium industry ‐ Where are the obstacles? ... 541 

Modelling of chemical reactions in metallurgical processes  ... 549 

Using CFD analysis to optimise top submerged lance furnace geometries  ... 555 

Numerical analysis of the temperature distribution in a martensic stainless steel   strip during hardening  ... 565 

Validation of a rapid slag viscosity measurement by CFD  ... 575 

Solidification modeling with user defined function in ANSYS Fluent  ... 583 

Cleaning of polycyclic aromatic hydrocarbons (PAH) obtained from ferroalloys plant  ... 587 

Granular flow described by fictitious fluids: a suitable methodology for process simulations  ... 593 

A multiscale numerical approach of the dripping slag in the coke bed zone of a   pilot scale Si‐Mn furnace  ...  599 

  INDUSTRIAL APPLICATIONS ... 605 

Use of CFD as a design tool for a phospheric acid plant cooling pond ... 607 

Numerical evaluation of co‐firing solid recovered fuel with petroleum coke in a   cement rotary kiln: Influence of fuel moisture ... 613 

Experimental and CFD investigation of fractal distributor on a novel plate and   frame ion‐exchanger  ...  621 

  COMBUSTION ... 631 

CFD modeling of a commercial‐size circle‐draft biomass gasifier  ... 633 

Numerical study of coal particle gasification up to Reynolds numbers of 1000  ... 641 

Modelling combustion of pulverized coal and alternative carbon materials in the   blast furnace raceway ... 647 

Combustion chamber scaling for energy recovery from furnace process gas:   waste to value ... 657 

  PACKED BED  ... 665 

Comparison of particle‐resolved direct numerical simulation and 1D modelling   of catalytic reactions in a packed bed  ... 667 

Numerical investigation of particle types influence on packed bed adsorber behaviour ... 675 

CFD based study of dense medium drum separation processes ... 683 

A multi‐domain 1D particle‐reactor model for packed bed reactor applications  ... 689 

  SPECIES TRANSPORT & INTERFACES ... 699 

Modelling and numerical simulation of surface active species transport   ‐ reaction in welding processes  ... 701 

Multiscale approach to fully resolved boundary layers using adaptive grids  ... 709 

Implementation, demonstration and validation of a user‐defined wall function   for direct precipitation fouling in Ansys Fluent  ... 717   

FREE SURFACE FLOW & WAVES ... 727  Unresolved CFD‐DEM in environmental engineering: submarine slope stability and  

other applications... 729  Influence of the upstream cylinder and wave breaking point on the breaking wave  

forces on the downstream cylinder ... 735  Recent developments for the computation of the necessary submergence of pump  

intakes with free surfaces ... 743  Parallel multiphase flow software for solving the Navier‐Stokes equations ... 752   

PARTICLE METHODS  ... 759  A numerical approach to model aggregate restructuring in shear flow using DEM in  

Lattice‐Boltzmann simulations  ... 761  Adaptive coarse‐graining for large‐scale DEM simulations  ... 773  Novel efficient hybrid‐DEM collision integration scheme  ... 779  Implementing the kinetic theory of granular flows into the Lagrangian  

dense discrete phase model  ... 785  Importance of the different fluid forces on particle dispersion in fluid phase  

resonance mixers ... 791  Large scale modelling of bubble formation and growth in a supersaturated liquid  ... 798   

FUNDAMENTAL FLUID DYNAMICS ... 807  Flow past a yawed cylinder of finite length using a fictitious domain method ... 809  A numerical evaluation of the effect of the electro‐magnetic force on bubble flow  

in aluminium smelting process  ... 819 

A DNS study of droplet spreading and penetration on a porous medium  ... 825 

From linear to nonlinear: Transient growth in confined magnetohydrodynamic flows  ... 831 

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MODELLING AND MEASUREMENTS IN THE ALUMINIUM INDUSTRY WHERE ARE THE OBSTACLES? -

Eirik MANGER¹

1 Hydro Aluminium, PMT, Hydrovegen 67, Porsgrunn, NORWAY

* E-mail: [email protected]

ABSTRACT

In this paper the necessity of obtaining experimental data with good enough quality for model verification is addressed.

Relevant examples from the aluminium industry are shown to illustrate some cases where measurements and model results work hand in hand on identifying bottlenecks and improving the situation. Moreover, measurements and their interpretation are briefly touched upon, trying to enlighten a few of the challenges on data collection in industrial environments and comparison with models.

Realising that measurements only uncovers parts of the real picture, an approach to estimate data interpretation errors is briefly outlined. A good model can and should rule out erroneous measurements – with the right use it can even give some guidelines on where to get good measurements.

Keywords: Measurements, CFD, Aluminium, Ducting.

NOMENCLATURE Greek Symbols

 Mass density, [kg/m³].

Latin Symbols C Constant, [-].

p Pressure, [Pa].

u Velocity, [m/s].

INTRODUCTION

The development in computer hardware combined with more sophisticated modelling tools have during the last decades opened up a wealth of new possibilities to understand and explain physical phenomena in complex systems. This is indeed true also for the aluminium industry, where the processes range from “simple” gas flow all the way to multiphase flow and phase changes coupled with electromagnetic forces.

Models are however still only approximations of the real processes, and comparison with measurements is crucial and necessary. A previous presentation Manger (2014) showed the importance of correct problem descriptions.

Along the same line, and equally important, are the access to and the quality of experimental data.

In this paper modelling combined with measurements are used to identify bottlenecks and other challenges in gas flow duct systems. Two different case studies will be used as examples, focusing on the area between matches and differences.

CASE STUDY I

The first case study is concentrated around a suction system for a quite old pot line, built in the mid 80’s.

Significant pressure drop increase in the ducting system during the last years has reduced the net pot suction rates and led to an unacceptable situation in terms of plant emissions. Measurements pointed towards deposits in the system, but there were questions on where and how much.

Original ducting system

Before moving on to the problem analysis, a brief overview of gas suction systems and their designs in aluminium plants is given.

Design

Gas suction systems for aluminium plants start at the individual pot by collecting fume gases from underneath the hooding. This is done via a gas channel with distributed openings along the pot’s length, usually located behind the anode beams at the top of the superstructure – see Figure 1. To avoid emissions from the pot, the suction rates must be high enough to keep the fume gases inside the pot hooding. Necessary rates will depend on i.a. hooding efficiency, size and temperature, typically varying from 4500 to 6500 Nm³/h/pot. Insufficient suction rates on the pots will cause undesirable emissions to the pot room and to the environment.

541

Figure 1: Gas channels at the top of the super structure.

Designing pot gas channels is a relatively straight- forward task, with a few guidelines:

- Pressure drop should be quite low - Deposits in the channel should be avoided - The suction should be even along the entire pot - The construction must be simple and robust Avoiding deposits in the pot gas channels can be (and often is) a challenge, particularly at the opposite end of the outlet where the channel dimensions lead to low velocities. This again changes the properties of the system and has undesirable effects.

The suction rate from each individual pot is controlled by dampers. The fume gas is led into one of several larger branches transporting the gas towards the Fume Treatment Plant, shortened FTP. Number of pots per branch varies, typically from 16 to 32, but there are systems with as many as 60 pots on one single line.

Figure 2 shows the particular gas systems of interest, including which pot numbers that are connected to the different sections. The system might not look impressive at first glimpse, but the reader should note that the total length of the channels is 500 metres, with channel cross sections varying from 2.5 m² to approximately 8 m². The main inlets to the FTP have a cross section of nearly 30 m².

Figure 2: Gas ducting system.

Modelling

To get an impression of the ducting system performance, several CFD models were established.

First the sizes of the opening slots in the pot gas channel were investigated. A simple model based the existing layout was established having ~24K hexahedral cells.

Constant density for air at 120°C was assumed, and the Relizable k- model handled turbulence. The flow distribution could then be determined by using a pressure inlet with a fixed outlet flux. Slot sizes decrease towards the outlet to compensate for the increased suction pressure (lower static pressure), mainly caused by increased gas velocity towards this side. The geometric model is shown in Figure 3, whereas the predicted static pressure just outside and inside the pot gas channel is shown in Figure 4.

Figure 3: Geometric model, pot gas channel.

Figure 4: Static pressure outside and inside the pot gas channel.

Analysing the flux through each of the slots revealed that the openings towards the outlet actually were too small, as can be seen in Figure 5. By adjusting the size of these, a more even suction from the pot could be achieved. In addition, the net pressure drop through this part of the system was reduced with nearly 25%.

The CFD model for the entire duct system became a bit more complicated. A mesh with approximately 3.8M polyhedral cells was constructed. Similar models and boundary conditions as for the hooding simulations were used, except that the inlet pressure from each pot had to be handled separately.

14-26

40-53 27-39

Direction East To FTP East Branch

Western Branch

542

Figure 5: Original and new suction distribution.

Adding the measured pressure drop through the interface towards the ducting branches (will be touched upon later) gave a reasonable estimate for the total inlet pressure drop into the channel. This pressure drop is modelled as a turbulent dynamic resistance on the form

2

2 1 u C p 



 ₍₁₎

The calculated pot suction rates for the unbalanced system are depicted in Figure 6. The results show, as expected, that the pots closest to the FTP intake have significantly higher suction rates compared against the end pots. Furthermore, the branch east of the FTP inlet generally have more suction compared to the western branch. This is also as expected, since there are a number of restrictions along the western flow path.

Figure 6: Calculated flow rates for an unbalanced system.

Before moving on to investigate if, and if so where, there are deposits in the system, the flow must be balanced theoretically. In real life this is done by measurements, so there might be some deviations to the actual damper settings, but this represents the closest match achievable between modelling and measurements.

The estimated resistance coefficients for balancing the system are shown in Figure 7. From correlations it can be shown that a resistance factor of around 4 represents blocking approximately half the available flow area.

After balancing the systems, the average flow rate is predicted to some 6000 Nm³/h/pot at 130°C and a suction pressure around 1500 Pa at the FTP inlets.

Figure 7: Estimated resistance coefficients for balancing the suction system.

Deposit analysis

With a balanced system in place, the search for obstacles and deposits could commence. Measured static pressures are shown in Figure 8. These correspond to effective suction pressures on the pots and were obtained by M. Karlsen (2016). Pots 1 to 13 in the series are connected to another FTP and not subject to analysis here, so focus should be on the pots in the range from 14 to 55. A first inspection shows that there is a big jump in suction pressure between the pots 40 and 41.

200 400 600 800 1 000 1 200 1 400 1 600 1 800

I-1 K-2 I-4 K-5 I-7 K-8 I-10 K-11 I-13 K-14 I - 16 K-17 I - 19 K-20 I - 22 K-23 I - 25 K-26 I - 28 K-29 I - 31 K-32 I - 34 K-35 I - 37 K-38 I - 40 K-41 I - 43 K-44 I - 46 K-47 I - 49 K-50 I - 52 K-53 I - 55

Static Pressure (Pa)

Static Pressure (Pa) ÅIIC Section 1-4

Figure 8: Measured suction pressure in the system.

Putting the simulation results on top of this shows that there is also a predicted jump in the pressure profile not seen in the measurements. In addition to the rather obvious restriction between pots 41 and 42, this indicates that something might be blocking the path from the eastern branch as well.

Figure 9: Comparison between simulated and measured pressure profile.

543

To investigate the effect of deposits and put numbers on the amount of material, the geometry was altered by blocking part of the lowest section in the channel dip, as can be seen in Figure 10. By trial error the deposit height in the channel dip was predicted to around one meter, leaving an open space only around 60 cm high from the originally 165 cm. Again, the pictures might not justify the dimensions – with one meter deposits as shown here, the blocked volume is some 12-13 m³.

Figure 10: Top; the original channel dip layout.

Bottom; channel dip partially filled with deposits.

The yellow line in Figure 11 shows the revised pressure profile with deposits in the channel dip. The match between measurements and simulations is now quite good for the eastern part of the system. By adding a restriction between the FTP inlets and the western branch, an even better agreement can be achieved, as shown with the green line. The amount of deposits and exact location for this have however not been followed further in the simulations.

Figure 11: Estimated resistance coefficients for balancing the suction system.

Optimising the pot gas channel

In a situation where every extra Pascal of suction pressure matters in trying to improve the emissions, especially for the end pots, also the construction of the pot channel interface towards the gas channel branch has been addressed. The original design had several obstructions and small openings, which can be seen in Figure 12. Streamlining, removing obstacles, and increasing the smallest openings with minor adjustments, as showed in Figure 13, reduce the pressure drop with approximately 25%, which also will contribute to larger net suction rates for the pots.

Figure 12: Original pot channel outlet.

Figure 13: Optimised pot channel outlet.

Current status

Comparing measurements and calculations strongly indicated that deposits reduced the pot suction rates significantly upstream the channel dip in the western branch. Opening some small hatches confirmed these conclusions, and actions could be planned.

Deposit removal

With evidence on significant amounts of deposits in the channel dip, it was decided to send in personnel to inspect the situation. Large amounts of rock solid material were found at the bottom, and the smallest opening height was measured to around 60 cm – in very good agreement with the simulations. It was further decided to try to remove some of the deposits.

Before starting the work, however, a there was a question asking how much of the material that should be removed to see an impact on the end pots. Thus, a series of simulations were run looking at the dependence between end pot suction rate and deposit height. Results from the simulations, plotted in Figure 14, show that there seems to be a minimum threshold value around 544

one meter. For opening heights larger than this, the net gain of removing deposits is relatively low per unit, whereas below this value the suction pressure on the end pots decrease rapidly.

Figure 14: Effect of smallest opening height on end pot suction rate.

Personnel has done a magnificent job in digging and removing deposited material from the channel dip.

Currently the smallest opening height has been increased to almost one meter, as recommended from the calculations. The duct system has responded well, again in quite good agreement with the model, and the suction pressure on the end pots is now close to 800 Pa.

Superstructure modifications

The proposed changes on the pot gas channel interface has also been accepted by the plant management. The changes are implemented when the pots are relined, and will slowly contribute to an even better emission picture.

Final comments

This work has shown how measurements combined with CFD models can be used to analyse and identify obstacles in a gas ducting system. The essence, perhaps not stated very directly, is that even quite simple models (no boundary layers, k- based turbulence model etc.) perform well enough when uncertainties on e.g.

geometry, deposits and measurements are added to the picture. There is no point in striving for the last 1%

accuracy in simulation models when other deviations easily can be a factor 10-20 above this. For us this is pragmatic modelling.

CASE STUDY II

The second case study is also concentrated around a suction system for a rather old pot line. Increasing the line current has altered the performance, and larger suction rates were needed to avoid substantial emissions.

Rather than looking at the entire system, the focus will be towards the suction rates from single pots. When analysing the system, there were large discrepancies between pitot tube measurements and the simulation results. To explain these differences a deep dive into measurement interpretation was needed, forming a basis of what could be referred to as model assisted measurements.

Superstructure gas channel

To get an understanding of the pots’ suction rates, the superstructure gas channels were simulated. Again a very simple model with some 54K polyhedral cells, constant density, Relizable k-, in combination with pressure inlet and fixed mass outflow, but it should still be able to capture the main features of the design. The geometric layout is shown in Figure 15, whereas the predicted velocities are plotted below in Figure 16. The velocities at the inlets are quite low, and the channel outlet velocity is around 16 m/s.

Figure 15: Gas channel design in the superstructure.

Figure 16: Predicted velocities in the superstructure gas channel.

In general, the pressure drop through the superstructure gas channel will depend on the flow rate as well the density – or implicitly, on temperature. Figure 17 shows the relationship between pressure drop and flow rate, given in units of Nm³/h (which actually is equivalent to a mass flow rate), for three different temperatures.

In addition, the pressure drop is given for two different states of the gas channel. The inlets from each hooding contain dampers to control and balance the flow. When fully open, the gas suction is not even along the pots.

When balancing the flow, the pressure drop is increased with some 30%.

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Figure 17: Gas channel design in the superstructure.

Ideally, calculated flow rates should of course match the measurements. However, when comparing these, there were large deviations, as can be seen in Figure 18. Here only the pressure drop curves for 140°C have been shown for simplicity. The estimated flow rates, from seven profile points across the pipe using a pitot tube, seem to indicate pressure drops of the order 800 Pa for flow rates around 6000 Nm³/h. The calculations predict 1100-1200 Pa at the same rates – or even higher if the superstructure gas channel was balanced.

Figure 18: Comparing measurements with simulation results.

Measurement interpretation

In search for explanations there were no reasons to disbelieve in the measurements – these were of excellent quality, with low variation and good repeatability. At the same time, adjusting and refining the CFD model had little impact on the calculated pressure drop.

After some discussions back and forth, the measurement interpretation came up as a point that might be questioned. Due to the channel outlet design, which has an expander from Ø200 to Ø460, and the location of the measurement point just downstream of this (access restrictions), the measured velocities showed a clear parabolic profile in the pipe, see Figure 19. Since the flow is highly turbulent, such a profile is not exactly expected, also indicating that the flow is not fully developed. In addition, for a circular pipe as measured here, the cross-sectional area covered by each point increases radially. Thus, utilizing our standard way of measurement interpretation, i.e. simple averaging, might not be good enough in this case.

The reader should note that the number of points in the velocity profile plot differs from the actual number of measurements points. Using an interpretation template, the user should specify both wall distance from first/last point, as well as the distances between the points. The values at the wall, constituting the first and the last points in the velocity profile, can either be set to zero or be found from extrapolation – representing a best possible reconstruction of the velocity profile.

Figure 19: Measured velocity profile and corresponding measurement points.

To overcome the hurdles on measurements/model discrepancy, a revised method on measurement interpretation was outlined and used. The method will be discussed briefly in the next section, but for now concentrate on the numbers found. The new interpretation puts the pressure drops as function of flow rates right on top of the model predictions, see Figure 20, and it was concluded that this was the main cause for the deviations.

Figure 20: Changing the measurement interpretation.

Model assisted measurements

It is no understatement to claim that out in the field, measurements must often be performed based on availability, which not necessarily corresponds to the optimal location. Based on the findings in this last project, there is now a recommendation on always combining measurements and models with the aim to say something about the conditions at the measurement location.

To give an example of how interpretation can influence the flow predictions from measurements, two rather simple geometries are considered – a straight pipe and an expander, the last one being showed in Figure 21.

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Figure 21: The expander, to be measured virtually.

The flow is simulated using a fixed velocity inlet, and monitoring points are added at two downstream locations to measure velocities virtually. These are then again used to calculate the flow rates (or rather the mean velocity). Three different approaches for calculating the mean velocity are considered:

- Simple arithmetic averaging - Area weighted averaging

- Linear reconstruction of the velocity profile The results from the virtual measurements are given in Table 1, whereas the calculated mean velocities and the deviations when using the different methods are shown in Table 2. Note that the real average velocity should be around 8.9 m/s.

As can be seen from the tables, there are only small discrepancies for the straight pipe section, independent of which calculation method used. For the expander, the story is however quite different. Close to the expander (at X=1.3), the mean velocity based on arithmetic averaging is overestimated with 50%. Using area based averages improves the figure somewhat, but there is still a deviation of almost 35%. The calculation based on linear reconstruction of the velocity profile is however superior to the others, with only 10% deviation even at this position (which is quite close to the expansion and far from optimal). Further downstream the expansion the deviations decrease.

Table 1: Virtual measurements in a pipe and an expansion.

Point No. X = 1.3 X = 2.0 X = 1.3 X = 2.0

1 8.96 9.13 8.7 8.8

2 8.97 9.23 14.5 9.7

3 8.97 9.23 15.3 10.3

4 8.97 9.23 15.3 12.5

5 8.97 9.23 15.3 10.3

6 8.97 9.23 14.5 9.7

7 8.96 9.13 8.7 8.8

Straight Expansion

Table 2: Estimated mean velocities based on the virtual measurements, and the deviations from real values.

Method/Variable X = 1.3 X = 2.0 X = 1.3 X = 2.0 Avg. Velocity

Simple Aritmetic 9.0 m/s 9.2 m/s 13.2 m/s 10.0 m/s Area Weighted 9.0 m/s 9.2 m/s 11.8 m/s 9.4 m/s Linear Flow Interp. 9.0 m/s 9.1 m/s 9.8 m/s 9.1 m/s

Deviation

Simple Aritmetic 1.9 % 4.6 % 49.8 % 13.8 % Area Weighted 1.9 % 4.3 % 34.2 % 7.0 % Linear Flow Interp. 1.8 % 3.9 % 11.2 % 3.2 %

Straight Expansion

The latter represents an example on how to use simulations in “assisting” with interpretation and perform quality control of measurements. In the future more work will follow this line.

CONCLUSION The conclusions are:

1. With the powerful CFD tools available today, simulation errors due to e.g. model selection or settings are often smaller than the measurement errors in an industrial environment. This is of course true only if the models are utilized in a correct manner.

2. Measurements, even though not of the best quality, can hand in hand with simulations prove to be a valuable tool for finding faults and obstacles in existing systems. Keep focus on the discrepancies.

3. Always have a knowledge of what is measured and how to interpret and convert the results into other numbers. A wrong interpretation and understanding will evidently lead to inaccurate figures and deviations.

ACKNOWLEDGEMENT

Interaction between measurements and modelling is a teamwork requiring expertise in both areas. The author wishes to acknowledge M. Karlsen and A. Dyrøy (2016) for excellent field work, discussions and contributions through the work.

REFERENCES

MANGER, E. (2014), “CFD in Problem Analysis and Optimisation – the Importance of Correct Boundary Conditions”, Proc. CFD 2014 – 10^th Int. Conf. on CFD in the Oil & Gas, Metallurgical and Process Industries, Trondheim, Norway, June 17-19.

KARLSEN, M. and DYRØY, A. (2016), Private Communications.

X=1.3

X=2.0

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