Numerical Simulations of Fluid Flows
In computational fluid dynamics (CFD) the NavierStokes equations are solved approximatly by means of numerical methods. Applications of flow simulations in the context of metrology are:
 design and optimization of measurement configurations,
 simulation and prediction of experiments, and
 determination of the influence of different parameters on measurement uncertainty.
Introduction
As mathematical description of the behavior of Newtonian viscous fluids we have the well known NavierStokes equations. Generally, analytic solutions are not available. Therefore, numerical methods as the finite volume method or the finite element method are used. Since the direct numerical solution (DNS) of turbulent flows is extremely expensive, it is limited to relatively simple problems. In case of turbulent flows, characterized by large Reynolds numbers, empirical turbulence models like Reynoldsaveraged NavierStokes (RANS), unsteady Reynoldsaveraged NavierStokes (URANS), and large eddy simulation (LES) are commonly used.
As an alternative to expensive experiments, numerical simulations of fluid flows provide the opportunity for
 prediction and validation of measurements
 risk evaluation
 determination of dependencies, e. g. material properties, geometry, causeeffect, case studies
 optimization of measurement procedures
Applications
Mathematical modeling and numerical simulation of multiphase flows in metrology
The aim of the EMPIR project „Multiphase flow reference metrology“ is to explain and reduce the uncertainty in multiphase flow metering in the oil and gas industries. Therefore, a typical multiphase flow measurement setup consisting of a 16 meter long horizontal pipe followed by a relatively complex measurement unit is examined experimentally and numerically. Within the working group 8.41, a variety of industrially relevant configurations with different oil, water, and gas flow rates are simulated with the commercial CFD code ANSYS Fluent. Depending on the prescribed superficial velocities of the gas and liquid phases, different flow patterns are observed at the end of the inflow section, which have an influence on the accuracy of the Venturi meter. The CFD simulations allow a visualization of the different structures in all parts of the geometry, even in areas that are hardly observable in experiments. Furthermore, the influence of different parameters (like the use of different fluids in the laboratories taking part in the experimental intercomparison of the project) on the pressure measurement in the Venturi tube has been investigated. An advantage of the simulation over the experiment is that it is possible to change only one parameter and keep the others constant. Thus, the influence of the different parameters can be investigated separately. [more]
Influence of uncertain parameters in pipe flow simulations
The flow in a pipe is influenced by different parameters, e.g., uncertain initial and boundary conditions, geometry variations due to manufacturing tolerances, or inaccurate material parameters. The uncertainty in such parameters leads to measurement errors of flow meters. For the application of flow meters under field conditions, which are characterized by disturbed inflow profiles, it must be ensured that the measurement error is below a certain threshold. The investigation of such uncertainties is done with the generalized polynomial chaos method together with classical fluid Dynamics.
CFD to provide support in particle metrology
Developing a national standard for soot mass concentration and opacity at PTB requires highsensitivity instrumentation for soot generation (CAST) in a wide range of particle sizes and particle number concentrations. Such high accuracy soot generators need also welldefined aerosol conditioning, dilution and homogenization process steps in order to vary e.g the particle number concentration over the legally relevant range. In order to optimize the soot spatial distribution and to develop effective mixing and dilution configuration we started to simulate such fluid conditioning units in different configurations. Threedimensional CFD simulations are carried out to estimate different mixing characteristics depending on the incoming flow rates and the angle of inclination at the junction where different pipes join together. In particular we consider namely mixing of three air flows at different dilution levels, on as short ways as possible, without long, steady flow distances. To analyse the propagation of mixing the variance for the the approach section is calculated. The work is done in cooperation with the Working Group 3.23 “Aerosols and particle measurements”.
CFD simulations of the temperature distribution in large storage tanks
Storage tanks for mineral oil and its derivatives can have a capacity of more than 50 million liter. Therefore a temperature change to some tenths of percent leads to a volume change of more than thousand liters. The exact measurement of the mean fluid temperature is necessary for the trading of great quantities.
In a scientific project, the mean temperature was measured in a real tank and also determined by extensive simulations. By using the CFD approach it was possible to transfer the measured data to other liquids, different weather conditions, and special filling situations.
Projects
 EMPIR 16ENG07 Multiphase Flow Reference Metrology (June 2017  May 2020)
 EMRP ENG58 Multiphase flow metrology in oil and gas production (June 2014  May 2017)
 EMRP NEW04 Novel mathematical and statistical approaches to uncertainty evaluation (August 2012  July 2015)
 Temperature distribution in large storage tanks (2010 – 2012)
Publications
• 
S. Knotek, S. Schmelter;M. Olbrich
Measurement: Sensors,
18
100317,
2021.
[DOI: 10.1016/j.measen.2021.100317]

• 
S. Schmelter, M. Olbrich, S. Knotek;M. Bär
Measurement: Sensors,
18
100154,
2021.
[DOI: 10.1016/j.measen.2021.100154]

• 
M. Olbrich, A. Hunt, T. Leonard, D. S. van Putten, M. Bär, K. Oberleithner;S. Schmelter
Comparing temporal characteristics of slug flow from tomography measurements and video observations.
Measurement: Sensors,
18
100222,
2021.
[DOI: 10.1016/j.measen.2021.100222]

• 
S. Schmelter, S. Knotek, M. Olbrich, A. Fiebach;M. Bär
Metrologia,
58(1),
014003,
2021.
[DOI: 10.1088/16817575/abd1c9]

• 
S. Schmidt, S. Flassbeck, S. Schmelter, E. Schmeyer, M. E. Ladd;S. Schmitter
Magnetic Resonance in Medicine,
85(6),
31543168,
2021.
[DOI: 10.1002/mrm.28641]

• 
M. Olbrich, E. Schmeyer, M. Bär, M. Sieber, K. Oberleithner;S. Schmelter
Flow Measurement and Instrumentation,
76
101814,
2020.

• 
M. Olbrich, M. Bär, K. Oberleithner;S. Schmelter
Statistical characterization of horizontal slug flow using snapshot proper orthogonal decomposition.
International Journal of Multiphase Flow,
134
103453,
2020.

• 
S. Schmelter, M. Olbrich, E. Schmeyer;M. Bär
Flow Measurement and Instrumentation,
73
101722,
2020.

• 
S. Schmelter, M. Olbrich, E. Schmeyer;M. Bär
Proceedings of the 18th International Flow Measurement Conference FLOMEKO 2019,
2019.

• 
M. Olbrich, E. Schmeyer, M. Bär, M. Sieber, K. Oberleithner;S. Schmelter
Proceedings of the 18th International Flow Measurement Conference FLOMEKO 2019,
2019.

• 
L. Riazy, T. Schäffter, M. Olbrich, J. A. Schueler, F. v. KnobelsdorffBrenkenhoff, T. Niendorf;J. SchulzMenger
Porous medium 3D flow simulation of contrast media washout in cardiac MRI reflects myocardial injury.
Magnetic Resonance in Medicine,
2019.
[DOI: 10.1002/mrm.27756]
(advance online publication)

• 
M. Olbrich, E. Schmeyer, L. Riazy, K. Oberleithner, M. Bär;S. Schmelter
J. Phys.: Conf. Series,
1065(9),
092015,
2018.

• 
S. Schmelter, M. Olbrich, E. Schmeyer;M. Bär
Proceedings of the North Sea Flow Measurement Workshop 2018,
2018.

• 
M. Straka, A. Fiebach, T. Eichler;C. Koglin
Flow Measurement and Instrumentation,
60
124133,
2018.

• 
A. Weissenbrunner, A. Fiebach, M. Juling;P. U. Thamsen
Eccomas Proceedia UNCECOMP,
(5393),
576587,
2017.
[DOI: 10.7712/120217.5393.16913]

• 
A. Fiebach, E. Schmeyer, S. Knotek;S. Schmelter
Proceedings of the 17th International Flow Measurement Conference FLOMEKO 2016,
2016.

• 
S. Knotek, A. Fiebach;S. Schmelter
Proceedings of the 17th International Flow Measurement Conference FLOMEKO 2016,
2016.

• 
A. Weissenbrunner, A. Fiebach, S. Schmelter, M. Bär, P. Thamsen;T. Lederer
Flow Measurement and Instrumentation,
2016.

• 
S. Schmelter, A. Fiebach;A. Weissenbrunner
Polynomchaos zur Unsicherheitsquantifizierung in Strömungssimulationen für metrologische Anwendungen.
tmTechnisches Messen,
83(2),
7176,
2016.

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G. Lindner, S. Schmelter, R. Model, A. Nowak, V. Ebert;M. Bär
J. Fluids Eng,
138(3),
031302,
2016.
[DOI: 10.1115/1.4031380]

• 
A. Weissenbrunner, A. Fiebach, S. Schmelter, M. Straka, M. Bär;T. Lederer
Proceedings of Imeko 2015 XXI World Congress Measurement in Research and Industry,
2015.

• 
S. Schmelter, A. Fiebach, R. Model;M. Bär
Int. J. Comp. Fluid. Dyn.,
29(68),
411422,
2015.

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G. Wendt, R. Jost, S. Schmelter;D. Werner
Technische Sicherheit,
11
1317,
2014.

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S. Schmelter, R. Model, G. Wendt;M. Bär
Proceedings of Flomeko 2013 16th International Flow Measurement Conference,
2013.

• 
K. Jousten, S. Pantazis, J. Buthig, R. Model, M. Wüest;J. Iwicki
Vacuum,
100
1417,
2013.
[DOI: 10.1016/j.vacuum.2013.07.037]

• 
R. Model, S. Schmelter, G. Lindner;M. Bär
In F. Pavese, M. Bär, J.R. Filtz, A. B. Forbes, L. Pendrill and K. Shirono, editor,
Volume 84
Publisher: World Scientific, New Jersey,
2012.

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H. Förster, W. Günther, G. Lindner;R. Model
Technische Sicherheit,
1
1827,
2011.

• 
S. Schmelter, G. Lindner, G. Wendt;R. Model
AIP Conf. Proc.
Volume 1389
, page 106109
2011.

• 
R. Model;U. Hammerschmidt
Thermal Conductivity 26/Thermal Expansion 14,
346357,
2005.

• 
R. Model
International Journal of Thermophysics,
26(1),
165178,
2005.
[DOI: 10.1007/s1076500523631]
