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Contents
• (Flow) metrology in short
• Numerical methods in flow metrology
• Calculation examples
• Conclusions
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Short introduction to metrology
• What about if…
• …one litre would not be equal?
• …the mass of one kilogram would vary?
• …nobody knows the exact time?
• ...the mixture of anesthesia gases would be unknown?
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Metrology - the science of measurement
• The definition of internationally accepted units of a measurement.
• The realisation of units of measurement by scientific methods.
• The establishment of traceability chains in documenting the accuracy of a measurement.
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Metrology - the science of measurement
• A traceability (or calibration) chain
• the documented relationship between the measurement result and a measurement standard of a national standard laboratory.
• Measurement result for mass flow
ccr UCIm
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Metrology - the science of measurement
An example of a calibration chain: small gas flow measurements in Finland.
M IK E S F lowC a lib ra tio n s fo r cu sto m e rs
M IK E S F lowL F E ca lib ra tio n sys tem
M IK E S F lowC a lib ra tio n s fo r cu sto m e rs
M IK E S F lowD W S ca lib ra tion sys tem
M IK E S M a ssN a tion a l s ta nd a rd fo r m a ss
M IK E S T im e a n d F re qu e n cyN a tion a l s ta nd a rd fo r t im e
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Numerical methods in flow metrology
• Velocity and temperature field before the meter
• Velocity and temperature field inside the meter
• Numerical simulations in evaluation of the measurement uncertainty
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Velocity and temperature field before the flow meter
• The indication of a flow meter might be depended on the velocity profile at upstream of the flow meter.
• Investigation of different flow profiles generated by
• 90o bend,
• double-elbow out of plane,
• diffuser or nozzle.
• Turbulent pipe flow
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Velocity and temperature field inside the meter
• More complex geometry
• Usually time depended situation
• Turbulent or laminar flow
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Monte Carlo simulation in evaluation of the measurement uncertainty
• Usually applied for non-linear measurement models
• Calculation
• Measurement model
• probability density functions
• model evaluation
• approximate distribution function
• estimate of the output quantity value and associated standard uncertainty.
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Simulation examples
• The flow conditioner before a turbine flow meter
• Study the efficiency of a new type compounded flow conditioner,
• evaluation and validation.
• A Vortex flow meter with an ultrasonic barrier
• Optimise the bluff body shape,
• minimise the pressure loss.
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The flow conditioner before a turbine flow meter
Straight length, double-elbow, diffuser and straight length
Flow conditioner at right and at the left the computational domain of it
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The flow conditioner before a turbine flow meter
The computatio-nal grid for gene-rating the veloci-ty field for the flow conditionerComputational grid for flow conditioner
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The flow conditioner before a turbine flow meter
• Calculation at volume flow rate 1000 m3/h
• RANS and k - RNG turbulence model
• Wall function
• Fully implicit (time) discretisation
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The flow conditioner before a turbine flow meter
Contours of axial and radial velocity vectors 10D upstream of the flow conditioner
Without conditioner
With single perforated plate
With compounded conditioner
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A Vortex flow meter with an ultrasonic barrier
• Traditionally pressure sen-sors are used to detect vor-tices generated by a bluff body pressure losses.
• New construction, where an ultrasonic barrier is used instead of pressure sensors smaller bluff body smaller pressure losses.
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A Vortex flow meter with an ultrasonic barrier
• Time depended simulation and structured grid
• Spatial discretisation
• convective terms: upwind biased discretisation,
• viscous terms: central difference.
• Time discretisation
• Runge-Kutta method or
• approximate factorisation.
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A Vortex flow meter with an ultrasonic barrier
Comparison between measured and simulated pressure fields behind the bluff body
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A Vortex flow meter with an ultrasonic barrier
Pressure field of the simulated vortex street of a triangular bluff body with a width of 24 mm facing the edge of the inflow
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Conclusions• Lower costs and better
data analysis
• Installation effects and measurement process
• Uncertainty calculation
• Evaluation of the magnitude of uncertainty components
• Mainly in universities, not yet in NMIs
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