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SUMMARY:Uncertainty Quantification in Thermofluids: Key Tools\, Applicatio
 ns and Perspectives - Pranay Seshadri\, Cambridge EDC
DTSTART:20180516T100000Z
DTEND:20180516T110000Z
UID:TALK105973@talks.cam.ac.uk
CONTACT:Mari Huhtala
DESCRIPTION:Uncertainties are ubiquitous throughout the field of applied t
 hermofluids. Thus\, developing techniques to identify and rigorously quant
 ify these uncertainties is of significant interest to companies up and dow
 n the U.K.\n\nThe field of uncertainty quantification has experienced a Re
 naissance of sorts over the past decade. It has become much more than the 
 science of running deterministic computer simulations at different boundar
 y conditions. It has grown to encompass novel techniques for exploring par
 ameter sensitivities\, for finding dimension reducing subspaces\, and for 
 gaining insight into the underlying thermofluid mechanics of simulation-dr
 iven problems using machine learning approaches. Succinctly stated\, its p
 hysical insight on a tight budget leading to statistically sound decision 
 making. \n\nIn this rather broad talk\, I present three different problems
  that reflect the _levels_ at which uncertainties need to be quantified an
 d the methods used. First\, there is the *sensor-level*\, where one tries 
 to convert a voltage change to a pressure or temperature. Uncertainties he
 re are grouped into random and systematic and need to be properly accounte
 d for by understanding the material properties of the sensor\, its range\,
  and calibration particulars. Next\, there is the *model-level*\, where on
 e typically uses a spatial average of a set of temperatures and pressures 
 as an input to estimate the performance of a component—the output of the
  model. Methods that use response surfaces are typically adopted here for 
 reducing the computational cost of running the models. Finally\, the outpu
 t of these models (and their uncertainties) inform a *system-level* exchan
 ge rate matrix\, where one tries to understand which uncertainties impact 
 whole-system cost and efficiency. Linearized or first-order methods are us
 ually adopted at this level for rapid analysis.\n\nI close this talk by of
 fering a glimpse of the techniques we are currently developing to address 
 uncertainty quantification at all three of the aforementioned levels. \n
LOCATION:Sir Arthur Marshalls\, CUED
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