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DTSTAMP:20260421T090512Z
LOCATION:Plenary Room (Bldg. 6 - 001)
DTSTART;TZID=Europe/Stockholm:20260629T194500
DTEND;TZID=Europe/Stockholm:20260629T194600
UID:submissions.pasc-conference.org_PASC26_sess124_pos107@linklings.com
SUMMARY:A Machine Learning Framework for CFD Applications
DESCRIPTION:Masumeh Gholamisheeri, Harry Durnberger, and Tim Powell (STFC)
 \n\nIn the present study, an automated framework is prepared that contains
  two modules, Computational Fluid Dynamics (CFD) simulations and surrogate
  modelling. CFD simulations are performed to model and make thermal assess
 ment of battery air cooling in different air stream conditions (i.e. strea
 m velocity and initial temperature) and various battery cell generated hea
 t. OpenFoam open-source code is used for the simulations. The surrogate mo
 delling is used with a primary aim to train a predictive model that approx
 imates the outcome of the CFD simulation based on previously provided CFD 
 data. Due to limited number of CFD results and having the results as a smo
 oth function of the input data, surrogate model is built based on Gaussian
  Process Regression (GPR). Quantities of Interest (QoI) were inlet velocit
 y, initial temperature and heat source for battery heat generation. This m
 ethod can also be useful for exploring large parameter spaces, performing 
 sensitivity analyses, or enabling faster design iterations, where running 
 many full CFD simulations would be too costly or time-consuming. It is not
 eworthy that machine learning is applied to the averaged data and not the 
 instantaneous fluctuations. Hence, the predictions are made for the trend 
 and not the seasonality of the result.\n\n
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