Predictivity: Definition and Application to a Tangentially FIred Combustion System

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Title Predictivity: Definition and Application to a Tangentially FIred Combustion System
Creator Parra-Alvarez, J.
Contributor Isaac, B., Smith, S., Smith, P.
Date 2018-09-17
Subject Machine-learning, V&UQ, Oxy-coal combustion
Description Paper from the AFRC 2018 conference titled Predictivity: Definition and Application to a Tangentially FIred Combustion System
Abstract Quantification of uncertainty and prediction under uncertainty are playing a bigger; role in simulation science nowadays than it was 10 years ago. Since the seminal work of; Kennedy-O'hagan1 it is understood that point-value estimates from model predictions,; lack real engineering value if not provided with valid intervals in which the model is; capable of forecasting. In this work, we used tools commonly used in artificial intelligence; and machine learning in order to define predictivity and determine the predictive; distribution function for the input parameters that control a tangentially-fired coal; combustion boiler quantities of interest (QoI's): heat flux to the wall, gas temperature; and oxygen concentration. The predictive posterior distribution informs the modelers; and the experimentalist the allowed ranges of simulation input parameters in which the; model produces useful predictions.
Type Event
Format application/pdf
Rights No copyright issues exist
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Metadata Cataloger Catrina Wilson
ARK ark:/87278/s6p3185p
Setname uu_afrc
Date Created 2018-12-12
Date Modified 2018-12-12
ID 1389186
Reference URL https://collections.lib.utah.edu/ark:/87278/s6p3185p