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Estimating convective heat transfer coefficients and uncertainty thereof using the general uncertainty management (GUM) framework

Håkansson, Andreas LU (2019) In Journal of Food Engineering 263. p.53-62
Abstract

The rate of convective heat transfer is often required to design and optimize food engineering processes. The temperature sensor technique is the most widely used method to measure convective heat transfer coefficients (h-values). Being able to reliably determine h-values is becoming increasingly important with the advances in sophisticated food engineering modelling using it as an empirical input. This contribution uses the general uncertainty management (GUM) framework to analyze the standard uncertainty resulting in the h-value obtained with sensor experiments. It is demonstrated (using a pilot scale air blast freezer example) that the reliability in estimating h-values is often overestimated when using the methodology suggested in... (More)

The rate of convective heat transfer is often required to design and optimize food engineering processes. The temperature sensor technique is the most widely used method to measure convective heat transfer coefficients (h-values). Being able to reliably determine h-values is becoming increasingly important with the advances in sophisticated food engineering modelling using it as an empirical input. This contribution uses the general uncertainty management (GUM) framework to analyze the standard uncertainty resulting in the h-value obtained with sensor experiments. It is demonstrated (using a pilot scale air blast freezer example) that the reliability in estimating h-values is often overestimated when using the methodology suggested in literature. Moreover, the uncertainty can be reduced substantially by an uncertainty-minimizing data analysis method (supplied with the study). This uncertainty analysis also provides a method for a priori determining under which conditions the temperature sensor technique will result in reliable estimations for a given food engineering application.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Baking, Convective heat transfer, Convective heat transfer coefficient, Drying, Freezing, Frying, Monte Carlo simulation, Uncertainty management
in
Journal of Food Engineering
volume
263
pages
10 pages
publisher
Elsevier
external identifiers
  • scopus:85066466870
ISSN
0260-8774
DOI
10.1016/j.jfoodeng.2019.05.031
language
English
LU publication?
yes
id
f895a0a6-2e1a-4074-bdb2-d56c59e037d3
date added to LUP
2019-06-17 06:55:53
date last changed
2020-12-22 03:47:02
@article{f895a0a6-2e1a-4074-bdb2-d56c59e037d3,
  abstract     = {<p>The rate of convective heat transfer is often required to design and optimize food engineering processes. The temperature sensor technique is the most widely used method to measure convective heat transfer coefficients (h-values). Being able to reliably determine h-values is becoming increasingly important with the advances in sophisticated food engineering modelling using it as an empirical input. This contribution uses the general uncertainty management (GUM) framework to analyze the standard uncertainty resulting in the h-value obtained with sensor experiments. It is demonstrated (using a pilot scale air blast freezer example) that the reliability in estimating h-values is often overestimated when using the methodology suggested in literature. Moreover, the uncertainty can be reduced substantially by an uncertainty-minimizing data analysis method (supplied with the study). This uncertainty analysis also provides a method for a priori determining under which conditions the temperature sensor technique will result in reliable estimations for a given food engineering application.</p>},
  author       = {Håkansson, Andreas},
  issn         = {0260-8774},
  language     = {eng},
  pages        = {53--62},
  publisher    = {Elsevier},
  series       = {Journal of Food Engineering},
  title        = {Estimating convective heat transfer coefficients and uncertainty thereof using the general uncertainty management (GUM) framework},
  url          = {http://dx.doi.org/10.1016/j.jfoodeng.2019.05.031},
  doi          = {10.1016/j.jfoodeng.2019.05.031},
  volume       = {263},
  year         = {2019},
}