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Fluid mechanics in food engineering

Accepted version
Peer-reviewed

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Type

Article

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Authors

Wilson, David Ian 
Chew, Yong Min John 

Abstract

Fluid mechanics underpins many aspects of food engineering. The capacity to model and simulate these has grown primarily via developments in codes and numerical methods in other fields. Newtonian flows, including free surfaces, are now handled routinely. The complexity and diversity of foods - particularly multiphase materials - present significant challenges in terms of (i) capturing detail at appropriate length scales; (ii) rheology; and (iii) devising reduced-order models that are both tractable and capture key features of the flow. Multiscale modelling approaches offer one route. Machine learning algorithms offer opportunities to handle large datasets, and reduce the dimensionality and order of modelling in fluid mechanics. Whether these can accurately predict physically meaningful flow phenomena even for well-defined problems remains to be seen.

Description

Keywords

30 Agricultural, Veterinary and Food Sciences, 3006 Food Sciences, Machine Learning and Artificial Intelligence, Bioengineering

Journal Title

CURRENT OPINION IN FOOD SCIENCE

Conference Name

Journal ISSN

2214-7993
2214-8000

Volume Title

Publisher

Elsevier BV