Digitalization in Thermodynamics

Esther Forte, Fabian Jirasek, Michael Bortz, Jakob Burger, Jadran Vrabec, Hans Hasse

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations

Abstract

Digitalization is about data and how they are used. This has always been a key topic in applied thermodynamics. In the present work, the influence of the current wave of digitalization on thermodynamics is analyzed. Thermodynamic modeling and simulation is changing as large amounts of data of different nature and quality become easily available. The power and complexity of thermodynamic models and simulation techniques is rapidly increasing, and new routes become viable to link them to the data. Machine learning opens new perspectives, when it is suitably combined with classical thermodynamic theory. Illustrated by examples, different aspects of digitalization in thermodynamics are discussed: strengths and weaknesses as well as opportunities and threats.

Original languageEnglish
Pages (from-to)201-214
Number of pages14
JournalChemie-Ingenieur-Technik
Volume91
Issue number3
DOIs
StatePublished - Mar 2019

Keywords

  • Digitalization
  • Machine learning
  • Pareto optimization
  • Thermodynamic models
  • Uncertainty propagation

Fingerprint

Dive into the research topics of 'Digitalization in Thermodynamics'. Together they form a unique fingerprint.

Cite this