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Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes

Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipesaf Ovadia Shoham
Bag om Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes

The objectives of this book are twofold: to provide insight and understanding of two-phase flow phenomena and to develop analytical tools for either designing two-phase flow systems or conducting research in this area. The traditional approach for two-phase flow prediction was based on the development of an empirical correlation from experimental data. This book presents the recent approach, in which mathematical mechanistic models are developed, based on the physical phenomena, to predict two-phase flow behavior. The models can be verified and refined with limited experimental data. However, as these models incorporate the physical phenomena and the important flow variables, they can be extended to different operational conditions and can enable scaleup with significant confidence.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9781555631079
  • Indbinding:
  • Paperback
  • Sideantal:
  • 408
  • Udgivet:
  • 27. juni 2013
  • Størrelse:
  • 254x178x21 mm.
  • Vægt:
  • 703 g.
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Leveringstid: 2-3 uger
Forventet levering: 23. januar 2025

Beskrivelse af Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes

The objectives of this book are twofold: to provide insight and understanding of two-phase flow phenomena and to develop analytical tools for either designing two-phase flow systems or conducting research in this area. The traditional approach for two-phase flow prediction was based on the development of an empirical correlation from experimental data. This book presents the recent approach, in which mathematical mechanistic models are developed, based on the physical phenomena, to predict two-phase flow behavior. The models can be verified and refined with limited experimental data. However, as these models incorporate the physical phenomena and the important flow variables, they can be extended to different operational conditions and can enable scaleup with significant confidence.

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