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An adaptive approach for prediction of propellant feedline dynamics in fluid network

Sivaguru S. Ravindran (Department of Mathematical Sciences, University of Alabama in Huntsville, Huntsville, Alabama, USA)
Alok K. Majumdar (Department of Propulsion System, NASA Marshall Space Flight Center, Huntsville, Alabama, USA)

International Journal of Numerical Methods for Heat & Fluid Flow

ISSN: 0961-5539

Article publication date: 20 June 2018

Issue publication date: 8 August 2018

139

Abstract

Purpose

This paper aims to propose an adaptive unstructured finite volume procedure for efficient prediction of propellant feedline dynamics in fluid network.

Design/methodology/approach

The adaptive strategy is based on feedback control of errors defined by changes in key variables in two subsequent time steps.

Findings

As an evaluation of the proposed approach, two feedline dynamics problems are formulated and solved. First problem involves prediction of pressure surges in a pipeline that has entrapped air and the second is a conjugate heat transfer problem involving prediction of chill down of cryogenic transfer line. Numerical predictions with the adaptive strategy are compared with available experimental data and are found to be in good agreement. The adaptive strategy is found to be efficient and robust for predicting feedline dynamics in flow network at reduced CPU time.

Originality/value

This study uses an adaptive reduced-order network modeling approach for fluid network.

Keywords

Acknowledgements

The work of S.S. Ravindran was supported in part by NASA grant #NNM16AA06A. This author was also supported in part by a grant from NASA Tech Excellence Program. The part of the work was conducted at Marshall Space Flight Center, Huntsville, Alabama, in the ER43/Thermal Analysis Branch. The authors would like to thank the Thermal Analysis Branch for their support.

Citation

Ravindran, S.S. and Majumdar, A.K. (2018), "An adaptive approach for prediction of propellant feedline dynamics in fluid network", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 28 No. 6, pp. 1335-1351. https://doi.org/10.1108/HFF-04-2017-0162

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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