Modeling of Coupled Turbulent Channel-Porous Media Flow Through a Deep Autoencoder-Echo State Network Framework

Xu Chu (), Sandeep Pandey, Yanchao Liu and Bernhard Weigand
Additional contact information
Xu Chu: University of Stuttgart, Cluster of Excellence SimTech
Sandeep Pandey: Technische Universität Ilmenau, Institute of Thermodynamics and Fluid Mechanics
Yanchao Liu: Institute of Aerospace Thermodynamics (ITLR)
Bernhard Weigand: Institute of Aerospace Thermodynamics (ITLR)

A chapter in High Performance Computing in Science and Engineering '23, 2026, pp 319-333 from Springer

Abstract: Abstract In this study, we propose a novel approach, namely the combined Convolutional Deep Autoencoder–Echo State Network (CDAE-ESN) model, for the analysis and forecasting of dynamics and low-order statistics in coupled turbulent channel-porous media flows. Such systems find wide applications in industrial settings, including transpiration cooling and smart interface engineering. However, the complex geometry of coupled flow systems presents additional challenges for purely data-driven models. Our results demonstrate that the integration of deep autoencoder and echo state network techniques enables effective modeling and prediction of dominant flow behaviors, particularly within the porous domain exhibiting laminar regimes. To enhance the model’s applicability across a broader range of data domains, we further employ fine-tuning on a dataset encompassing varying porosities. The achieved average statistics exhibit a reasonable agreement, underscoring the efficacy of our proposed approach.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-91312-9_22

Ordering information: This item can be ordered from
http://www.springer.com/9783031913129

DOI: 10.1007/978-3-031-91312-9_22

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().