Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows

Roberto Pacciani, Michele Marconcini, Francesco Bertini, Simone Rosa Taddei, Ennio Spano, Yaomin Zhao, Harshal D. Akolekar, Richard D. Sandberg and Andrea Arnone
Additional contact information
Roberto Pacciani: Department of Industrial Engineering, University of Florence, Via Santa Marta, 3, 50139 Florence, Italy
Michele Marconcini: Department of Industrial Engineering, University of Florence, Via Santa Marta, 3, 50139 Florence, Italy
Francesco Bertini: GE Avio Aero, Via I maggio 99, 10040 Rivalta di Torino, Italy
Simone Rosa Taddei: GE Avio Aero, Via I maggio 99, 10040 Rivalta di Torino, Italy
Ennio Spano: GE Avio Aero, Via I maggio 99, 10040 Rivalta di Torino, Italy
Yaomin Zhao: Center for Applied Physics and Technology, HEDPS, College of Engineering, Peking University, Beijing 100871, China
Harshal D. Akolekar: Department of Mechanical Engineering, University of Melbourne, Melbourne, VIC 3010, Australia
Richard D. Sandberg: Department of Mechanical Engineering, University of Melbourne, Melbourne, VIC 3010, Australia
Andrea Arnone: Department of Industrial Engineering, University of Florence, Via Santa Marta, 3, 50139 Florence, Italy

Energies, 2021, vol. 14, issue 24, 1-17

Abstract: This paper presents an assessment of machine-learned turbulence closures, trained for improving wake-mixing prediction, in the context of LPT flows. To this end, a three-dimensional cascade of industrial relevance, representative of modern LPT bladings, was analyzed, using a state-of-the-art RANS approach, over a wide range of Reynolds numbers. To ensure that the wake originates from correctly reproduced blade boundary-layers, preliminary analyses were carried out to check for the impact of transition closures, and the best-performing numerical setup was identified. Two different machine-learned closures were considered. They were applied in a prescribed region downstream of the blade trailing edge, excluding the endwall boundary layers. A sensitivity analysis to the distance from the trailing edge at which they are activated is presented in order to assess their applicability to the whole wake affected portion of the computational domain and outside the training region. It is shown how the best-performing closure can provide results in very good agreement with the experimental data in terms of wake loss profiles, with substantial improvements relative to traditional turbulence models. The discussed analysis also provides guidelines for defining an automated zonal application of turbulence closures trained for wake-mixing predictions.

Keywords: low-pressure turbine; wake mixing; transition; machine learning; explicit algebraic Reynolds stress model; laminar kinetic energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/24/8327/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/24/8327/ (text/html)

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:gam:jeners:v:14:y:2021:i:24:p:8327-:d:699482

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().