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Energies
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Transition/Turbulence Models for Turbomachinery Applications
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Transition/Turbulence Models for Turbomachinery Applications

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 8456

Special Issue Editors

Dr. Michele Marconcini

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Florence, via di Santa Marta, 3, 50139 Florence, Italy
Interests: development of computational fluid dynamics (CFD) techniques for steady/unsteady viscous solvers
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Pacciani

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Florence, via di Santa Marta, 3, 50139 Florence, Italy
Interests: computational fluid dynamics; CFD simulation; fluid mechanics; numerical simulation; computational fluid mechanics; numerical modeling aerodynamics; numerical analysis; modeling and simulation; engineering thermodynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Current aerodynamic tools used for the design of turbomachinery components frequently fail to predict the flow details in blade passages. With the performance achieved by the current generation of turbomachinery the design optimization of the components at the flow details level is one of the options the designers have to further improve performance, durability, and environmental impact of future aeroengines and power plants. In consideration of such requirements, the accuracy of RANS/URANS methodologies has become questionable and researchers in the field of CFD are progressively moving towards the study of High-Fidelity approaches in order to gain further insights in turbomachinery flows. On the other hand, due to the excessive computational requirements, DNS and LES approaches are yet far to come for practical design purposes.

Most of the deficiencies of traditional CFD approaches are to be attributed to turbulence and transition models failure. The deployment of three-dimensional time resolved experimental investigations in conjunction with High-Fidelity calculations can result in remarkable new insights into the physics at work in turbomachinery flows. Machine-learning techniques driven by LES, DNS and fine resolved experimental results, appear as enablers to exploit high-fidelity flow data to improve turbulence and transition closures, by including new terms in their formulations or tuning the existing ones without modelling assumptions. 

This Special Issue invites high-quality research papers covering a wide range of topics related to turbulence and transition modelling and measurements. The papers are expected to provide contributions, and data, and ideas for improving the RANS/URANS approaches currently used in turbomachinery design and analysis.

Prof. Dr. Michele Marconcini
Prof. Dr. Pacciani Roberto
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • turbulence modelling
  • transition modelling
  • turbulence measurements
  • turbomachinery flows
  • scale resolving simulations
  • machine learning for turbulence and transition modelling

Published Papers (3 papers)

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Research

17 pages, 1504 KiB  
Article
Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows
by Roberto Pacciani, Michele Marconcini, Francesco Bertini, Simone Rosa Taddei, Ennio Spano, Yaomin Zhao, Harshal D. Akolekar, Richard D. Sandberg and Andrea Arnone
Energies 2021, 14(24), 8327; https://doi.org/10.3390/en14248327 - 10 Dec 2021
Cited by 10 | Viewed by 2569
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Transition/Turbulence Models for Turbomachinery Applications)
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17 pages, 1512 KiB  
Article
Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning
by Harshal D. Akolekar, Fabian Waschkowski, Yaomin Zhao, Roberto Pacciani and Richard D. Sandberg
Energies 2021, 14(15), 4680; https://doi.org/10.3390/en14154680 - 1 Aug 2021
Cited by 25 | Viewed by 2292
Abstract
Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop [...] Read more.
Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of Re2is=100,000. Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model. This is the first known study which makes use of ‘CFD-driven’ machine learning to enhance the transition prediction for a non-canonical flow. Full article
(This article belongs to the Special Issue Transition/Turbulence Models for Turbomachinery Applications)
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20 pages, 34823 KiB  
Article
Large Eddy Simulations of Strongly Non-Ideal Compressible Flows through a Transonic Cascade
by Jean-Christophe Hoarau, Paola Cinnella and Xavier Gloerfelt
Energies 2021, 14(3), 772; https://doi.org/10.3390/en14030772 - 1 Feb 2021
Cited by 9 | Viewed by 2458
Abstract
Transonic flows of a molecularly complex organic fluid through a stator cascade were investigated by means of large eddy simulations (LESs). The selected configuration was considered as representative of the high-pressure stages of high-temperature Organic Rankine Cycle (ORC) axial turbines, which may exhibit [...] Read more.
Transonic flows of a molecularly complex organic fluid through a stator cascade were investigated by means of large eddy simulations (LESs). The selected configuration was considered as representative of the high-pressure stages of high-temperature Organic Rankine Cycle (ORC) axial turbines, which may exhibit significant non-ideal gas effects. A heavy fluorocarbon, perhydrophenanthrene (PP11), was selected as the working fluid to exacerbate deviations from the ideal flow behavior. The LESs were carried out at various operating conditions (pressure ratio and total conditions at inlet), and their influence on compressibility and viscous effects is discussed. The complex thermodynamic behavior of the fluid generates highly non-ideal shock systems at the blade trailing edge. These are shown to undergo complex interactions with the transitional viscous boundary layers and wakes, with an impact on the loss mechanisms and predicted loss coefficients compared to lower-fidelity models relying on the Reynolds-averaged Navier–Stokes (RANS) equations. Full article
(This article belongs to the Special Issue Transition/Turbulence Models for Turbomachinery Applications)
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