Knowledge enhanced modeling of low-pressure turbine profile loss by combining physical-based and data-driven methods

Changxing Liu, Zhengping Zou, Chao Fu, Jun Zeng and Wei Li

Energy, 2025, vol. 320, issue C

Abstract: Appropriate modeling methods are essential for establishing models of energy systems, especially when the physical mechanisms are complex. A modeling method organically combined physical-based and data-driven is developed to ensure accuracy, generalization and interpretability while keeping a reasonable amount of required data. Utilizing the method, a comprehensive loss model for low-pressure turbine profile is established. The model considers freestream turbulence intensity and periodic wakes and accounts for flow modes of separation-reattachment and separation-non-reattachment. The model not only predicts loss at design and off-design points but also provides transition and reattachment locations. For physics-based modeling, the flow is spatially and temporally divided and modeled. In the data-driven part, over 450 sets of experimental data are utilized for coefficients optimization and validation. The model demonstrates high accuracy, with a relative error of less than 8 % for trailing edge momentum thickness and less than 3 % and 4 % for transition and reattachment locations, respectively. In the context of low-pressure turbine profile design guidelines, a front-loaded and high leading-edge load design is recommended. However, excessive leading-edge load can lead to increased loss, the leading-edge load integral (LEI) is suggested not to exceed 0.6. An appropriate front-loaded and high LEI design can achieve optimal performance.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422500756X
Full text for ScienceDirect subscribers only

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:eee:energy:v:320:y:2025:i:c:s036054422500756x

DOI: 10.1016/j.energy.2025.135114

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().