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A Data-Centric Approach to Loss Mechanisms

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Senior, Alistair C 
Miller, Robert J 


jats:titleAbstract</jats:title> jats:pBreaking down the total loss in a turbomachine into a number of low order physical models is a powerful way of developing loss models. A problem, however, is that in complex flows it is often not clear how to put together these physical models in the correct way. An additional problem is that the designer often doesn’t know whether all of the underlying low order physical models are correct or whether a more general, and accurate, physical model is yet to be discovered. In practice this problem often leads to loss models of low accuracy, which only work in a limited part of the overall design space. This paper shows that machine learning can be used to augment a designer in the process of developing loss models for complex flows. It is shown that it helps both in understanding how to put together the underlying physical models in a more accurate way, and also in discovering new, and more general, underlying physical models. The paper illustrates the new method using the problem of compressor and turbine profile loss. This problem was chosen because it is well understood and therefore is a good way of validating the new method. However, surprisingly the new method is shown to be able to develop a new profile loss model which is more accurate than previous models. This is shown to have been achieved by the machine learning finding a new, more general, underlying model for trailing edge mixing loss.</jats:p>



40 Engineering, 33 Built Environment and Design, 3303 Design

Journal Title

Volume 13A: Turbomachinery — Axial Flow Fan and Compressor Aerodynamics

Conference Name

ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition

Journal ISSN

Volume Title


American Society of Mechanical Engineers
Engineering and Physical Sciences Research Council (2114731)
EPSRC (2114731)