A Data-Centric Approach to Loss Mechanisms

Abstract

Breaking down the total loss in a turbomachine into a number of low order, physical models is a powerful way of developing loss models for informing design decisions. Better loss models lead to better design decisions. A problem, however, is that in complex flows it is often not clear how to break a flow down physically without making assumptions. An additional problem is that the designer often doesn't know what assumptions should be made to derive the most accurate and general physical models. 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 is able to help a designer discover new, more accurate and general, physical models, highlighting to a designer what assumptions should be made to retain the physics important to the problem. 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 and general 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.