Numerical Evaluation of Entropy Generation in Isolated Airfoils and Wells Turbines

In recent years, a number of authors have studied entropy generation in Wells turbines. This is potentially a very interesting topic, as it can provide important insights into the irreversibilities of the system, as well as a methodology for identifying, and possibly minimizing, the main sources of loss. Unfortunately, the approach used in these studies contains some crude simplifications that lead to a severe underestimation of entropy generation and, more importantly, to misleading conclusions. This paper contains a re-examination of the mechanisms for entropy generation in fluid flow, with a particular emphasis on RANS equations. An appropriate methodology for estimating entropy generation in isolated airfoils and Wells turbines is presented. Results are verified for different flow conditions, and a comparison with theoretical values is presented.Regione Autonoma Sardegna (funding for University of Cagliari co-authors

Dimension Reduction via Gaussian Ridge Functions

Ridge functions have recently emerged as a powerful set of ideas for subspace-based dimension reduction. In this paper we begin by drawing parallels between ridge subspaces, sufficient dimension reduction and active subspaces, contrasting between techniques rooted in statistical regression and those rooted in approximation theory. This sets the stage for our new algorithm that approximates what we call a Gaussian ridge function---the posterior mean of a Gaussian process on a dimension-reducing subspace---suitable for both regression and approximation problems. To compute this subspace we develop an iterative algorithm that alternates between optimizing over the Stiefel manifold to compute the subspace and optimizing the hyperparameters of the Gaussian process. We demonstrate the utility of the algorithm on two analytical functions, where we obtain near exact ridge recovery, and a turbomachinery case study, where we compare the efficacy of our approach with three well-known sufficient dimension reduction methods: SIR, SAVE, and CR. The comparisons motivate the use of the posterior variance as a heuristic for identifying the suitability of a dimension-reducing subspace.The first author's work was supported by a Rolls-Royce fellowship. The second author's work was supported by Magdalene College, Cambridg