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Wall-modeled large eddy simulation (LES) is a practical tool for solving wall-bounded flows with less computational cost by avoiding the explicit resolution of the near-wall region. However, its use is limited in flows that have high non-equilibrium effects like separation or transition. This study aims to present a novel methodology of using high-fidelity data and machine learning (ML) techniques to capture these non-equilibrium effects.

A precursor to this methodology has already been tested in Radhakrishnan et al. (2021) for equilibrium flows using LES of channel flow data. In the current methodology, the high-fidelity data chosen for training includes direct numerical simulation of a double diffuser that has strong non-equilibrium flow regions, and LES of a channel flow. The ultimate purpose of the model is to distinguish between equilibrium and non-equilibrium regions, and to provide the appropriate wall shear stress. The ML system used for this study is gradient-boosted regression trees.

The authors show that the model can be trained to make accurate predictions for both equilibrium and non-equilibrium boundary layers. In example, the authors find that the model is very effective for corner flows and flows that involve relaminarization, while performing rather ineffectively at recirculation regions.

Data from relaminarization regions help the model to better understand such phenomenon and to provide an appropriate boundary condition based on that. This motivates the authors to continue the research in this direction by adding more non-equilibrium phenomena to the training data to capture recirculation as well.

The concept of hinterland is changing with a globalized economy, new needs between airports, stakeholders and the tourist destination challenge new dimensions of operation in the territory. Identifying new factors and actors in the influence zone of the hinterland will allow us to stage the importance of airports in the regional economy and the positive effects derived from these. The aim of this paper is to analyse the hinterland of Málaga Costa del Sol airport and its territorial and economic dimensions. Moreover, to provide an updated and clearer definition of hinterland, assuming future implications for airport operators, management of tourist destination by destination marketing organizations and scholars and practitioners interested in this topic. The results revealed that Málaga’s airport is modifying the hinterland of airport and its area of influence in economic and urban development terms.