![]() ![]() In principle, when particles of fluid have a property of a streamlined flow, and they follow a smooth path without interfering with one another, it is called laminar flow. The distinction between laminar and turbulent flow is eminently important in aerodynamics and hydrodynamics because the type of flow has a profound impact on how momentum and heat are transferred. Laminar-Turbulent flow is a fluid dynamics phenomenon that refers to the motion of particles as they move through a substance. 1.3 lists the contributions of our study to overcome the issues mentioned in Sect. 1.2 details the issues in the automation of laminar-turbulent flow localisation, and Sect. 1.1 explains the laminar-turbulent flow phenomenon for those who has no fluid dynamics background, Sect. In the light of the workflow above, Sect. 3.2) to the design team back.įinally, the design team considers those processed images and investigation results to decide if the original design is satisfactory or if it needs some revisions. That is why our main focus is to optimise this step in this study.Īfter a long analysis and annotation period on those measurement images, the investigation team delivers the results (such as laminar-turbulent boundaries, or coordinate points of those regions corresponding to the reference markers as explained in Sect. Nevertheless, the main issue here, each time the experts may need to re-calibrate the parameters in the processing tool because those parameters may become obsolete if the observation data varies due to changing conditions during the course of the measurements. ![]() However, those investigations might be entirely manual, or semi-automatic such that experts can draw the flow boundary lines or they can benefit from some image processing tools to determine the parameters which will be later utilised for separating the laminar-turbulent regions. In the fourth step, experts in the investigation team determine the laminar-turbulent regions on the measurement images. In the third step, thermographic measurements are collected from the region of interest on the aircraft body, as some of the examples can be seen in Sect. The installation environment might be in wind tunnels on the ground, or on flight as detailed in Sect. In the second step, a new measurement system is installed to examine the devised or revised aircraft body components. In the first step, the design team devises or revises wings, stabilisers or blades. Finally, in order to improve the robustness of the proposed architecture, a self-supervised learning strategy is adopted by exploiting 23.468 non-labelled laminar-turbulent flow observations.īefore elaborating on the objective, having a clear picture of high-level workflow for design, test and analysis iterations would be useful as illustrated in Fig. Contrary to the existing automatic flow localisation techniques in the literature which mostly rely on homogeneous and clean data, the competency of our proposed approach in automatic flow segmentation is examined on the mixture of diverse thermographic observation sets exposed to different levels of noise. For this aim, a flow segmentation architecture composed of two consecutive encoder-decoder is proposed, which is called Adaptive Attention Butterfly Network. This study investigates whether it is possible to separate flow regions with current deep learning techniques. ![]() Some recent efforts have dealt with the automatic localisation of laminar-turbulent flow but they are still in infancy and not robust enough in noisy environments. Many of the laminar-turbulent flow localisation techniques are strongly dependent upon expert control even-though determining the flow distribution is the prerequisite for analysing the efficiency of wing & stabiliser design in aeronautics. ![]()
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