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首页> 外文期刊>Computer Methods in Applied Mechanics and Engineering >Development of an algorithm for reconstruction of droplet history based on deposition pattern using computational fluid dynamics and convolutional neural network
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Development of an algorithm for reconstruction of droplet history based on deposition pattern using computational fluid dynamics and convolutional neural network

机译：基于计算流体动力学和卷积神经网络，基于沉积模式的液滴历史重建算法的开发

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摘要

Liquids are one of the fundamental components for the functionality of a wide range of mechanical equipment (e.g. for lubrication, cooling, hydraulic, etc.). However, they could lead to secondary issues such as corrosion or contamination, caused, for instance, due to leakage of the liquid. As investigating the equipment during their operation is not always possible (e.g. for rotary machinery or in case of high-temperature working conditions), locating the origin of the leakage could be a challenging task, especially if the only traces left behind are a few droplets. In the present work, an algorithm for prediction of the leakage position, i.e. position of the injector, is developed. In order to guarantee intelligence and enhance flexibility, the designed algorithm is powered by a machine learning approach, Convolutional Neural Network. The developed algorithm is based upon using different deposition patterns, calculated by numerical simulations, as the input. The robust algorithm is designed to be so intelligent that it could ideally traceback (predict) the leakage location (i.e. reconstruct the droplet history) by only an image of the deposition. (C) 2020 Elsevier B.V. All rights reserved.
机译：液体是各种机械设备的功能的基本组件之一（例如，用于润滑，冷却，液压等）。然而，它们可能导致诸如腐蚀或污染的次要问题，例如由于液体泄漏而导致的腐蚀或污染。由于在操作期间调查设备并不总是可能的（例如用于旋转机械或在高温工作条件的情况下），定位泄漏的起源可能是一个具有挑战性的任务，特别是如果唯一留下的痕迹是几滴。在本作工作中，开发了一种用于预测泄漏位置的算法，即注射器的位置。为了保证智能和增强灵活性，设计的算法由机器学习方法，卷积神经网络供电。开发的算法基于使用不同的沉积图案，通过数值模拟计算为输入。稳健的算法被设计为如此智能，即它可以通过仅沉积的图像来理解（预测）泄漏位置（即重建液滴历史）。 （c）2020 Elsevier B.v.保留所有权利。

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