Neural network reconstruction of fluid flows from tracer-particle displacements
Neural network reconstruction of fluid flows from tracer-particle displacements
复制标题
根据示踪粒子位移进行流体流动的神经网络重建
DOI：
10.1007/s003480000217
复制
发表时间：
2001
影响因子：
2.4
通讯作者：
G. Labonté
中科院分区：
文献类型：
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作者：
G. Labonté
摘要本文展示了利用人工神经网络对粒子跟踪测速（PTV）数据进行后处理的一些优点。本文研究的是对颗粒图像进行匹配，去除明显的异常值后得到的数据。我们表明，很容易产生简单的反向传播神经网络，它可以过滤剩余的随机噪声并在测量之间进行插值。他们通过执行一种特殊形式的非线性全局回归来做到这一点，这使得他们能够重建照片所覆盖的整个领域的流体流动。这是通过训练这些神经网络来学习流体动力学函数f来获得的，该函数将流体粒子在时间t的位置x映射到时间t + Δt的位置x。当从大约2到4对PTV照片中提供成对匹配的粒子位置（x, x）作为示例时，它们可以以高精度做到这一点。我们表明，无论它们是在精确数据上训练还是在嘈杂数据上训练，它们都能以如此精确的方式学习插值，以至于它们的输出在理论输出的一个像素内。我们通过使用它们来绘制整个流线或流剖面，通过从单个起点迭代来证明它们的准确性。
Abstract We demonstrate some of the advantages of using artificial neural networks for the post-processing of particle-tracking velocimetry (PTV) data. This study is concerned with the data obtained after particle images have been matched and the obvious outliers have been removed. We show that it is easy to produce simple back-propagation neural networks that can filter the remaining random noise and interpolate between the measurements. They do so by performing a particular form of non-linear global regression that allows them to reconstruct the fluid flow for the entire field covered by the photographs. This is obtained by training these neural networks to learn the fluid dynamics function f that maps the position x of a fluid particle at time t to its position X at time t + Δt. They can do so with a high degree of precision when provided with pairs of matching particle positions (x, X) from only about 2 to 4 pairs of PTV photographs as exemplars. We show that whether they are trained on exact or on noisy data, they learn to interpolate with such a precision that their output is within one pixel of the theoretical output. We demonstrate their accuracy by using them to draw whole streamlines or flow profiles, by iteration from a single starting point.