基于人工智能的多相管流扩展区域非侵入降阶模型
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes
摘要：
管道中多相流的建模对高分辨率计算流体动力学（CFD）模型提出了重大挑战，这是由于该域的高纵横比（长度与直径之比）。在海底应用中，管道长度可以是几百公里，而管道直径只有几英寸。在本文中，我们提出了一种新的基于AI的非侵入式降阶模型，该模型在域分解框架（AI-DDNIROM）中能够对比训练中使用的域大得多的域进行预测。这是通过使用区域分解;降维;训练神经网络对单个子域进行预测;以及通过使用子域迭代技术来收敛整个域的解决方案来实现的。为了找到低维空间，我们探索了几种类型的自动编码器网络，这些网络以其准确和压缩信息的能力而闻名。自动编码器的性能进行评估的两个对流为主的问题：流过去的圆柱体和弹状流在管道中。为了及时做出预测，我们利用了一个对抗网络，该网络旨在学习训练数据的分布，以及学习特定输入和输出之间的映射。这类网络已显示出产生现实产出的潜力。整个框架适用于多相弹状流在水平管的一个AI-DDNIROM的高保真CFD模拟的长度为10米，纵横比为13：1，并通过模拟的长度为98米，纵横比几乎为130：1的管道的流动进行测试。从CFD模拟获得的流量的统计数据进行比较的AI-DDNIROM的预测，以证明我们的方法的成功。
英文摘要：
The modelling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over diameter) of the domain. In subsea applications, the pipe length can be several hundreds of kilometres versus a pipe diameter of just a few inches. In this paper, we present a new AI-based non-intrusive reduced-order model within a domain decomposition framework (AI-DDNIROM) which is capable of making predictions for domains significantly larger than the domain used in training. This is achieved by using domain decomposition; dimensionality reduction; training a neural network to make predictions for a single subdomain; and by using an iteration-by-subdomain technique to converge the solution over the whole domain. To find the low-dimensional space, we explore several types of autoencoder networks, known for their ability to compress information accurately and compactly. The performance of the autoencoders is assessed on two advection-dominated problems: flow past a cylinder and slug flow in a pipe. To make predictions in time, we exploit an adversarial network which aims to learn the distribution of the training data, in addition to learning the mapping between particular inputs and outputs. This type of network has shown the potential to produce realistic outputs. The whole framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m with an aspect ratio of 13:1, and tested by simulating the flow for a pipe of length 98 m with an aspect ratio of almost 130:1. Statistics of the flows obtained from the CFD simulations are compared to those of the AI-DDNIROM predictions to demonstrate the success of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
