Extending the Capabilities of Data-Driven Reduced-Order Models to Make Predictions for Unseen Scenarios: Applied to Flow Around Buildings
Extending the Capabilities of Data-Driven Reduced-Order Models to Make Predictions for Unseen Scenarios: Applied to Flow Around Buildings
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DOI：
10.3389/fphy.2022.910381
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
C. Heaney;Xiangqi Liu;Hanna Go;Zef Wolffs;P. Salinas;Ionel M. Navon;C. Pain
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文献类型：
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作者：
C. Heaney;Xiangqi Liu;Hanna Go;Zef Wolffs;P. Salinas;Ionel M. Navon;C. Pain
我们提出了一种数据驱动或非侵入式降阶模型（NIROM），它能够对比用于生成快照或训练数据的域大得多的域进行预测。这种发展依赖于一种新的采样训练数据的方式（这使NIROM摆脱了对原始问题域的依赖）和域分解方法（以与子采样方法一致的方式划分看不见的几何形状）的组合。该方法扩展了降阶模型的当前能力，以概括，即，来预测看不见的场景。该方法被应用到一个二维的测试情况下，模拟过去的建筑物在一个中等的雷诺数使用计算流体动力学（CFD）代码的空气随时间变化的混沌流。3D问题的程序类似，但是，作为概念验证，2D测试用例在这里被认为是足够的。降阶模型包括一种用于获取快照的采样技术;一个用于降维的卷积自动编码器;一个用于预测的对抗网络;所有这些都设置在域分解框架内。选择自动编码器进行降维，因为文献中已经证明，这些网络可以比基于奇异值分解的传统（线性）方法更有效地压缩信息。为了保持预测的现实性，利用了对抗网络的属性。为了证明其泛化能力，一旦训练，该方法被应用于具有不同建筑物布置的更大域。从降阶模型的流量的统计特性进行比较，从CFD模型，以建立如何现实的预测。
We present a data-driven or non-intrusive reduced-order model (NIROM) which is capable of making predictions for a significantly larger domain than the one used to generate the snapshots or training data. This development relies on the combination of a novel way of sampling the training data (which frees the NIROM from its dependency on the original problem domain) and a domain decomposition approach (which partitions unseen geometries in a manner consistent with the sub-sampling approach). The method extends current capabilities of reduced-order models to generalise, i.e., to make predictions for unseen scenarios. The method is applied to a 2D test case which simulates the chaotic time-dependent flow of air past buildings at a moderate Reynolds number using a computational fluid dynamics (CFD) code. The procedure for 3D problems is similar, however, a 2D test case is considered sufficient here, as a proof-of-concept. The reduced-order model consists of a sampling technique to obtain the snapshots; a convolutional autoencoder for dimensionality reduction; an adversarial network for prediction; all set within a domain decomposition framework. The autoencoder is chosen for dimensionality reduction as it has been demonstrated in the literature that these networks can compress information more efficiently than traditional (linear) approaches based on singular value decomposition. In order to keep the predictions realistic, properties of adversarial networks are exploited. To demonstrate its ability to generalise, once trained, the method is applied to a larger domain which has a different arrangement of buildings. Statistical properties of the flows from the reduced-order model are compared with those from the CFD model in order to establish how realistic the predictions are.