Purpose

This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.

Design/methodology/approach

In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.

Findings

The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.

Practical implications

The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.

Originality/value

The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.

中文翻译：

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MLFV：一种新颖的机器学习特征向量方法，用于预测湍流热和流体流动的特征

 目的

本研究旨在引入一种新颖的机器学习特征向量（MLFV）方法，利用机器学习来克服耗时的计算流体动力学（CFD）模拟，以可接受的精度快速预测湍流特性。

设计/方法论/途径

在此方法中，CFD 快照被编码在张量中作为输入训练数据。然后，MLFV通过棒状滤波器（称为特征向量）学习数据之间的关系，通过在其上定义函数来学习特征。为了证明 MLFV 的准确性，该方法仅基于 10 个 CFD 数据来预测经过创新型 Dolphin 湍流器的湍流的速度、温度和湍流动能场。

 发现

结果表明，MLFV 和 CFD 等高线以及散点图在R ² ≃ 1 的预测数据和求解数据之间具有良好的一致性。此外，误差百分比等高线和直方图揭示了 MAPE = 7.90E-02、1.45E 时预测的高精度-02、7.32E-02 和 NRMSE = 1.30E-04、1.61E-03、4.54E-05 分别用于预测 Re = 20,000 时的速度、温度、湍流动能场。

 实际意义

该方法可以在各种 CFD 模拟中拥有最先进的应用，并且能够基于小数据进行训练，这在所需测试的数量方面是实用且合乎逻辑的。

 原创性/价值

本文介绍了一种名为 MLFV 的新颖、创新且超快速的方法，以解决与传统 CFD 方法相关的耗时挑战，实时预测湍流热和流体流动的物理特性，并具有基于小数据的训练优势可接受的准确性。