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Developing Machine-Learning Algorith...

Zhang, Zexia,

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Developing Machine-Learning Algorithms for Predicting Turbulent Flows in Large-Scale Waterways, Oceanic Environments, and Utility-Scale Wind Farms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Developing Machine-Learning Algorithms for Predicting Turbulent Flows in Large-Scale Waterways, Oceanic Environments, and Utility-Scale Wind Farms // Zexia Zhang.
Author:	Zhang, Zexia,
Description:	1 electronic resource (211 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
Subject:	Mechanical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31556907
ISBN:	9798342728379

Developing Machine-Learning Algorithms for Predicting Turbulent Flows in Large-Scale Waterways, Oceanic Environments, and Utility-Scale Wind Farms /
Zhang, Zexia,

Developing Machine-Learning Algorithms for Predicting Turbulent Flows in Large-Scale Waterways, Oceanic Environments, and Utility-Scale Wind Farms /Zexia Zhang. - 1 electronic resource (211 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

Accurately predicting turbulent flow is critical for infrastructure design, operation, and risk assessment in engineering. For example, in wind farms, the power production of wind turbines is affected by atmospheric stability and wake overlapping. Turbulent river flows can undermine the foundation of hydraulic structures and change the morphology of meandering rivers. Thus, accurate prediction of turbulent flows can help prevent drawbacks and utilize advantages in engineering practices. However, the conventional high-fidelity computational fluid dynamics (CFD) prediction approach, such as large eddy simulation (LES), is computationally expensive for resolving high-Reynolds turbulent flows in wind farms and natural waterways due to the high demands for computational grid nodes in large-scale flow domains and the long sampling time required to calculate the turbulence statistics.This thesis proposes a machine learning (ML)-based framework that has the potential to significantly impact the field of engineering. It augments the efficiency of LES in predicting turbulence statistics, such as mean velocity fields and turbulence kinetic energy, with high accuracy. Specifically, the proposed ML algorithm uses instantaneous snapshots of LES to predict turbulence statistics, reducing the computational resource demands of LES by over 80% for generating statistical samples. The efficiency, accuracy, and generality of the proposed ML algorithm are widely validated for the prediction of (a) the turbulent flow of large-scale rivers with wall-mounted bridge piers and marine hydrokinetic turbines, (b) bed deformation of large-scale meandering rivers, (c) ocean wave-wind interactions, and (d) turbine wake flows in utility-scale wind farms, demonstrating its capability in various engineering applications.

English

ISBN: 9798342728379Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Turbulence statistics

Developing Machine-Learning Algorithms for Predicting Turbulent Flows in Large-Scale Waterways, Oceanic Environments, and Utility-Scale Wind Farms /
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520 $a Accurately predicting turbulent flow is critical for infrastructure design, operation, and risk assessment in engineering. For example, in wind farms, the power production of wind turbines is affected by atmospheric stability and wake overlapping. Turbulent river flows can undermine the foundation of hydraulic structures and change the morphology of meandering rivers. Thus, accurate prediction of turbulent flows can help prevent drawbacks and utilize advantages in engineering practices. However, the conventional high-fidelity computational fluid dynamics (CFD) prediction approach, such as large eddy simulation (LES), is computationally expensive for resolving high-Reynolds turbulent flows in wind farms and natural waterways due to the high demands for computational grid nodes in large-scale flow domains and the long sampling time required to calculate the turbulence statistics.This thesis proposes a machine learning (ML)-based framework that has the potential to significantly impact the field of engineering. It augments the efficiency of LES in predicting turbulence statistics, such as mean velocity fields and turbulence kinetic energy, with high accuracy. Specifically, the proposed ML algorithm uses instantaneous snapshots of LES to predict turbulence statistics, reducing the computational resource demands of LES by over 80% for generating statistical samples. The efficiency, accuracy, and generality of the proposed ML algorithm are widely validated for the prediction of (a) the turbulent flow of large-scale rivers with wall-mounted bridge piers and marine hydrokinetic turbines, (b) bed deformation of large-scale meandering rivers, (c) ocean wave-wind interactions, and (d) turbine wake flows in utility-scale wind farms, demonstrating its capability in various engineering applications.
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650 4 $a Computer science. $3 523869
650 4 $a Fluid mechanics. $3 528155
653 $a Turbulence statistics
653 $a Computational fluid dynamics
653 $a Machine learning
653 $a Large eddy simulation
653 $a Turbulent river flows
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690 $a 0548
690 $a 0984
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710 2 $a State University of New York at Stony Brook. $b Civil Engineering. $e degree granting institution. $3 3830037
720 1 $a Burgueño, Rigoberto $e degree supervisor.
720 1 $a Khosronejad, Ali $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-05B.
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