자료유형  

 학위논문

Control Number  

0016934564

International Standard Book Number  

9798380482707

Dewey Decimal Classification Number  

600

Main Entry-Personal Name  

Iskhakova, Anna.

Publication, Distribution, etc. (Imprint  

[S.l.] : North Carolina State University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(208 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

General Note  

Advisor: Bolotnov, Igor A.;Dinh, Nam T.

Dissertation Note  

Thesis (Ph.D.)--North Carolina State University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Flow boiling is an efficient heat transfer mechanism used in industrial thermal applications including nuclear power plants. Extensive experimental, theoretical, and computational studies on physics that govern flow boiling fluid dynamics and heat transfer conducted over the past century have created a comprehensive knowledge and database. However, this knowledge is highly empirical and predicting the flow boiling behavior in new settings (fluids, system conditions, geometries, surfaces) remains elusive due to the inherently stochastic nature of the boiling process and multiple parameters that greatly influence heat transfer rate (e.g., contact angle, surface imperfections, etc.). In the presented research, a small contribution to the flow boiling process understanding has been made using PHASTA (Parallel Hierarchic Adaptive Stabilized Transient Analysis) solver. PHASTA is a highly scalable, parallel, finite-element code that can be used to conduct high resolution analysis of various flow phenomena. The level-set method is used in tandem with fluid flow and heat transfer equations to capture the interface dynamics and interfacial interactions.Recently, PHASTA capabilities have been extended to flow boiling simulations in heatexchanger's complex-geometry channels. For this purpose, code modifications were made to ensure robust modeling of level-set function interactions with multiple walls and accurate contact angle modeling in subsequent bubble generation processes. PHASTA flow boiling capabilities have been assessed against data from validation experiments conducted at Texas A&M University (TAMU) with a single nucleation site in a vertical rectangular channel. Parametric contact angle and nucleation cavity studies have been performed to evaluate their influence on bubble dynamics. Subsequently, PHASTA is used to perform numerical studies of flow boiling in a heat-exchanger channel with offset strip fins (OSF). Notably, several nucleation sites were seeded in the simulation domain. Contact angle and wall superheat sensitivity studies are conducted for each of channel configurations, bubble statistics and heat transfer coefficient data are collected. Substantial heat transfer enhancement is found near channel walls due to bubbles' presence.Although DNS with interface-capturing have become computationally affordable for relatively short transients in small parts of heat exchangers, DNS methods are not practical for design analysis and optimization to inform engineering-scale applications. Within the PHASTA technology, the path forward discussed is to perform machine learning (ML) guided coarse grid (CG) simulations for real-size heat exchangers. The CG correction workflow is proposed that allows to use a ML-predicted level-set field in the CG simulation thereby decreasing the error associated with under resolved interface. In this workflow a couple of advanced ML algorithms are explored, namely, super resolution and Fourier neural operator (FNO). Super resolution technique uses multiple convolutional layers to predict output values on a finer mesh using CG input data. FNO allows to predict a solution of a PDE given initial and boundary conditions. The general idea is to integrate this workflow with CG PHASTA simulations with prior ML algorithms training on high-fidelity (HF) PHASTA data. The tests conducted with FNO for predicting HF level-set fields based on analytical and numerical data for training provided encouraging results. Thus, the whole workflow implementation has a potential to bridge the gap between high resolution data for local physical phenomena and global parameters' evaluation at the component scale.

Subject Added Entry-Topical Term  

Friction.

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Heat transfer.

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Growth models.

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Physics.

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Control algorithms.

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Nuclear reactors.

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Viscosity.

Subject Added Entry-Topical Term  

Heat exchangers.

Subject Added Entry-Topical Term  

Bubbles.

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Phase transitions.

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Engineering.

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Time series.

Subject Added Entry-Topical Term  

Contact angle.

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Geometry.

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Hydraulics.

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Linear algebra.

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Interfaces.

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Hydraulic engineering.

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Mathematics.

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Nuclear engineering.

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Thermodynamics.

Added Entry-Corporate Name  

North Carolina State University.

Host Item Entry  

Dissertations Abstracts International. 85-04B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:643580