東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Deep Learning Strategies for Critical Heat Flux Detection in Pool Boiling.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Learning Strategies for Critical Heat Flux Detection in Pool Boiling./
作者:	Al-Hindawi, Firas.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	83 p.
附註:	Source: Masters Abstracts International, Volume: 83-02.
Contained By:	Masters Abstracts International83-02.
標題:	Industrial engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647927
ISBN:	9798535547640

Deep Learning Strategies for Critical Heat Flux Detection in Pool Boiling.
Al-Hindawi, Firas.

Deep Learning Strategies for Critical Heat Flux Detection in Pool Boiling. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 83 p.

Source: Masters Abstracts International, Volume: 83-02.

Thesis (M.Sc.)--Arizona State University, 2021.

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

Image-based deep learning (DL) models are employed to enable the detection of critical heat flux (CHF) based on pool boiling experimental images. Most machine learning approaches for pool boiling to date focus on a single dataset under a certain heater surface, working fluid, and operating conditions. For new datasets collected under different conditions, a significant effort in re-training the model or developing a new model is required under the assumption that the new dataset has a sufficient amount of labeled data. This research is to explore supervised, semi-supervised, and unsupervised machine learning strategies that are formulated to adapt to two scenarios. The first is when the new dataset has limited labeled data available. This scenario was addressed in chapter 2 of this thesis, where Convolutional Neural Networks (CNNs) and Transfer learning (TL) were used in tackling such situations. The second scenario is when the new dataset has no labeled data available at all. In such cases, this research presents a methodology in Chapter 3, where one of the state-of-the-art Generative Adversarial Networks (GANs) called Fixed-Point GAN is deployed in collaboration with a regular CNN model to tackle the problem. To the best of my knowledge, the approaches presented in chapters 2 and 3 are the first of their kind to utilize TL and GANs to solve the boiling heat transfer problem within the heat transfer community and are a step forward towards obtaining a one-for-all general model.

ISBN: 9798535547640Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Boiling crisis

Deep Learning Strategies for Critical Heat Flux Detection in Pool Boiling.
LDR:02687nmm a2200373 4500 001 2344042
005 20220513115355.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798535547640
035 $a (MiAaPQ)AAI28647927
035 $a AAI28647927
040 $a MiAaPQ $c MiAaPQ
100 1 $a Al-Hindawi, Firas. $3 3682746
245 1 0 $a Deep Learning Strategies for Critical Heat Flux Detection in Pool Boiling.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 83 p.
500 $a Source: Masters Abstracts International, Volume: 83-02.
500 $a Advisor: Wu, Teresa;Yoon, Hyunsoo.
502 $a Thesis (M.Sc.)--Arizona State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Image-based deep learning (DL) models are employed to enable the detection of critical heat flux (CHF) based on pool boiling experimental images. Most machine learning approaches for pool boiling to date focus on a single dataset under a certain heater surface, working fluid, and operating conditions. For new datasets collected under different conditions, a significant effort in re-training the model or developing a new model is required under the assumption that the new dataset has a sufficient amount of labeled data. This research is to explore supervised, semi-supervised, and unsupervised machine learning strategies that are formulated to adapt to two scenarios. The first is when the new dataset has limited labeled data available. This scenario was addressed in chapter 2 of this thesis, where Convolutional Neural Networks (CNNs) and Transfer learning (TL) were used in tackling such situations. The second scenario is when the new dataset has no labeled data available at all. In such cases, this research presents a methodology in Chapter 3, where one of the state-of-the-art Generative Adversarial Networks (GANs) called Fixed-Point GAN is deployed in collaboration with a regular CNN model to tackle the problem. To the best of my knowledge, the approaches presented in chapters 2 and 3 are the first of their kind to utilize TL and GANs to solve the boiling heat transfer problem within the heat transfer community and are a step forward towards obtaining a one-for-all general model.
590 $a School code: 0010.
650 4 $a Industrial engineering. $3 526216
650 4 $a Artificial intelligence. $3 516317
650 4 $a Datasets. $3 3541416
650 4 $a Video recordings. $3 575241
650 4 $a Neural networks. $3 677449
650 4 $a Classification. $3 595585
653 $a Boiling crisis
653 $a Boiling crisis detection
653 $a Machine learning
653 $a Pool boiling
653 $a Supervised machine learning
653 $a Unsupervised machine learning
690 $a 0546
690 $a 0800
710 2 $a Arizona State University. $b Industrial Engineering. $3 2098642
773 0 $t Masters Abstracts International $g 83-02.
790 $a 0010
791 $a M.Sc.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647927