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 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Hydrogen Energy Storage: From Predic...

Banerjee, Tanumoy,

FindBook

Google Book

Amazon

博客來

Hydrogen Energy Storage: From Predictive Interaction With Metallic Materials to Thermodynamic Analysis of System Integration /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hydrogen Energy Storage: From Predictive Interaction With Metallic Materials to Thermodynamic Analysis of System Integration // Tanumoy Banerjee.
作者:	Banerjee, Tanumoy,
面頁冊數:	1 electronic resource (228 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
標題:	Mechanical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31334741
ISBN:	9798384018841

Hydrogen Energy Storage: From Predictive Interaction With Metallic Materials to Thermodynamic Analysis of System Integration /
Banerjee, Tanumoy,

Hydrogen Energy Storage: From Predictive Interaction With Metallic Materials to Thermodynamic Analysis of System Integration /Tanumoy Banerjee. - 1 electronic resource (228 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

Hydrogen is a versatile element with the potential to revolutionize various industries, including energy, transportation, and material science. However, its behavior and interactions at the atomic level are complex, making it challenging to accurately predict its properties and behavior in different environments. These items are important to effectively use hydrogen in different societal applications. In the realm of energy storage, hydrogen's role is particularly significant. Hydrogen energy storage offers a promising avenue for addressing intermittency issues in renewable energy sources such as wind and solar power. Research was performed to investigate hydrogen-based energy storage and its integration with a coal-fired plant. Additionally, metal hydride-based hydrogen storage system modeling was also investigated and shows promise for application in natural gas combined cycle (NGCC) plants. These investigations employed multi-scale modeling, a computational approach that integrates different levels of detail, ranging from quantum mechanics to classical molecular dynamics, to system level process modeling of the diverse phenomena occurring in hydrogen systems. Machine learning techniques, on the other hand, have gained prominence in recent years as powerful tools for data-driven discovery and prediction. Multiscale modelling and machine learning techniques can enhance our understanding of hydrogen and its interactions with other materials. This integrated approach was also applied to facilitate the discovery of new materials with tailored hydrogen-related properties, reducing the reliance on more expensive and time-consuming approaches. 

English

ISBN: 9798384018841Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Hydrogen energy storage

Hydrogen Energy Storage: From Predictive Interaction With Metallic Materials to Thermodynamic Analysis of System Integration /
LDR:03239nmm a22004453i 4500 001 2438610
005 20260727134401.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798384018841
035 $a (MiAaPQD)AAI31334741
035 $a AAI31334741
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Banerjee, Tanumoy, $e author. $0 (orcid)0000-0002-0862-6871 $3 3829972
245 1 0 $a Hydrogen Energy Storage: From Predictive Interaction With Metallic Materials to Thermodynamic Analysis of System Integration / $c Tanumoy Banerjee.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (228 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
500 $a Advisors: Romero, Carlos Committee members: Kundu, Animesh; Balasubramanian, Ganesh; Webb, Edmund, III; Saha, Sandip Kumar.
502 $b Ph.D. $c Lehigh University $d 2024.
520 $a Hydrogen is a versatile element with the potential to revolutionize various industries, including energy, transportation, and material science. However, its behavior and interactions at the atomic level are complex, making it challenging to accurately predict its properties and behavior in different environments. These items are important to effectively use hydrogen in different societal applications. In the realm of energy storage, hydrogen's role is particularly significant. Hydrogen energy storage offers a promising avenue for addressing intermittency issues in renewable energy sources such as wind and solar power. Research was performed to investigate hydrogen-based energy storage and its integration with a coal-fired plant. Additionally, metal hydride-based hydrogen storage system modeling was also investigated and shows promise for application in natural gas combined cycle (NGCC) plants. These investigations employed multi-scale modeling, a computational approach that integrates different levels of detail, ranging from quantum mechanics to classical molecular dynamics, to system level process modeling of the diverse phenomena occurring in hydrogen systems. Machine learning techniques, on the other hand, have gained prominence in recent years as powerful tools for data-driven discovery and prediction. Multiscale modelling and machine learning techniques can enhance our understanding of hydrogen and its interactions with other materials. This integrated approach was also applied to facilitate the discovery of new materials with tailored hydrogen-related properties, reducing the reliance on more expensive and time-consuming approaches. 
546 $a English
590 $a School code: 0105
650 4 $a Mechanical engineering. $3 649730
650 4 $a Thermodynamics. $3 517304
650 4 $a Energy. $3 876794
650 4 $a Mechanics. $3 525881
653 $a Hydrogen energy storage
653 $a Hydrogen storage
653 $a Molecular simulation
653 $a Multi-principal element alloys
653 $a Predictive modeling
690 $a 0548
690 $a 0346
690 $a 0348
690 $a 0791
710 2 $a Lehigh University. $b Mechanical Engineering. $e degree granting institution. $3 3770463
720 1 $a Romero, Carlos $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-02B.
790 $a 0105
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31334741