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Hydrogen Energy Storage: From Predic...

Banerjee, Tanumoy,

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hydrogen Energy Storage: From Predictive Interaction With Metallic Materials to Thermodynamic Analysis of System Integration // Tanumoy Banerjee.
Author:	Banerjee, Tanumoy,
Description:	1 electronic resource (228 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
Subject:	Mechanical engineering. -
Online resource:	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 /
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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. 
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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
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