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Experimental Validation of Degradation in Nuclear Equipment-Piping Systems and its Detection Using Deep Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Experimental Validation of Degradation in Nuclear Equipment-Piping Systems and its Detection Using Deep Learning./
作者:	Sauers, Serena Lynn Viola.
面頁冊數:	1 online resource (88 pages)
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Nuclear engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798379656447

Experimental Validation of Degradation in Nuclear Equipment-Piping Systems and its Detection Using Deep Learning.
Sauers, Serena Lynn Viola.

Experimental Validation of Degradation in Nuclear Equipment-Piping Systems and its Detection Using Deep Learning. - 1 online resource (88 pages)

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.Sc.)--North Carolina State University, 2023.

Includes bibliographical references

There Has Been A Resurgence Of Interest In Nuclear Power Due To The Increasing Demand For Clean Energy As A Result Of Climate Change. Nuclear Power Plants (Npps) Do Not Directly Generate Any Carbon Emissions, And Their Utilization Will Allow Countries To Meet Their Net-zero Emission Goals. However, In The United States, The Majority Of Npps In Operation Are At Or Past Their Design Life. As A Result, Critical Equipment In Npps Such As The Equipment-piping Systems Are Subject To Degradation Over Time From Flow-accelerated Corrosion Under Continuous Cyclic Loading From Operational Vibrations. The Failure Of Degraded Equipment-piping Systems Can Be Detrimental To The Regular Functionality Of An Npp. It Is Essential To Monitor These Systems To Ensure The Structural Integrity And Safety Of Npps. Current Monitoring Methods For Npp Equipment-piping Systems Are Expensive And Time-consuming. Piping Systems In Npps Can Span Across Several Miles, Making It Difficult For Maintenance Operators To Effectively Monitor Them Manually. Thus, There Is A Need For An Automated, Efficient, And Cost-effective Method To Detect Degradation In Nuclear Equipment-piping Systems. Structural Health Monitoring (Shm) Practices That Implement Artificial Intelligence (Ai) Methods Have Proven To Be Effective In Making Predictions About The Location And Severity Of Degradation Using Digital Twin Technology. However, The Applicability Of Simulation-based Studies Is Not Proven Until Experimental Validation Can Be Conducted. This Research Explores The Validity Of An Ai-based Condition Monitoring Framework For Nuclear Equipment-piping Systems Through Experimental Design And Laboratory Setup. The Vibration Response From The Experimental System And Its Corresponding Digital Twin (Dt) Is Collected And Signal Processing Techniques Are Used To Extract Damage-sensitive Quantities. Deep Learning Algorithms Are Developed To Make Predictions About Degraded Locations And The Results From The Experimental And Simulated Data Are Compared.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379656447Subjects--Topical Terms:

655622
Nuclear engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Experimental Validation of Degradation in Nuclear Equipment-Piping Systems and its Detection Using Deep Learning.
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