Physics-Informed Machine Learning for Void Fraction Prediction in Upward and Downward Two-Phase Flows

Research output: Contribution to journal › Conference article › peer-review

Original language	English
Pages (from-to)	1262-1265
Number of pages	4
Journal	Transactions of the American Nuclear Society
Volume	132
Issue number	1
DOIs	https://doi.org/10.13182/T140-48655
State	Published - 2025
Event	ANS Annual Conference, 2025 - Chicago, United States Duration: Jun 15 2025 → Jun 18 2025

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@article{89a66dd6a7914c2c86e865608c3526de,

title = "Physics-Informed Machine Learning for Void Fraction Prediction in Upward and Downward Two-Phase Flows",

author = "Davide Rotilio and Jacob Petersen and Robert Salko and Xingang Zhao",

year = "2025",

doi = "10.13182/T140-48655",

language = "English",

volume = "132",

pages = "1262--1265",

journal = "Transactions of the American Nuclear Society",

issn = "0003-018X",

number = "1",

note = "ANS Annual Conference, 2025 ; Conference date: 15-06-2025 Through 18-06-2025",

}

TY - JOUR

T1 - Physics-Informed Machine Learning for Void Fraction Prediction in Upward and Downward Two-Phase Flows

AU - Rotilio, Davide

AU - Petersen, Jacob

AU - Salko, Robert

AU - Zhao, Xingang

PY - 2025

Y1 - 2025

UR - https://www.scopus.com/pages/publications/105016693176

U2 - 10.13182/T140-48655

DO - 10.13182/T140-48655

M3 - Conference article

AN - SCOPUS:105016693176

SN - 0003-018X

VL - 132

SP - 1262

EP - 1265

JO - Transactions of the American Nuclear Society

JF - Transactions of the American Nuclear Society

IS - 1

T2 - ANS Annual Conference, 2025

Y2 - 15 June 2025 through 18 June 2025

ER -