AI RESEARCH
A Methodology for Thermal Limit Bias Predictability Through Artificial Intelligence
arXiv CS.AI
•
ArXi:2603.14648v1 Announce Type: cross Nuclear power plant operators face significant challenges due to unpredictable deviations between offline and online thermal limits, a phenomenon known as thermal limit bias, which leads to conservative design margins, increased fuel costs, and operational inefficiencies. This work presents a deep learning based methodology to predict and correct this bias for Boiling Water Reactors (BWRs), focusing on the Maximum Fraction of Limiting Power Density (MFLPD) metric used to track the Linear Heat Generation Rate (LHGR) limit.