Verification of the PWR Core Solver Coupling the Neutron Code nTRACER with Artificial Neural Networks for Thermal-Hydraulic Feedback

Marianna Papadionysiou^1*Gregory Delipei²Maria Avramova²Hakim Ferroukhi³Kostadin Ivanov²

• ¹Ecole polytechnique federale de Lausanne, Lausanne, Switzerland
• ²NC State University, Raleigh, United States
• ³Paul Scherrer Institut PSI, Villigen, Switzerland

Paul Scherrer Institute (PSI) and North Carolina State University are developing a high-resolution multi-physics core solver for Pressurized Water Reactor analysis in Cartesian geometry, using the neutron transport code nTRACER and two Machine Learning (ML) models providing thermal-hydraulic (T/H) feedback. This work presents the coupling of nTRACER/ML, comparing the solver with the verified core solver nTRACER/CTF, in terms of accuracy and computation costs, for steady state and cycle analysis, with the presented results and their interpretation focusing primarily on performance improvements. nTRACER/ML shows strong agreement with nTRACER/CTF in power and coolant predictions for Hot Full Power, with maximum deviations of 1.01% in assembly power and 2.63 °C in coolant temperature (Root Mean Square Error [RMSE]: 0.65 °C). Fuel temperature predictions are also good, with centerline temperature differences reaching up to 32.96 °C and an RMSE of 5.55 °C. As exposure increases, power deviations grow - up to 3.33% axially and 4.13% in 3D assembly power at 392.3 EFPDs. Coolant temperature discrepancies decrease with burnup, while fuel temperature errors increase, with centerline differences peaking at 36.24 °C (RMSE: 8.30 °C). Despite its limitations, nTRACER/ML shows sufficient agreement with nTRACER/CTF in both neutronic and T/H metrics, making it a practical low-fidelity alternative for high-resolution simulations, particularly since it runs 3–4 times faster than CTF while using only about 1% of the CPU time.