Nuclear Power Plants contribute about a fourth of global low-carbon electricity production mainly through Pressurized Water Reactors (PWRs), which comprise about 70% of all reactors worldwide. Chalk River Unidentified Deposit (CRUD) poses significant challenges for PWRs. CRUD which is formed from the buildup of fouling deposits in the Reactor Coolant System (RCS) might lead to increased hydraulic resistance, increased occupational radiation exposure, CRUD Induced Power Shift (CIPS), and CRUD Induced Localized Corrosion (CILC). These CRUD deposits on the reactor core ultimately impact reactor operation and cause significant economic losses. Given the challenges associated with observing CRUD deposition in real-time, the modeling of CRUD growth and effects has gained significant interest over the past decades, as various simulation tools have been developed to predict CRUD formation, transport, and deposition in PWRs. This study examines some important simulation codes used in CRUD deposition modeling in PWRs, categorizing them into three generations: legacy, transitional, and state-of-the-art codes. Legacy Codes such as VIPRE-1, laid the foundation with basic thermal-hydraulic models. Transitional Codes such as BOA, introduced more sophisticated models and improved computational efficiency meanwhile State-of-the-Art codes such as VERA suite of codes, provide high-fidelity multi-physics simulations. However, the high-fidelity multi-physics simulations offered by State-of-the-Art codes often require extensive computational resources and time to run. Additionally, this study discusses the role of machine learning in CRUD modeling especially the use of convolutional neural networks to build surrogate models.  These models emulate the high-fidelity simulations with acceptable accuracy while operating much faster and requiring fewer computational resources. However, there has been very limited research that apply machine learning techniques for CRUD modeling. This leaves a considerable void in the existing literature.
 

CRUD,Deposition,simulation codes,Modeling,machine learning