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At least 631 records · Page 35

Lower-length-scale modeling of chemical additions, corrosion and fission products in select MSR base salts

Molten salts play a crucial role in advancing Generation IV nuclear energy technology, with chloride-based salts like NaCl-UCl 3 garnering significant attention due to their distinctive properties. However, the corrosive nature of molten salts causes the dissolution of chromium (Cr), leading to the formation of CrCl 2 and CrCl 3 species in molten chlorides. Moreover, the radioactive decay of nuclear fuel gives rise to fission products, including Cs, Sr, and I chlorides. The first part of this report presents a comprehensive study utilizing ab initio molecular dynamics (AIMD) simulations to investigate the properties of eutectic NaCl-UCl 3 molten salt in the presence of corrosion products (CrCl 2 and CrCl 3 ) and fission product (CsCl). The study focuses on essential structural and thermophysical properties such as density, mixing energy, coordination numbers (CN), and Radial Distribution Functions (RDF) of the salts with varying compositions of corrosion products and fission product, covering a range from 0% to 13.5%. The results offer valuable insights into the behavior of corrosion and fission products in uranium-based molten salts, providing essential data that can be used as input to the MTDB-TC and MSTDB-TP property databases being developed by the NEAMS program. Due to their favorable characteristics such as low melting points, high boiling points, and low costs, MgCl 2 +NaCl+KCl (MNK) eutectic salts have recently attracted attention as high-temperature heat transfer fluids. The incorporation of LiCl into MNK salts can further reduce their melting points and increase their specific heat capacities, which is de- sirable for high-temperature heat transfer applications. The second part of this report presents the development and validation of a new shell-model potential for the MgCl 2 +NaCl+KCl+LiCl system, which captures the polarization of Cl anions. The extensive comparison with experimental data and AIMD simulations demonstrates the accuracy and reliability of the potential. Furthermore, using this potential, we elucidate the intricate network structure in MNK eutectic salts that is formed through polyhedron sharing. This research contributes to a fundamental understanding of the atomic structures and thermophysical properties of multi-component molten salts, which is crucial for future development of heat transfer fluids for applications in molten salt reactors.

36 MATERIALS SCIENCE↗

Lower length scale model for palladium attack of silicon carbide in TRISO fuel

TRistructural ISOtropic (TRISO) particle fuels rely on silicon carbide (SiC) as the primary barrier for metallic fission product (FP) release. Palladium (Pd) generated by fission degrades the SiC layer, resulting in the formation of lamellar layers of palladium silicides (PdxSi) and carbon (C) perpendicular to the direction of attack. The Pd attack has been hypothesized to be responsible for failure of the SiC layer and enhance FP release. To better understand and quantify Pd attack of SiC in TRISO particles, a multiscale, mechanistic model of Pd transport is being developed by the NEAMS program. Previous work provided an initial hypothesis for modeling lamellar microstructure formation in SiC due to Pd attack using a phase-field model. The work described in this report builds on the previous model by using molecular dynamics (MD) simulations to parameterize the phase-field model kinetics, and build a reduced order model in BISON using the improved mesoscale Pd penetration model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Digital Twin Framework for PIP-II Linac: AI-Driven Multi-Scale Modeling from Ion Source to 800 MeV

The PIP-II linac will enable >1.2 MW beam power for DUNE, requiring unprecedented operational reliability across its warm front-end (RFQ, MEBT) and five distinct SRF sections operating at 162.5/325/650 MHz. We present a comprehensive digital twin framework uniquely combining a fully differentiable fast beam transport code with neural network surrogates trained on high-fidelity PIC simulations, capturing space charge and nonlinear dynamics beyond traditional envelope codes while achieving 10⁴ speedup at <1% accuracy. End-to-end differentiability enables gradient-based optimization across 500+ parameters simultaneously previously impossible with conventional tools while the model incorporates static/dynamic errors and serves as a virtual commissioning platform for diverse hardware integration. The framework facilitates reinforcement learning for pulsed/CW mode transitions, predictive maintenance through anomaly detection, and autonomous tuning algorithm development with real-time execution capability. Validation against physics simulations shows excellent agreement for the front-end, with initial results demonstrating potential for 30% commissioning time reduction and proactive fault mitigation, providing a scalable blueprint for operating next-generation high-intensity accelerators.

Pathak, Abhishek [Fermilab] (ORCID:000000021704208↗