Idaho National Laboratory

Edge effects on the pressure drop through triply periodic minimal surface heat exchangers

Triply periodic minimal surface (TPMS) geometries offer significant potential for compact, high-performance heat exchangers, but confinement-induced edge effects in small-scale test articles can distort the measured pressure drop and its extrapolation to full-scale performance. T...

Socio‐Technical Risk‐Informed AI‐Driven Automation Trustworthiness Evaluation (ST‐RATE): Part 1‐Conceptualization

ABSTRACT The growing integration of artificial intelligence (AI)‐driven technologies into nuclear power plant (NPP) operations and maintenance holds significant potential to enhance safety, operational efficiency, and reliability. However, deploying such AI‐driven automation tech...

Gamma Radiolysis of Hydroxylamine in Aqueous Nitric Acid Solutions

Abstract The radiolytic stability of hydroxylamine (NH3OH+) in concentrated nitric acid (HNO3) is directly relevant to plutonium finishing in used nuclear fuel reprocessing, where NH3OH+ serves as both a plutonium reductant and nitrous acid (HNO2) scavenger under high-radiation c...

Radiolysis of ZrO2 Surface Modified with Polyphenols

Abstract The surface of ZrO2 was modified with various aromatic polyphenols to study their impact on the surface chemistry induced by γ radiolysis. Molecular hydrogen (H2) production in the radiolysis of water adsorbed on zirconium oxide (ZrO2) is enhanced over that observed in b...

Strategies to Improve Project Results Using Relational Capital in Agile and Hybrid Agile Software Development

ABSTRACT Improvements in Agile and Hybrid Agile project management hold immense potential, promising significant cost savings. These methodologies place the customer at the center of the software development process, providing valuable business insights and offering feedback, val...

Assessment of beryllium use in nuclear power generation

Abstract Beryllium has extraordinary physical, mechanical, and nuclear properties that make it an ideal material for improving nuclear reactor physics, particularly for compact and high-performance reactors such as fission microreactors and fusion reactors. However, beryllium and...

Machine-Learning Evaluation of a Magnetostrictive Acoustic-Emission Sensor Developed for Nuclear Structural Health Monitoring

Acoustic emission (AE) sensing is widely used for structural health monitoring, but conventional piezoelectric sensors can be constrained in high-temperature and radiation environments. This study evaluates whether an Idaho National Laboratory-developed magnetostrictive AE sensor...

An analysis of energy needs and seawater-extraction approaches for the integration of primary magnesium production with nuclear power

The cost of primary magnesium production is highly dominated by the energy required to produce the metal. This, along with the current geographical concentration of its production, makes the magnesium price vulnerable as energy-production paradigms begin to rapidly change. In thi...

Preconditioning a Hybridizable Discontinuous Galerkin Method for Navier–Stokes at High Reynolds Number

Abstract. We introduce a preconditioner for a hybridizable discontinuous Galerkin discretization of the linearized Navier–Stokes equations at high Reynolds number. The preconditioner is based on an augmented Lagrangian (AL) approach of the full discretization. Unlike standard gra...

Design of a neutron irradiation campaign for electron beam welded reactor pressure vessel steel

The objective of this study is to document a neutron irradiation experiment campaign intended to enable evaluation of embrittlement in electron beam (EB) welded SA508, Grade 3 reactor pressure vessel (RPV) steel for small modular reactors. Arc welds in RPVs have historically expe...

Model predictive control with state estimation and reduced-order physics for nuclear reactor operation during unanticipated communication transients

Advanced nuclear reactors are increasingly being designed for remote operation and higher levels of autonomy, particularly for small and microreactor applications where on-site staffing and frequent fuel handling may be impractical. These new operational paradigms would require n...

Linear and Nonlinear Learning from Spectroelectrochemical Data: Interrogation of PLS and CNN Behavior Under Experimental Scarcity

Spectroelectrochemistry (SEC) provides unique information-rich datasets by coupling molecular spectroscopic fingerprints with electrochemical activity. Despite its richness, direct machine-learning (ML) analysis of SEC data under realistic experimental constraints and scarcity re...

The Potential of Operando DRIFT Spectroscopy for Studying Degradation Mechanisms of Metal Oxide‐Based Electrochemical Cells

ABSTRACT High‐temperature electrochemical oxide devices are promising for many applications. These devices are known to be prone to degradation, yet the mechanisms remain poorly understood. In this perspective, we summarize the techniques most used to study degradation in metal o...

Separation and Recovery of High-Purity Dysprosium from Electric Vehicle Scrap Permanent Magnets Using Cyanex 572, a Cationic Extractant in a Membrane Solvent Extraction Process

Abstract Rare earth elements (REEs), especially heavy rare earth elements such as dysprosium (Dy), have gained immense attention globally due to their widespread use in many state-of-the-art technologies. Consequently, demand for REEs is increasing rapidly in comparison to their...