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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development of chemometric models to classify solid-state U materials by micro-Raman spectroscopy

Discerning uranium (U) particles found in environmental sampling is of interest for monitoring the peaceful use of nuclear material. In this study, a soft independent modeling of class analogy (SIMCA) library was successfully developed for the classification of a four-class system consisting of α-U 3 O 8 , UO 2 , UO 2 (NO 3 ) 2 ·6H 2 O (UNH), and UO 2 O 2 ·4H 2 O (studtite) by Raman spectroscopy in the presence of matrix particulates and additional outliers. Spectral variability between numerous particles of each type revealed appreciable differences as a function of particle size with respect to hydration state and potential oxide phase within each class. Interclass variability was accounted for using both unsupervised and supervised chemometric models. The supervised SIMCA model displayed reasonable sensitivity for each U class and a high degree of specificity by returning whether a spectrum belonged to one class or not. This work demonstrates how Raman spectral features and chemometrics can be used to distinguish U materials from one another and from matrix materials such as flint clay. Combining the outlined chemometric approach with Raman mapping sequences could provide a rapid, nondestructive technique to characterize the chemical composition of a diverse collection of U compounds amid background samples for environmental sampling, nuclear forensics, and industrial applications.

Actinide↗
DOE OSTI2024

Performance stability of plastics for neutron-gamma pulse shape discrimination

The introduction of the first commercially available plastic scintillators with pulse shape discrimination (PSD) offered by Eljen Technology marked progress towards potentially replacing liquid scintillators in neutron detection. However, use of these plastics over several recent years has revealed an important flaw in these materials: the eventual degradation of scintillation light output and PSD performance. Here, studies described in this paper considered possible reasons for this degradation. Experiments conducted with numerous lab-prepared and commercial EJ-276 samples showed that the main factor affecting the instability is oxidation that involves highly reactive radicals generated during the polymerization process or from the further breakdown of polymer chains under oxygen/air exposure. Based on the obtained results, the stability of scintillation performance for PPO (2,5-Diphenyloxazole)-based PSD plastics has been improved through modifications of the composition via the utilization of scintillation dyes and compounds with antioxidant properties that diminish the effects of oxidation. Elements of these studies were used in the commercial production of the most recent EJ-276D PSD plastic version for fast neutron detection and 6Li-loaded plastics for thermal neutron and antineutrino detection applications. Performance projections of the new PSD plastics indicate a likely degradation of less than 10 % over 5–10 years in comparison to previous EJ-276 that might lose up to 30–40 % of the scintillation light during 1–2 years of storage or deployment under ambient conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Use of carbon electrodes to reduce mobile ion concentration and improve reliability of metal halide perovskite photovoltaics

Ion migration is one of the prime reasons for the rapid degradation of metal halide perovskite solar cells (PSCs), and we report on a method for quantifying mobile ion concentration (No) using a transient dark current measurement. We perform both ex-situ and in-situ measurements on PSCs and study the evolution of No in films and devices under a range of temperatures. We also study the effect of device architecture, top electrode chemistry, and metal halide perovskite composition and dimensionality on No. Two-dimensional perovskites are shown to reduce the ion concentration along with inert C electrodes that do not react with halides by ~99% while also improving mechanical reliability by ~250%. We believe this work can provide design guidelines for the development of stable PSCs through the lens of minimizing mobile ions and their evolution over time under operational conditions.

14 SOLAR ENERGY↗
DOE OSTI2025

CFD modeling of near-wall combustion and unburned methane prediction in natural gas spark ignition engines

Natural gas-powered engines play a critical role in gas drilling, compression, and transmission sectors, but methane (CH 4 ) from engine combustion slip can be significant over their lifespan, contributing to atmospheric pollution and signaling reduced engine efficiency. Here, to address this challenge, computational fluid dynamics (CFD) simulations offer valuable insights into the in-cylinder combustion process, enabling the optimization of combustion strategies and engine designs to minimize unburned CH 4 slip. This study aims to evaluate and improve combustion models for simulating the combustion process and predicting unburned CH 4 concentrations in natural gas spark-ignition (SI) engines, including engines that are part of combined reformer-engine systems. Specifically, the performance of two flamelet-based combustion models—the Extended Coherent Flame Model (ECFM) and the G-equation model—was assessed using experimental engine data collected under varying excess-air ratio (λ) conditions and fuel compositions, including natural gas and syngas blends. In addition, to enhance the predictive capabilities of the G-equation model, a flame-wall interaction (FWI) sub-model was integrated into its framework. The effects of its model parameters, such as quenching and influence distance, on combustion behavior and unburned methane predictions were analyzed in detail. The ECFM tended to predict delayed combustion phasing under diluted mixture conditions, resulting in overprediction of unburned CH 4 concentrations. In contrast, the G-equation model provided reasonable predictions of combustion pressure, while representing higher the CH 4 reduction rate across the operating condition compared to experimental data. Incorporating the FWI sub-model—with the quenching distance calculated based on a pressure-dependent relation (P -0.48 ) and a fixed influence distance of 1.5 mm—further improved the G-equation model’s accuracy in predicting CH 4 reduction rates without compromising its ability to simulate the combustion process.

Combustion model↗
DOE OSTI2025

Impact of Oxalic Acid Consumption and pH on the In Vitro Biological Control of Oxalogenic Phytopathogen Sclerotinia sclerotiorum

The phytopathogenic fungus Sclerotinia sclerotiorum has a wide host range and causes significant economic losses in crops worldwide. This pathogen uses oxalic acid as a virulence factor; for this reason, the degradation of this organic acid by oxalotrophic bacteria has been proposed as a biological control approach. However, previous studies on the potential role of oxalotrophy in biocontrol did not investigate the differential effect of oxalic acid consumption and the subsequent pH alkalinisation on fungal growth. In this study, confrontation experiments on different media using a wild-type (WT) strain of S. sclerotiorum and an oxalate-deficient mutant (strain Δoah) with the soil oxalotrophic bacteria Cupriavidus necator and Cupriavidus oxalaticus showed the combined effect of media composition on oxalic acid production, pH, and fungal growth control. Oxalotrophic bacteria were able to control S. sclerotiorum only in the medium in which oxalic acid was produced. However, the deficient Δoah mutant was also controlled, indicating that the consumption of oxalic acid is not the sole mechanism of biocontrol. WT S. sclerotiorum acidified the medium when inoculated alone, while for both fungi, the pH of the medium changed from neutral to alkaline in the presence of bacteria. Therefore, medium alkalinisation independent of oxalotrophy contributes to fungal growth control.

Estoppey, Aislinn (ORCID:0000000347410835)↗
DOE OSTI2025

Paper or Plastic? Multiscale Material Handling Properties of Two Model Municipal Solid Waste Streams

Purpose: Municipal Solid Waste (MSW) is a potentially valuable sustainable feedstock for fuel and chemical production due to its carbon-rich content and low cost. This study aims to assess the material handling properties of paperand plastic-rich MSW feedstocks to mitigate equipment failure and processing downtime. Methods: The material handling properties of crumbled MSW feedstocks were measured using apowder rheometer with mass flow hopper calculations to assess handling performance. Inverse gas chromatography was use to measure the surface energy differences between feedstocks. Electron microscopy and Raman spectroscopy was used to evaluate microscale features that may contribute to material handling differences. Results: Plastic and paper rich feedstocks crumbled to a nominal 2 mm particle size were observed to have similar flow and handling characteristics with reasonable hopper outlets. 2 mm plastic rich crumbles, with their higher bulk density, exhibited superior flow performance. By contrast, 4 mm material required significantly larger hopper outlets, indicating poor flowability. Paper rich and 4 mm plastic rich samples displayed broad particle size distributions, which contributed to particle interlocking, jamming, and other flow issues. Electron microscopy revealed that plastic rich samples were significantly smoother, enhancing their flowability compared to the rougher, paper rich materials. Conclusions: This study establishes critical material handling baselines for processing MSW as a viable feedstock for fuel and chemical production. The findings highlight the importance of optimizing particle size and feedstock composition to improve flowability and handling performance.

09 BIOMASS FUELS↗
DOE OSTI2024

Effect of Co on twin formation and magnetic properties of Sm(Fe,Ti,V) 12 alloys

Transferring the excellent intrinsic magnetic properties of SmFe 12 -based compounds to their extrinsic properties remains the main challenge in the development of high-performance SmFe12-based permanent magnets. Twin formation is one of the reasons for the inability to achieve high coercivity and remanence. Here we have shown that the addition of Co in Sm(Fe 1-x Co x ) 10–11 M 1–2 alloys, where M=Ti and V, leads to an increase in twin density. Microstructural characterizations revealed that the atomic arrangement in the twin boundary changes depending on the stabilizing element, which directly influences the local intrinsic magnetic properties. Theoretical investigations showed that the critical grain size at which twin formation can be hindered by grain size reduction decreases when the stabilizer changes from V to Ti. Furthermore, this study shows that the alloy composition influences not only the intrinsic magnetic properties but also the twin formation energy and its grain size dependence, crucial for the design of SmFe12-based permanent magnets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fluid-like shock compression of dilute polymer nanocomposites

The shock Hugoniot of heterogenous mixtures of discrete particles has been experimentally investigated for porous agglomerates and fluids, while the study of full-density solids has been primarily limited to compressed powders and high-dimensional composites. By dispersing ceria nanoparticles in a polyethylene matrix, we are able to examine the hydrodynamic behavior of a nonporous, heterogenous solid in thermal equilibrium during weak shock compression. Phase-driven discontinuities in the Hugoniot particle velocity–shock velocity (u−D) relationship of pure polyethylene are replicated in the nanocomposites but are shifted to lower velocity and to higher pressure with higher particle concentration. The results are explained using an isothermal, two-velocity fluid model under the hydrodynamic approximation. The model, which assumes a theoretical equation-of-state for ceria and either a low-order or high-order fit to the measured polyethylene Hugoniot, reasonably predicts the Hugoniot for two different polyethylene/ceria nanocomposites. Using the model, the mixture Hugoniot is shown to be insensitive to the Hugoniot of the stiffer constituent when the moduli are sufficiently disparate, while dependence on particle density and volume fraction is preserved through fluid-like motion.

Moore, Nathan W. [Sandia National Laboratories (SN↗
DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗
DOE OSTI2026

Conceptual design study of neutron detectors for safeguards measurement of an irradiated pebble

Nuclear material control and accounting (MC&A) of pebble-bed reactors (PBRs) is challenging because a PBR utilizes hundreds of thousands of identical, unmarked pebbles that are continuously recirculated through the core. To develop tools that enable the implementation of international safeguards, especially in the context of MC&A of spent pebbles, we designed and simulated three neutron detection concepts to determine fissile content in individual pebbles: a differential die-away (DDA) detector, a californium interrogation prompt neutron (CIPN) detector, and a passive neutron albedo reactivity (PNAR) detector using Monte Carlo calculations. Burnup calculations were performed on the spent pebbles from the PBMR-400 classic PBR. The varying neutron and gamma source terms, and isotopic compositions in the spent pebbles calculated at various burnup levels were used in the neutron detector models. DDA was found to be sensitive to the number of passes a pebble has had through the core and to the fissile content contained in a spent pebble. Optimization in the DDA design further increased the neutron count rates and thus reduced counting uncertainty. Meanwhile, passive neutron counting using the same detector body could distinguish pebbles with different numbers of passes, but its response was dominated by neutron-emitting actinides and was not sensitive to fissile content. On the other hand, the PNAR technique was not viable for a single pebble but performed reasonably for a 27-pebble array, which suggested potential use for verification measurements of containers filled with 27 or more spent pebbles.

CIPN↗
DOE OSTI2026

Upgrading Biogas through in situ Conversion of Carbon Dioxide to Biomethane in Anaerobic Digesters

Organic waste streams generated by wastewater treatment plants, agricultural operations, and food processing industries represent an important yet underutilized opportunity for renewable energy production in the United States. Through anaerobic digestion, these waste streams can produce biogas, a mixture primarily composed of methane (CH4) and carbon dioxide (CO2), that can be upgraded to pipeline-quality natural gas. However, most existing upgrading technologies remove CO2 from biogas rather than utilizing it, leaving a significant portion of the potential energy unused. This project investigates a novel biological upgrading approach that converts CO2 into additional CH4 by supplying hydrogen (H2) to specialized microorganisms capable of performing hydrogenotrophic methanation. The main challenges associated with biological biogas upgrading are related to hydrogen supply, gas-liquid mass transfer, and process stability. First, due to the high cost of hydrogen gas, it is preferable that H2 be produced on-site using renewable energy sources such as wind or solar power. Second, hydrogen has low solubility in liquids, which limits its availability to microorganisms and requires strategies to improve gas dissolution and transfer within the reactor. Third, process inhibition may occur as a result of increased pH caused by CO2 consumption or elevated H2 partial pressure, both of which can negatively affect methanogenic activity. Although research in these areas has advanced during the course of this project, these challenges have not yet been fully resolved. To date, the biological systems that have achieved the highest methane concentrations are typically ex-situ reactors, where operational conditions can be more easily controlled. For this reason, the findings of the present project remain highly relevant. The project goal was to develop an innovative system that can accomplish biogas upgrading via biological conversion of CO2 to CH4, in a novel hybrid approach that combines the advantages of both in-situ and ex-situ systems. The proposed system employs a three-phase upflow anaerobic bioreactor with H2 delivery through a gas-permeable membrane, enabling efficient hydrogen transfer and microbial conversion. Under optimized operating conditions, the system achieved 99% H2 consumption and 90% CO2 conversion. A subsequent gas cleaning stage was implemented to further improve gas quality and meet target purity standards. The upgraded gas composition reached 97.7% CH4, 2.2% CO2, and 0.97% O2, while H2S concentrations remained below detection limits. In addition, a flue gas-driven inorganic thermoelectric generator (TEG) system was designed and experimentally validated as a potential source of electricity for H2 production. The system consisted of six TEG modules connected in series and achieved an open-circuit voltage of 4.5 V and a maximum power output of 224 mW at a temperature difference of approximately 53.5 °C, demonstrating effective conversion of waste heat into electrical power under simulated flue gas conditions. Finally, a comprehensive techno-economic analysis was completed to evaluate the capital and operating costs associated with the proposed system. The results provide important insights to guide future scale-up, optimization, and potential deployment of integrated biological biogas upgrading technologies.

09 BIOMASS FUELS↗
DOE OSTI2025

Triangle Method for Dense ReLU Layers [SWR-25-72]

This software is an implementation of the methods for initializing and training neural networks to be more efficient per parameter, described more fully below and in the related publication: In theory, depth should make a ReLU network EXPONENTIALLY more efficient by enabling it to produce an exponential number of piecewise linear sections in its output. This reasoning is largely based on the work of mathematicians that have hand-constructed networks that make good use of depth. In practice however, even very deep ReLU networks that have been randomly initialized will behave identically to their shallow counterparts - missing an entire exponential dimension of efficiency. The triangle method is a first attempt at realizing the exponential potential of deep networks. Instead of randomly setting weights, we force pairs of neurons in each layer learn to build triangles (i.e. functions from [0,1] -> [0,1] that look like triangles). This is a very efficient pattern for generating lots of linear pieces because composing two triangular functions doubles the number of pieces with each composition. The triangle method is more than just a different initialization, it is a new paradigm of training. Instead of making direct updates to the matrix weights, we do an extra step of backpropagation to collect the derivatives of the loss function with respect to the shapes of the triangles, training them to tilt left or right. This process essentially holds the networks hand throughout the loss landscape and forces it to always use depth effectively by producing triangular shapes internally. This can produce several orders of magnitude of improvement on convex one-dimensional regression problems. Much more theoretical work is needed to realize its full potential beyond this context, but the implementation in this repository will still work in arbitrary numbers of dimensions. The file Triangle_Method.py is a generalized form of the method that will build each neuron its own custom 1-d convex activation function (with exponential efficiency). Example usage on one dimensional problems can be found in Example_Usage.ipynb and an example of using this in a real neural network can be found in Example_VGG16_CIFAR10.ipynb.

Milkert, Max [National Renewable Energy Laboratory↗
DOE OSTI2026

Effect of Storage Conditions on Efficacy of Poly(ethylenimine)-Alumina CO 2 Sorbents

Solid amine sorbents are one of the primary components of DAC technologies that allow for the removal of ultradilute CO 2 from the atmosphere. A main drawback in the implementation of solid amine sorbents in industrial-scale DAC applications is their instability under certain operational or storage conditions over an extended period. In this work, the effect of storage temperature and gas composition in the storage headspace on the long-term stability of a poly(ethylenimine)-alumina (PEI/γ-Al 2 O 3 ) sorbent is explored. PEI/γ-Al 2 O 3 sorbents with 70 and 100% pore filling are aged under varying gases (N 2 , O 2 , Ar, 0.04% CO 2 −N 2 , CO 2 , and ambient air) in an oven (40 °C), at common ambient indoor temperature conditions (23 °C), or in a freezer (−4 °C). The CO 2 sorption capacity, as measured by thermogravimetric analysis (TGA), along with FTIR spectra of the fresh and aged sorbents, reveal that at 23 and −4 °C, storage under ambient air or inert gas (Ar) provides reasonable long-term stability, with <13% degradation over 12 and 5 months of storage. Interestingly, with storage at 40 °C, similar levels of deactivation were observed under pure O 2 and N 2 after 4 months of storage, which suggests that nonoxidative thermal reactions can occur under prolonged storage conditions under N 2 . In contrast, with storage under CO 2 , sorbent degradation is substantially suppressed compared to storage under N 2 , ambient air, O 2 , or Ar, yielding sorbents with no observable loss in capacity after 2 months, compared to a 66, 63, and 62% loss under N 2 , ambient air, and N 2 in the same period at 40 °C, respectively. Overall, these findings provide guidance for practical amine sorbent storage in academic or industrial settings where amine sorbents are used for carbon capture.

Atmospheric chemistry↗
DOE OSTI2025

Anion-Exchange-Membrane Electrolysis with Alkali-Free Water Feed

Hydrogen is a green and sustainable energy vector that can facilitate the large-scale integration of intermittent renewable energy, renewable fuels for heavy transport, and deep decarbonization of hard-to-abate industries. Anion-exchange-membrane water electrolyzers (AEM-WEs) have several achieved or expected competitive advantages over other electrolysis technologies, including the use of precious metal-free electrocatalysts at both electrodes, fluorine-free hydrocarbon-based ionomeric membranes and bipolar plates based on inexpensive materials. Contrasting the analogous proton-exchange-membrane system (PEM-WE), where pure water is circulated (no support electrolyte), the current generation of AEM-WEs necessitates the circulation of a dilute aqueous alkaline electrolyte for reaching high energy efficiency and durability. For several reasons, including but not limited to lower cost of balance-of-plant, lower operating cost and improved device's lifetime, achieving high cell efficiency and performance using an alkali-free water feed is highly desirable. In this review, we develop and build a foundational understanding of AEM-WEs operating with pure water, as well as discuss the effects of operating with natural water feeds like seawater. After a discussion of the possible advantages of pure-water-fed AEM-WEs, we cover the thermodynamic and kinetic processes involved in AEM-WE, followed by a detailed review of materials and components and their integration in the device. We highlight the influence of electrolyte composition and alkali/electrolyte-free feed on the membrane-electrode assembly, ionomers, electrocatalysts, porous transport layer, bipolar plates and operating configuration. We provide evidence for how the pure water feed engenders several issues related to the degradation of device components and propose mitigation strategies.

Electrodes↗
DOE OSTI2024

Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

Glassy materials form the basis of many modern applications, including nuclear waste immobilization, touch-screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, in this work, we explore the application of an open-source pre-trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics-informed machine learning (PIML)-predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top-performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Production of Creep-Resistant FeCrAl-ODS

Four ODS FeCrAl alloys with base alloy composition Fe-16Cr-4Al-2Mo-0.2Y (wt.%) were mechanically alloyed with minor additions of CeO 2 and Y 2 O 3 additions and were successfully extruded into rectangular-shaped bars. Two oxide compositions were explored, which were designated NC with oxide addition of 0.5CeO 2 plus 0.2Yin solution of the base alloy powder and PC with additions of 0.5CeO 2 and 0.7Y 2 O 3 to elevate the O level. The microstructure of both alloys revealed by SEM and EBSD to consist of very small grains with sizes of ~450-520 nm and weak texture components. Sub-size tensile specimens fabricated from both alloys were tensile tested from room temperature to 800ºC. The results for both alloys from room temperature to 400ºC showed very high strengths, but low ductilities, especially uniform strain. From 500ºC to 800ºC, the strengths decreased rapidly, and the ductilities increased significantly. The reason for the rapid decreases in tensile strengths above 500ºC for both alloys is not clear, but will require advanced characterization techniques including atom probe tomography, to determine the size and number density of the nano-size oxide particles, and TEM/EFTEM, for investigating the dispersion of nano-size oxide particles on grain boundaries to assess the Zener grain boundary pinning effect, which will hinder grain coarsening at high temperatures that may enhance the Hall Petch strengthening mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

Molten-salt reactors (MSRs) represent a promising solution for next-generation nuclear energy, offering advantages in safety, fuel efficiency, and waste minimization. However, their liquid-fueled design presents unique challenges for spent fuel management, making post-shutdown waste characterization essential for developing effective strategies. Despite this need, there is a notable absence of visualization platforms specifically tailored to the unique characteristics and analytical requirements of MSR waste management. Existing tools in the nuclear industry are primarily designed for reactor operations or generic data exploration and lack both integration with MSR-specific multiphysics frameworks and the ability to simultaneously visualize time-dependent thermal fields, chemical composition evolution, and radiation distribution patterns. To address these limitations, this paper presents an immersive virtual reality (VR) visualization platform that processes and displays high-fidelity multiphysics simulation output from the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework in real-time, using Unity. The platform visualizes MSR waste characteristics such as nuclide decay, salt cooling, and corrosion by using Exodus II output data and running on a VR headset. It includes a user-friendly interface with features such as visibility toggling, cross-sectional slicing, and time-series animation for exploring simulation data. These capabilities support experimental design, stakeholder engagement, and public communication by making complex reactor behavior more accessible and understandable. By enhancing spatial reasoning and reducing cognitive load, this immersive environment fosters more effective communication and decision-making in MSR waste management.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Corrosion Analysis of 121103072 (SAVY-4000)

A surveillance feedlist for fiscal year (FY) 2022 was developed with the intent to target containers with contents known to generate corrosive gasses. One SAVY-4000 (hereafter “SAVY”) container with a serial number 121103072 was selected due to the reasonable wattage and known molten salt extraction (MSE) material corrosive behavior. The material was measured at 3.08 W with approximately 200 g of material placed inside of the SAVY for 6.07 years. The inner packaging configuration included a ¼ Qt stainless steel slip top inner container and a sPVC bag-out bag enclosing the inner container. Visual observations of the container during retrieval revealed several concerning features on the exterior of the container, notably on the lid. Fig. 1 shows the container lid along with an inset image further magnifying the features of interest. The corroded tamper indicating device (TID) wire and the corroded radioactive material tag wire indicated that corrosive gas species for steel were produced during storage. Although the TID wire and rad tag wire are not the same composition as the SAVY body and lid, these are often used as an indicator of potential corrosion inside of the SAVY container. The oxide residing inside of the filter holes and significant buildup around one hole provided further evidence supporting the presence of corrosive species inside of the container.

36 MATERIALS SCIENCE↗