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2,053 records · Page 11

Strategies for Fabricating Molybdenum Structures Using Laser Powder Bed Fusion

Advances in manufacturing techniques are viewed as enabling technologies for development of high performance nuclear fuel forms that couple high uranium density with improvements to key properties such as thermal conductivity unattainable through conventional fabrication routes. Additive manufacturing (AM) enables the fabrication of complex fuel geometries that are difficult or impossible to achieve using conventional manufacturing methods. Melting-based AM processes, such as laser powder bed fusion (LPBF), provide high geometric resolution (>200 µm depending on the feature) across a variety of metal alloys, including those suitable for high-temperature fuel cladding applications, such as Nb, W, and Mo. Molybdenum is particularly attractive due to its high thermal conductivity, low thermal expansion, and excellent mechanical stability at elevated temperatures. However, its high melting temperature and brittle nature at low temperatures pose significant challenges during LPBF processing. Rapid solidification inherent to LPBF induces high residual stresses, often leading to post-solidification cracking, which limits the manufacturability of Mo components via this method.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS

Thin Film Sensors for Fission Surface Power

Physical sensors fabricated with thin films could result a significant savings in space and weight with improved reliability for monitoring the long-term operation of fission surface power systems. Thin film sensors of 1 µm or less are attractive for FSP applications because they can be incorporated onto component surfaces with minimal machining and the additional weight to a system is minimal compared to thick film-, wire-, or foil-based sensors. The thin, surface fabrication of the sensors is also expected to make them less susceptible to deep dose effects that affect thicker sensors. An overview of thin film sensors using thermocouples and resistive elements designed, fabricated, and demonstrated at GRC for aerospace applications exceeding 900°C is presented.

Physical Sensors

A New Era for Modern Bubble Chamber Technology: Cooling and Real-Time Control for the MAMBA Neutrino Bubble Chamber∗

Future neutrino experiments such as DUNE will be limited less by statistics than by how well neutrino--nucleus interactions are understood, and the cleanest way to improve that understanding is to measure interactions with light nuclei such as hydrogen or deuterium. The detector best suited to the job, the bubble chamber, has not been built for a neutrino beam in about fifty years. MAMBA (Modern Adaptive Modular Bubble chamber Archetype) is a small prototype at Fermilab intended to bring the technology back with modern cryogenics and automation, cycling continuously at 1~Hz. This paper summarizes my work on two of its subsystems during a summer internship. A new solid copper thermal link brought the coldhead to 21.3~K in a commissioning cooldown, near the 20~K operating target. An Industrial Shields Raspberry Pi programmable logic controller (PLC) running OpenPLC was characterized at a median round-trip response of 0.64~ms over 20,000 trials, with 0.66\% of trials exceeding 1~ms. Both results support continuous cycling.

Williams, Nicholas [Fermilab; DuPage Coll.]

Applications Technology Satellites Battery and Power System Design

A summary of the ATS battery design which is onboard the Applications Technology Satellite (ATS) is provided. The 15 ampere hour nickel cadmium cells were manufactured by Gulton, 19 series connected cells per battery, and there are two batteries in each spacecraft. The operating design life was two years in a synchronous orbit, and a maximum depth of discharge of 50 percent. The design temperature for the batteries in the spacecraft was 0 to 25 C, and the charge control consisted of 1 volt versus temperature on a constant percentage voltage. Also, C/10 current limit, and a commandable trickle charge rate, using C/20 or C/60. The undervoltage was sent across a 9 cell and a 10 cell group, and it was set at one volt average per group on either group.

F E Ford

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data

Bosch Carbon Catalyst Regeneration in Water II: Analysis of Bosch Catalyst Beads Throughout the Initial Regeneration Process

Catalyst integrity is an important factor in reactor longevity since it changes the effectiveness of a reaction. A Carbon Formation Reactor (CFR), used within Series Bosch (S-Bosch), utilizes an iron alloy catalyst to convert carbon monoxide and hydrogen into water and solid carbon. The catalyst promotes the formation of carbon to such an extent that pressure-causing clogs form within the reactor and reaction sites on the catalyst become limited. To remove carbon from both the reactor and the surface of the iron alloy catalyst, a technique was developed that utilizes water, an In-Situ Resource Utilization (ISRU) Resource, and agitation to regenerate the reactor. To ascertain catalyst integrity throughout this regenerative process, sample catalyst beads were taken before and after the carbon formation reaction, and then after the regeneration process. Additionally, wastewater from the regeneration process was sampled. The sampled beads and wastewater were observed under a Scanning Electron Microscopy (SEM) microscope and the beads were chemically etched to determine if the catalyst had degraded at any point during this process, and if catalyst was lost. Minimal catalyst degradation was observed after carbon formation in the CFR.

regeneration

Bosch Carbon Catalyst Regeneration in Water II: Analysis of Bosch Catalyst Beads Throughout the Initial Regeneration Process

Catalyst integrity is an important factor in reactor longevity since it changes the effectiveness of a reaction. A Carbon Formation Reactor (CFR), used within Series Bosch (S-Bosch), utilizes an iron alloy catalyst to convert carbon monoxide and hydrogen into water and solid carbon. The catalyst promotes the formation of carbon to such an extent that pressure-causing clogs form within the reactor and reaction sites on the catalyst become limited. To remove carbon from both the reactor and the surface of the iron alloy catalyst, a technique was developed that utilizes water, an In-Situ Resource Utilization (ISRU) Resource, and agitation to regenerate the reactor. To ascertain catalyst integrity throughout this regenerative process, sample catalyst beads were taken before and after the carbon formation reaction, and then after the regeneration process. Additionally, wastewater from the regeneration process was sampled. The sampled beads and wastewater were observed under a Scanning Electron Microscopy (SEM) microscope and the beads were chemically etched to determine if the catalyst had degraded at any point during this process, and if catalyst was lost. Minimal catalyst degradation was observed after carbon formation in the CFR.

Series Bosch

A Python Tool for Aqueous Plutonium Nitrate Density Law Input Preprocessing in MCNP6

Here, this work develops a predictive density tool in Python, named Plutonium Nitrate Solutions (PuNS), to reduce bias and uncertainty in nuclear criticality safety calculations for plutonium nitrate systems. The Pitzer method and an empirical method were implemented into the PuNS tool to generate atom densities for use in MCNP6 material cards. These material cards are directly prepared into an MCNP6 input text file and are calculated based on customizable user inputs of plutonium content, nitric acid content, temperature, and plutonium isotope weight percentages. The PuNS tool is validated and verified against the International Criticality Safety Benchmark Evaluation Project Handbook experiments and is observed to predict densities within a root mean square error of 0.89% for the Pitzer method and 1.82% for the empirical method. These errors in density lead to up to 1569 pcm difference in MCNP6 calculated k eff for the Pitzer method and up to a 1751 pcm difference for the empirical method when compared to experimental benchmarks. Simultaneous work is also being performed at Los Alamos National Laboratory and the University of New Mexico to create a similar tool for plutonium chloride solutions, named Plutonium Chloride Solution, which aims to provide the accreditation of the chlorine absorption. These capabilities will not only provide more accurate models but also facilitate an improved understanding of solution systems and a potential relaxation in the conservatism of current aqueous plutonium processing criticality safety limits.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA

Nanoscale Observation and Control of Quasiparticle Induced Magnetic Noise in a Superconducting Resonator

Superconducting circuits are arguably taking a leading role in driving the ongoing quantum technological revolution. A detailed knowledge of the microscopic fluctuating electromagnetic properties plays an important role in advancing the circuitry design, testing, and material integration of cutting-edge superconducting quantum electronics. Here, in this work, we report scanning nitrogen-vacancy (NV) quantum sensing of local magnetic noise environment of an on-chip superconducting resonator. We find that quasiparticle-induced fluctuating magnetic fields can drive NV spin relaxation, which shows a peak value around the superconducting transition point of niobium at the thermal equilibrium state. External microwave driving at the resonator mode frequency significantly increases the quasiparticle density, leading to enhancement of magnetic noise. We further perform optically detected magnetic resonance measurements to demonstrate quasiparticle magnetic noise mediated off-resonant dipole coupling between the NV center and niobium resonator. Our Letter reports experimental observation of the Hebel-Slichter peak signature by an external sensor outside of a superconductor. The presented study also highlights the advantages of quantum sensors in investigating miniaturized superconducting devices, providing insights into their future performance improvements.

Li, Senlei [Georgia Institute of Technology]

Smart building HVAC control challenge: experience and solutions from the ADRENALIN project

A smart building HVAC control competition crowdsourced and compared algorithms on fair and equal ground using the standardized BOPTEST framework. The competition attracted 138 participants, but only 9% submitted valid solutions for the final stage, highlighting the complexity of advanced HVAC control design. The winning solutions showed significant potential to reduce energy use and cost by shifting demand, without compromising occupant comfort. Across scenarios, thermal energy cost reductions of 36–76% relative to a baseline, were achieved. In peak heat periods, the cost reduction leveraged limited energy use reduction (0–15%), but more significant energy price reduction (34–62%). This shows smart controls' ability to avoid as much as possible consumption during the morning peak hours, when spot prices are tendentially the highest. Hosting the competition has highlighted challenges in creating competitions that both are fair and promotes solutions that are transferable to real life implementation.

BOPTEST

Prime Time for Model-Predictive Control? Assessing the Technical and Market Readiness of Advanced Controls in Buildings

Despite three decades of extensive research and field testing that have consistently validated the benefits of Model Predictive Control (MPC) in building applications, the technology has seen limited market adoption. This paper evaluates the readiness of MPC for widespread deployment, showcases recent demonstrations and field tests across diverse building types, including residential, small commercial, large commercial, and campus settings. Our results demonstrate that MPC can optimize system operations to achieve load shifting, minimize curtailment of on-site generation, and reduce energy costs by up to 80 %, while maintaining or improving occupant comfort. We also show that MPC can effectively control large assets, such as MW-sized thermal storage systems, and respond to dynamic pricing signals. However, achieving scale remains difficult due to labor-intensive workflows, reliance on a “PhD-in-the-loop” for MPC design and maintenance, susceptibility to fragile data infrastructure, and persistent workforce education and acceptance barriers. To bridge this gap, we outline a transition from bespoke, labor intensive prototypes toward streamlined, segment-targeted deployment strategies that leverage model templates, semantic tools, and generative AI. By automating control configuration and reducing engineering effort, these recommendations provide a pathway for transforming successful research demonstrations into scalable, market ready solutions for MPC-based controls.

Pritoni, Marco

Progress and Perspectives: Zirconium Electrodeposition from Different Electrolytes

Zirconium (Zr) possesses outstanding properties, including exceptional chemical resistance and a high melting point, and is therefore desirable for use in a wide variety of challenging environments, such as in nuclear reactors and the chemical processing industry. To minimize the amount of Zr required for any given application, developing methods for generating metallic Zr coatings is highly advantageous. Owing to Zr’s highly negative reduction potential, electrodeposition in traditional solvents at near ambient temperatures remains challenging and thus not well understood. Due to the extreme conditions required to deposit this metal, extensive work has been conducted in molten salt electrolytes, however the broad applicability of this methodology is limited due to its corrosivity. The primary focus of this article is to present an overview of Zr’s electrochemical behavior and to consolidate the efforts of researchers in exploring electrodeposition techniques for Zr involving aqueous, organic, ionic liquid, deep eutectic, and molten salt solvents. With this information, we highlight trends across solvent systems and opportunities for future research.

42 ENGINEERING

Autonomous Power Expert Fault Diagnostic System for Space Station Freedom Electrical Power System Testbed

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control to the Space Station Freedom Electrical Power System (SSF/EPS) testbed being developed and demonstrated at NASA Lewis Research Center. The objectives of the program are to establish artificial intelligence technology paths, to craft knowledge-based tools with advanced human-operator interfaces for power systems, and to interface and integrate knowledge-based systems with conventional controllers. The Autonomous Power EXpert (APEX) portion of the APS program will integrate a knowledge-based fault diagnostic system and a power resource planner-scheduler. Then APEX will interface on-line with the SSF/EPS testbed and its Power Management Controller (PMC). The key tasks include establishing knowledge bases for system diagnostics, fault detection and isolation analysis, on-line information accessing through PMC, enhanced data management, and multiple-level, object-oriented operator displays. The first prototype of the diagnostic expert system for fault detection and isolation has been developed. The knowledge bases and the rule-based model that were developed for the Power Distribution Control Unit subsystem of the SSF/EPS testbed are described. A corresponding troubleshooting technique is also described.

Long V Truong