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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 217 records · Page 12

On the character of turbulent-like flows in self-consistent models of core-collapse supernovae

Neutrino-driven convection plays a crucial role in the development of core-collapse supernova (CCSN) explosions. However, the complex mechanism that triggers the shock revival and the subsequent explosion has remained inscrutable for many decades. Multidimensional simulations suggest that the growth of fluid instabilities, especially development of convection, will determine the morphology of the explosion. We have performed 3D simulations using spherical-polar coordinates covering a reduced angular extent (90° computational domain), and with angular resolutions of 2°, 1°, 1/2°, and 1/4°, to study the development of turbulent-like flows in core-collapse supernova explosions on a time scale of order 100 ms. We have employed the multi-physics Chimera code that includes detailed nuclear physics and spectral neutrino transport. Coarse resolution models do not develop an inertial range, presumably due to the bottleneck effect, such that the energy is prevented from cascading down to small scales and tends to accumulate at large scales. High-resolution models instead, start to recover the k −5/3 scaling of Kolmogorov's theory. Stochasticity and few simulation samples limit our ability to predict the development of explosions. Over the simulated time period, our models show no clear trend in improving (or diminishing) conditions for explosion as the angular resolution is increased. However, we find that disordered flow provides an effective pressure, as characterized by the Reynolds stress, behind the shock. This contribution, ~40%–50% of the thermal pressure, aids shock revival and thus the development of the explosion. Finally, we show that the kinetic energy power spectrum of reduced angular extent and full 4π models are consistent, thus indicating that a 90° computational domain is an adequate configuration to study the character of turbulence in CCSNe.

Casanova Bustamante, Jordi↗

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, https://doi.org/10.5880/GFZ.4.4.2023.001).

54 ENVIRONMENTAL SCIENCES↗

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics and exposomics studies is to fully characterize the small molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversi-ty present within the metabolome and exposome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemis-try methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alter-native route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solu-tion. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectros-copy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics and exposomics studies. We expect this review will inspire researchers in the field of small molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Metabolism↗

Permittivity Threshold and Thermodynamics of Integer Charge-Transfer Complexation for an Organic Donor–Acceptor Pair

Molecular or chemical-based charge doping provides a means to change the properties (e.g. conductivity) of a material through a complete or partial transfer of charge between its constituents. Understanding how local material environment impacts this process is thus important for controlling the properties of organic materials. This paper presents spectroscopic investigations of molecular charge transfer interactions between a pair of common charge-doping and -transporting materials (2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane, F4TCNQ, and N,N’-Diphenyl-N-N’-di-p-tolylbenzene-1,4-diamine, MPDA). Equilibrium constants associated with charge transfer were determined for donor-acceptor pairs dissolved in a series of solvents covering a range of values of permittivity. A threshold for highly favorable charge separation was observed to occur at a permittivity of ~8-9, with more and less favorable charge separation observed in solvents of higher and lower permittivity, respectively, but with chloroform (permittivity=4.81) exhibiting an anomalously favorable charge separation. Temperature-dependent studies were undertaken to assess the thermodynamics of charge separation. In 1,2- dichloroethane (permittivity=10.36) and chlorobenzene (permittivity=5.62), charge transfer was observed to be both enthalpically and entropically favorable. Charge separation in chloroform is exothermic and entropically disfavored, indicating that specific, inner-shell solvent-solute interactions stabilize the charge-separated complex and result in a net increase in local solution structure. Furthermore, our results provide insight on how modification to the chemical environment may be utilized to support stable charge transfer for molecular doping applications and requiring only modest changes in local permittivity.

36 MATERIALS SCIENCE↗

Energy Intensity Baselining and Tracking Guidance

Each company joining the U.S. Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) commits to establishing an energy consumption and energy intensity (EI) baseline and to tracking its energy performance over a 10-year period against that baseline. The baseline must reflect a company’s energy consumption over a 12-month period, covering all its U.S.-based operations. Energy consumption is calculated by fuel type in terms of primary energy (also known as source energy). EI is broadly defined as the amount of energy consumed per unit of output produced. For this guidance document and for the program, the term energy performance represents an evaluation of a facility’s capacity to use energy efficiently. Metrics used to assess a facility’s energy performance can include EI, energy consumption, improvements in EI, etc. Establishing an energy baseline and tracking system is a critical first step in effectively managing energy use. Developing a baseline can help a company understand energy use within the corporation and give it a point of comparison to evaluate future efforts to improve energy performance. It can also support efforts to validate a company’s energy management activities, improve comparative analyses when using benchmarks, and help in predicting future energy needs. In addition, a company that normalizes its performance data can determine highly defensible measures of energy savings generated through implemented energy efficiency projects. Establishing a baseline and tracking energy performance is also a requirement for ISO 50001 certification. Although basic energy data can be collected through utility bills, most manufacturers will have to perform additional analyses to develop accurate and robust energy baselines and tracking systems. Energy is consumed in many ways within the manufacturing sector and can come from multiple sources. Energy is sometimes generated and sold to other parties or captured and reused on-site. External events can exert a significant impact on a facility or company’s energy use independent of any purposeful efforts to improve energy efficiency. Operational changes, such as production shifts—which may be inevitable for some companies over the 10-year period covered by the program—can also make a big difference in energy use. Since Better Plants asks companies to account for all their U.S.-based operations, mergers, acquisitions, and divestitures can also have significant implications for a company’s energy metrics. This document aims to demystify the sometimes complex baselining process. It devotes special attention to the task of normalizing and adjusting energy consumption to account for external factors, such as weather and production changes. A key recommendation is that companies use regression analysis to normalize their energy consumption data whenever possible. Regression analysis is a statistical technique that estimates the dependence of a variable (i.e., energy use in the context of Better Plants) on one or more independent variables such as ambient temperature, while controlling for the influence of other variables at the same time. A properly developed regression analysis can provide a reliable estimate of energy savings resulting from energy improvement strategies and projects by accounting for the effects of variables such as annual production levels and weather. DOE has developed a companion Energy Performance Indicator software tool (EnPI) to simplify the baselining process. This tool can run regression models, calculate changes in EI at the facility level, and automatically compile facility-level data into a corporate-wide metric. Note that although the relevant equations used to calculate EI are provided in this document, the EnPI tool will automatically perform most calculations for the user. Additionally, Better Plants Partners (Partners) can call on their Technical Account Manager (TAM) to help them establish a baseline and assist with the necessary calculations to track progress.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A computational pipeline to generate a synthetic dataset of metal ion sorption to oxides for AI/ML exploration

The charged mineral/electrolyte interfaces are ubiquitous in the surface and subsurface–including the surroundings of the geological disposal sites for radioactive waste. Therefore, understanding how ions interact with charged surfaces is critically important for predicting radionuclide mobility in the case of waste leakage. At present, the Surface Complexation Models (SCMs) are the most successful thermodynamic frameworks to describe ion retention by mineral surfaces. SCMs are interfacial speciation models that account for the effect of the electric field generated by charged surfaces on sorption equilibria. These models have been successfully used to analyze and interpret a broad range of experimental observations including potentiometric and electrokinetic titrations or spectroscopy. Unfortunately, many of the current procedures to solve and fit SCM to experimental data are not optimal, which leads to a non-transferable or non-unique description of interfacial electrostatics and consequently of the strength and extent of ion retention by mineral surfaces. Recent developments in Artificial Intelligence (AI) offer a new avenue to replace SCM solvers and fitting algorithms with trained AI surrogates. Unfortunately, there is a lack of a standardized dataset covering a wide range of SCM parameter values available for AI exploration and training–a gap filled by this study. Here, we described the computational pipeline to generate synthetic SCM data and discussed approaches to transform this dataset into AI-learnable input. First, we used this pipeline to generate a synthetic dataset of electrostatic properties for a broad range of the prototypical oxide/electrolyte interfaces. The next step is to extend this dataset to include complex radionuclide sorption and complexation, and finally, to provide trained AI architectures able to infer SCMs parameter values rapidly from experimental data. Here, we illustrated the AI-surrogate development using the ensemble learning algorithms, such as Random Forest and Gradient Boosting. These surrogate models allow a rapid prediction of the SCM model parameters, do not rely on an initial guess, and guarantee convergence in all cases.

Li, Chunhui↗

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Year 2024

A new version of this dataset is available at doi:10.15485/3001338 and is the first citation in the 'Related References' section. It is expands on this dataset by appending another water year of data collection and additional logger sites.This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 12 August 2024. Instruments were deployed at 14 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 shows smaller-scale snow patterns across a nearby meadow.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.Dataset update 2025-03-03: Resolved header and datetime formatting inconsistencies within LoggerData.zip files KP1_Air, KP3_Air, KP3_Ground, KP6_Ground, AP3_Ground, AP4_Air, and AP6_Air.Dataset update 2025-11-18: Modified abstract and related references sections to include new version of dataset.

54 ENVIRONMENTAL SCIENCES↗

Toward particle accelerator machine state embeddings as a modality for large language models

Understanding and diagnosing the state of a particle accelerator requires navigating high-dimensional control system data, often involving hundreds of interdependent parameters. We propose a novel multimodal embedding framework that jointly learns representations of machine states from both numerical control system readouts and natural language descriptions. This enables the translation of complex machine conditions into human-readable summaries while maintaining fidelity to the underlying physical system. The obtained embeddings are subsequently adapted to an open-weights large language model via cross-attention conditioning. We demonstrate a first implementation trained on European XFEL machine state data. This work covers the embedding model architecture, training methodology, and presents initial examples demonstrating the model's capabilities in action. Due to the general concept of machine state, the model can be easily adapted to other facilities and control system environments.

Accelerator Physics↗

Develop a weather-aware climate model to understand and predict extremes and associated power outages and renewable energy shortages with uncertainty-aware and physics-informed machine learning

Focal Area(s): The focus area is predictive modeling through the use of AI techniques and AI-derived model components with a particular emphasis on extreme weather in Atmospheric Science and power outages and shortages in Energy Science. Science Challenge: Predicting weather extremes (e.g., heavy precipitation, strong wind, and large hailstones), and weather-related power system outages and shortages can mitigate economic losses, save lives, support renewables integration, and improve power system resiliency. However, currently, the poor reliability and large uncertainty associated with the weather extreme prediction in the current climate models make the problem intractable. The key challenges are: (1) physical factors like green-house gases (GHGs), aerosols, and land use and land cover (LULC) can significantly impact extreme storms, but the understanding of these impacts is limited, particularly globally; (2) the convective permitting resolutions needed to model severe convective storms and their impacts are computationally prohibitive with global climate models (GCMs); (3) interactions between weather extremes and power system outages are complex and subject to great uncertainty. Current outage prediction models are short lead (~ 3 days), which do not allow for long-time planning of energy production and distribution. Moreover, we have limited capacity to predict weather events leading to sustained shortages in a renewable-energy-dominated power system. These challenges drive motivation for mechanistic understanding and reliable and efficient predictive modeling of extremes and their impacts from the sub-seasonal to long term projections.

54 ENVIRONMENTAL SCIENCES↗

Data analytics for intermodal freight transportation applications

With the growth of intermodal freight transportation, it is important that transportation planners and decision-makers are knowledgeable about freight flow data to make informed decisions. This is particularly true with Intelligent Transportation Systems (ITS) offering new capabilities for intermodal freight transportation. Specifically, ITS enables access to multiple different data sources, but they have different formats, resolutions, and time scales. Thus, knowledge of data science is essential to be successful in future ITS-enabled intermodal freight transportation systems. This chapter discusses the commonly used descriptive and predictive data analytic techniques in intermodal freight transportation applications. These techniques cover the entire spectrum of univariate, bivariate, and multivariate analyses. In addition to illustrating how to apply these techniques manually, this chapter will also show how to apply them using the statistical software R. Additional exercises are provided for those who wish to apply the described techniques to more complex problems.

Huynh, Nathan↗

A simulation study of carbon storage with active reservoir management

As part of the Integrated Midcontinent Stacked Carbon Storage Hub (IMSCS-Hub) project led by Battelle Memorial Institute, a study was conducted to determine the feasibility of storing carbon dioxide (CO 2 ) in the stacked saline rock formations of the Sleepy Hollow Field (SHF), located in Red Willow County, southern Nebraska. A series of CO 2 injection simulation scenarios, with and without active reservoir management (ARM; brine extraction), were evaluated to investigate the feasibility of storing 50+ million tonnes (Mt) of CO 2 . The results indicated CO 2 injection combined with ARM may enable permanent storage of 50+ Mt of CO 2 . The area of review (AOR), the area in which underground sources of drinking water (USDWs) might be endangered during CO 2 injection, was assessed for the simulation scenarios. In comparison to a case without ARM, brine extraction resulted in a much smaller AOR, covering an area of 42 square miles (108.8 km 2 ), roughly one-fourth the size of an AOR resulting from CO 2 injection without ARM. Our findings, presented in this paper, indicate that CO 2 injection with ARM can improve the CO 2 storage capacity of a geologic storage complex up to 100% while also reduce the rate of pressure buildup in the subsurface, resulting in a 75% reduction in AOR. This may help in lowering carbon capture and storage project costs, risks, and effort needed to meet monitoring requirements for a storage project.

54 ENVIRONMENTAL SCIENCES↗

Oxidation and Reduction of Polycrystalline Cerium Oxide Thin Films in Hydrogen

This study investigates the oxidation state of ceria thin films' surface and subsurface under 100 mTorr hydrogen using ambient pressure X-ray photoelectron spectroscopy. We examine the influence of the initial oxidation state and sample temperature (25-450 °C) on the interaction with hydrogen. Our findings reveal that the oxidation state during hydrogen interaction involves a complex interplay between oxidizing hydride formation, reducing thermal reduction, and reducing formation of hydroxyls followed by water desorption. In all studied conditions, the subsurface exhibits a higher degree of oxidation compared to the surface, with a more subtle difference for the reduced sample. The reduced samples are significantly hydroxylated and covered with molecular water at 25 °C. We also investigate the impact of water vapor impurities in hydrogen. We find that although 1 × 10 -6 Torr water vapor oxidizes ceria, it is probably not the primary driver behind the oxidation of reduced ceria in the presence of hydrogen.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An Extensive Catalog of Early-type Dwarf Galaxies in the Local Universe: Morphology and Environment

We present an extensive catalog of 5405 early-type dwarf (dE) galaxies located in the various environments, i.e., clusters, groups, and fields, of the local universe (z < 0.01). The dEs are selected through visual inspection of the Legacy survey's g–r–z combined tricolor images. The inspected area, covering a total sky area of 7643 deg 2 , encompasses two local clusters (Virgo and Fornax), 265 groups, and the regions around 586 field galaxies of M K < -21 mag. The catalog aims to be one of the most extensive and publicly accessible collections of data on dEs, despite its complex completeness limits, which may not accurately represent its statistical completeness. The strength of the catalog lies in the morphological characteristics, including nucleated, tidal, and ultradiffuse dEs. The two clusters contribute nearly half (2437 out of 5405) of the dEs, and the 265 groups contribute 2103 dEs. There are 864 dEs in 586 fields, i.e., ~1.47 dEs per field. Using a standard definition commonly used in literature, we identify 100 ultradiffuse galaxies (UDGs), which compose ~2% of the dE population. We find that 40% of our sample dEs harbor a central nucleus, and among the UDG population, a majority (79%) are nonnucleated. About 1.3% of dEs suffer from ongoing tidal disturbance by nearby massive galaxies, and only 0.03% show a sign of recent dwarf–dwarf mergers. The association between dEs and their nearest bright neighbor galaxies suggests that dEs are more likely created where their neighbors are non-star-forming ones.

79 ASTRONOMY AND ASTROPHYSICS↗

Sludge Removal: Success and Partnership with T Plant - 20510

U.S. DOE contractor CH2M Hill Plateau Remediation Company (CHPRC) has safely and efficiently managed the removal of highly radioactive sludge from the 105-K West Reactor fuel storage basin (105- KW Basin) near the Columbia River. In mid-September 2019, sludge retrieval was completed after 21 storage containers were safely loaded with highly radioactive sludge and transported approximately 13 miles from the 105-KW Basin to T Plant, located in the 200 West Area on the Central Plateau of the Hanford Site. Sludge removal represents the last major source term reduction necessary before the K West Reactor and fuel storage basin can transition to closure activities in preparation for final deactivation and demolition. The technical complexity of the sludge retrieval process, coupled with the challenging physical and radiological characteristics of the waste, necessitated a methodical and deliberate approach using unique design and operational solutions to safely conduct the work. This challenge was further complicated by the need to plan and conduct the work in two separate facilities, each subject to the controls and requirements of its own nuclear facility safety basis. The project overcame these challenges through creating an integrated team that actively engaged, communicated and coordinated each phase of the project to ensure successful completion. This paper will cover how these key elements of the Sludge Removal Project led to its success: - Integrating project planning and management; - Lessons learned from design and construction; - Preparing for a successful campaign (lessons learned from testing and start-up); - Lessons learned from operating in two separate facilities, transporting sludge containers on the Hanford Site and ensuring compliance with a range of regulatory and safety basis requirements. The establishment of an integrated group spanning two facilities and multiple organizations created a team capable of overcoming the challenges necessary to successfully plan and execute the sludge retrieval mission. Sharing lessons learned from this successful project can enhance the ability of teams across the DOE complex to successfully plan and execute complex projects. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Years 2024 and 2025

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 8 August 2025. Instruments were deployed at 19 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 (Avery Picnic) provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 (Emerald Lake) shows smaller-scale snow patterns across a nearby meadow. Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗

An Updated Visual Orbit of the Directly Imaged Exoplanet 51 Eridani b and Prospects for a Dynamical Mass Measurement with Gaia

We present a revision to the visual orbit of the young, directly imaged exoplanet 51 Eridani b using four years of observations with the Gemini Planet Imager. The relative astrometry is consistent with an eccentric (e=0.53{sub -0.13}{sup +0.09}) orbit at an intermediate inclination (i=136{sub -11}{sup +10} °), although circular orbits cannot be excluded due to the complex shape of the multidimensional posterior distribution. We find a semimajor axis of 11.1{sub -1.3}{sup +4.2} au and a period of 28.1{sub -4.9}{sup +17.2} yr, assuming a mass of 1.75 M{sub ⊙} for the host star. We find consistent values with a recent analysis of VLT/SPHERE data covering a similar baseline. We investigate the potential of using the absolute astrometry of the host star to obtain a dynamical mass constraint for the planet. The astrometric acceleration of 51 Eri derived from a comparison of the Hipparcos and Gaia catalogs was found to be inconsistent at the 2σ–3σ level with the predicted reflex motion induced by the orbiting planet. Potential sources of this inconsistency include a combination of random and systematic errors between the two astrometric catalogs and the signature of an additional companion within the system interior to current detection limits. We also explored the potential of using Gaia astrometry alone for a dynamical mass measurement of the planet by simulating Gaia measurements of the motion of the photocenter of the system over the course of the extended 8 yr mission. We find that such a measurement is only possible (>98% probability) given the most optimistic predictions for the Gaia scan astrometric uncertainties for bright stars and a high mass for the planet (≳3.6 M {sub Jup}).

79 ASTRONOMY AND ASTROPHYSICS↗

Expert evaluation of LLM world models: A high-T c superconductivity case study

Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the ability of LLM systems to understand the literature at the level of an expert. We construct an expert-curated database of 1,726 scientific papers that covers the history of the field, and a set of 67 expert-formulated questions that probe deep understanding of the literature. We then evaluate six different LLM-based systems for answering these questions, including both commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. Experts then evaluate the answers of these systems against a rubric that assesses balanced perspectives, factual comprehensiveness, succinctness, and evidentiary support. Among the six systems, two using RAG on curated literature outperformed existing closed models across key metrics, particularly in providing comprehensive and well-supported answers. We discuss promising aspects of LLM performances as well as critical short-comings of all the models. The set of expert-formulated questions and the rubric will be valuable for assessing expert level performance of LLM based reasoning systems.

36 MATERIALS SCIENCE↗

Study of reference burnup steps optimization in fuel segment data file generation for NEXUS/ANC9 code system

For any two-step core design code system, the cross-section files are the primary factor to determine the accuracy of the system prediction. For a once-through cross-section system to cover all potential applicable conditions, the system may need to perform tens of thousands state points of lattice calculations. These calculations take a significant amount of CPU times and computing power, especially when a fine or ultra-fine energy group library is used. As computer power increases, so does the calculational complexity. Therefore, it is important to make the lattice calculations effective and efficient. Based on the Westinghouse core design code system NEXUS/ANC9, this work focuses on the reference burnup steps used by the lattice code when performing the calculations. Following the fundamental cross-section methodology and analyzing the contribution of each individual terms, this study provides an applicable solution of the optimized reference burnup steps. The test results show that, with the existing cross-section methodology, it is possible to significantly reduce the lattice calculation cases and cross-section file generation time without sacrificing the accuracy of system prediction. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗