Revolutionizing Knowledge Management with Project I.R.I.S., Intelligent Retrieval and Information System
Invited presentation to CLSAC conference, November 2024
Garasi, Christopher J.↗
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Round Robin Measurements of Molten Salt Properties for LiF-NaF-KF (FLiNaK) and NaCl-KCl Mixtures
The development, operation, and regulation of nuclear reactors that utilize molten salts as fuel or as heat transfer media require knowledge of the thermal properties of the salt systems and quantification of the corresponding uncertainties. Knowledge of molten salt properties is also necessary for applications in material synthesis, processing, separations, solar thermal power generation, and energy storage. A round robin was conducted with national laboratory and university participants from twenty-one laboratories in five countries to compare property measurements, to better understand uncertainties, and to identify possible best practices. Two salt mixtures, each from a common batch, were distributed to participants for evaluation: equimolar NaCl-KCl and 45.0LiF-13.7NaF-41.3KF mol % (FLiNaK). Measurements were performed to determine the major constituent composition, oxygen content, density, thermal expansivity, melting point, and thermal conductivity. Error analysis was performed on each measurement for uncertainty quantification for each type of property that was explored. Finally, the resulting discussion of the methodologies used in this work is meant to lay the groundwork for the development of standard methods and reference materials for future high-temperature property measurements on halide melts.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Indicators of Global Climate Change 2023: annual update of key indicators of the state of the climate system and human influence
Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC). Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open-data, open-science approach, is to make annually updated reliable global climate indicators available in the public domain. As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO 2 e yr -1 over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO 2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here.
54 ENVIRONMENTAL SCIENCES↗
Diurnal Trends and Meteorological Factors Influencing the Variability of Fluorescent Bioaerosol in Mt. Crested Butte, Colorado During SAIL
Abstract Despite the crucial role of bioaerosol particles (BAP) in our climate system, local ecosystems, and human health, our grasp on their atmospheric interactions is hampered by a lack of high‐resolution and long‐term data, which is essential for understanding their abundance and variability in response to meteorological conditions. To discern these relationships in a high‐altitude mountainous terrain is even less well‐studied. Therefore, we deployed a Wideband Integrated Bioaerosol Sensor (WIBS‐Neo) for three months during the first biologically active season of the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in summer 2022 on Mt. Crested Butte in Colorado (elevation 3,140 m ASL). Here, we report real‐time fluorescent BAP (FBAP) data collected from 15 June to 13 September 2022, sampled from within the ARM Aerosol Observing System (AOS). To our knowledge, these are the first high altitude (>3,000 m ASL) continuous measurements of FBAP made in North America. During this deployment, we observed on average 21% and as many as 48% (hourly maximum) of particles within the detection size range ( to ) of the WIBS as FBAP. Our analysis presents the diurnal cycles for seven distinct types of FBAP, showing unique patterns and, in some cases, correlations with temperature and solar radiation cycles. Abundance and composition of FBAP varied with relative humidity and precipitation. Precipitation events appeared to both cause emission and removal of FBAP, whereas dust and smoke events had no significant effect, highlighting the critical role of meteorology on FBAP at high altitudes.
54 ENVIRONMENTAL SCIENCES↗
Path Forward: Materials Data Modernization for ASME Codes and Standards in the Artificial Intelligence Era
Development of the ASME Materials Properties Database was initiated in the early 2010s to support the ASME Codes and Standards. As information technologies advance at an accelerated pace with the artificial intelligence era on the horizon, the ASME Materials Properties Database must be further modernized from a database to a knowledgebase to ride the wave of digital information revolution and effectively support the ASME Codes and Standards in the new era. This paper is intended to provide an overview of the ASME Materials Properties Database and discuss a roadmap for its future development to facilitate understanding of and participation from different sectors of the Codes and Standards community. Further, it first reviews the basic concepts of data, information, knowledge, database, and database system as well as the pros and cons in different types of data management and then discusses the path forward for a desired evolution of the database into a self-explanatory and machine-readable knowledgebase that is consistent with human cognitive processes for the Codes and Standards development and, furthermore, provides resources for data processing and analysis to reach an eventual goal of streamlining the Codes and Standards development from the initial inquiry, throughout data submission, analysis, …, to Codes and Standards rule establishment for final publication.
36 MATERIALS SCIENCE↗
Enrichable consortia of microbial symbionts degrade macroalgal polysaccharides in Kyphosus fish
ABSTRACT Coastal herbivorous fishes consume macroalgae, which is then degraded by microbes along their digestive tract. However, there is scarce genomic information about the microbiota that perform this degradation. This study explores the potential of Kyphosus gastrointestinal microbial symbionts to collaboratively degrade and ferment polysaccharides from red, green, and brown macroalgae through in silico study of carbohydrate-active enzyme and sulfatase sequences. Recovery of metagenome-assembled genomes (MAGs) from previously described Kyphosus gut metagenomes and newly sequenced bioreactor enrichments reveals differences in enzymatic capabilities between the major microbial taxa in Kyphosus guts. The most versatile of the recovered MAGs were from the Bacteroidota phylum, whose MAGs house enzyme collections able to decompose a variety of algal polysaccharides. Unique enzymes and predicted degradative capacities of genomes from the Bacillota (genus Vallitalea ) and Verrucomicrobiota (order Kiritimatiellales ) highlight the importance of metabolic contributions from multiple phyla to broaden polysaccharide degradation capabilities. Few genomes contain the required enzymes to fully degrade any complex sulfated algal polysaccharide alone. The distribution of suitable enzymes between MAGs originating from different taxa, along with the widespread detection of signal peptides in candidate enzymes, is consistent with cooperative extracellular degradation of these carbohydrates. This study leverages genomic evidence to reveal an untapped diversity at the enzyme and strain level among Kyphosus symbionts and their contributions to macroalgae decomposition. Bioreactor enrichments provide a genomic foundation for degradative and fermentative processes central to translating the knowledge gained from this system to the aquaculture and bioenergy sectors. IMPORTANCE Seaweed has long been considered a promising source of sustainable biomass for bioenergy and aquaculture feed, but scalable industrial methods for decomposing terrestrial compounds can struggle to break down seaweed polysaccharides efficiently due to their unique sulfated structures. Fish of the genus Kyphosus feed on seaweed by leveraging gastrointestinal bacteria to degrade algal polysaccharides into simple sugars. This study reconstructs metagenome-assembled genomes for these gastrointestinal bacteria to enhance our understanding of herbivorous fish digestion and fermentation of algal sugars. Investigations at the gene level identify Kyphosus guts as an untapped source of seaweed-degrading enzymes ripe for further characterization. These discoveries set the stage for future work incorporating marine enzymes and microbial communities in the industrial degradation of algal polysaccharides.
59 BASIC BIOLOGICAL SCIENCES↗
Dynamic and Responsive Distributed Energy Resource Education Solutions for Building, Fire, and Safety Department Officials (Final Technical Report)
From April 2021 through March 2024, the Interstate Renewable Energy Council (IREC) led a collaborative project to develop a free online clearinghouse of educational resources about solar photovoltaics (PV), energy storage systems (ESS), electric vehicle supply equipment (EVSE), and grid-interactive efficient building (GEB) technologies. Two websites—the Clean Energy Clearinghouse and CleanEnergyTraining.org—housed over 70 educational resources. Over the course of the three-year project, 154,272 unique visitors accessed the learning materials. Learner feedback was overwhelmingly positive. Even through the end of the project, there was sustained demand for education and communication. A primary innovation of the project was to drive multiple complementary audiences to the same place. Building owners, designers, installation contractors and developers, authorities having jurisdiction (AHJs), and fire service personnel all benefit from a shared understanding of clean energy technologies, including safety and code-related requirements. When considering the impact on the target audience, the project team worked with partners and advisors to inform resource creation and delivery in such a way as to address key motivational factors of the target audience and compel each user to seek additional information on the topic and return to the Clean Energy Clearinghouse website as their central location for more information. Resources were intentionally developed to be concise—five to 15 minutes—and accessible, meaning not overly technical. Providing basic information demystified the technologies and invited the professional to explore additional learning opportunities. Awardee and partner collaboration was key to project success. IREC facilitated collaboration among the other Topic 2 awardees, Southface and New Buildings Institute (NBI). The three awardees shared relevant information gained through discovery and validation questionnaires that informed product development and reduced duplication of effort by coordinating the development of complementary, and not competing, educational resources. Inspired by this collaboration, IREC brought on additional partners even in the final year of the project. Five regional energy efficiency organizations were part of the project, which expanded the connection between efficiency and distributed energy resources. We also included resources on the Clearinghouse that were developed through other federally funded projects, such as the Buildings Energy Efficiency Frontiers & Innovation Technologies (BENEFIT) program. The website was developed with the learner in mind, and not solely the funding source. Feedback from stakeholders throughout the project, and especially in its final year, indicated the need for continued education and facilitated communication among stakeholders to further the safe and widespread adoption of clean energy.
14 SOLAR ENERGY↗
Northeast Freight Corridor Charging Plan (Roadmap Report)
Final report produced as part of grant awarded to National Grid. The final report is a roadmap of 39 prioritized sites. These sites would create a minimum viable network of charging infrastructure, enabling the electrification of trucks across the Northeast
02 PETROLEUM↗
Functional characterization of glycosyltransferases in duckweed to enable predictive biology
Glycosyltransferases (GTs) catalyze the formation of glycosidic linkages to produce almost all complex carbohydrates. This project used a multi-disciplinary, high-throughput (HTP) biochemical and computational biology approach focused on duckweed as a model energy crop, to study carbohydrate metabolic processes. To achieve this, developed and carried out out high-throughput (HTP) functional characterization of plant glycosyltransferases (GTs) role of enzymatic microenvironments be assessed through a combined proteomic and computational biology approach, and the combined data was used to populate deep-learning frameworks to predict plant GT function. Functional validation achieved through this research is being used to assign gene function and study plant processes at the systems level to efficiently link the genome sequence with gene function. Together, the combined approaches used within this study provide a foundation for how computational prediction, in combination with high-throughput functional validation, can be used to study plant processes at the systems level and translate knowledge gained to efficiently link genome sequence with gene function in a species agnostic manner.
09 BIOMASS FUELS↗
Autonomous Ion Mass Spectrometer Sentry (AIMSS)
Satellite measurements of plasma density and temperature help predict the intensity of geomagnetic storms, which can impact satellites, communications, and power systems. Accurate forecasting requires knowledge of plasma composition. However, typical orbital mass spectrometers have difficulty distinguishing between nitrogen (N+) and oxygen (O+) ions due to their similar masses. This limitation makes it difficult to validate modern space weather models, which depend on accurate knowledge of ion composition in near-Earth space. N+ and O+ ions behave differently in space due to differences in their origin, transport, and loss mechanisms. Improving our ability to distinguish between these ions is critical for understanding and predicting space weather.
47 OTHER INSTRUMENTATION↗
Advancing the Limits of InSAR to Detect Crustal Displacement from Low-Magnitude Earthquakes through Deep Learning
Detecting surface deformation associated with low-magnitude (M w ≤ 5) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the M w = 3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring.
58 GEOSCIENCES↗
From Rules to Reasoning: A Survey of Large Language Model-Based Approaches to Scientific Hypothesis and Idea Generation
Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.
AI-driven discovery↗
Crystal Nucleation Kinetics and Mechanism: Influence of Interaction Potential
Modulating liquid-to-solid transitions and the resulting crystalline structure for tailored properties is much desired. Colloidal systems are exemplary to this end, but the fundamental knowledge gaps in relating the influence of intermolecular interactions to crystallization behavior continue to hinder progress. In this study, we address this knowledge gap by studying nucleation and growth in systems with modified Lennard-Jones potential. Specifically, we study the commonly used 12-6 potential and a softer 7-6 potential. The thermodynamic state point for the study is chosen such that both systems are investigated at the same level of supercooling and pressure. Under these conditions, we find that the nucleation rate for both systems is comparable. Interestingly, the nucleation pathways and resulting crystal structures are different. In the 12-6 system, nucleation and growth occur predominantly through the FCC structure. Softening the potential alters the critical nucleus composition and introduces two distinct nucleation pathways. One pathway predominantly leads to the nucleus with a body-centered cubic (BCC) structure, while the other favors the face-centered cubic (FCC) arrangement. Our study illustrates that polymorph selection can be achieved through modifications to intermolecular interactions without impacting nucleation kinetics. The results have significant implications in designing approaches for polymorph selection and modulating self-assembly mechanisms.
36 MATERIALS SCIENCE↗
Generation IV International Forum – Knowledge Management and Knowledge Preservation: An initiative from the Education and Training Working Group
Dr. Patricia Paviet is the Chair of the Generation IV International Forum – Education and Training Working Group. As such, she is overseeing and directing several initiative: The GIF webinar series and the Pitch your Gen IV Research competition. This group has launched a new initiative on knowledge management and knowledge preservation of advanced reactor systems and how lessons learned are passed on in an international context. The paper is a summary of the various GIF member countries’ strategies to preserve and transfer the knowledge of advanced reactors.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings
Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.
ASHRAE guideline 36↗
Reinforcement Learning to Enhance Optimal Operation of Resilient Community Energy Systems
This paper presents a novel model-free multi-agent Reinforcement Learning (RL) control method to enhance the resilience of community energy systems in island mode, which coordinates multiple objectives without the necessity of identifying system models that require expert knowledge. Specifically, a community-level coordinator agent is designed to allocate renewable energy resources among different buildings, and multiple building-level agents are developed to optimize load schedules based on limited energy resources and requirements of building loads and occupants’ comfort. In a two-day evaluation, our RL approach demonstrated a similar performance against MPC without requiring system models and formulation of optimization problems as required in MPC.
ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
Small-Scale Irrigation: Improving Food Security under Changing Climate and Water Resource Conditions in Ethiopia
We develop a new systems modeling tool that integrates knowledge from hydrology, agriculture, and economics to understand the effect of small-scale irrigation on food security and groundwater sustainability in Ethiopia. Irrigation is an effective tool to mitigate climate impacts and improve agricultural yields. Small-scale irrigation, such as decentralized groundwater irrigation, is well suited for developing countries where smallholder farming communities are widely dispersed and can only afford small infrastructure investment. We study the underlying interdependencies between food and water systems in Ethiopia, where small-holder agriculture is the foundation of the nation’s economy and climate variability has led to great challenges to its food security. Our coupled market and crop model with groundwater module captures the interdependencies of climate, water availability (including irrigation), crop yield, farmland allocation, crop production, transport and consumption based on a system approach across multiple spatial scales. We study the implication of small-scale irrigation to Ethiopia’s food security and water resource conditions as a “what-if” question by comparing an irrigation scenario to the calibrated baseline in 2015, a year of significant drought and crop failure over a large portion of Ethiopia. Our model offers fresh insights into geographic disparities in outcomes that are driven by baseline climate variability, soil fertility, and market conditions. In general, we find that small-scale irrigation can potentially improve food security through increases in food consumption, but it requires policy support to direct the increases of production to domestic consumption while maintaining a sustainable groundwater condition. By using Ethiopia as an example, we show the strength of our model to study how water infrastructure resources support critical functions and service in water and food systems.
Zhang, Ying↗
Droplet Entrainment in Steam Supply System of Water-Cooled Small Modular Reactors: Experiment and Modeling Approaches
Droplet entrainment in steam-flow is a prominent phenomenon that needs adequate safety and risk analysis of postulated transient and accident scenarios—including experimental investigation and representative modeling and simulation (M&S)—for small modular reactor (SMR) system design and demonstration. This study identifies knowledge gaps by evaluating experimental and computational fluid dynamics modeling approaches to support early-stage reactor system design, testing, and model evaluation. Previous studies reported in the literature for steam-flow entrainment primarily focused on gigawatt capacity pressurized water reactor (PWR) systems. However, entrainment phenomena are even more prominent for PWR-type SMRs due to their more compact integrated designs, which need further research and development. To fill the research gaps, this study provides insight by specifying the phenomena of interest by leveraging the lessons learned from past research, adopting advanced M&S techniques and advanced instrumentation and control. The findings and recommendations are applicable for evaluating steam-flow entrainment models and for designing integral effect test and separate effect test facilities for gaining reactor design approvals.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗