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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains

Natural language processing (NLP) tasks (text classification, named entity recognition, etc.) have seen amazing improvements over the last few years. This is due to models such as BERT that achieve deep knowledge transfer by using a large pre-trained model, then fine-tuning the model on specific tasks. The BERT architecture has shown even better performance on domain-specific tasks when the model is pre-trained using domain-relevant texts. Here, inspired by these recent advancements, we have developed NukeLM, a nuclear-domain BERT model pre-trained on 1.5 million abstracts from the DOE Office of Scientific and Technical Information (OSTI) database. This NukeLM model is then fine-tuned for the classification of research articles into either binary classes (related to the nuclear fuel cycle (NFC) or not) or multiple categories related to the subject of the article. We show that continued pre-training of a BERT-style architecture prior to fine-tuning results in greater performance in both article classification tasks. This information is critical for properly triaging manuscripts, a necessary task for better understanding citation networks that publish in the nuclear space and uncovering new areas of research in the nuclear (or nuclear relevant) domain.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

The DECOVALEX international collaboration on modeling of coupled subsurface processes and its contribution to confidence building in radioactive waste disposal

Abstract The long-lived radiotoxicity of the high-level radioactive waste generated by nuclear power plants requires safe isolation from the biosphere for many hundreds of thousands of years. An international consensus has emerged that such isolation can best be provided by disposal in mined geologic repositories, a strategy that today is pursued by most countries dealing with radioactive waste. However, the need to predict the performance of such repositories over very long time periods generates large uncertainties that have to be accounted for in safety assessments. The findings from such safety assessments need to be conveyed to all stakeholders in a clear way, such that public confidence in geologic disposal solutions can be achieved. It is suggested here that close international collaboration on the technical aspects of geologic waste disposal has helped, and will continue to help, building trust and increasing confidence. This paper discusses a particular international collaboration initiative referred to as DECOVALEX, which brings together multiple teams and disciplines to collectively tackle complex experimental and modeling challenges related to geologic disposal. By describing how DECOVALEX works and by providing joint research examples, a case is made that such international collaboration contributes to knowledge transfer and confidence building in radioactive waste disposal science.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Sim-to-real supervised domain adaptation for radioisotope identification

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.

Lalor, Peter W.↗
DOE OSTI2015

Leaching characteristics of toxic constituents from coal fly ash mixed soils under the influence of pH

Highlights: • The impact of pH on the leaching of elements and metals from fly ash mixed soils. • Generally Ca, Cd, Mg, and Sr follows a cationic leaching pattern. • The leaching of As and Se shows an oxyanionic leaching pattern. • The leaching behavior of elements does not change based on material type. • Different fly ash types show different abilities in immobilizing trace elements. - Abstract: Leaching behaviors of Arsenic (As), Barium (Ba), Calcium (Ca), Cadmium (Cd), Magnesium (Mg), Selenium (Se), and Strontium (Sr) from soil alone, coal fly ash alone, and soil-coal fly ash mixtures, were studied at a pH range of 2–14 via pH-dependent leaching tests. Seven different types of soils and coal fly ashes were tested. Results of this study indicated that Ca, Cd, Mg, and Sr showed cationic leaching pattern while As and Se generally follows an oxyanionic leaching pattern. On the other hand, leaching of Ba presented amphoteric-like leaching pattern but less pH-dependent. In spite of different types and composition of soil and coal fly ash investigated, the study reveals the similarity in leaching behavior as a function of pH for a given element from soil, coal fly ash, and soil-coal fly ash mixtures. The similarity is most likely due to similar controlling mechanisms (e.g., solubility, sorption, and solid-solution formation) and similar controlling factors (e.g., leachate pH and redox conditions). This offers the opportunity to transfer knowledge of coal fly ash that has been extensively characterized and studied to soil stabilized with coal fly ash. It is speculated that unburned carbon in off-specification coal fly ashes may provide sorption sites for Cd resulting in a reduction in concentration of these elements in leachate from soil-coal fly ash mixture. Class C fly ash provides sufficient CaO to initiate the pozzolanic reaction yielding hydrated cement products that oxyanions, including As and Se, can be incorporated into.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

OPERATOR 4.0 FOR HYBRID MANUFACTURING

Hybrid manufacturing, a combination of additive and subtractive manufacturing capabilities in one system, has recently become a more viable production option across several industries. Although current hybrid manufacturing research covers a broad range of topics, there is a lack of focus on how this new technology impacts both the designer and the operator of hybrid systems. This paper identifies areas of literature across design theory and Industry/Operator 4.0 research efforts and presents a path for applying this research to hybrid manufacturing users. The unique relationship between operator and designer is highlighted as they learn new strategies and develop new intuitive judgements over time to become the first experienced/expert users of hybrid manufacturing. The potential impact of excessive cognitive workload due to the novel combination of processes is discussed. This paper begins a critical discussion about proper knowledge transfer to other hybrid designers and operators, as well as towards efforts of monitoring, inspecting, and automating hybrid manufacturing processes.

Fillingim, Blane↗
DOE OSTI2021

Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy

Atom probe tomography, and related methods, probe the three-dimensional architecture of a material. The software tools that microscopists use, and how these tools are connected into workflows, makes a substantial contribution to the accuracy and precision of such a material characterization experiment. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced that collaborating is useful. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Na-Ion Battery Anodes: Materials and Electrochemistry

The intermittent nature of renewable energy sources, such as solar and wind, calls for sustainable electrical energy storage (EES) technologies for stationary applications. Li will be simply too rare for Li-ion batteries (LIBs) to be used for large-scale storage purposes. In contrast, Na-ion batteries (NIBs) are highly promising to meet the demand of grid-level storage because Na is truly earth abundant and ubiquitous around the globe. Furthermore, NIBs share a similar rocking-chair operation mechanism with LIBs, which potentially provides high reversibility and long cycling life. Here, it would be most efficient to transfer knowledge learned on LIBs during the last three decades to the development of NIBs. Following this logic, rapid progress has been made in NIB cathode materials, where layered metal oxides and polyanionic compounds exhibit encouraging results. On the anode side, pure graphite as the standard anode for LIBs can only form NaC 64 in NIBs if solvent co-intercalation does not occur due to the unfavorable thermodynamics. In fact, it was the utilization of a carbon anode in LIBs that enabled the commercial successes. Anodes of metal-ion batteries determine key characteristics, such as safety and cycling life; thus, it is indispensable to identify suitable anode materials for NIBs.

25 ENERGY STORAGE↗
DOE OSTI2020

Spatiotemporal Variability in Transport and Reactive Processes Across a First- to Fifth-Order Fluvial Network

Fluvial networks integrate and transport constituents from terrestrial and aquatic ecosystems. To date, most research on water quality dynamics has focused on process understanding at individual streams and, as a result, there is a lack of studies analyzing how small-scale, physical and biogeochemical drivers scale across fluvial networks. We performed tracer tests in five stream orders of the Jemez River continuum in New Mexico, USA, to quantify reach-scale hyporheic exchange during two different seasonal periods to address: how do hyporheic zone contributions to riverine processing change with space and time? And does the spatiotemporal variability of hyporheic exchange scale across fluvial networks? Combining conservative (i.e., bromide) and reactive (i.e., resazurin) tracer analyses with solute transport modeling, we found a dominance of reaction-limited transport conditions in space (for all stream orders) and time (two contrasting flow regimes). Moreover, we found a trend toward more reaction-limited conditions with increasing discharge across all stream orders. While our studied fluvial network did not consistently follow the expectations of decreasing hyporheic exchange contributions with increasing stream order, as it has been previously suggested from modeling studies, we found that processing rate coefficients and Damköhler numbers consistently decreased along the continuum with increasing discharge. Our findings suggest that knowledge transferability of hyporheic zone processing along fluvial networks may be possible if the scaling patterns that we found hold true in other catchments. Thus, more research on scaling patterns along fluvial networks is highly needed.

Gootman, Kaylyn S.↗
DOE OSTI2019

The emergence of microbiome centres

As microbiome science expands, academic centres scramble to fill many needs, from service provider to industry liaison. This newly created network aims to share strategies and accelerate knowledge transfer, and invites others to join the efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Design strategies for nonaqueous multivalent-ion and monovalent-ion battery anodes

The inability of current battery technologies to keep up with the performance requirements of industry is pushing forward developments in electrochemistry. Specifically, the battery's negative electrode, the anode, presents many unique chemical, physical and engineering challenges. Lithium-based battery technologies have dominated the past decade, but concerns about the limited supply of lithium in the Earth's crust have led researchers to look towards alternative metal-ion technologies. Furthermore, various alkali metals (such as sodium and potassium) and alkali earth metals (such as magnesium and calcium) have attracted significant research interest. In this Review, we analyse these technologies in a coherent manner, addressing the problems of each type of anode, rather than those of specific types of metal-ion batteries. Covering direct metal plating and stripping, intercalation-based, alloy-based and conversion-reaction-based anode technologies, this analysis will offer the reader a comprehensive understanding of the behaviour of different metal-ion anodes and of what can be learned by transferring knowledge between these different systems. Increasing demand for energy-storage systems will inevitably stress the Earth's lithium supply; thus, the research focus is shifting towards other alkali and alkali earth metals. In summary, this Review compares and connects strategies to enable different multivalent and monovalent metal-ion battery anodes, including metal anodes and intercalation-based, alloy-based and conversion-reaction-based anodes.

36 MATERIALS SCIENCE↗
DOE OSTI2019

EcoFAB: Advancing microbiome science through standardized fabricated ecosystems

The vital role microbiomes play in ecosystems and for human health is now apparent. Great progress has been made by using model systems to gain insight into host-microbe interactions, yet lack of standardized reproducible experimental systems and methods greatly limits microbiome science. Recent successes highlight the importance of developing these types of systems to deconstruct mechanisms mediating host-microbe interactions. Standardization of ‘fabricated ecosystems’ would enable detailed mechanistic interrogation and more effective knowledge transfer between researchers – analogous to the significant impact model organisms and cell systems have had in molecular and cell biology. To advance these efforts we advocate for strategies and overarching themes to enable communities of scientists to work together to develop standardized laboratory microbial consortia for studying host-associated microbiomes.

Zengler, Karsten↗
DOE OSTI2025

Leveraging prior mean models for faster Bayesian optimization of particle accelerators

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Contribution of electrolyte in nanoscale electrolysis of pure and buffered water by particulate photocatalysis

Photocatalysis using semiconductor powders in suspension performs reduction and oxidation reactions at nanometer-scale distances. Such short distances between the reduction (cathode) and the oxidation (anode) sites enable photocatalytic water splitting to generate H 2 and O 2 from pure water without a supporting electrolyte, which is otherwise impossible in conventional electrode systems due to the high solution resistance. A CrO x /Pt/SrTiO 3 model photocatalyst achieves high efficiency under UV irradiation in ultra-pure water splitting at rates (>1 μmol-H 2 per cm 2 per h) corresponding to electrocatalysis on the order of mA cm -2 . The introduction of an unbuffered supporting electrolyte did not improve the photocatalytic rates, consistent with the negligible ohmic losses (<1 mV) numerically calculated using the Poisson–Nernst–Planck equations. The Nernstian potential loss resulting from pH gradients became apparent at high photocatalytic rates (>100 mV when rate >1 μmol-H 2 per cm 2 per h) even when the distance between redox sites was below 10 nm. Substantial improvements in photocatalytic rates were observed when buffer ions were introduced into near-neutral pH media by not only circumventing pH gradients but introducing kinetically facile H + reduction to H 2 instead of the kinetically sluggish direct reduction of H 2 O to H 2 . In this work, the quantitative descriptions of the electric potential, concentration gradients, and catalytic performance in nanoscale water electrolysis are presented with emphasis on (1) the advantages of performing redox reactions at the nanoscale, (2) the use of electrolyte engineering at near-neutral pH as a universal and effective strategy, and (3) the effectiveness of transferring knowledge from electrocatalysis to photocatalysis, where the potential is quantitatively defined regarding the former and poorly quantified regarding the latter.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Improved annotation of the insect vector of citrus greening disease: biocuration by a diverse genomics community

The Asian citrus psyllid ( Diaphorina citri Kuwayama) is the insect vector of the bacterium Candidatus Liberibacter asiaticus (CLas), the pathogen associated with citrus Huanglongbing (HLB, citrus greening). HLB threatens citrus production worldwide. Suppression or reduction of the insect vector using chemical insecticides has been the primary method to inhibit the spread of citrus greening disease. Accurate structural and functional annotation of the Asian citrus psyllid genome, as well as a clear understanding of the interactions between the insect and CLas, are required for development of new molecular-based HLB control methods. A draft assembly of the D. citri genome has been generated and annotated with automated pipelines. However, knowledge transfer from well-curated reference genomes such as that of Drosophila melanogaster to newly sequenced ones is challenging due to the complexity and diversity of insect genomes. To identify and improve gene models as potential targets for pest control, we manually curated several gene families with a focus on genes that have key functional roles in D. citri biology and CLas interactions. This community effort produced 530 manually curated gene models across developmental, physiological, RNAi regulatory and immunity-related pathways. As previously shown in the pea aphid, RNAi machinery genes putatively involved in the microRNA pathway have been specifically duplicated. A comprehensive transcriptome enabled us to identify a number of gene families that are either missing or misassembled in the draft genome. In order to develop biocuration as a training experience, we included undergraduate and graduate students from multiple institutions, as well as experienced annotators from the insect genomics research community. The resulting gene set (OGS v1.0) combines both automatically predicted and manually curated gene models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models

In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. One common pattern emerging in such applications is frequent checkpointing of the state of the learning model during training, needed in a variety of scenarios: analysis of intermediate states to explain features and correlations with training data, exploration strategies involving alternative models that share a common ancestor, knowledge transfer, resilience, etc. However, with increasing size of the learning models and popularity of distributed data-parallel training approaches, simple checkpointing techniques used so far face several limitations: low serialization performance, blocking I/O, stragglers due to the fact that only a single process is involved in checkpointing. This paper proposes a checkpointing technique specifically designed to address the aforementioned limitations, introducing efficient asynchronous techniques to hide the overhead of serialization and I/O, and distribute the load over all participating processes. Experiments with two deep learning applications (CANDLE and ResNet) on a pre-Exascale HPC platform (Theta) shows significant improvement over state-of-art, both in terms of checkpointing duration and runtime overhead.

checkpointing↗
DOE OSTI2024

Underwater unexploded ordnance discrimination based on intrinsic target polarizabilities – A case study

Seabed unexploded ordnance that resulted partly from the high failure rate among munitions from more than 80 years ago and from decades of military training and testing of weapons systems poses an increasing concern all around the world. Although existing magnetic systems can detect clusters of debris, they are not able to tell whether a munition is still intact requiring special removal (e.g. in situ detonation) or is harmless scrap metal. The marine environment poses unique challenges, and transferring knowledge and approaches from land to a marine environment has not been easy and straightforward. On land, the background soil conductivity is much lower than the conductivity of the unexploded ordnance and the electromagnetic response of a target is essentially the same as that in free space. For those frequencies required for target characterization in the marine environment, the seawater response must be accounted for and removed from the measurements. The system developed for this study uses fields from three orthogonal transmitters to illuminate the target and four three-component receivers to measure the signal arranged in a configuration that inherently cancels the system's response due to the enclosing seawater, the sea–bottom interface and the air–sea interface for shallow deployments. The system was tested as a cued system on land and underwater in San Francisco Bay – it was mounted on a simple platform on top of a support structure that extended 1 m below and allowed the diver to place metal objects to a specific location even in low-visibility conditions. The measurements were stable and repeatable. Furthermore, target responses estimated from marine measurements matched those from land acquisition, confirming that the seawater and air–sea interface responses were removed successfully. Thirty-six channels of normalized induction responses were used for the classification, which was done by estimating the target principal dipole polarizabilities. Our results demonstrated that the system can resolve the intrinsic polarizabilities of the target, with clear distinctions between those of symmetric intact unexploded ordnance and irregular scrap metal. The prototype system was able to classify an object based on its size, shape and metal content and correctly estimate its location and orientation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗