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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Experimental and Computational Analysis of NEAMS Pebble Bed Reactors

The Advanced High-Temperature Pebble Bed Reactor concept leverages a particle-based fuel format consisting of discrete spherical graphite pebbles arrayed in a packed bed architecture. Thermal regulation achieved via flow of gas (e.g., helium) or liquid (e.g., molten salt) coolants through the void spaces between pebbles in the bed (characteristic pebble diameters: ~ 6 cm, gas cooled; ~ 3 cm, liquid cooled). This design is of interest owing to inherent advantages including passive safety features and highly efficient heat transfer characteristics that enable high power densities to be achieved. Fully leveraging these advantages requires a fundamental understanding of the complex coolant flow structure within the pebble-shaped fuel bed. Unfortunately, complexities associated with non-invasively probing the micro-scale flow phenomena within the complex network of void spaces between pebbles have hindered efforts to characterize the underlying transport phenomena. Without this fundamental foundation, it becomes challenging to rationally construct macroscopic transport models based upon volume averages of micro-scale parameters that accurately capture the observed flow behavior. Consequently, this lack of fundamental knowledge has limited previous work to coarse comparisons between theory and extremely limited experimental data. Moreover, these comparisons have been made only in terms of integral or macroscopic results. Therefore, multipoint measurements with a high level of spatial and temporal resolution are critically needed to fully map the complex flow patterns and to provide data at high spatial density to permit accurate volume averaging in the pebble bed. For this project, we performed a coordinated experimental and computational effort to quantitatively map the full-field velocity and temperature fields in the interstitial spaces within the pebble bed. The major accomplishments of this project included the important knowledge acquired for the flow mixing and heat transfer in the interstitial pore scales, the effects of flow mixing in the near-wall and far-wall regions, and the by-pass flows of pebble bed systems. The produced high-resolution experimental data (not broadly available in existing literature) were then used for the numerical studies enabling the refinement and validation of standard simulation tools (Nek5000).

42 ENGINEERING↗
DOE OSTI2019

Technology Applicable to Remote Hazardous Constituents and Waste Stream Grouping

Originally, Rocky Flats transuranic (TRU) waste was managed from waste streams that were composed of similar chemical constituents and were generated from identical processes. Early waste processing at Idaho National Laboratory (INL) maintained the Rocky Flats record until INL began generating their own item description codes (IDCs). Ultimately TRU waste management at INL was driven to combine wastes from all IDCs that were assigned by Rocky Flats to a summary category group regardless of whether the chemical constituents in the waste were known or not. At this point, the Waste Isolation Pilot Plant (WIPP) certified program requirements to process the TRU waste according to waste streams was nearly eliminated. In April 2018, operations in the Sludge Repackage Project (SRP) area were halted by a radiological materials release accident in which drums containing waste experienced a loss of containment after over-pressurization occurred. Four SRP drums, repackaged in the Accelerated Retrieval Project (ARP) V area, had an adverse, delayed chemical reaction event occur when prohibited materials from one container were mixed with chemically-incompatible materials from other containers in the repackaging process. This incident was the direct result of processing unknowns under a protocol where the components of the waste were added to incompatible materials resulting in a run-away reaction. After a Difficult Waste Team (DWT) and Los Alamos National Laboratory – Carlsbad Operations (LANL-CO) subject matter expert (SME) visit in September 2019, it was determined by the team that all of the work observed on the September visit was performed upstream of the certified program and that the Visual Examination (VE) and other methods typically used by the certified program were being used for Acceptable Knowledge (AK) augmentation. This augmentation was needed in order to determine what the constituents in the waste might be, and is based primarily on collections of VE results. The waste at INL that is being processed in what is considered to be the Priority 2 (yellow) and Priority 3 (red) lists either needs AK to be completed before certified characterization can occur or is waiting to be processed because the waste has been mixed with unknown wastes resulting in a lack of information that could have impact of the ability to certify and ship the waste to the WIPP.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Iodine Mass Tracking Research and Development Needs for Pyrochemical Fuel Cycles

This report was generated jointly by Argonne National Laboratory (ANL) and Idaho National Laboratory to provide a high-level summary of the current knowledge on the behavior of fission product iodine during reprocessing of used nuclear fuel, as well as provide recommended path forward for research and development activities to fill particular knowledge gaps. The focus of this work is on pyrochemical processing as is applied to light water reactor (LWR) oxide-based used nuclear fuels (UNF), however, some discussion of electrorefiner behavior from metal fuel processing equipment is also included.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Modeling and simulation to investigate the electrification potential of medium- and heavy-duty vehicle fleets

This project involves developing and integrating new modeling tools to simulate the dynamics of electric medium- and heavy-duty fleet vehicle adoption. A technical and economic modeling tool, combining a data-driven hardware cost model with a cost-optimal charging strategy microsimulation, enables tailored analysis of the costs and benefits of electrifying individual fleets. Next, a novel text synthesis process, applied to a curated corpus of literature, quantifies trade-offs between technical, economic, and other factors in the fleet vehicle procurement decision. The outcomes of these tasks combine with knowledge from recent literature on fleet decision processes to specify the vehicle procurement model used by fleets in an agent-based model of the medium- and heavy-duty electric vehicle market. This model embodies an especially disaggregated approach to adoption modeling, internalizing factors and dynamics that conventional adoption models externalize. In particular, explicitly modeling the formation and diffusion of opinions among agents enables experiments that conventional models cannot support. Demonstrations show, for example, that increasing the extent of interactions between populations with different proclivities to electric vehicles has an asymmetrical outcome. High-proclivity electric vehicle adoption is generally unaffected as interactions increase, but low-proclivity adoption is accelerated. By representing individual fleets' requirements and costs at a high level of detail, incorporating an adoption decision model informed by a wide body of empirical research, and broadening the array of variables and dynamics available for experimentation, this integrated model offers a new way to understand the urgent challenge of eliminating emissions from the most emissions-intensive transportation sectors.

Trinko, David A.↗
DOE OSTI2023

FEOTS v0.0.0: a new offline code for the fast equilibration of tracers in the ocean

Abstract. In this paper we introduce a new software framework for the offline calculation of tracer transport in the ocean. The Fast Equilibration of Ocean Tracers Software (FEOTS) is an end-to-end set of tools to efficiently calculate tracer distributions on a global or regional sub-domain using transport operators diagnosed from a comprehensive ocean model. To the best of our knowledge, this is the first application of a transport matrix model to an eddying ocean state. While a Newton–Krylov-based equilibration capability is still under development and not presented here, we demonstrate in this paper the transient modeling capabilities of FEOTS in an application focused on the Argentine Basin, where intense eddy activity and the Zapiola Anticyclone lead to strong mixing of water masses. The demonstration illustrates progress in developing offline passive tracer simulation capabilities, while highlighting the challenges of the impulse response functions approach in capturing tracer transports by a non-linear advection scheme. Our future work will focus on improving the computational efficiency of the code to reduce time-to-solution, using different basis functions to better represent non-linear advection operators, applying FEOTS to a parent model with unstructured grids (Ocean Model for Prediction Across Scales, MPAS-Ocean), and fully implementing a Newton–Krylov steady-state solver.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Figure Descriptive Text Extraction Using Ontological Representation

Abstract Experimental research publications provide figure form resources including graphs, charts, and any type of images to effectively support and convey methods and results. To describe figures, authors add captions, which are often incomplete, and more descriptions reside in body text. This work presents a method to extract figure descriptive text from the body of scientific articles. We adopted ontological semantics to aid concept recognition of figure-related information, which generates human- and machine-readable knowledge representations from sentences. Our results show that conceptual models bring an improvement in figure descriptive sentence classification over word-based approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Multi-Kernel Adaptive Support Vector Machine for Scalable Predictive Maintenance

Application of data-driven solutions across an industry is challenging, since the data are often stored locally, and increasing privacy and security concerns restrict access to the data. In addition, it is highly unlikely that all potential data patterns are captured in a single data source. Because it is highly unlikely that all potential data patterns are captured in a single data source, machine learning (ML) models developed from a single source cannot be robust enough. An alternative is to train the ML model at each source and develop a distributed knowledge discovery and aggregation approach to build global knowledge. In this paper, we develop and demonstrate a distributed ML model, federated transfer learning (FTL), using a multi-kernel-based adaptive support vector machine (MK-A-SVM). For federated learning (FL), the multi-kernel (MK) approach enables feature-specific model aggregation under data heterogeneity; whereas for transfer learning (TL) the adaptive model enables utilization of an aggregated model from a different task. The proposed approach is validated using nuclear power plant (NPP) vertical motor-driven pump data to predict the health condition of vertical motor-driven pumps as an anomaly detection. The efficiency of the proposed approach is also quantified and compared with neural network.

42 ENGINEERING↗
DOE OSTI2022

Acceptance Test of WCTC with LEU Fuel at the IVG.1M Research Reactor Site in Kazakhstan

The water-cooled technological channels (WCTC) with low-enriched uranium (LEU) fuel, in the amount required for conversion, were delivered to the IVG.1M site by the manufacturer in February 2021 and thereafter the site acceptance test (SAT) of the WCTCs and the fuel elements started immediately. The paper provides an overview of the SAT conducted between March and November 2021 by the designated experts of the reactor operator and the manufacturer. It includes the introduction of the IVG.1M reactor and its unique WCTCs, and the inspection methodology to verify the conformity of the quality of the LEU fuel with the Technical Design (reference document). The paper presents results of the non-destructive and destructive tests, the outcomes of the thermohydraulic measurements, as well as the evaluation and corrective actions (if any), including the amendments (modifications) of the Technical Design initiated by the manufacturer based on the test results. Finally, the paper draws conclusions on the effectiveness of the SAT method used, captures consolidated experiential knowledge and shares lessons learned that can be used in general when planning and performing fuel verification.

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

Exercise Design Guidance for Solar Cybersecurity

The intent of this resource is to provide recommendations on how State Energy Offices and Public Utility Commissions might design an energy emergency exercise, drill, or other simulation focused on solar cybersecurity scenarios . Exercise practitioners, planners, or facilitators interested in exploring solar cybersecurity incident response, preparedness, recovery, or mitigation may find this a valuable resource . This is an advanced supplementary resource for persons or entities with prior exercise experience and knowledge . It should not be used as a baseline educational resource for how to conduct and evaluate an exercise . It is based on a set of standard concepts, terms, and procedures that are common among the exercise community . This tool may be referenced in conjunction with the Hypothetical Solar Cyberattack Scenarios and Impacts tool within the CATSS Toolkit

14 SOLAR ENERGY↗
DOE OSTI2025

Bridging Atomic Solvation Environment with Electrochemical Properties for the Bis(trifluoromethylsulfonyl)imide-Based Divalent Cation Electrolytes for the Next-Generation Energy Storage Systems

A deep molecular-level understanding of the multivalent electrolyte and its correlation with the electrochemical properties is crucial for designing optimized electrolytes for next-generation rechargeable batteries. Comprehensive knowledge of the atomic level of the solvation structure and its connection with electrochemical stability and ion transport properties is especially critical. However, the interaction of these three components coupled with clear atomistic insights is lacking in the literature. Here, our current contribution evaluates representative electrolytes with the bis(trifluoromethanesulfonyl)imide (TFSI) anions for multivalent cations of Mg, Ca, and Zn, at different ionic conditions with and without a cosolvated environment in ether-based solvent. Two critical problems are investigated: first, resolving the solvation structures in the electrolyte solutions as a function of concentrations through pair distribution function analysis and the corresponding electrochemical transport properties; second, unmasking the quantitative correlation of the atomistic environment with both electrochemical kinetics and cation dependence. We discovered that the magnesium- and calcium-based electrolytes display versatile coordination lengths but poor average anodic stability due to ion pairing with TFSI - . On the contrary, the zinc-based electrolytes show the shortest solvent coordination lengths, shielding the Zn cation from rigid solvent interactions and resulting in the highest anodic stabilities. Calcium-based electrolytes exhibit the longest and most concentration-independent coordination lengths. This work provides valuable insights into the molecular structural and electrochemical features of diverse multivalent electrolyte systems with cations in various solvation environments, emphasizing the importance of the solvation structure and construction in designing high-performance electrolytes.

cation coordination↗
DOE OSTI2021

Tissue scale agent-based simulation of premalignant progressions in Barrett’s esophagus

Barrett’s esophagus (BE) is a benign condition of the distal esophagus that initiates a multistage pathway to esophageal adenocarcinoma (EAC). Short of frequent intrusive (and costly) surveillance, effective screening for neoplasia in BE populations is yet to be established since progressors are rare and virtually undetectable without routine biopsies, which often sample only a small portion of the BE tissue. As a result, reliable estimation of the true prevalence of dysplasia in a BE population and evidence-based optimization of screening for at-risk individuals is challenging. Data-driven microsimulations, i.e., model-generated instances of disease history in a predefined virtual population, have found utility in the EAC screening literature as low-overhead alternatives to real-world hypothesis testing of optimal interventions for dysplasia. Despite the successes, computational limitations, paucity of knowledge and data on Barrett’s dysplasia, and the complexities of disease progression as a multiscale multiphysics process have hindered the treatment of disease progression in BE as a spatial process. Agent-based modeling of nucleation and proliferation processes in dysplasia warrants exploration in this context as an approximation that operates at a trade-off between computational tractability and precise representation of the composition and physics of the substrate (tissue). In this study, we describe spatially resolved simulations of premalignant progression toward EAC in a coarse-grained model of Barrett’s tissue that resolves the metaplastic tissue at a length scale of 0.42 mm (~3300 crypts/mm 2 ). Finally, the model is calibrated to reproduce historical high-grade dysplasia prevalence when model-generated patients are screened using the Seattle protocol.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Physics-Informed Deep Neural Network Method for Limited Observability State Estimation

The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, increasing the fluctuations of the injected power. In this paper, we consider the case when the distribution grid becomes partially observable, due to for example cyber attacks, and the state estimation problem is under-determined. We present a new methodology that leverages a deep neural network (DNN) to estimate the grid state. The standard DNN training method is modified to explicitly incorporate the physical information of the grid topology and line/shunt admittance. We show that our method leads to a superior accuracy of the estimation when compared to the case when no physical information is provided. Finally, we compare the performance of our method to the standard state estimation approach, which is based on the weighted least squares with pseudo-measurements, and show that our method performs significantly better with respect to the estimation accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The effect of dogbone sample size in the tensile testing of TATB-based plastic‑bonded explosive materials

Mechanical properties are of interest for many plastic‑bonded explosive (PBX) materials with tensile properties being of particular interest. Direct tensile measurements using dogbone-shaped samples are considered the gold standard, but they are fairly large, making testing more costly and less desirable from a safety perspective. We investigated whether the measured tensile strength depends on the dogbone specimen size, which to our knowledge, has not been reported in the literature for PBX materials. Understanding this should inform the feasibility of employing smaller samples and how sample size should be considered when comparing PBX dogbone values in the literature. The TATB-based PBX dogbone sample size was varied by (a) scaling all dimensions proportionally and (b) varying only the length of the samples. It was observed that the measured tensile peak stress (strength) was a function of the sample size, and was more dependent on the diameter (cross-sectional area) than the length of the samples. Since peak stress is calculated as peak force normalized to the diameter of the sample, one might not expect an explicit diameter dependence for the peak stress. Therefore, these results suggest there may be an additional strengthening effect as the sample diameter is increased.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The hidden roots of wetland methane emissions

Abstract Wetlands are the largest natural source of methane (CH 4 ) globally. Climate and land use change are expected to alter CH 4 emissions but current and future wetland CH 4 budgets remain uncertain. One important predictor of wetland CH 4 flux, plants, play an important role in providing substrates for CH 4 ‐producing microbes, increasing CH 4 consumption by oxygenating the rhizosphere, and transporting CH 4 from soils to the atmosphere. Yet, there remain various mechanistic knowledge gaps regarding the extent to which plant root systems and their traits influence wetland CH 4 emissions. Here, we present a novel conceptual framework of the relationships between a range of root traits and CH 4 processes in wetlands. Based on a literature review, we propose four main CH 4 ‐relevant categories of root function: gas transport, carbon substrate provision, physicochemical influences and root system architecture. Within these categories, we discuss how individual root traits influence CH 4 production, consumption, and transport (PCT). Our findings reveal knowledge gaps concerning trait functions in physicochemical influences, and the role of mycorrhizae and temporal root dynamics in PCT. We also identify priority research needs such as integrating trait measurements from different root function categories, measuring root‐CH 4 linkages along environmental gradients, and following standardized root ecology protocols and vocabularies. Thus, our conceptual framework identifies relevant belowground plant traits that will help improve wetland CH 4 predictions and reduce uncertainties in current and future wetland CH 4 budgets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Controlling Host Responses to Infection

Pathogen invasion of host cells causes a myriad of functional changes including alterations of chromatin accessibility often limiting defense responses, shunting of cellular resources to centers of viral replication, and rearrangement of intracellular membranes to facilitate genome reproduction and progeny release. Systems biology approaches provide global snapshots of pathogen induced changes following infection and provide a variety of tools to begin to define how cellular homeostasis is disrupted, but improvements on these tools are required to determine how cellular functions are altered post infection. Chromatin accessibility techniques, biochemical assays to assess the activity of epigenetic enzymes, scalable sample collection platforms, and activity-based probes were used to characterize how human respiratory viruses modify host responses in infected human lungs over time. These studies enhanced our knowledge of how pathogens usurp the host environment during infection and identify additional targets for future evaluations of medical countermeasures.

59 BASIC BIOLOGICAL SCIENCES↗