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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

DECOVALEX-2023 Task F Specification (Rev. 9)

This report is the revised (Revision 9) Task F specification for DECOVALEX-2023. Task F is a comparison of the models and methods used in deep geologic repository performance assessment. The task proposes to develop a reference case for a mined repository in a fractured crystalline host rock (Task F1) and a reference case for a mined repository in a salt formation (Task F2). Teams may choose to participate in the comparison for either or both reference cases. For each reference case, a common set of conceptual models and parameters describing features, events, and processes that impact performance will be given, and teams will be responsible for determining how best to implement and couple the models. The comparison will be conducted in stages, beginning with a comparison of key outputs of individual process models, followed by a comparison of a single deterministic simulation of the full reference case, and moving on to uncertainty propagation and uncertainty and sensitivity analysis. This report provides background information, a summary of the proposed reference cases, and a staged plan for the analysis.

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

Analytical Assessment of Impurities Found in Process Gases using the InfiTOF, a High Resolution, Low Mass, Variable Time-of-Flight Mass Spectrometer

The JEOL InfiToF is a high-resolution, low mass (HRLM) mass spectrometer (MS) with the application potential to meet analytical needs for tritium handling systems, such as those at the Savannah River Tritium Facilities (SRTE) and those used in fusion research. The Service Level Agreement (SLA) #144 line 27 “InfiTOF R&D” has concluded its first year of efforts under Tritium Recycle and Renewal. The project goals for FY22 were: (1) Production of a finalized modification list for tritium hardened components in the InfiTOF, (2) perform an assessment on impurity gases commonly found in process gases from Tritium Facilities and (3) begin developing a set of standardized work instructions that will be used as a basis for operations on the InfiTOF.

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

DECOVALEX-2023: Task F Specification (Revision 10)

This report is the revised (Revision 10) Task F specification for DECOVALEX-2023. Task F is a comparison of the models and methods used in deep geologic repository performance assessment. The task proposes to develop a reference case for a mined repository in a fractured crystalline host rock (Task F1) and a reference case for a mined repository in a salt formation (Task F2). Teams may choose to participate in the comparison for either or both reference cases. For each reference case, a common set of conceptual models and parameters describing features, events, and processes that impact performance will be given, and teams will be responsible for determining how best to implement and couple the models. The comparison will be conducted in stages, beginning with a comparison of key outputs of individual process models, followed by a comparison of a single deterministic simulation of the full reference case, and moving on to uncertainty propagation and uncertainty and sensitivity analysis. This report provides background information, a summary of the proposed reference cases, and a staged plan for the analysis.

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

Integrated Biomass Supply Analysis and Logistics Model (IBSAL) 2.0

The Integrated Biomass Supply Analysis and Logistics (IBSAL) 2.0 models a feedstock supply shed's harvest and transport process to estimate the cost of delivered feedstock. The model sets up a supply shed of fields, harvests the fields using a series of sequential harvest operations with different crew types, and transports the harvested feedstock to a storage facility or refinery. The model reports many metrics; the most important ones include the crew resources required for harvest and transport operations, equipment utilization, fuel usage, amount of dry mass harvested, the cost for each operation, and the expected time to complete the harvest with the provided resources. IBSAL 2.0 is a complete redesign around the ExtendSim database, and it is faster and easier to reapply the model to different feedstocks.

09 BIOMASS FUELS↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
DOE OSTI2016

Pore Space Connectivity and the Transport Properties of Rocks

Pore connectivity is likely one of the most important factors affecting the permeability of reservoir rocks. Furthermore, connectivity effects are not restricted to materials approaching a percolation transition but can continuously and gradually occur in rocks undergoing geological processes such as mechanical and chemical diagenesis. Here, we compiled sets of published measurements of porosity, permeability and formation factor, performed in samples of unconsolidated granular aggregates, in which connectivity does not change, and in two other materials, sintered glass beads and Fontainebleau sandstone, in which connectivity does change. We compared these data to the predictions of a Kozeny-Carman model of permeability, which does not account for variations in connectivity, and to those of Bernabé et al. (2010, 2011) model, which does [Bernabé Y., Li M., Maineult A. (2010) Permeability and pore connectivity: a new model based on network simulations, J. Geophys. Res. 115, B10203; Bernabé Y., Zamora M., Li M., Maineult A., Tang Y.B. (2011) Pore connectivity, permeability and electrical formation factor: a new model and comparison to experimental data, J. Geophys. Res. 116, B11204]. Both models agreed equally well with experimental data obtained in unconsolidated granular media. But, in the other materials, especially in the low porosity samples that had undergone the greatest amount of sintering or diagenesis, only Bernabé et al. model matched the experimental data satisfactorily. In comparison, predictions of the Kozeny-Carman model differed by orders of magnitude. The advantage of the Bernabé et al. model was its ability to account for a continuous, gradual reduction in pore connectivity during sintering or diagenesis. Though we can only speculate at this juncture about the mechanisms responsible for the connectivity reduction, we propose two possible mechanisms, likely to be active at different stages of sintering and diagenesis, and thus allowing the gradual evolution observed experimentally.

58 GEOSCIENCES↗
DOE OSTI2022

GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling

We present GIGA-Lens: a gradient-informed, GPU-accelerated Bayesian framework for modeling strong gravitational lensing systems, implemented in TensorFlow and JAX. The three components, optimization using multistart gradient descent, posterior covariance estimation with variational inference, and sampling via Hamiltonian Monte Carlo, all take advantage of gradient information through automatic differentiation and massive parallelization on graphics processing units (GPUs). We test our pipeline on a large set of simulated systems and demonstrate in detail its high level of performance. The average time to model a single system on four Nvidia A100 GPUs is 105 s. The robustness, speed, and scalability offered by this framework make it possible to model the large number of strong lenses found in current surveys and present a very promising prospect for the modeling of ${ \mathcal O }({10}^{5})$ lensing systems expected to be discovered in the era of the Vera C. Rubin Observatory, Euclid, and the Nancy Grace Roman Space Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Urban influence on the concentration and composition of submicron particulate matter in central Amazonia

An understanding of how anthropogenic emissions affect the concentrations and composition of airborne particulate matter (PM) is fundamental to quantifying the influence of human activities on climate and air quality. The central Amazon Basin, especially around the city of Manaus, Brazil, has experienced rapid changes in the past decades due to ongoing urbanization. Herein, changes in the concentration and composition of submicron PM due to pollution downwind of the Manaus metropolitan region are reported as part of the GoAmazon2014/5 experiment. A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and a suite of other gas- and particle-phase instruments were deployed at the T3 research site, 70 km downwind of Manaus, during the wet season. At this site, organic components represented 79±7 % of the non-refractory PM 1 mass concentration on average, which was in the same range as several upwind sites. However, the organic PM 1 was considerably more oxidized at T3 compared to upwind measurements. Positive-matrix factorization (PMF) was applied to the time series of organic mass spectra collected at the T3 site, yielding three factors representing secondary processes (73±15 % of total organic mass concentration) and three factors representing primary anthropogenic emissions (27±15 %). Fuzzy c-means clustering (FCM) was applied to the afternoon time series of concentrations of NO y , ozone, total particle number, black carbon, and sulfate. Four clusters were identified and characterized by distinct air mass origins and particle compositions. Two clusters, Bkgd-1 and Bkgd-2, were associated with background conditions. Bkgd-1 appeared to represent near-field atmospheric PM production and oxidation of a day or less. Bkgd-2 appeared to represent material transported and oxidized for two or more days, often with out-of-basin contributions. Two other clusters, Pol-1 and Pol-2, represented the Manaus influence, one apparently associated with the northern region of Manaus and the other with the southern region of the city. A composite of the PMF and FCM analyses provided insights into the anthropogenic effects on PM concentration and composition. The increase in mass concentration of submicron PM ranged from 25 % to 200 % under polluted compared with background conditions, including contributions from both primary and secondary PM. Furthermore, a comparison of PMF factor loadings for different clusters suggested a shift in the pathways of PM production under polluted conditions. Nitrogen oxides may have played a critical role in these shifts. Increased concentrations of nitrogen oxides can shift pathways of PM production from HO 2 -dominant to NO-dominant as well as increase the concentrations of oxidants in the atmosphere. Consequently, the oxidation of biogenic and anthropogenic precursor gases as well as the oxidative processing of preexisting atmospheric PM can be accelerated. This combined set of results demonstrates the susceptibility of atmospheric chemistry, air quality, and associated climate forcing to anthropogenic perturbations over tropical forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Algal Pretreatment Improves Biofuels Yield and Value; Highlights in Science, NREL (National Renewable Energy Laboratory)

One of the major challenges associated with algal biofuels production in a biorefinery-type setting is improving biomass utilization in its entirety, increasing the process energetic yields and providing economically viable and scalable co-product concepts. We demonstrate the effectiveness of a novel, integrated technology based on moderate temperatures and low pH to convert the carbohydrates in wet algal biomass to soluble sugars for fermentation, while making lipids more accessible for downstream extraction and leaving a protein-enriched fraction behind. This research has been highlighted in the Green Chemistry journal article mentioned above and a milestone report, and is based on the work the researchers are doing for the AOP projects Algal Biomass Conversion and Algal Biofuels Techno-economic Analysis. That work has demonstrated an advanced process for algal biofuel production that captures the value of both the algal lipids and carbohydrates for conversion to biofuels. With this process, as much as 150 GGE/ton of biomass can be produced, 2-3X more than can be produced by terrestrial feedstocks. This can also reduce the cost of biofuel production by as much as 40%. This also represents the first ever design case for the algal lipid upgrading pathway.

09 BIOMASS FUELS↗
DOE OSTI2019

Detecting faults in turbine generators

Turbine generator faults can be detected by receiving a current signal from the turbine generator, and synchronously sampling the current signal to obtain a set of current signal samples that are evenly spaced in the phase domain, in which phase differences between adjacent current signal samples in the set of current signal samples are substantially the same. The process includes generating a frequency spectrum of the current signal samples, identifying one or more excitations in the frequency spectrum, and detecting a fault in the turbine generator based on the one or more excitations in the frequency spectrum.

Qiao, Wei↗
DOE OSTI2022

Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study: Preprint

This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the Model Predictive Control (MPC) algorithm based on Virtual Phase-Link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems to enable the deployment including sensing, processing, and actuating components. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days including the steps took, real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment with the changes in delay and energy consumption.

deployment↗
DOE OSTI2023

Task 2.3: Continuous Counter-Current Chromatography

This task in the Separations Consortium focuses on the development of counter-current chromatography (CCC), which is an advanced liquid-liquid chromatography method. Today, CCC is primarily practiced in batch mode operation, but continuous operation will be required for at-scale deployment in a biorefinery setting. In this task, we are developing a continuous CCC process in collaboration with a company that builds CCC units. This work will be demonstrated on the biorefining challenge of lignin valorization. Specifically, we demonstrate use of continuous CCC for both monomer-monomer and monomer-oligomer separations in multiple lignin streams of relevance to the biorefining industry. Breakthroughs in this area would be useful for BETO goals in both sustainable aviation fuel and biochemicals production, including directly contributing to BETO's 2030 lignin valorization goal. Our approach includes developing new computational modeling approaches to optimize both batch and continuous CCC processes, experimental work to determine optimal solvent systems for multiple lignin streams sourced from BETO-funded projects and industrial collaborators, and techno-economic analysis and life cycle assessment to identify the most impactful areas to ultimately enable this approach in the biorefinery. This task overall will enable high-resolution, multi-component, continuous separations at scale, demonstrated on a grand challenge biorefining problem.

BIOMASS FUELS↗
DOE OSTI2018

Radiological Characterization for Decommissioning: International Good Practice and Experience - 18396

The 'Task Group on Radiological Characterization for Decommissioning (TGRCD)' functions under the umbrella of the Nuclear Energy Agency' (NEA) 'Working Party on Decommissioning and Dismantling (WPDD),' which is currently reporting to NEA Radioactive Waste Management Committee (RWMC). The TGRCD task group, which is composed of international experts, has been working since 2014 on a commitment to provide a high-level guidance for characterization practitioners working in nuclear decommissioning. The work has focused on facility characterization from a waste and materials perspective. Radiological characterization is a key activity enabling planning and implementation of decommissioning of nuclear facilities. Effective characterization allows the extent, location and nature of contamination to be determined providing crucial information to facilitate facility dismantling, the management of material and arising waste, protection of workers, the public, the environment and associated cost estimation. The report is written for characterization practitioners who carry out the tactical planning, preparation, optimization and implementation of characterization to support the decommissioning of nuclear facilities and the management of associated materials and waste. It compiles recent experience of NEA member countries in radiological characterization, including the experience of international experts and practitioners; the learning from international case studies and international conferences; and national approaches to the application of international and national regulations, standards and guidance documents. Using this comprehensive approach and evaluation studies, the report identifies relevant good practices and sets out set out practical guidance covering all stages of the characterization process as related to decommissioning. This paper provides a summary of the work conducted and the main findings from the detailed report. (authors)

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

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

This paper is the basis for a presentation help at the 2026 Georgia Tech Fault & Disturbance Analysis Conference, which can be found at OSTI # 3168287 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danovo Energy Solutions]↗
DOE OSTI2012

What Can Meta-Analyses Tell Us About the Reliability of Life Cycle Assessment for Decision Support?

The body of life cycle assessment (LCA) literature is vast and has grown over the last decade at a dauntingly rapid rate. Many LCAs have been published on the same or very similar technologies or products, in some cases leading to hundreds of publications. One result is the impression among decision makers that LCAs are inconclusive, owing to perceived and real variability in published estimates of life cycle impacts. Despite the extensive available literature and policy need formore conclusive assessments, only modest attempts have been made to synthesize previous research. A significant challenge to doing so are differences in characteristics of the considered technologies and inconsistencies in methodological choices (e.g., system boundaries, coproduct allocation, and impact assessment methods) among the studies that hamper easy comparisons and related decision support. An emerging trend is meta-analysis of a set of results from LCAs, which has the potential to clarify the impacts of a particular technology, process, product, or material and produce more robust and policy-relevant results. Meta-analysis in this context is defined here as an analysis of a set of published LCA results to estimate a single or multiple impacts for a single technology or a technology category, either in a statistical sense (e.g., following the practice in the biomedical sciences) or by quantitative adjustment of the underlying studies to make them more methodologically consistent. One example of the latter approach was published in Science by Farrell and colleagues (2006) clarifying the net energy and greenhouse gas (GHG) emissions of ethanol, in which adjustments included the addition of coproduct credit, the addition and subtraction of processes within the system boundary, and a reconciliation of differences in the definition of net energy metrics. Such adjustments therefore provide an even playing field on which all studies can be considered and at the same time specify the conditions of the playing field itself. Understanding the conditions under which a meta-analysis was conducted is important for proper interpretation of both the magnitude and variability in results. This special supplemental issue of the Journal of Industrial Ecology includes 12 high-quality metaanalyses and critical reviews of LCAs that advance understanding of the life cycle environmental impacts of different technologies, processes, products, and materials. Also published are three contributions on methodology and related discussions of the role of meta-analysis in LCA. The goal of this special supplemental issue is to contribute to the state of the science in LCA beyond the core practice of producing independent studies on specific products or technologies by highlighting the ability of meta-analysis of LCAs to advance understanding in areas of extensive existing literature. The inspiration for the issue came from a series of meta-analyses of life cycle GHG emissions from electricity generation technologies based on research from the LCA Harmonization Project of the National Renewable Energy Laboratory (NREL), a laboratory of the U.S. Department of Energy, which also provided financial support for this special supplemental issue. (See the editorial from this special supplemental issue [Lifset 2012], which introduces this supplemental issue and discusses the origins, funding, peer review, and other aspects.) The first article on reporting considerations for meta-analyses/critical reviews for LCA is from Heath and Mann (2012), who describe the methods used and experience gained in NREL's LCA Harmonization Project, which produced six of the studies in this special supplemental issue. Their harmonization approach adapts key features of systematic review to identify and screen published LCAs followed by a meta-analytical procedure to adjust published estimates to ones based on a consistent set of methods and assumptions to allow interstudy comparisons and conclusions to be made. In a second study on methods, Zumsteg and colleagues (2012) propose a checklist for a standardized technique to assist in conducting and reporting systematic reviews of LCAs, including meta-analysis, that is based on a framework used in evidence-based medicine. Widespread use of such a checklist would facilitate planning successful reviews, improve the ability to identify systematic reviews in literature searches, ease the ability to update content in future reviews, and allow more transparency of methods to ease peer review and more appropriately generalize findings. Finally, Zamagni and colleagues (2012) propose an approach, inspired by a meta-analysis, for categorizing main methodological topics, reconciling diverging methodological developments, and identifying future research directions in LCA. Their procedure involves the carrying out of a literature review on articles selected according to predefined criteria.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enhanced joining strength in additive-manufactured polylactic-acid structures fused by embedded heated metallic meshes

Additively manufactured thermoplastic polymers, such as polylactic acid (PLA), hold significant promise for sustainable engineering structures, including wind turbine blades. Upscaling these structures beyond the limitations of 3D printer build volumes is a challenge; fusion joining presents a potential solution. This paper introduces a displacement-controlled resistance welding process for PLA, as an alternative to the typical force-controlled methods. Here, we investigated the bonding quality of resistance-welded and adhesive-bonded PLA beams through three-point bending and measured the surface deformations using digital image correlation. Different metal meshes (30%/0.11 mm Ni—Cu, 34%/0.07 mm Ni—Cu, and 36%/0.25 mm Co—Ni) served as heating elements. The process parameters were varied for the 34%/0.07 mm Ni—Cu mesh to identify an optimum set of parameters. Results showed that this optimized displacement-controlled welding achieved 94% of the original strength of monolithic samples. This indicates that the new welding process not only ensures high-quality bonding and fine surface finishing but also promotes sustainability, recyclability, and economic efficiency in various polymer and composite structural applications.

Additive manufacturing↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2012

Search for heavy vector-like quarks coupling to light quarks in proton-proton collisions at root s=7 TeV with the ATLAS detector

This Letter presents a search for singly produced vector-like quarks, Q, coupling to light quarks, q. The search is sensitive to both charged current (CC) and neutral current (NC) processes, pp {yields} Qq {yields} Wqq{prime} and pp {yields} Qq {yields} Zqq{prime} with a leptonic decay of the vector gauge boson. In 1.04 fb{sup -1} of data taken in 2011 by the ATLAS experiment at a center-of-mass energy {radical}s = 7 TeV, no evidence of such heavy vector-like quarks is observed above the expected Standard Model background. Limits on the heavy vector-like quark production cross section times branching ratio as a function of mass m{sub Q} are obtained. For a coupling {kappa}{sub qQ} = v/m{sub Q}, where v is the Higgs vacuum expectation value, 95% C.L. lower limits on the mass of a vector-like quark are set at 900 GeV and 760 GeV from CC and NC processes, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗