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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Superuniversality from disorder at two-dimensional topological phase transitions

We investigate the effects of quenched randomness on topological quantum phase transitions in strongly interacting two-dimensional systems. We focus first on transitions driven by the condensation of a subset of fractionalized quasiparticles (“anyons”) identified with “electric charge” excitations of a phase with intrinsic topological order. All other anyons have nontrivial mutual statistics with the condensed subset and hence become confined at the anyon condensation transition. Furthermore, using a combination of microscopically exact duality transformations and asymptotically exact real-space renormalization group techniques applied to these two-dimensional disordered gauge theories, we argue that the resulting critical scaling behavior is “superuniversal” across a wide range of such condensation transitions and is controlled by the same infinite-randomness fixed point as that of the 2D random transverse-field Ising model. We validate this claim using large-scale quantum Monte Carlo simulations that allow us to extract zero-temperature critical exponents and correlation functions in (2+1)D disordered interacting systems. We discuss generalizations of these results to a large class of ground-state and excited-state topological transitions in systems with intrinsic topological order as well as those where topological order is either protected or enriched by global symmetries. When the underlying topological order and the symmetry group are Abelian, our results provide prototypes for topological phase transitions between distinct many-body localized phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

When more data hurts: Optimizing data coverage while mitigating diversity-induced underfitting in an ultrafast machine-learned potential

Machine-learned interatomic potentials (MLIPs) are becoming an essential tool in materials modeling. However, optimizing the generation of training data used to parametrize the MLIPs remains a significant challenge. This is because MLIPs can fail when encountering local environments too different from those present in the training data. The difficulty of determining a priori the environments that will be encountered during molecular dynamics simulation necessitates diverse, high-quality training data. Here, this study investigates how training data diversity affects the performance of MLIPs using the Ultra-Fast force field (UF 3 ) to model amorphous silicon nitride. We employ expert and autonomously generated data to create the training data and fit four force field variants to subsets of the data. Our findings reveal a critical balance in training data diversity: insufficient diversity hinders generalization, while excessive diversity can exceed the MLIP's learning capacity, reducing simulation accuracy. Specifically, we found that the UF 3 variant trained on a subset of the training data, in which nitrogen-rich structures were removed, offered vastly better prediction and simulation accuracy than any other variant. By comparing these UF 3 variants, we highlight the nuanced requirements for creating accurate MLIPs, emphasizing the importance of application-specific training data to achieve optimal performance in modeling complex material behaviors.

ab initio molecular dynamics↗
DOE OSTI2020

Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques

A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling

Online deep learning (ODL) has become an important methodology for modeling time-based performance of computer systems. An open problem is the intelligent selection of features from raw workload traces of computer systems. The best methods are overly sensitive to noisy data, causing frequent feature changes and re-training. Using all available features inflates training time and introduces model artifacts if some features should have been dropped. We present WinnowML, a method for automatically determining the most relevant feature subset for a predictive time-series model. WinnowML combines existing feature ranking algorithms and a history of each feature's ranking to iteratively rank a feature set to lower prediction error and maximize long term relevance. From this ranked feature set, the most relevant and stable subset is selected to train a model. Experimentally, we show how WinnowML can lower a model's mean absolute relative error up to 42% on average compared to the closest performing approach. Additionally, we lower the fluctuation in feature ranking and selection up to 65%. We also demonstrate how to combine WinnowML and a model search tool to provide improvements in performance of up to 14.5% when compared to using all the feature available.

Bel, Oceane MS↗
DOE OSTI2023

A Novel Resilience-Oriented Cellular Grid Formation Approach for Distribution Systems with Behind-the-Meter Distributed Energy Resources

This paper presents a resilience-oriented cellular grid formation approach to achieve scalable and reconfigurable community microgrid operations for distribution systems with behind-the-meter distributed energy resources. A set of interconnected solar photovoltaics, energy storage systems, and load is termed as a cell, implying a subset of the grid that can operate independently using its own resources. Cells are identified such that each cell inherently has sufficient energy resources to black start and can provide a certain level of backup power for its load under the loss of utility power supply. The proposed cell formation approach builds on a unique self-organizing map-based method (SomRes) to quantify a system's resilience. Using SomRes and a non-dominated sorting-based genetic algorithm (NSGA-II), a fast and efficient cell formation algorithm is developed to identify cells in a distribution system that are resilient against extreme events. The efficacy of the proposed approach is demonstrated on a numerical model of a real distribution feeder in Colorado, United States.

distributed energy resource↗
DOE OSTI2021

A Data-Driven Method for Estimating Behind-the-Meter Photovoltaic Generation in Hawaii

Due to the increasing penetration of distributed behind-the-meter photovoltaic (PV) systems and the installed utility revenue metering limited to monitoring only the net power import/export of the household, it is increasingly challenging for utilities to effectively plan and operate the grid. This paper proposes a methodology that estimates behind-the-meter PV generation using a selected subset of monitored PV systems. It is a data-driven approach, and the PV output is estimated utilizing a statistic regression model. A Minimum Redundancy Maximum Relevance (MRMR) algorithm is applied to preselect the optimal subset of the monitored PV systems. The performance of this approach is compared with a spatial interpolation method and a model-based approach. The proposed method is validated using high-resolution meter data recorded from 18 residential rooftop PV systems located on the island of Maui, Hawaii.

Data-driven modeling↗
DOE OSTI2024

Transmission Interface Limits for High-Spatial Resolution Capacity Expansion Modeling

Large-scale capacity expansion models typically rely on estimates of the power transfer limits between modeled zones. Accurate estimation of these interface transfer limits (ITLs) requires modeling the underlying transmission network. Here we expand on a maximum flow optimization method that uses linearized power flow to estimate transfer limits. We apply this method to a data set of the U.S. transmission network to estimate ITLs between U.S. counties. By calculating ITLs using different subsets of the network, we evaluate how the size of the network used in the estimation affects the results. The results show diminishing returns to ITL accuracy after six hops, suggesting that a network subset can reasonably be used to approximate ITLs. The county-level estimates produced in this study will support more spatially resolved capacity expansion modeling and will help inform policy making at local and national levels.

capacity planning↗
DOE OSTI2021

Microbial Community Shifts Reflect Losses of Native Soil Carbon with Pyrogenic and Fresh Organic Matter Additions and Are Greatest in Low-Carbon Soils

ABSTRACT Soil organic carbon (SOC) plays an important role in regulating global climate change, carbon and nutrient cycling in soils, and soil moisture. Organic matter (OM) additions to soils can affect the rate at which SOC is mineralized by microbes, with potentially important effects on SOC stocks. Understanding how pyrogenic organic matter (PyOM) affects the cycling of native SOC (nSOC) and the soil microbes responsible for these effects is important for fire-affected ecosystems as well as for biochar-amended systems. We used an incubation trial with five different soils from National Ecological Observatory Network sites across the United States and 13 C-labeled 350°C corn stover PyOM and fresh corn stover OM to trace nSOC-derived CO 2 emissions with and without PyOM and OM amendments. We used high-throughput sequencing of rRNA genes to characterize bacterial, archaeal, and fungal communities and their responses to PyOM and OM in soils that were previously stored at −80°C. We found that the effects of amendments on nSOC-derived CO 2 reflected the unamended soil C status, where relative increases in C mineralization were greatest in low-C soils. OM additions produced much greater effects on nSOC-CO 2 emissions than PyOM additions. Furthermore, the magnitude of the microbial community composition change mirrored the magnitude of increases in nSOC-CO 2 , indicating that a specific subset of microbes was likely responsible for the observed changes in nSOC mineralization. However, PyOM responders differed across soils and did not necessarily reflect a common “charosphere.” Overall, this study suggests that soils that already have low SOC may be particularly vulnerable to short-term increases in SOC loss with OM or PyOM additions. IMPORTANCE Soil organic matter (SOM) has an important role in global climate change, carbon and nutrient cycling in soils, and soil moisture dynamics. Understanding the processes that affect SOM stocks is important for managing these functions. Recently, understanding how fire-affected organic matter (or “pyrogenic” organic matter [PyOM]) affects existing SOM stocks has become increasingly important, due to both changing fire regimes and interest in “biochar,” pyrogenic organic matter that is produced intentionally for carbon management or as an agricultural soil amendment. We found that soils with less SOM were more prone to increased losses with PyOM (and fresh organic matter) additions and that soil microbial communities changed more in soils that also had greater SOM losses with PyOM additions. This suggests that soils that already have low SOM content may be particularly vulnerable to short-term increases in SOM loss and that a subset of the soil microbial community is likely responsible for these effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Intranasal Nanoparticle Vaccination Elicits a Persistent, Polyfunctional CD4 T Cell Response in the Murine Lung Specific for a Highly Conserved Influenza Virus Antigen That Is Sufficient To Mediate Protection from Influenza Virus Challenge

We report lung-localized CD4 T cells play a critical role in the control of influenza virus infection and can provide broadly protective immunity. However, current influenza vaccination strategies primarily target influenza hemagglutinin (HA) and are administered peripherally to induce neutralizing antibodies. We have used an intranasal vaccination strategy targeting the highly conserved influenza nucleoprotein (NP) to elicit broadly protective lung-localized CD4 T cell responses. The vaccine platform consists of a self-assembling nanolipoprotein particle (NLP) linked to NP with an adjuvant. We have evaluated the functionality, in vivo localization, and persistence of the T cells elicited. Our study revealed that intranasal vaccination elicits a polyfunctional subset of lung-localized CD4 T cells that persist long term. A subset of these lung CD4 T cells localize to the airway, where they can act as early responders following encounter with cognate antigen. Polyfunctional CD4 T cells isolated from airway and lung tissue produce significantly more effector cytokines IFN-γ and TNF-α, as well as cytotoxic functionality. When adoptively transferred to naive recipients, CD4 T cells from NLP:NP-immunized lung were sufficient to mediate 100% survival from lethal challenge with H1N1 influenza virus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Characterization of Pseudomonas aeruginosa and Acinetobacter calcoaceticus-baumannii complex traumatic wound isolates

Healing of traumatic wounds is complicated by infecting pathogens, with Pseudomonas aeruginosa and members of the Acinetobacter calcoaceticus-baumannii complex among the most common infectious agents. However, a full understanding of genotypic and phenotypic differences between circulating wound isolates is lacking. To address this, traumatic wounds were sampled at Emory University Hospital, and 24 isolates were characterized; we focused on P. aeruginosa and Acinetobacter due to their prevalence and tendency for antibiotic resistance. Even though these species are renowned for antibiotic resistance, only two of the tested isolates could be classified as multidrug resistant. Whole-genome sequencing and analysis revealed that isolates from different patients were genetically distinct; however, longitudinal isolates from the same patient were closely related and appeared to represent chronic colonization by the same strain. Phylogenetic analysis revealed that laboratory strains (PAO1 and AB5075) that were isolated decades previously and from different locations grouped closely to subsets of the recent wound isolates. Given the importance of biofilm formation in infection, the ability of the isolates to form biofilms was assessed; all isolates formed biofilms but showed temporal and magnitude differences. Analysis of a subset of isolates revealed that planktonic P. aeruginosa was resistant to human serum-mediated killing. While the same was true for the majority of Acinetobacter isolates, one showed planktonic sensitivity that was abrogated when grown in a biofilm. Taken together, these data reveal genotypic and phenotypic differences in circulating isolates of P. aeruginosa and A. baumannii.

Acinetobacter↗
DOE OSTI2020

Threshold $\pi ^-$ photoproduction on the neutron

Recent data from the PIONS@MAX-lab Collaboration, measuring the total cross section of the incoherent pion photoproduction reaction, γd → π – pp, near threshold, have been used to extract the E 0+ multipole and total cross section of the reaction γn → π – p, also near threshold. These are the first measurements of the reaction γd → π – pp in the threshold region. The value of E 0+ is extracted through a fit to the deuteron data in a photoproduction model accounting for final-state interactions. The model takes an S-wave approximation for the elementary reaction γn → π – p with E 0+ = const in the threshold region. The fit over all the 6 deuteron data points gives the value E 0+ = –31.86 ± 0.8 (in units 10 –3 /mπ). We explore the dependence of our results on the choice of data subsets included in the fit. Here, the obtained values of E 0+ , for different subsets, have overlapping errors and agree with previous determinations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems: Preprint

We present the PowerGridworld software package to provide users with a light-weight, modular, and customizable framework for creating power systems-focused, multi-agent gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). While many frameworks exist for training multi-agent (MA) RL policies, none exist to rapidly prototype and develop the environments themselves, especially in the context of heterogeneous (composite, multi-device) power systems where power flow solutions are required to define grid-level variables and costs. PowerGridworld is an open-source software package that helps to fill this gap. To highlight PowerGridworld's key features, we present two case studies and demonstrate learning multi-agent RL policies using both OpenAI's MADDPG and RLLib's PPO algorithms where, in both cases, at least some subset of agents incorporate elements of the power flow solution at each time step as part of their reward (negative cost) structures.

MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Layered Double Perovskites

Successful strategies for the design of crystalline materials with useful function are frequently based on the systematic tuning of chemical composition within a given structural family. Perovskites with the formula ABX 3 , perhaps the best-known example of such a family, have a vast range of elements on A, B, and X sites, which are associated with a similarly vast range of functionality. Layered double perovskites (LDPs), a subset of this family, are obtained by suitable slicing and restacking of the perovskite structure, with the additional design feature of ordered cations and/or anions. In addition to inorganic LDPs, we also discuss hybrid (organic-inorganic) LDPs here, where the A-site cation is a protonated organic amine. Several examples of inorganic LDPs are presented with a discussion of their ferroic, magnetic, and optical properties. The emerging area of hybrid LDPs is particularly rich and is leading to exciting discoveries of new compounds with unique structures and fascinating optoelectronic properties. We provide context for what is important to consider when designing new materials and conclude with a discussion of future opportunities in the broad LDP area.

Materials Science↗
DOE OSTI2021

The updated ITPA global H-mode confinement database: description and analysis

The multi-machine ITPA Global H-mode Confinement Database has been upgraded with new data from JET with the ITER-like wall and ASDEX Upgrade with the full tungsten wall. This paper describes the new database and presents results of regression analysis to estimate the global energy confinement scaling in H-mode plasmas using a standard power law. Various subsets of the database are considered, focusing on type of wall and divertor materials, confinement regime (all H-modes, ELMy H or ELM-free) and ITER-like constraints. Apart from ordinary least squares, two other, robust regression techniques are applied, which take into account uncertainty on all variables. Regression on data from individual devices shows that, generally, the confinement dependence on density and the power degradation are weakest in the fully metallic devices. Using the multi-machine scalings, predictions are made of the confinement time in a standard ELMy H-mode scenario in ITER. The uncertainty on the scaling parameters is discussed with a view to practically useful error bars on the parameters and predictions. One of the derived scalings for ELMy H-modes on an ITER-like subset is studied in particular and compared to the IPB98(y,2) confinement scaling in engineering and dimensionless form. Transformation of this new scaling from engineering variables to dimensionless quantities is shown to result in large error bars on the dimensionless scaling. Regression analysis in the space of dimensionless variables is therefore proposed as an alternative, yielding acceptable estimates for the dimensionless scaling. The new scaling, which is dimensionally correct within the uncertainties, suggests that some dependencies of confinement in the multi- machine database can be reconciled with parameter scans in individual devices. This includes vanishingly small dependence of confinement on line-averaged density and normalized plasma pressure (β), as well as a noticeable, positive dependence on effective atomic mass and plasma triangularity. Extrapolation of this scaling to ITER yields a somewhat lower confinement time compared to the IPB98(y, 2) prediction, possibly related to the considerably weaker dependence on major radius in the new scaling (slightly above linear). Further studies are needed to compare more flexible regression models with the power law used here. In addition, data from more devices concerning possible ‘hidden variables’ could help to determine their influence on confinement, while adding data in sparsely populated areas of the parameter space may contribute to further disentangling some of the global confinement dependencies in tokamak plasmas.

Database↗
DOE OSTI2026

Pyctos

Pyctos is a concolic testing framework for standard, dynamically-typed Python. Pyctos is able to generate exhaustive test inputs reaching 100% coverage for a subset of pure Python in the absence of type annotations, even where modern fuzzers would fail. Pyctos's underlying reasoning engine is the CVC5 SMT solver, though Z3 is also supported. Pyctos also supports a growing subset of the standard library and some third-party libraries, such as NumPy.

Washbourne, ErickN [Lawrence Livermore National La↗
DOE OSTI2022

Novel Anti-LY6G6D/CD3 T-Cell–Dependent Bispecific Antibody for the Treatment of Colorectal Cancer

Abstract New therapeutics and combination regimens have led to marked clinical improvements for the treatment of a subset of colorectal cancer. Immune checkpoint inhibitors have shown clinical efficacy in patients with mismatch-repair–deficient or microsatellite instability–high (MSI-H) metastatic colorectal cancer (mCRC). However, patients with microsatellite-stable (MSS) or low levels of microsatellite instable (MSI-L) colorectal cancer have not benefited from these immune modulators, and the survival outcome remains poor for the majority of patients diagnosed with mCRC. In this article, we describe the discovery of a novel T-cell–dependent bispecific antibody (TDB) targeting tumor-associated antigen LY6G6D, LY6G6D-TDB, for the treatment of colorectal cancer. RNAseq analysis showed that LY6G6D was differentially expressed in colorectal cancer with high prevalence in MSS and MSI-L subsets, whereas LY6G6D expression in normal tissues was limited. IHC confirmed the elevated expression of LY6G6D in primary and metastatic colorectal tumors, whereas minimal or no expression was observed in most normal tissue samples. The optimized LY6G6D-TDB, which targets a membrane-proximal epitope of LY6G6D and binds to CD3 with high affinity, exhibits potent antitumor activity both in vitro and in vivo. In vitro functional assays show that LY6G6D-TDB–mediated T-cell activation and cytotoxicity are conditional and target dependent. In mouse xenograft tumor models, LY6G6D-TDB demonstrates antitumor efficacy as a single agent against established colorectal tumors, and enhanced efficacy can be achieved when LY6G6D-TDB is combined with PD-1 blockade. Our studies provide evidence for the therapeutic potential of LY6G6D-TDB as an effective treatment option for patients with colorectal cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Are 100 Ensemble Members Enough to Capture the Remote Atmospheric Response to +2°C Arctic Sea Ice Loss?

This study presents results from the Polar Amplification Multimodel Intercomparison Project (PAMIP) single-year time-slice experiments that aim to isolate the atmospheric response to Arctic sea ice loss at global warming levels of +2°C. Using two general circulation models (GCMs), the ensemble size is increased up to 300 ensemble members, beyond the recommended 100 members. After partitioning the response in groups of 100 ensemble members, the reproducibility of the results is evaluated, with a focus on the response of the midlatitude jet streams in the North Atlantic and North Pacific. Both atmosphere-only and coupled ocean–atmosphere PAMIP experiments are analyzed. Substantial differences in the midlatitude response are found among the different experiment subsets, suggesting that 100-member ensembles are still significantly influenced by internal variability, which can mislead conclusions. Despite an overall stronger response, the coupled ocean–atmosphere runs exhibit greater spread due to additional ENSO-related internal variability when the ocean is interactive. The lack of consistency in the response is true for anomalies that are statistically significant according to Student’s t and false discovery rate tests. This is problematic for the multimodel assessment of the response, as some of the spread may be attributed to different model sensitivities whereas it is due to internal variability. Here, we propose a method to overcome this consistency issue that allows for more robust conclusions when only 100 ensemble members are used.

58 GEOSCIENCES↗