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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Emergent Ergodicity at the Transition between Many-Body Localized Phases

Strongly disordered systems in the many-body localized (MBL) phase can exhibit ground state order in highly excited eigenstates. The interplay between localization, symmetry, and topology has led to the characterization of a broad landscape of MBL phases ranging from spin glasses and time crystals to symmetry protected topological phases. Understanding the nature of phase transitions between these different forms of eigenstate order remains an essential open question. In this work, we conjecture that no direct transition between distinct MBL orders can occur in one dimension; rather, an ergodic phase always intervenes. Motivated by recent advances in Rydberg-atom-based quantum simulation, we propose an experimental protocol where the intervening ergodic phase can be diagnosed via the dynamics of local observables.

1-dimensional spin chains↗
DOE OSTI2024

Power Profile Monitoring and Tracking Evolution of System-Wide HPC Workloads

The power & energy demands of HPC machines have grown significantly. Modern exascale HPC systems require tens of megawatts of combined power for computing resources and cooling facilities at full capacity. The current energy trend is not sustainable for future HPC systems, and there is a need to work toward the energy efficiency aspect of HPC performance. Energy awareness of the HPC applications at the job level is essential for running an efficient HPC system. This work aims to develop a pipeline to provide a production-level system-wide overview of the HPC workloads' power profile while handling evolving workloads exhibiting new power trends. We developed an open-set classification model for HPC jobs based on the properties of power profiles to continuously provide a system-wide holistic view of recently completed jobs. The pipeline helps continuously monitor the job-level power usage pattern of HPC and enables us to capture the new trends in applications' power behavior. We employed a comprehensive set of techniques to generate job-level data, custom-designed feature extraction methods to extract critical features from jobs' power profiles, clustering techniques powered by generative modeling, and open-set classification for identifying job profiles into known classes or an unknown set. With extensive evaluations, we demonstrate the effectiveness of each component in our pipeline. We provide an analysis of the resulting clusters that characterize the power profile landscape of the Summit supercomputer from more than 60K jobs executed in a year. The open-set classification classifies the known data sets into known classes with high accuracy and identifies unknown data noints with over 85% accuracy.

Karimi, Ahmad Maroof↗
DOE OSTI2020

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology

Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Science Breakthroughs 2030. Final report

Agriculture is a fundamental societal activity, characterized by many different landscapes, crops, markets, and participants. Food, agricultural, and biofuels products are central to the daily life of all citizens, though most do not recognize the fragility of the environment that brings forth this abundance. As is noted in the 2012 report from the President's Council of Advisors on Science and Technology, Agricultural Preparedness and the United States Agricultural Research Enterprise (PCAST, 2012) the food and agricultural system faces constant challenges in: Managing new pests, pathogens, and invasive plants. Increasing the efficiency of water use. Growing food in a changing climate. Reducing the environmental footprint of agriculture. Managing the production of bioenergy. Producing safe and nutritious food. Assisting with global food security and maintaining abundant yields. Science Breakthroughs 2030 was organized to identify the most compelling research directions in food and agriculture, in particular those empowered by the application of insights and tools from disciplines of science and engineering not typically associated with food and agricultural research. A committee appointed by the Chairman of the National Research Council explored ideas for research directions with input from the scientific community, with the objective of producing a report describing ambitious and achievable scientific pathways to address major problems and create new opportunities in food and agriculture. Following numerous meetings, a jamboree, and town hall, the appointed committee prepared a report that has subsequently become a reference for federal agencies supporting research in the food and agricultural space. It highlights five key areas for research investment with broad application across food and agriculture: integrated systems research; sensor development; data mining and information sciences, genomics; and the microbiome.

09 BIOMASS FUELS↗
DOE OSTI2013

Clean Energy Finance: Challenges and Opportunities of Early-Stage Energy Investing (Presentation)

Characterized by a changing landscape and new opportunities, today's increasingly complex energy decision space will need innovative financing and investment models to appropriately assess risk and profitability. This report provides an overview of the current state of clean energy finance across the entire spectrum but with a focus on early stage investing, and it includes insights from investors across all investment classes. Further, this report aims to provide a roadmap with the mechanisms, limitations, and considerations involved in making successful investments by identifying risks, challenges, and opportunities in the clean energy sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The Evolving Solar Energy Innovation Ecosystem in Puerto Rico

This report presents initial findings from the research project, “Developing socially and economically generative, resilient PV-energy systems for low- and moderate-income communities: Applications for Puerto Rico.” This material is based upon work supported primarily by the US Department of Energy’s Office of Energy Efficiency and Renewable Energy under the Solar Energy Technology Office Fiscal Year 2018 Funding Program award number DE-EE0008570 and in part by the National Science Foundation (NSF) and the Department of Energy (DOE) under NSF CA No. EEC-1041895. This project is a joint effort of Arizona State University’s Center for Energy and Society; the University of Puerto Rico-Mayaguez’s Center for Hemispheric Cooperation in Research and Education in Engineering and Applied Science, Sustainable Energy Center, and National Institute for Energy and Island Sustainability; and the National Renewable Energy Laboratory. The project seeks to understand the place of energy in low-income communities in Puerto Rico, the opportunities for solar energy to provide solutions to the energy challenges facing these communities, and the capacity of the solar energy innovation ecosystem in Puerto Rico to deliver relevant solar solutions for these communities. We define the solar energy innovation ecosystem as the diverse actors and stakeholders whose collective networks and actions—and the flows among them of ideas, knowledge, data, influence, money, and equipment, as well as the rules that govern those flows—are essential to the success of renewable energy markets and projects. This report presents results from the first year of the project’s research, which has concentrated on several key goals: (1) characterizing the trajectory of solar energy development in Puerto Rico in the three years since Hurricane María; (2) mapping and characterizing the current solar energy innovation ecosystem in Puerto Rico, including the technical potential for solar energy in Puerto Rico, and the business and policy landscape shaping solar developments; and (3) beginning to characterize the broad landscape of community energy initiatives underway in Puerto Rico. To pursue these goals, we engaged in a systematic review of solar energy developments in Puerto Rico and conducted interviews with over 100 members of the solar energy innovation, including policy, business, finance, research, and civil society leaders, as well as solar users. Solar technical potential data for Puerto Rico was developed by the National Renewable Energy Laboratory (NREL) and is publicly available at: https://data.nrel.gov/submissions/144. The documentation for the NREL dataset is included as Appendix A.

14 SOLAR ENERGY↗
DOE OSTI2015

Characterization of Flow Paths, Residence Time and Media Chemistry in Complex Landscapes to Integrate Surface, Groundwater and Stream Processes and Inform Models of Hydrologic and Water Quality Response to Land Use Activities; Savannah River Site

The objective of this report is to document the methodology used to calculate the three hydro-geomorphic indices: C Index, Nhot spot, and Interflow Contributing Area (IFC Area). These indices were applied in the Upper Four Mile Creek Watershed in order to better understand the potential mechanisms controlling retention time, path lengths, and potential for nutrient and solute metabolism and exchange associated with the geomorphic configurations of the upland contributing areas, groundwater, the riparian zone, and stream channels.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics

Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. Here we demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Evaluating Coastal Landscape Response to Sea-Level Rise in the Northeastern United States - Approach and Methods

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

terrain↗
DOE OSTI2022

Cosmogenic nuclide and solute flux data from central Cuban rivers emphasize the importance of both physical and chemical mass loss from tropical landscapes

We use 25 new measurements of in situ produced cosmogenic 26 Al and 10 Be in river sand, paired with estimates of dissolved load flux in river water, to characterize the processes and pace of landscape change in central Cuba. Long-term erosion rates inferred from 10 Be concentrations in quartz extracted from central Cuban river sand range from 3.4–189 Mg km –2 yr –1 (mean 59, median 45). Dissolved loads (10–176 Mg km –2 yr –1 ; mean 92, median 97), calculated from stream solute concentrations and modeled runoff, exceed measured cosmogenic- 10 Be-derived erosion rates in 18 of 23 basins. This disparity mandates that in this environment landscape-scale mass loss is not fully represented by the cosmogenic nuclide measurements. The 26 Al / 10 Be ratios are lower than expected for steadystate exposure or erosion in 16 of 24 samples. Depressed 26 Al/ 10 Be ratios occur in many of the basins that have the greatest disparity between dissolved loads (high) and erosion rates inferred from cosmogenic nuclide concentrations (low). Depressed 26 Al/ 10 Be ratios are consistent with the presence of a deep, mixed, regolith layer providing extended storage times on slopes and/or burial and extended storage during fluvial transport. River water chemical analyses indicate that many basins with lower 26 Al/ 10 Be ratios and high 10 Be concentrations are underlain at least in part by evaporitic rocks that rapidly dissolve. Our data show that when assessing mass loss in humid tropical landscapes, accounting for the contribution of rock dissolution at depth is particularly important. In such warm, wet climates, mineral dissolution can occur many meters below the surface, beyond the penetration depth of most cosmic rays and thus the production of most cosmogenic nuclides. Our data suggest the importance of estimating solute fluxes and measuring paired cosmogenic nuclides to better understand the processes and rates of mass transfer at a basin scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Dual transposon sequencing profiles the genetic interaction landscape in bacteria

Gene redundancy complicates systematic characterization of gene function as single-gene deletions may not produce discernible phenotypes. We report dual transposon sequencing (dual Tn-seq), a platform for assaying the fitness of a comprehensive double mutant pool in parallel. Dual Tn-seq couples random barcode transposon site sequencing with the Cre-lox system, enabling deep sampling of 73% of the 1.3 million possible double gene deletions in Streptococcus pneumoniae. The genetic interactions identified span a wide range of biochemical processes, revealing new factors in presumably well-studied pathways, exemplified by a cytidine triphosphate synthase PyrJ. Moreover, this approach should permit further investigation of growth condition–specific genetic interactions. Because dual Tn-seq does not require the construction of a large array of single mutants, it should be readily adaptable to various microorganisms.

CTP synthesis↗
DOE OSTI2021

Spurious regulatory connections dictate the expression-fitness landscape of translation factors

During steady-state cell growth, individual enzymatic fluxes can be directly inferred from growth rate by mass conservation, but the inverse problem remains unsolved. Perturbing the flux and expression of a single enzyme could have pleiotropic effects that may or may not dominate the impact on cell fitness. Here, we quantitatively dissect the molecular and global responses to varied expression of translation termination factors (peptide release factors, RFs) in the bacterium Bacillus subtilis. While endogenous RF expression maximizes proliferation, deviations in expression lead to unexpected distal regulatory responses that dictate fitness reduction. Molecularly, RF depletion causes expression imbalance at specific operons, which activates master regulators and detrimentally overrides the transcriptome. Through these spurious connections, RF abundances are thus entrenched by focal points within the regulatory network, in one case located at a single stop codon. Such regulatory entrenchment suggests that predictive bottom-up models of expression-fitness landscapes will require near-exhaustive characterization of parts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Evaluating the potential for bird‐habitat models to support biodiversity‐friendly urban planning

Abstract Urban expansion poses a major threat to wildlife populations. Biodiversity‐friendly urban landscapes could deliver benefits for both wildlife and people, by incorporating conservation and ecosystem services objectives. Well‐designed urban developments could also soften the ecological impacts of urbanization. However, delivering urban landscapes that integrate biodiversity requirements effectively remains challenging. Ecological models, designed to predict wildlife population responses to alternative urban designs, could prove effective in supporting the creation of biodiversity‐friendly urban landscapes. Here, we combine national‐scale bird abundance data with high resolution, spatially explicit habitat data to characterize relationships between bird densities and urban landscape form in Britain. From these analyses and cross validation, we evaluate the potential for well‐parameterized, species‐specific models to be used to predict bird densities in novel or modified urban areas. Our analyses indicate that responses of bird abundance to urban habitat are species‐specific and complex, with few variables consistently affecting a large proportion of species. However, contiguous areas of greenspace within urban sites are preferential for accommodating breeding birds, compared to a more fragmented arrangement of multiple, small greenspace patches. In combination, the bird‐habitat relationships identified could successfully predict observed variation in abundance for most bird species considered. Further evaluation of habitat descriptor variables, spatial scales of species' habitat use and analytical modelling approaches may be needed to improve the predictive ability of bird‐habitat models for certain species, particularly waterbirds and those observed less frequently in urban areas. Synthesis and applications . We modelled breeding bird abundance in built‐up areas with respect to the characteristics and contexts of urban environments. While most variables were important for multiple species, responses overall were species‐specific, so simple assemblage metrics, like diversity, will not describe the variation in bird communities well. However, the results illustrate the potential of an evidence‐based, spatially explicit evaluation of urban development impacts on biodiversity, by predicting the consequences for bird numbers. Subject to verification of predictive ability, practitioners can apply the models to compare, for example, land‐sparing and sharing within developments, or to quantify the biodiversity requirements for effective offsetting. This would be facilitated by incorporation into an online tool allowing user‐determined input scenarios.

Plummer, Kate E.↗
DOE OSTI2024

Fine-scale contemporary recombination variation and its fitness consequences in adaptively diverging stickleback fish

Despite deep evolutionary conservation, recombination rates vary greatly across the genome and among individuals, sexes and populations. Yet the impact of this variation on adaptively diverging populations is not well understood. Here we characterized fine-scale recombination landscapes in an adaptively divergent pair of marine and freshwater populations of threespine stickleback from River Tyne, Scotland. Through whole-genome sequencing of large nuclear families, we identified the genomic locations of almost 50,000 crossovers and built recombination maps for marine, freshwater and hybrid individuals at a resolution of 3.8 kb. We used these maps to quantify the factors driving variation in recombination rates. We found strong heterochiasmy between sexes but also differences in recombination rates among ecotypes. Hybrids showed evidence of significant recombination suppression in overall map length and in individual loci. Recombination rates were lower not only within individual marine–freshwater-adaptive loci, but also between loci on the same chromosome, suggesting selection on linked gene ‘cassettes’. Through temporal sampling along a natural hybrid zone, we found that recombinants showed traits associated with reduced fitness. Our results support predictions that divergence in cis-acting recombination modifiers, whose functions are disrupted in hybrids, may play an important role in maintaining differences among adaptively diverging populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Layered nested Markov chain Monte Carlo

A configurational sampling algorithm based on nested layerings of Markov chains (Layered Nested Markov Chain Monte Carlo or L-NMCMC) is presented for simulations of systems characterized by rugged free energy landscapes. The layerings are generated using a set of auxiliary potential energy surfaces. The implementation of the method is demonstrated in the context of a rugged, two-dimensional potential energy surface. The versatility of the algorithm is next demonstrated on a simple, many body system, namely, a canonical Lennard-Jones fluid in the liquid state. In that example, different layering schemes and auxiliary potentials are used, including variable cutoff distances and excluded volume tempering. In addition to calculating a variety of properties of the system, it is also shown that L-NMCMC, when combined with a free-energy perturbation formalism, provides a straightforward means to construct approximate free-energy surfaces at no additional computational cost using the sampling distributions of each auxiliary Markov chain. In conclusion, the proposed L-NMCMC scheme is general in that it could be complementary to any number of methods that rely on sampling from a target distribution or methods that exploit a hierarchy of time scales and/or length scales through decomposition of the potential energy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Community-Based Transactive Coordination Mechanism for Enabling Grid Edge Systems

The changing landscape of the electricity industry, characterized by a surge in distributed energy resources (DERs) and proactive customers, necessitates practical solutions for coordinated operations especially at the distribution-level. This paper introduces a community-based transactive coordination mechanism designed to incentivize customers for providing localized and system-level services reflected through real-time prices. The work presents a bidding approach for communities with DERs, such as solar photovoltaic (PV) and battery energy storage systems (BESS), to formulate their price-responsiveness for retail energy coordination, emphasizing a community-centric model. By sending bidding curves to a third-party, the mechanism enables customers with DER assets to actively participate in localized coordination with the load serving entity (LSE), thereby supplementing each other’s and even the utilities needs through a shared energy economy. The proposed transactive mechanism is implemented leveraging a co-simulation framework that integrates a distribution grid simulator and Python-based agents for performance evaluation. Collaboration with a local utility to access real distribution feeder models and consumption profiles yields simulation results demonstrating the potential to reduce costs by 12\% for communities with DERs like PV and BESS.

Community-based coordination, grid-edge systems, R↗
DOE OSTI2026

Compilation and utilization of a sorghum transcriptome compendium for gene regulatory network analysis and crop trait engineering

Sorghum bicolor (Sorghum) is a drought and heat tolerant C4 grass crop used to produce grain, forage, biofuels, and other bioproducts. Genetic improvement of sorghum hybrid crops is aided by a large and diverse germplasm, sorghum's diploid inbreeding genetics, and a relatively small genome that has facilitated genomic research. Over the past 20 years, the sorghum research community characterized the cytogenetic and recombinant landscapes of sorghum's 10 chromosomes, sequenced and annotated the sorghum genome, and used that information to identify genes/alleles that modulate flowering time, plant height, seed shattering, and other important traits. More recently, >1000 RNA-seq transcriptome profiles were collected from 15 sorghum genotypes to help understand the genetic basis of variation in growth and development of sorghum stems, tillers, roots, and leaves, and the regulation of biosynthetic pathways that produce epicuticular wax, dhurrin, and RFOs, compounds that contribute to sorghum's resilience. Transcriptome studies were designed to identify differentially expressed genes that are co-expressed during development or in response to a treatment to enable construction of gene regulatory networks. Co-expression and network analysis identified transcription factors and their cognate binding sites in target gene promoters and signaling pathways that modulate gene regulatory networks providing gene editing targets for further trait optimization. RNA-seq data from >20 experiments targeting sorghum organs, tissues, cell types, developmental stages, and responses to environmental conditions (i.e., diel, day-length, shading, water-deficit, temperature) has been compiled in a sorghum transcriptome compendium. The goal of this resource paper is to describe compendium content, accessibility, and a compendium data analysis pipeline and to illustrate the types of information that can be derived from the compendium with a focus on the elucidation of gene regulatory networks useful for guiding the improvement of sorghum traits through gene editing.

RNA-seq↗