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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Perspectives on AI Architectures and Co-design for Earth System Predictability

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

58 GEOSCIENCES↗
DOE OSTI2021

Biological Carbon Sequestration for Climate and Soil Health

The United States recently launched the Net Zero World Initiative on decarbonization. As temperatures and sea levels rise, strong action is needed to mitigate high atmospheric CO 2 levels. In response to this need, a joint workshop between the US Department of Energy’s Oak Ridge National Laboratory (ORNL) and Lawrence Berkeley National Laboratory (LBNL) was convened to assess the challenges and opportunities within biological carbon (C) capture. The virtual workshop on Biological Carbon Sequestration for Climate and Soil Health focused on three aspects of biological C capture research: natural systems, engineered systems, and modeling. For this workshop, the scope of natural systems included unmanaged (e.g., grasslands, wetlands, natural forests) and managed (e.g., agricultural lands, commercial forests, forest biofuels) ecosystems. The engineered systems group covered topics in genetically modified plants and microbes, synthetic microbiomes, and altered plant–microbe interactions for enhancing C capture, storage, and utilization. The modeling group focused on computational tools and data collection to predict and assess the effectiveness of new biocarbon sequestration advances. Participant expertise included plant and microbial systems biology, synthetic biology, soil biogeochemistry, and techno-economic analysis. Together, participants outlined key scientific advances in support of biological C capture and storage. Among the major findings of the meeting were the need to better measure C flux within natural, agricultural, and fabricated ecosystems for experimentation; better understand the role of soil composition, plant roots, and the rhizosphere (i.e., the soil surrounding the plant root) in the underground storage of organic C; and design plant–microbe systems for maximal C storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m −2 ) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m −2 (mean ≈ 350 g m −2 ), while predicted values ranged from 0 to ∼4000 g m −2 (mean ≈ 275 g m −2 ), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m −2 and R 2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

Climate change↗
DOE OSTI2025

Optimization problems governed by systems of PDEs with uncertainties

This paper reviews current theoretical and numerical approaches to optimization problems governed by partial differential equations (PDEs) that depend on random variables or random fields. Such problems arise in many engineering, science, economics and societal decision-making tasks. This paper focuses on problems in which the governing PDEs are parametrized by the random variables/fields, and the decisions are made at the beginning and are not revised once uncertainty is revealed. Examples of such problems are presented to motivate the topic of this paper, and to illustrate the impact of different ways to model uncertainty in the formulations of the optimization problem and their impact on the solution. A linear–quadratic elliptic optimal control problem is used to provide a detailed discussion of the set-up for the risk-neutral optimization problem formulation, study the existence and characterization of its solution, and survey numerical methods for computing it. Different ways to model uncertainty in the PDE-constrained optimization problem are surveyed in an abstract setting, including risk measures, distributionally robust optimization formulations, probabilistic functions and chance constraints, and stochastic orders. Furthermore, approximation-based optimization approaches and stochastic methods for the solution of the large-scale PDE-constrained optimization problems under uncertainty are described. Some possible future research directions are outlined.

Heinkenschloss, Matthias [Rice Univ., Houston, TX ↗
DOE OSTI2020

Overview of the OECD-NEA Working Party on International Nuclear Data Evaluation Cooperation (WPEC)

The OECD Nuclear Energy Agency (NEA) Working Party on International Nuclear Data Evaluation Cooperation (WPEC) was established in 1989 to facilitate collaboration in nuclear data activities. Over its thirty year history, different Subgroups have been created to address topics in nearly every aspect of nuclear data, including: experimental measurements, evaluation, validation, model development, quality assurance of databases and the development of software tools. WPEC has recently completed activities on fission yield evaluation, the general nuclear database structure (GNDS) to replace the ENDF-6 format, methods to provide feedback to evaluation, studies of specific capture cross sections, new methods in thermal scattering kernel evaluation and the Collaborative International Evaluated Library Organisation (CIELO) Pilot Project. Ongoing activities in GNDS application programming interface (API) development, methods for covariance evaluation and quality assurance in nuclear data validation using the International Criticality Safety Benchmark Evaluation Project (ICSBEP) database are complemented by the work of two Expert Groups that oversee the High-Priority Request List (HPRL) for Nuclear Data and the continuous development of the GNDS. New activities on the use of integral experiments for nuclear data validation and adjustment, as well as the use of the Shielding Integral Benchmark Archive and Database (SINBAD) for validation have begun and will be coordinated alongside future Subgroups. After three decades we will review the status of WPEC, how it integrates other collections and activities organised by the NEA and how it dovetails with the initiatives of the IAEA and other bodies to effectively coordinate international activities in nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Addressing Human and Organizational Factors in Nuclear Industry Modernization: A Sociotechnically Based Strategic Framework

The modernization of nuclear power plants will require an advanced concept of operations, involving an integrated set of tightly coupled systems in which all stakeholders act in a coordinated manner. For this modernization effort to be enabled, we developed a human and organizational factors approach based on a broad sociotechnical framework. Starting from core human factors principles, we conducted a literature review of the methods and approaches relevant to the modernization problem. These included not only core disciplines such as cognitive systems engineering, systems theoretic accident modeling and processes, human systems integration, resilience engineering, and macroergonomics but also related topics of safety culture and organizational change. From this literature, we developed a conceptual framework centered around the work system with its four interacting components: people, technology, process, and governance. In an effective work system, these four components are jointly optimized according to three systems criteria: efficiency, effectiveness, and safety. System failure may result from excessive emphasis on any one criterion. The actual work of attaining joint optimization in a given work system can be accomplished by utilizing three high-level functions: knowledge elicitation, knowledge representation, and cross-functional integration. Finally, we illustrated the utility of this approach by applying it to practical problems and case studies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Process Model Development and Experimental Investigation for Spent Fuel Disposal in Crystalline Rocks: FY20 Report.

The U.S. Department of Energy Office of Spent Fuel Waste Disposition (SFWD) established in fiscal year 2010 (FY10) the Spent Fuel Waste Science & Technology (SFWST) Program (formerly the Used Fuel Disposition Campaign - UFDC) program to conduct the research and development (R&D) activities related to storage, transportation and disposal of used nuclear fuel and high level nuclear waste. The Mission of the SFWST is: To identify alternatives and conduct scientific research and technology development to enable storage, transportation and disposal of used nuclear fuel and wastes generated by existing and future nuclear fuel cycles. Significant progress has been made in FY20 in both experimental and modeling arenas in evaluation of used fuel disposal in crystalline rocks, especially in model demonstration using field data. The work covers a wide range of research topics identified in the R&D plan.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Advanced Guide to Understanding Power System Model Results for Long-Term Resource Plans

This guide is for public utility commission staff, state energy office staff, and other stakeholders who review model results for utility integrated resource plans (IRPs). It builds on the "Beginner's Guide to Understanding Power System Model Results for Long-Term Resource Plans," and delves into more advanced topics that were not included or not fully covered in the beginner's guide. The topics included here are evolving and may or may not be important to the resource plan you are evaluating. Due to limitations in resources, it's also not possible to give the highest level of detail or attention to every topic within a plan. An important part of planning for an uncertain future involves deciding which topics deserve the most focus and resources. It's all about prioritizing efforts where they will have the most impact or be most beneficial despite uncertainties ahead. As with the beginner's guide, we intend for this guide to help improve decision-making in the electricity planning process by strengthening understanding and dialogue between electricity system planners and relevant stakeholders. The following information is designed to allow you to better engage in the planning process by evaluating results, asking questions, and thinking through what is most important. We allocate significant discussion in the guide to electricity demand evolution, demand-side resources, and resource adequacy. These topics have garnered increasing attention, hence their prominence in this guide. Other topics are also presented, but their incorporation into the planning process might vary based on interest and relevance. The absence of certain topics doesn't diminish their importance; rather, it reflects the reality that in planning, trade-offs necessitate prioritizing certain aspects over others.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Editorial: Predicting near-earth space environment: new perspective and capabilities in the AI age

Editorial on the Research Topic Predicting near-earth space environment: new perspective and capabilities in the AI age The near-Earth space environment is not only an operational hazard for space missions, but also a scientific laboratory for advancing our understanding and prediction of space plasma populations. This Research Topic is organized around three interconnected themes: observational datasets, machine-learning (ML) model development, and the discovery of new physical insights through those models. Its primary goal is to highlight the emerging capabilities in space environment prediction that are enabled, or will be enabled, by integrating advanced techniques—including AI/ML methods—with long-term curated datasets.

58 GEOSCIENCES↗
DOE OSTI2024

Ab initio generalized Langevin equation

We introduce a machine learning–based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori–Zwanzig formalism, under the constraint of the fluctuation–dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Spatially resolved land and grid model of carbon neutrality in China

China has committed to achieve net carbon neutrality by 2060 to combat global climate change, which will require unprecedented deployment of negative emissions technologies, renewable energies (RE), and complementary infrastructure. At terawatt-scale deployment, land use limitations interact with operational and economic features of power systems. To address this, we developed a spatially resolved resource assessment and power systems planning optimization that models a full year of power system operations, sub-provincial RE siting criteria, and transmission connections. Our modeling results show that wind and solar must be expanded to 2,000 to 3,900 GW each, with one plausible pathway leading to 300 GW/yr combined annual additions in 2046 to 2060, a three-fold increase from today. Over 80% of solar and 55% of wind is constructed within 100 km of major load centers when accounting for current policies regarding land use. Large-scale low-carbon systems must balance key trade-offs in land use, RE resource quality, grid integration, and costs. Under more restrictive RE siting policies, at least 740 GW of distributed solar would become economically feasible in regions with high demand, where utility-scale deployment is limited by competition with agricultural land. Effective planning and policy formulation are necessary to achieve China’s climate goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Structure and Dynamics of Domains in Ferroelectric Nanostructures – Phase-Field Modeling (Final Technical Report)

Domain pattern formation is one of the most common phenomena in nature and is a topic of immense interest in many fields ranging from materials science and physics to chemistry and biology. This DOE sponsored research project explored the basic science concerning the thermodynamic stability of mesoscale polarization domain patterns and their temporal evolution mechanisms during formation and subsequent switching in ferroelectric nanostructures and heterostructures. The project employed the computational phase-field method in combination of microelasticity and electrostatic theories. The research was carried out in close collaboration with a number of experimental groups who used High Resolution Transmission Electron Microscopy (HRTEM), in situ TEM with Scanning Probe Microscopy (SPM), or Piezoresponse Force Microscopy (PFM) to characterize the domain structures and dynamics of ferroelectric thin films and heterostructures and who grow high-quality ferroelectric and multiferroic thin films using advanced growth techniques such as Molecular Beam Epitaxy (MBE), Pulsed Laser Deposition (PLLD), and sputtering. The research efforts of the project help establish the phase-field method as the most powerful method for understanding and predicting domain structures in ferroelectric thin films and nanostructures. The findings of the project led to the basic understanding of stability and switching mechanisms of ferroelectric domains under different mechanical boundary conditions and under either homogeneous capacitor configurations or local fields using metallic probes as electrodes and the emergence of charged domain walls during domain switching. The project predicted the spatial length scales, temperature ranges, and electromechanical conditions for different polar states in heterostructures and guided the discovery of both transient and stable novel polarization states containing vortex lattices in oxide superlattices. The project resulted in 134 journal publications and 6 PhD theses with all the PhD graduates currently working in either academia or industry within the United States. The basic understanding on the stability of mesoscale polar states and pattern evolution achieved by the project improved our ability to control and engineer properties of ferroelectric thin films and heterostructures for potential applications in nanoscale electronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ecological niche models display nonlinear relationships with abundance and demographic performance across the latitudinal distribution of Astragalus utahensis (Fabaceae)

Abstract The potential for ecological niche models (ENMs) to accurately predict species' abundance and demographic performance throughout their geographic distributions remains a topic of substantial debate in ecology and biogeography. Few studies simultaneously examine the relationship between ENM predictions of environmental suitability and both a species' abundance and its demographic performance, particularly across its entire geographic distribution. Yet, studies of this type are essential for understanding the extent to which ENMs are a viable tool for identifying areas that may promote high abundance or performance of a species or how species might respond to future climate conditions. In this study, we used an ensemble ecological niche model to predict climatic suitability for the perennial forb Astragalus utahensis across its geographic distribution. We then examined relationships between projected climatic suitability and field‐based measures of abundance, demographic performance, and forecasted stochastic population growth (λ s ). Predicted climatic suitability showed a J‐shaped relationship with A. utahensis abundance, where low‐abundance populations were associated with low‐to‐intermediate suitability scores and abundance increased sharply in areas of high predicted climatic suitability. A similar relationship existed between climatic suitability and λ s from the center to the northern edge of the latitudinal distribution. Patterns such as these, where density or demographic performance only increases appreciably beyond some threshold of climatic suitability, support the contention that ENM‐predicted climatic suitability does not necessarily represent a reliable predictor of abundance or performance across large geographic regions.

Baer, Kathryn C.↗
DOE OSTI2026

A System-Level Cost Modeling Framework for Design for Remanufacturing: A Case Study of an Agricultural Machine Transmission

Remanufacturing offers significant environmental and economic benefits by restoring end-of-life products to as-new conditions. Although extensive research has been conducted on the topic, the adoption of remanufacturing practices remains limited across various industries. A primary barrier to broader implementation is the substantial upfront investment required, which necessitates reliable cost modeling to justify potential future savings. Most existing models treat components independently and ignore inter-component dependencies. We develop a probabilistic, system-level cost modeling framework that integrates reliability, reusability, and a dependency matrix to capture cascading effects across components over multiple life cycles. Our model identifies those critical components that maximize remanufacturing benefits across a product's many lives. A toy example and an industry case study (John Deere PowrQuad transmission subassembly) illustrate how design alternatives affect cumulative cost. Using a Monte Carlo simulation (MCS) to perform life cycle cost analysis on the system with different design changes, we show the normalized average cost savings after three remanufacturing cycles. Accounting for dependencies meaningfully alters cost projections and ignoring them underestimates accumulated cost by up to 20% in our examples. Furthermore, the results of our study confirm that accounting for component interdependencies is necessary to produce cost estimates that meet industry standards.

Life Cycle Analysis and Design↗
DOE OSTI2021

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Foundations of plasma standards

Abstract The field of low-temperature plasmas (LTPs) excels by virtue of its broad intellectual diversity, interdisciplinarity and range of applications. This great diversity also challenges researchers in communicating the outcomes of their investigations, as common practices and expectations for reporting vary widely in the many disciplines that either fall under the LTP umbrella or interact closely with LTP topics. These challenges encompass comparing measurements made in different laboratories, exchanging and sharing computer models, enabling reproducibility in experiments and computations using traceable and transparent methods and data, establishing metrics for reliability, and in translating fundamental findings to practice. In this paper, we address these challenges from the perspective of LTP standards for measurements, diagnostics, computations, reporting and plasma sources. This discussion on standards, or recommended best practices, and in some cases suggestions for standards or best practices, has the goal of improving communication, reproducibility and transparency within the LTP field and fields allied with LTPs. This discussion also acknowledges that standards and best practices, either recommended or at some point enforced, are ultimately a matter of judgment. These standards and recommended practices should not limit innovation nor prevent research breakthroughs from having real-time impact. Ultimately, the goal of our research community is to advance the entire LTP field and the many applications it touches through a shared set of expectations.

data and reaction mechanisms↗
DOE OSTI2024

Learning Operators for Structure-Informed Surrogate Models

This report summarizes the work performed under the author's two-year John von Neumann LDRD project, which involves the non-intrusive surrogate modeling of dynamical systems with remarkable structural properties. After a brief introduction to the topic, technical accomplishments and project metrics are reviewed including peer-reviewed publications, software releases, external presentations and colloquia, as well as organized conference sessions and minisymposia. The report concludes with a summary of ongoing projects and collaborations which utilize the results of this work.

97 MATHEMATICS AND COMPUTING↗