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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Risk-averse optimization for resilience enhancement of complex engineering systems under uncertainties

With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable to external disturbances. Hence, managing potential disruptive events during the design, operating, and recovery phase of an engineered system and therefore improving the system’s resilience is an important yet challenging task. Here, to ensure system resilience after the occurrence of failure events, this study proposes a mixed-integer linear programming (MILP) based restoration framework using heterogeneous dispatchable agents. The scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Moreover, different from conventional SO using deterministic equivalent formulations, the CVaR risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves a large-scale MILP problem and thus an adequate decomposition technique i.e. modified Lagrangian dual decomposition, is also employed to achieve tractable computational complexity. Case study results based on the IEEE 37-bus test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.

42 ENGINEERING↗
DOE OSTI2022

Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints

Jointing techniques like the Self-Piercing Riveting (SPR), Resistance Spot Welding (RSW) and Rivet-Weld (RW) joints are used for mass production of dissimilar material joints due to their high performance, short cycle time, and adaptability. However, the service life and safety usage of these joints can be largely impacted by the galvanic corrosion due to the difference in equilibrium potentials between the metals with the presence of electrolyte. Here, in this paper, we focus on Al-Fe galvanic corrosion and develop physics-informed machine learning based surrogate model for statistical corrosion analysis, which enables the reliability analysis of dissimilar material joints under corrosion environment. In this study, a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode. Geometric and environmental factors including crevice gap, roughness of anode, conductivity, and the temperature of the electrolyte are investigated. Further, a thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al joints. It is found that the electrolyte conductivity has the largest effects on the material loss and needs to be managed closely for better corrosion control. This will help in designing and manufacturing joints with improved corrosion performance.

42 ENGINEERING↗
DOE OSTI2021

EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics

Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data Set Analysis to Reduce Uncertainty in Formula Assignments of Ultrahigh Resolution Mass Spectra

Environmental samples contain a vast array of organic compounds with diverse elemental compositions and heteroatom content. Molecular formula assignments of ultrahigh resolution mass spectra (HRMS) hold promise for elucidating the molecular composition of these compounds. However, the need to account for an assortment of heteroatoms increases the uncertainty associated with individual assignments – and ultimately the ecological, biological, and biogeochemical insights gleaned from the assignments. To address this challenge, we introduce a formula assignment strategy that leverages HRMS data sets to improve assignment confidence, filter false assignments, and mitigate bias in assignment routines. The strategy, implemented using CoreMS, first identifies the highest confidence assignment for a recurring ion in a data set by assessing the mass accuracy and isotopologue similarity of all assignments to the ion across the data set. The second component of the strategy examines the consistency of mass errors for an assigned ion throughout a data set and flags formulas with statistically unlikely deviations in mass error. Here, we illustrate the application and utility of the strategy by comparing its results against documented misassignment patterns within a set of oceanographic samples that were measured with 21 T Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Because the efficacy of our strategy improves with data set size, it is particularly useful for enhancing assignment confidence in large HRMS data sets common in studies of environmental systems.

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

Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N, and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only achieve class-balanced accuracies of more than 0.95 but also accurately quantify uncertainty. Here, we also show that including multiple XANES modalities improves predictions notably on average, demonstrating a “multimodal advantage” over any single modality. In addition to structure refinement, our approach can be generalized to broad applications with molecular design pipelines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.

Rutz, Jonathan J.↗
DOE OSTI2022

Representing Socio‐Economic Uncertainty in Human System Models

Abstract Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Constraining Bedrock Groundwater Residence Times in a Mountain System With Environmental Tracer Observations and Bayesian Uncertainty Quantification

Groundwater residence time distributions provide fundamental insights on the hydrological processes within watersheds. Yet, observations that can constrain groundwater residence times over broad timescales remain scarce in mountain catchment studies. We use environmental tracers (CFC-12, SF 6 , 3 H, and 4 He) to investigate groundwater residence times along a hillslope in the East River Watershed, Colorado, USA. We develop a Bayesian inference framework that applies a Markov-chain Monte Carlo (MCMC) approach to estimate noble gas recharge temperature, elevation, and excess-air parameters and the resulting environmental tracer concentrations. MCMC is then used to propagate the environmental tracer uncertainties to estimates of groundwater mean residence times inferred with lumped parameter models. All samples contain 3 H, CFC-12, and SF 6 in addition to terrigenic 4 He, suggesting a mixture of water characterized by modern and premodern residence times. 4He exponential mean residence times range from hundreds of years at the upslope well to thousands of years at the toe-slope well assuming average crustal production rates. We find that binary mixing residence time distributions with separate young and old mixing fractions are needed to predict the 4 He, CFC-12, SF 6 , and 3 H observations, supporting the importance of flow path mixing in this bedrock system. Our findings that the fractured bedrock hosts groundwater with a mixture of residence times ranging from decades to millennia suggest variable recharge dynamics and flow path mixing along the hillslope and highlight the importance of characterizing groundwater systems with observations that are sensitive to transport over a broad range of residence times.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ground Heat Flux Reconstruction Using Bayesian Uncertainty Quantification Machinery and Surrogate Modeling

Ground heat flux (G 0 ) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G 0 is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G 0 across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G 0 data (measured or modeled) for snow-free period as a reference. When observed G 0 is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state-of-the-art uncertainty quantification methods, the developed G 0 reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dual Impacts of Space Heating Electrification and Climate Change Increase Uncertainties in Peak Load Behavior and Grid Capacity Requirements in Texas

Around 60% of households in Texas currently rely on electricity for space heating. As decarbonization efforts increase, non‐electrified households could adopt electric heat pumps, significantly increasing peak (highest) electricity demand in winter. Simultaneously, anthropogenic climate change is expected to increase temperatures, the potential for summer heat waves, and associated electricity demand for cooling. Uncertainty regarding the timing and magnitude of these concurrent changes raises questions about how they will jointly affect the seasonality of peak demand, firm capacity requirements, and grid reliability. This study investigates the net effects of residential space heating electrification and climate change on long‐term demand patterns and load shedding potential, using climate change projections, a predictive load model, and a direct current optimal power flow (DCOPF) model of the Texas grid. Results show that full electrification of residential space heating by replacing existing fossil fuel use with higher efficiency heat pumps could significantly improve reliability under hotter futures. Less efficient heat pumps may result in more severe winter peaking events and increased reliability risks. As heating electrification intensifies, system planners will need to balance the potential for greater resource adequacy risk caused by shifts in seasonal peaking behavior alongside the benefits (improved efficiency and reductions in emissions).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate-related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Temporal Variability in Reservoir Surface Area Is an Important Source of Uncertainty in GHG Emission Estimates

Ebullitive methane (CH 4 ) emissions in lentic ecosystems tend to concentrate at river-lake interfaces and within shallow littoral zones. However, inconsistent definitions of the littoral zone and static representations of the lake or reservoir surface area contribute to major uncertainties in greenhouse gas (GHG) emissions estimates, particularly in reservoirs with large water-level fluctuations. This study examines temporal variation in littoral and total surface areas of US reservoirs and demonstrates how different methods and data sources lead to discrepencies in reservoir GHG emissions at large scales and over time. We also explore variability in remotely sensed water occurrence according to maximum surface area, reservoir purposes, and hydrologic regions. Notably, the largest relative variability in surface area is exhibited by small reservoirs with a maximum surface area <1 km 2 and non-hydroelectric reservoirs. Additionally, we use a case study of measured CH 4 emissions from the southeastern United States (Douglas Reservoir) to illustrate the effects of varying surface area on reservoir-wide GHG estimates. Upscaled CH 4 emissions in Douglas Reservoir differed by nearly two-fold depending on the source of total surface area data and whether estimates accounted for seasonal fluctuations in surface area. During seasonal drawdown in Douglas Reservoir, relative littoral area varies non-linearly; periods of lower pool elevation (and thus larger relative littoral area) likely contribute disproportionately high CH 4 emission rates compared to the commonly sampled summer season when water levels are at full-pool elevation. Improved GHG monitoring and upscaling techniques require accounting for temporal variability in reservoir surface extent and littoral area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Cloud Feedback Uncertainty in the Equatorial Pacific Across CMIP6 Models

Cloud feedback is the largest uncertainty in estimating Equilibrium Climate Sensitivity. In this study we focus on the equatorial Pacific, where CMIP6 model cloud feedback spread is notably large. Cloud radiative effects in this region are relevant for the global climate. Our findings show that models predict a consistent shift towards the ascent regime in response to El Nino-like sea surface warming. Models diverge in terms of the radiative impact due to differences in cloud characteristics in ascent and subsidence regimes. Using the observed relationship between circulation regime and cloud radiative effect, we find a reduction in the regional mean cloud feedback estimate from 0.77 to 0.22 W m -2 K -1 , though this does not substantially lessen the model spread in total feedback. Pathways to reduce this spread include: improving confidence in the regional ocean warming pattern, and using observations and models to understand cloud type and circulation interactions.

CMIP6↗
DOE OSTI2023

Hadronic uncertainties versus new physics for the W boson mass and Muon g – 2 anomalies

There are now two single measurements of precision observables that have major anomalies in the Standard Model: the recent CDF measurement of the W mass shows a 7σ deviation and the Muon g – 2 experiment at FNAL confirmed a long-standing anomaly, implying a 4.2σ deviation. Doubts regarding new physics interpretations of these anomalies could stem from uncertainties in the common hadronic contributions. We demonstrate that these two anomalies pull the hadronic contributions in opposite directions by performing electroweak fits in which the hadronic contribution was allowed to float. The fits show that including the g – 2 measurement worsens the tension with the CDF measurement and conversely that adjustments that alleviate the CDF tension worsen the g – 2 tension beyond 5σ. This means that if we adopt the CDF W mass measurement, the case for new physics in either the W mass or muon g – 2 is inescapable regardless of the size of the SM hadronic contributions. Lastly, we demonstrate that a mixed scalar leptoquark extension of the Standard Model could explain both anomalies simultaneously.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Disentangling climate and policy uncertainties for the Colorado River post-2026 operations

Abstract Lakes Mead and Powell in the Colorado River Basin underpin water and hydropower supply for the western United States. While the policies currently regulating the basin will expire by 2026, planning remains challenging due to intertwined climate variability and policy uncertainties. Based on streamflow projections from 10 dynamically downscaled CMIP6 global climate models and unique methods that add and remove internal variability, we evaluate future conditions at Powell and Mead under existing and alternative policies. Due to projected streamflow declines, under existing policy, both reservoirs will face substantial risks (>80% likelihood) of reaching dead pool before 2060. Adopting recently proposed alternative policies reduces but doesn’t eliminate such risks. All policies also exhibit tipping points where reservoir levels can change rapidly with a slight change in streamflow. A sustainable policy may require larger reductions to further reduce the reservoirs’ dead pool risks and provide better buffers from sudden changes.

Science & Technology - Other Topics↗
DOE OSTI2021

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies. Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection. This approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the ensemble. This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗