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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Climate science to inform adaptation policy: Heat waves over India in the 1.5°C and 2°C warmer worlds

Developing a better scientific understanding of anthropogenic climate change and climate variability, especially the prediction/projection of climate futures with useful temporal and geographical resolution and quantified uncertainties, and using that knowledge to inform adaptation planning and action will become crucially important in the coming years. Generating such policy-relevant knowledge may be particularly important for developing countries such as India. It is with this backdrop that, in this paper, we analyze future heat waves in India by using observations and a large number of model simulations of historical, + 1.5 °C, and + 2.0 °C warmer worlds. In both the future scenarios, there is an increased probability of heat waves during June and July when the Indian monsoon is in full swing and humidity is high, which makes the heat events even more of a health risk. While the highest temperatures in heat waves may not increase much in future climates, the duration and areal extent of the heat waves will most likely increase, leading to the emergence of new heat wave-prone zones in India. The results indicate that the joint frequencies of the longest duration and large area events could be nearly threefold greater in the + 1.5 °C and fivefold greater in the + 2.0 °C future scenarios compared to historical simulations. Thus, overall, the study indicates a substantial increase in the risk of heat events that typically elicit warnings from forecasters. The likely widespread and persistent nature of heat wave events in the future, as revealed by this study, will require planning and adaptation measures beyond the short-term disaster planning frameworks currently in place. Exploring what these measures might look like is beyond the scope of this study, but it underlines the importance of developing climate knowledge with high temporal and geographical resolution capable of informing adaptation policy and planning.

+1.5 °C and +2.0 °C↗
DOE OSTI2023

Machine learning from RANS and LES to inform coarse grid simulations

Nuclear system thermal hydraulic analysis has historically relied on computationally inexpensive 1D codes. However, such tools are unable to capture multiscale multidimensional effects in large nuclear reactor enclosures. On the other hand, simulations with higher fidelity can be too expensive for such purposes. One of the ways to reduce computational cost is to perform simulations on a coarse grid, which, unfortunately, introduces large discretization errors. In this paper, two high-to-low data-driven approaches are investigated: (1) a coarse grid turbulence model to predict eddy viscosity and (2) correction of errors in coarse grid velocity fields. The approaches aim to reduce grid- and turbulence model-induced errors in coarse grid Reynolds-averaged Navier–Stokes (RANS) simulations. Two sources of high-fidelity data, RANS and large eddy simulations (LES), are explored. To extract the eddy viscosity from the LES data, an inverse optimization problem is solved. However, the LES eddy viscosity is shown to be comparable to the RANS eddy viscosity in terms of error reduction. Therefore, the directly available RANS eddy viscosity was used to develop a coarse grid data-driven turbulence model. Additionally, error correction in velocity is used to reduce the remaining uncertainties and bring the results closer to reality. In conclusion, the performance of the frameworks is demonstrated for a scaled upper plenum of a gas-cooled reactor facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Geothermal district energy systems coupled with seasonal underground thermal energy storage: a U.S. techno-economic screening by climate and geology

In the United States, cooling-dominated commercial building loads can cause geothermal heat pump-based district energy systems to accumulate a long-term subsurface thermal imbalance, motivating the incorporation of seasonal underground thermal energy storage. We developed a transferable workflow to evaluate geothermal district systems that pair ground heat exchangers with seasonal underground thermal energy storage. Using standardized hourly loads for seven commercial buildings and a uniform cost framework, we simulated ten U.S. cities with a physics-based ground heat exchanger model, subsurface storage simulations, and economic assessment to isolate the roles of climate and hydrogeology. In cooling-dominated cities, underground thermal energy storage supplied the majority of annual cooling, cutting electricity use and summer peaks substantially while achieving levelized costs comparable to or below conventional chiller-boiler plants. In cooler climates, the storage share shrunk, required borefield size and costs rose, and levelized cost of energy increased nearly linearly with declining underground thermal energy storage fraction, indicating storage fraction as the primary economic lever. Sensitivity analysis showed capital risk dominated by borefield drilling and surface heating, ventilation, and air-conditioning and piping, with underground thermal energy storage costs secondary. This workflow provides a transparent foundation for site-specific design and screening of next-generation geothermal district energy systems.

25 ENERGY STORAGE↗
DOE OSTI2026

Economic NMPC for a Reversible Solid Oxide Cell

Reversible solid oxide fuel cells (rSOCs) offer the flexibility to operate in tandem with the electric grid by switching between fuel cell and electrolysis modes based on real-time electricity prices. However, their complex, tightly coupled dynamic behavior poses significant challenges in determining optimal operating strategies. In this work, we present an economic nonlinear model predictive control (E-NMPC) framework to optimize the operation of rSOCs. The proposed E-NMPC is applied to a detailed rSOC flowsheet model that includes a utility scale rSOC module as well as balance-of-plant equipment necessary for thermal management. Our results demonstrate that in fuel cell mode, the E-NMPC strategy reduces hydrogen consumption compared to conventional set-point tracking NMPC, while maintaining the same level of electricity output. Also, in electrolysis mode, the E-NMPC yields a marginal improvement in hydrogen production. In addition, we explore the integration of a battery with the rSOC system to enhance flexibility in meeting electricity production and consumption targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2023

A comparison of template vs. direct model fitting for redshift-space distortions in BOSS

The growth of large-scale structure, as revealed in the anisotropic of clustering of galaxies in the low redshift Universe at z < 2, provides a stringent test of our cosmological model. The strongest current constraints come from the BOSS and eBOSS surveys, with uncertainties on σ 8 , the amplitude of clustering on an 8 h –1 Mpc scale, of less than 10 per cent. A number of different approaches have been taken to fitting this signal, leading to discrepancies of up to 1σ in the measurements of the amplitude of fluctuations at late times. We compare in some detail two of the leading approaches, one based on fitting a template cosmology whose amplitude and length scales are allowed to float with one based on varying the underlying parameters of a cosmological model directly, when fitting to the BOSS DR12 data. Holding the input data, scale cuts, window functions and modeling framework fixed we are able to isolate the cause of the differences and discuss the implications for future surveys.

redshift surveys↗
DOE OSTI2023

Multinucleon transfer mechanism in Gd 160 + W 186 collisions in stochastic mean-field theory

The multinucleon transfer mechanism in 160 Gd + 186 W collisions is investigated in the framework of quantal transport description, based on the stochastic mean-field (SMF) theory. The SMF theory provides a microscopic approach for nuclear dynamics beyond the time-dependent Hartree-Fock approach by including mean-field fluctuations. Finally, cross sections for the primary fragment production are determined in the quantal transport description and compared with the available data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Three-dimensional Hydrodynamic Simulations of Convective Nuclear Burning in Massive Stars Near Iron Core Collapse

Nonspherical structure in massive stars at the point of iron core collapse can have a qualitative impact on the properties of the ensuing core-collapse supernova explosions and the multimessenger signals they produce. Strong perturbations can aid successful explosions by strengthening turbulence in the postshock region. Here we report on a set of 4π 3D hydrodynamic simulations of O- and Si-shell burning in massive star models of varied initial masses using MESA and the FLASH simulation framework. We evolve four separate 3D models for roughly the final 10 minutes prior to and including iron core collapse. We consider initial 1D MESA models with masses of 14, 20, and 25 M ⊙ to survey a range of O/Si-shell density and compositional configurations. Further, we characterize the convective shells in our 3D models and compare them to the corresponding 1D models. In general, we find that the angle-average convective speeds in our 3D simulations near collapse are three to four times larger than the convective speeds predicted by MESA at the same epoch for our chosen mixing length parameter of α MLT = 1.5. In three of our simulations, we observe significant power in the spherical harmonic decomposition of the radial velocity field at harmonic indices of ℓ = 1–3 near collapse. Our results suggest that large-scale modes are common in massive stars near collapse and should be considered a key aspect of presupernova progenitor models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

An Investigation of Time Distributions for Task Primitives to Support the HUNTER Dynamic Human Reliability Analysis

As an effort to support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method. GOMS-HRA was developed to provide cognition-based time and human error probability information for dynamic HRA calculation in the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework. HUNTER is a framework to support the dynamic modelling of human error in conjunction with other modelling tools. In this paper, we investigate time distributions using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which suggests an HRA data collection framework to complement full-scope simulator research as well as collect input data for dynamic HRA using simplified simulators such as the Rancor Microworld Simulator. In this study, time required for GOMS-HRA task primitives to satisfy thirteen statistical distributions is investigated. Then, the time distributions from student operators and professional operators are compared and discussed. As a result, this study identified several time distributions on five GOMS-HRA task primitives at a statistically significant level. According to analyses to date, a greater number of significant time distributions was found in abnormal or emergency operating procedures rather than standard operating procedures. In the future, it is expected that the result of this study can provide objective reference on elapsed time data for task primitives as well as help to realistically simulate scenarios within dynamic HRA.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Novel Preprocessing Techniques for Enhancing Flowability of Miscanthus

Purpose-grown energy crops possess significant potential as promising feedstocks for biofuel and biochemical production. However, similar to other biomass feedstocks, their utilization has been hindered by handling and feeding challenges such as clogging and segregation, which prevent biorefineries from operating at full capacity. These challenges arise primarily due to particle attributes including irregular shape, high flexibility, and high compressibility, leading to interlocking, high compaction, and significant friction under stress consolidation. To enhance operational reliability, it is crucial to focus on preprocessing techniques to address these issues, alongside accurately characterizing the mechanical and physical properties and flow performance of these feedstocks. This study investigates novel preprocessing techniques, including pelletization and torrefaction, to fundamentally alter these particle attributes and improve flowability. Miscanthus, an herbaceous energy crop, was the primary focus of this study. Samples were processed using a Forest Concepts Crumbler rotary shear system and an oscillating multi-stack screen bed to achieve two nominal (baseline) particle sizes: 6mm and 2mm. The study evaluated the efficiency and energy consumption of size reduction, revealing that while 2mm particles required more energy, they exhibited better flowability and higher yield strength compared to 6mm particles. Shear and compression tests assessed the material's shear strength, internal friction, and elastic modulus, while wedge-shaped hopper tests measured the critical arching distance and mass flow rate. The results provide valuable insights into the mechanical properties and processing efficiency of Miscanthus, contributing to the development of a preprocessing framework that enhances energy efficiency and material flowability in bioenergy production. However, further preprocessing and development are required to optimize this framework fully.

09 - BIOMASS FUELS↗
DOE OSTI2026

Validation of OpenPronghorn for Periodic Hill Flow Separation

OpenPronghorn is an open-source, MOOSE-based thermal-hydraulics simulation tool used for advanced reactor analysis. As an open-source code, it offers a transparent framework for validating governing equations, assumptions, and numerical methods against established benchmarks. This study evaluates OpenPronghorn's RANS turbulence model against the ERCOFTAC Case 81 periodic hill benchmark, a standard test case for separated turbulent flow featuring curved-wall separation, recirculation, shear-layer development, and reattachment. Streamwise velocity profiles predicted by OpenPronghorn were compared to reference LES data at multiple x/h locations. Results show that OpenPronghorn captures the overall trend of the velocity profiles, but the largest discrepancies occur in the separated-flow region, where turbulence is highly anisotropic and strongly affected by adverse pressure gradients and wall curvature—conditions that are inherently difficult for standard RANS models to resolve. Future work will test alternative k-e model variants and correction terms to improve prediction accuracy in this region.

42 - ENGINEERING↗
DOE OSTI2026

Functionalized Metal–Organic Framework Thin Films for Stable and Efficient Electrochemical Water Oxidation under Near-Neutral Conditions

The development of molecularly modified metal− organic frameworks (MOFs) for electrochemical water oxidation has emerged as a promising strategy for efficient artificial photosynthesis. In this study, a ruthenium-based water oxidation catalyst (WOC) was incorporated into the UiO-67 framework, forming RuM−UiO-67 films grown directly on conductive FTO substrates. These modified films demonstrate efficient water oxidation activity at near-neutral pH (pH 6), operating at a low overpotential of ∼600 mV. The catalyst exhibits a turnover frequency (TOF) of (0.32 ± 0.02) s −1 at 1.5 V versus the normal hydrogen electrode (NHE) in buffered solution (pH 6) for the oxygen evolution reaction. Notably, incorporation into the MOF results in a 12-fold increase in electroactive surface coverage compared to a monolayer of the same catalyst on bare FTO. Faradaic efficiency analysis revealed incomplete conversion to O 2 , and follow-up iodometric analysis confirmed the formation of H 2 O 2 as a competing two-electron oxidation product during electrocatalysis. These results highlight the utility of MOF-based architectures for maximizing the catalyst accessibility and stability under electrochemical water oxidation conditions.

catalysts↗
DOE OSTI2020

The C‐Terminal Domain of α‐Synuclein Confers Steric Stabilization on Synaptic Vesicle‐Like Surfaces

Abstract While α‐synuclein, an intrinsically disordered protein linked to Parkinson's disease, has been shown to associate with membrane organelles, its overall cellular function remains nebulous. α‐Synuclein binds to membranes through its amino‐terminal domain (first ≈100 residues), but there is no consensus on the biophysical function of the carboxyl‐terminal domain (last ≈40 residues) due, in part, to its lack of strong interaction partners and persisting intrinsic disorder even when membrane bound. Here, by directly applying force on α‐synuclein bound to spherical nanoparticle‐supported lipid bilayers (SSLBs) and tracking higher‐order structural changes through small‐angle X‐ray scattering, strong evidence is presented that α‐synuclein sterically stabilizes membrane surfaces through its carboxyl‐terminal domain. Full‐length α‐synuclein dramatically increases the critical osmotic pressure at which SSLBs cluster ( P C ≈ 1.3 × 10 5 Pa) compared to α‐synuclein without the carboxyl‐terminal domain ( P C ≈ 1.9 × 10 4 Pa) at physiological salt and temperature conditions. This clustering of α‐synuclein‐bound SSLBs is shown to be reversible and sensitive to monovalent/divalent salt, both features of grafted polyelectrolyte‐mediated steric stabilization. In elucidating the biophysical function of α‐synuclein in the framework of polymer science, it is demonstrated that the carboxyl‐terminal domain can potentially utilize its persisting intrinsic disorder to functionalize membrane surfaces.

Chung, Peter J.↗
DOE OSTI2024

First Principles Study of Aluminum Doped Polycrystalline Silicon as a Potential Anode Candidate in Li‐ion Batteries

Addressing sustainable energy storage remains crucial for transitioning to renewable sources. While Li‐ion batteries have made significant contributions, enhancing their capacity through alternative materials remains a key challenge. Micro‐sized silicon is a promising anode material due to its tenfold higher theoretical capacity compared to conventional graphite. However, its substantial volumetric expansion during cycling impedes practical application due to mechanical failure and rapid capacity fading. A novel approach is proposed to mitigate this issue by incorporating trace amounts of aluminum into the micro‐sized silicon electrode using ball milling. Density functional theory (DFT) is employed to establish a theoretical framework elucidating how grain boundary sliding, a key mechanism involved in preventing mechanical failure is facilitated by the presence of trace aluminum at grain boundaries. This, in turn, reduces stress accumulation within the material, reducing the likelihood of failure. To validate the theoretical predictions, capacity retention experiments are conducted on undoped and Al‐doped micro‐sized silicon samples. In conclusion, the results demonstrate significantly reduced capacity fading in the doped sample, corroborating the theoretical framework and showcasing the potential of aluminum doping for improved Li‐ion battery performance.

25 ENERGY STORAGE↗
DOE OSTI2021

Green’s functions on a renormalized lattice: An improved method for the integer quantum Hall transition

Highlights: • Speed up of numerical calculations by exact renormalization group steps. • Practical approach to handle delicate scaling analysis. • Absence of evidence to prefer marginal corrections over power-law corrections. We introduce a performance-optimized method to simulate localization problems on bipartite tight-binding lattices. It combines an exact renormalization group step to reduce the sparseness of the original problem with the recursive Green’s function method. We apply this framework to investigate the critical behavior of the integer quantum Hall transition of a tight-binding Hamiltonian defined on a simple square lattice. In addition, we employ an improved scaling analysis that includes two irrelevant exponents to characterize the shift of the critical energy as well as the corrections to the dimensionless Lyapunov exponent. We compare our findings with the results of a conventional implementation of the recursive Green’s function method, and we put them into broader perspective in view of recent development in this field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Postsynthetic Oxidation of the Coordination Site in a Heterometallic Metal-Organic Framework: Tuning Catalytic Behaviors

Postsynthetic modification (PSM) in metal-organic frameworks (MOFs) can introduce multiple functionalities and alter the structural function for the desired application. However, the PSM of the coordination site faces the challenges of structural collapse or incompatibility between the original metal site and the newly formed coordination group. Herein, we developed a novel concept of introducing “primary” and “secondary” nodes, coexisting in a water-stable, Zr-based heterometallic MOF, [Zr6(µ3-OH)8(OH)8][CuI4(L1)4]2 (1-SH-a, H2L1 = 6-mercaptopyridine-3-carboxylic acid). The post-synthetic oxidation at the coordination site was successfully achieved in the “secondary” nodes [CuI4(L1)4]4-, while the robust “primary” nodes [Zr6(µ3-OH)8(OH)8]8+ stabilized the whole framework to form [Zr6(µ3-OH)8(OH)8][(CuI0.44CuII0.56(OH)0.56)4(L2)4]2 (1-SO3H, H2L2 = 6-sulfonicotinic acid). PSM of 1-SH-a to form 1-SO3H dramatically tuned the catalytic properties toward the styrene oxide ring-opening reaction to give a regioselectivity of primary alcohol (A) of ~99% compared with 1-SH-a (~71%).

Han, Yi↗