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DOE OSTI2022

The Challenges of Modeling Defect Behavior and Plasticity across Spatial and Temporal Scales: A Case Study of Metal Bilayer Impact

Atomistic molecular dynamics (MD) and a microstructural dislocation density-based crystalline plasticity (DCP) framework were used together across time scales varying from picoseconds to nanoseconds and length scales spanning from angstroms to micrometers to model a buried copper–nickel interface subjected to high strain rates. The nucleation and evolution of defects, such as dislocations and stacking faults, as well as large inelastic strain accumulations and wave-induced stress reflections were physically represented in both approaches. Both methods showed similar qualitative behavior, such as defects originating along the impactor edges, a dominance of Shockley partial dislocations, and non-continuous dislocation distributions across the buried interface. The favorable comparison between methods justifies assumptions used in both, to model phenomena, such as the nucleation and interactions of single defects and partials with reflected tensile waves, based on MD predictions, which are consistent with the evolution of perfect and partial dislocation densities as predicted by DCP. This substantiates how the nanoscale as modeled by MD is representative of microstructural behavior as modeled by DCP.

36 MATERIALS SCIENCE↗

NASA NTRS1985

Turbulent disruptions from the Strauss equations

Preliminary results are reported from application of a three-dimensional spectral method model to the solution of the Strauss (1976) reduced MHD equations. The investigation was focused on describing MHD turbulence in a current-carrying bounded magnetofluid. A cylindrical geometry with a square cross-section was considered, with the walls being rigid perfect conductors with free-slip boundary conditions. A uniform magnetic field and the electric current density both point in the z-direction. Initial conditions are specified which feature small amounts of random noise expressed as Fourier modes. Linearized equations are defined for tracing the movement to equlibrium conditions or other temporal development. The model is further refined with nonlinear equations to examine the effects of the appearance of disruptions. Comparisons are drawn between solutions obtained with linear and nonlinear equations, with an eye to the associated physical realities.

Dahlburg, J. P.↗

NASA NTRS2013

Challenges in Quantifying Pliocene Terrestrial Warming Revealed by Data-Model Discord

Comparing simulations of key warm periods in Earth history with contemporaneous geological proxy data is a useful approach for evaluating the ability of climate models to simulate warm, high-CO2 climates that are unprecedented in the more recent past. Here we use a global data set of confidence-assessed, proxy-based temperature estimates and biome reconstructions to assess the ability of eight models to simulate warm terrestrial climates of the Pliocene epoch. The Late Pliocene, 3.6-2.6 million years ago, is an accessible geological interval to understand climate processes of a warmer world4. We show that model-predicted surface air temperatures reveal a substantial cold bias in the Northern Hemisphere. Particularly strong data-model mismatches in mean annual temperatures (up to 18 C) exist in northern Russia. Our model sensitivity tests identify insufficient temporal constraints hampering the accurate configuration of model boundary conditions as an important factor impacting on data- model discrepancies. We conclude that to allow a more robust evaluation of the ability of present climate models to predict warm climates, future Pliocene data-model comparison studies should focus on orbitally defined time slices.

Salzmann, Ulrich↗

NASA NTRS2022

Soil Respiration Phenology Improves Modeled Phase of Terrestrial Net Ecosystem Exchange in Northern Hemisphere

In the northern hemisphere, terrestrial ecosystems transition from net sources of CO2 to the atmosphere in winter to net ecosystem carbon sinks during spring. The timing (or phase) of this transition, determined by the balance between ecosystem respiration (RECO) and primary production, is key to estimating the amplitude of the terrestrial carbon sink. We diagnose an apparent phase bias in the RECO and net ecosystem exchange (NEE) seasonal cycles estimated by the Terrestrial Carbon Flux (TCF) model framework and investigate its link to soil respiration mechanisms. Satellite observations of vegetation canopy conditions, surface meteorology, and soil moisture from the NASA SMAP Level 4 Soil Moisture product are used to model a daily carbon budget for a global network of eddy covariance flux towers. Proposed modifications to TCF include: the inhibition of foliar respiration in the light (the Kok effect); a seasonally varying litterfall phenology; an O2 diffusion limitation on heterotrophic respiration (RH); and a vertically resolved soil decomposition model. We find that RECO phase bias can result from bias in RECO magnitude and that mechanisms which reduce northern spring RECO, like substrate and O2 diffusion limitations, can mitigate the phase bias. A vertically resolved soil decomposition model mitigates this bias by temporally segmenting and lagging RH. Applying these model enhancements at Continuous Soil Respiration (COSORE) sites verifies their improvement of RECO and NEE skill compared to in situ observations (up to ∆RMSE = −0.76 g C m−2 d −1 35 ). Ultimately, these mechanisms can improve prior estimates of NEE for atmospheric inversion studies.

Soil respiration↗

DOE OSTI2023

Core collapse supernova gravitational wave emission for progenitors of 9.6, 15, and 25 M ⊙

Here, we present gravitational wave emission predictions based on three core collapse supernova simulations corresponding to three different progenitor masses. The masses span a large range, between 9.6 and 25M ⊙ , are all initially nonrotating, and are of two metallicities: zero and solar. We compute both the temporal evolution of the gravitational wave strains for both the plus and the cross polarizations, as well as their spectral decomposition and characteristic strains. The temporal evolution of our zero metallicity 9.6M ⊙ progenitor model is distinct from the temporal evolution of our solar metallicity 15M ⊙ progenitor model and our zero metallicity 25M ⊙ progenitor model. In the former case, the high-frequency gravitational wave emission is largely confined to a brief time period ~75 ms after bounce, whereas in the latter two cases high-frequency emission does not commence until ~125 ms after bounce or later. The excitation mechanisms of the high-frequency emission in all three cases correspond to proto-neutron star convection and accretion onto the proto-neutron star from the convective gain layer above it, with the former playing the dominant role for most of the evolution. The low-frequency emission in all three models exhibits very similar behavior. At frequencies below ~250 Hz, gravitational waves are emitted by neutrino-driven convection and the standing accretion shock instability (SASI). This emission extends throughout the simulations when a gain region is present. In all three models, explosion is observed at ~125, ~500, and ~250 ms after bounce in the 9.6, 15, and 25M ⊙ progenitor models, respectively. At these times, the low-frequency gravitational wave emission is joined by very low-frequency emission, below ~10 Hz. These very low-frequency episodes are the result of explosion and begin at the above designated explosion times in each of our models. Our characteristic strains tell us that, in principle, all three gravitational wave signals would be detectable by current-generation detectors for a supernova at a distance of 10 kpc. However, our 9.6M ⊙ progenitor model is a significantly weaker source of gravitational waves, with strain amplitudes approximately 5–10 times less than in our other two models. The characteristic strain for this model tells us that such a supernova would be detectable only within a much more narrow frequency range around the maximum sensitivity of today’s detectors. Finally, in our 9.6M ⊙ progenitor model, we see very high-frequency gravitational radiation, extending up to ~2000 Hz. This feature results from the interaction of shock- and deleptonization-induced convection with perturbations introduced in the progenitor by nuclear burning during core collapse. While unique to the 9.6M ⊙ progenitor model analyzed here, this very high-frequency emission may, in fact, be a generic feature of the predictions for the gravitational wave emission from all core collapse supernova models when simulations are performed with three-dimensional progenitors.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

A scalable transformer model for real-time decision making in neutron scattering experiments

The U.S. Department of Energy's (DOE's) neutron research facilities at Oak Ridge National Laboratory (ORNL), including the High Flux Isotope Reactor (HFIR) and the Spallation Neutron Source (SNS), are a state-of-the-art neutron scattering facility that allows researchers to study the structure and dynamics of materials at the atomic scale. At the SNS, neutrons are measured using the time-of-flight (TOF) technique as they move through a neutron beamline to interact with a sample. Large volumes of neutron scattering data are collected and recorded in neutron event mode. Optimal productivity of the TOF instrument is limited due to the lack of real-time data analysis tools. The large amount of data generated by the experiments can be challenging to process and analyze in real time, particularly for experiments that require rapid feedback and adjustment of experimental parameters. The regular computer/workstation cannot keep up with the experiment speed to provide real-time feedback to adjust experimental parameters, so connecting the supercomputers available to the neutron facility is necessary to achieve real-time data analysis and experiment steering. To address this challenge, we exploit the Frontier supercomputer at Oak Ridge Leadership Computing Facility (OLCF) to train a scalable temporal fusion transformer model for real-time decision making of TOF neutron scattering experimentation. Here, in this paper, we present the results using Frontier to provide the processing power needed to rapidly process and analyze large volumes of single-crystal diffraction data collected at TOPAZ, a neutron time-of-flight Laue single-crystal diffractometer at the SNS.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A Regime-Switching Spatio-Temporal GARCH Method for Short-Term Wind Forecasting

The growth of wind energy poses challenges to the integration of wind energy into the power grid. Within a wind farm, the conditions of local wind exhibit sizeable variations in very short term period and temporal wind speed patterns vary from turbine to turbine. Hence, short-term wind forecasting has been adopted to assist power system operations. In this work, we propose a wind plant-level short term wind speed and power forecasting methodology considering turbine contributions. The proposed model utilizes spatio-temporal dependencies and nonstationarity to accommodate the characteristics of wind farm data by using a novel regime-switching spatiotemporal generalized autoregressive conditional heteroscedasticity (RS-stGARCH) model. Case studies based on 2 years of data from a wind farm shows that the proposed RS-stGARCH method outperforms benchmark models by up to 21.10% for wind speed forecasting and up to 58.62% for the wind power forecasting.

ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Prediction of laser beam spatial profiles in a high-energy laser facility by use of deep learning

We adapt the significant advances achieved recently in the field of generative artificial intelligence/machine-learning to laser performance modeling in multipass, high-energy laser systems with application to high-shot-rate facilities relevant to inertial fusion energy. Advantages of neural-network architectures include rapid prediction capability, data-driven processing, and the possibility to implement such architectures within future low-latency, low-power consumption photonic networks. Four models were investigated that differed in their generator loss functions and utilized the U-Net encoder/decoder architecture with either a reconstruction loss alone or combined with an adversarial network loss. We achieved inference times of 1.3 ms for a 256 × 256 pixel near-field beam with errors in predicted energy of the order of 1% over most of the energy range. It is shown that prediction errors are significantly reduced by ensemble averaging the models with different weight initializations. These results suggest that including the temporal dimension in such models may provide accurate, real-time spatiotemporal predictions of laser performance in high-shot-rate laser systems.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

A seamless approach for evaluating climate models across spatial scales

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1987

Galactic chemical evolution - Z versus ln(1/mu) relationship

The ways in which a radial abundance gradient in a disk galaxy may be interpreted are reexamined, asking how metallicity Z depends upon gas mass fraction mu in analytic models characterized by a temporally varying history of metal-poor infall. Six different such models are studied, and it is shown that the metallicity versus gas fraction relationship is well approximated by an expression which is the analog of the Z = y ln(1/mu) relation for closed evolution. The approximation appears valid when the infall rate is nonneglible, but not so great that it dominates the gas budget.

Clayton, Donald D.↗

DOE OSTI2021

Ultrafast formation of domain walls of a charge density wave in SmTe 3

We study ultrafast x-ray diffraction on the charge density wave (CDW) of SmTe 3 using an x-ray free-electron laser. The high momentum and time resolution afforded by the x-ray laser enabled capturing fine wave-vector and time-dependent features of the CDW that originate from fast (in time) and sharp (in real space) variations of the CDW lattice distortion, which we attribute to an inversion of the order parameter. These domain inversions occur near the surface and are caused by the short penetration depth of the near-infrared pump with the wavelength centered at 800 nm, resulting in CDW domain walls perpendicular to the sample surface. These domain walls break the CDW long-range order on the scale of the x-ray probe depth, controlled experimentally by the x-ray incidence angle and suppress the diffraction intensity of the CDW for times much longer than the ~1–ps recovery of the electronic gap observed in time and angle-resolved photoemission spectroscopy. We model the spatial and temporal dependences of the order parameter using a simple Ginzburg-Landau model with all the parameters obtained from the published literature. We find reasonable agreement between the calculated and the measured diffraction across the momentum, time, fluence, and incidence angle dependence without adjusting any parameters. In conclusion, we reconstruct the spatial and temporal dependences of the lattice order parameter and find that at long times, depending on the pump fluence, multiple domain walls remain at distances of a few nanometers from the surface.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Gridded Sub-daily Climate Forcings for North America Based on Daymet and GSWP3 (Daymet-GSWP3)

To support high spatial and temporal resolution land surface modeling, this dataset provides 3-hourly time step historic weather forcing at 1-km spatial resolution for the entire North America. The latest Daymet V4 data provides gridded historic daily weather observations at 1-km spatial resolution from 1980 to 2014. Using sub-daily temporal information from the Global Soil Wetness Project Phase 3 (GSWP3), Daymet was further temporally downscaled to 3-hourly time steps and provided in the format required for land surface model simulations. The process of temporal downscaling preserves the relative magnitude in each sub-daily time step from GSWP3 while maintaining the total and average values from Daymet for each day. This results in a blended 1980-2014 Daymet-GSWP3 dataset. Available variables include surface air temperature, precipitation, specific humidity, shortwave and longwave radiation, wind speed, and pressure. These data can be used as a high-resolution meteorological forcing dataset to support high-resolution land surface modeling where accurate meteorological forcing datasets built from historic observations and/or reanalysis datasets are desirable.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Gridded Sub-daily Climate Forcings for North America Based on Daymet and GSWP3 (Daymet-GSWP3)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

High temporal resolution generation expansion planning for the clean energy transition

As power systems integrate increasing quantities of wind, solar and energy storage resources, it is important to revisit power system capacity expansion modeling methods and assumptions that have been utilized in thermal- dominated systems. We conduct a series of case study analyses using a simplified representation of the Electric Reliability Council of Texas (ERCOT) system to demonstrate how least-cost capacity expansion outcomes are impacted by changes in model resolution across two temporal dimensions: 1) the number of considered representative periods, and 2) the system dispatch interval. First, we find that the least-cost generation portfolio can differ significantly for small changes in the number of representative days, but largely converges to the 365-day result once 104 representative days are considered. Furthermore, systems with wind, solar and storage resources were more sensitive to changes in the number of representative days than a thermal-dominated system. Second, we find that considering five-minute dispatch resolution consistently results in least-cost generation portfolios with less solar capacity and more energy storage capacity than corresponding scenarios with hourly dispatch intervals. This suggests that hourly dispatch representation fails to capture the intra-hour volatility of solar generation, and therefore also overlooks opportunities for storage resources to provide system value by balancing this volatility. Collectively these results indicate that capacity expansion modelers should revisit conventional approaches to temporal representation when conducting analyses of deeply decarbonized power systems to ensure that such analyses are robust and actionable. To our knowledge, this is the first study to analyze capacity expansion outcomes with five-minute dispatch resolution in this manner.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRSDate not supplied

Characterizing Spatiotemporal Uncertainty in Interpolated Meteorological Data

Interpolated meteorological data invariably contain errors. These errors have structure in time and space, particularly autocorrelation, which can cause the effects of errors to compound when model outputs are aggregated temporally or spatially. One way to account for this uncertainty is with a probabilistic model from which samples can be drawn that are coherent with respect to underlying spatial and temporal covariance structure. This work describes a probabilistic method for spatial interpolation of point-wise meteorological time series. Observational data from weather stations are generally sparse in space and dense in time (but sometimes missing). The method works by projecting time series onto orthogonal basis vectors and spatially interpolating each resulting component independently. Under suitable assumptions, and data transformations to better satisfy those assumptions, Gaussian process regression provides a complete description of the joint predictive distribution over a Gaussian random field. Spatiotemporally coherent realizations are generated as the sum of conditional (spatial) simulations of each orthogonal (temporal) component. Data-derived and generic orthogonal bases are considered. In addition to spatial interpolation, imputation of missing observational data is examined. The method is applied using near-surface air temperature over the Western United States and validated by comparing theoretical versus actual coverage of predictive distributions and analyzing the degree to which spatial and temporal covariance structure is reproduced. Computational considerations, relating to conditional simulation of random fields, are also addressed.

Conor T Doherty↗

DOE OSTI2020

What can the GRACE seasonal cycle tell us about lake-aquifer interactions?

Lake-aquifer interactions have been the subject of investigation and debate for decades. Traditional investigation methods include measurement of water flux across the groundwater-surface water interface, application of heat and environmental tracer methods, conducting numerical simulations of the water flow, and mass balance-based approaches. We first review, evaluate and describe the different approaches that have been applied for examining lake-aquifer interactions and propose an additional complementary approach. While the leakage from lakes and its slow movement through porous media (laminar flow) is well characterized by existing methods, modeling the rapid and turbulent flow through preferred pathways (faults/karst) remains a challenging task. To better understand the nature, and the full scale, of the lake aquifer interactions, and using Lake Nasser and the underlying Dakhla subbasin of the Nubian Sandstone Aquifer System (NSAS) in the Western Desert of Egypt (area: 0.66 x 10 6 km 2 ) as a test site, we examine, model, and correlate temporally and spatially, the variations in Gravity Recovery and Climate Experiment terrestrial water storage (GRACE TWS ) solutions, with precipitation, lake levels, area, and water volume. We review current GRACE applications in hydrology and present our novel approach. Findings include: firstly, large seasonal fluctuations (peak: Nov./Dec.; trough: July/Aug.) in Lake Nasser's surface water levels (average annual fluctuations: from 3.7 m to 7.5 m), area (average area: 3622 km 2 but up to 4530 km 2 ), and volume (annual average: 13.4 km 3 , and up to 34 km 3 ) are observed throughout years 2006 to 2015. These fluctuations are accompanied by an increase in GRACE TWS (average: 50 ± 13 mm/yr, up to 77 ± 18 mm/yr) over Lake Nasser and by a progression of a front of increasing GRACE TWS values (> 50 ± 13 mm) away from the lake reaching distances of up to 700 km some 3 to 5 months following peak lake level periods. The areas witnessing the seasonal increase in GRACE TWS display a progressive increase in phase and decrease in amplitude with distance from the lake. Secondly, the negligible precipitation over the Dakhla subbasin cannot account for the observed seasonal GRACE TWS patterns and neither can the leakage signal from Lake Nasser. Thirdly, overall similarities in the distribution of GRACE TWS seasonal spatial patterns are observed. During high lake level periods (e.g., Dec. 2007; Feb. 2015) additional, and more distant, areas from Lake Nasser saw increases in GRACE TWS and vice versa during low lake level periods (e.g., July 2006; Aug. 2010). These observations are consistent with Lake Nasser being the main source of modern recharge for the Dakhla subbasin and suggest a new conceptual model for the subbasin: a slow groundwater flow through a porous matrix and a rapid groundwater flow along a network of faults, fractures, and karst topography across the matrix. We suggest that the proposed conceptual model for the interaction between Lake Nasser and the Dakhla subbasin could be applicable to aquifers of similar geologic, climatic, and hydrologic settings worldwide and that approaches similar to those advocated here could be used to investigate the validity of this suggestion.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Assessing Cloud and Precipitation Properties on Temporal and Spatial Scales Using LASSO Simulations over ENA

Low clouds and precipitation representation remain a major source of uncertainty in Earth System Models (ESMs), particularly due to challenges in representing their sub-grid variability and scale-dependent sampling. This study evaluates the performance of preliminary simulations from the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project over the Eastern North Atlantic (ENA), with a focus on liquid water path (LWP), ice water path (IWP), cloud fraction (CF), and surface precipitation simulated across closed-cell, open-cell, and transitional cloud regimes. Using LES (100 m horizontal grid spacing) driven by ERA5 and MERRA-2 reanalyses, we assess the representativeness of ground-based point observations by analyzing their correspondence to model-resolved spatial and temporal means. Results suggest that observational sampling of at least 6 hours is required to achieve consistency with domain-scale averages, in particular for observations that exhibit pronounced sub-grid heterogeneity, such as precipitation. ERA5-forced simulations exhibit improved spatial coherence and agreement with domain-averaged quantities when compared to MERRA-2 runs, with performance discrepancies largest for convective cloud conditions due to differences in forcing fidelity and temporal resolution. These findings highlight the importance of regime-aware model evaluation strategies and potentially demonstrate how LES can inform observation-model comparison practices and the development of cloud and precipitation parameterizations in ESMs.

Liang, Jiakun [University of Hawai'i at Manoa] (OR↗

DOE OSTI2025

An interregional optimization approach for time series aggregation in continent-scale electricity system models

Modeling electric power systems with high shares of weather-dependent resources requires tradeoffs between temporal, spatial, and operational resolution. Many studies perform time series aggregation using clustering algorithms to reduce the temporal dimension, but when modeling continent-scale electricity systems that are large enough to contain multiple independent weather systems, this approach requires large numbers of representative periods to minimize errors in regional wind and solar capacity factors. Here, a new optimization-based approach for representative period selection and weighting is introduced that minimizes regional errors in average renewable capacity factors and electricity demand. The method delivers higher regional fidelity with fewer representative periods than alternative clustering methods when applied to wind, solar, and demand profiles for the contiguous United States. When representative periods are selected from multiple weather years, the optimized method reproduces regional averages with lower error than a complete 365-day time series from any single weather year. The method identifies only representative (as opposed to outlying) periods but can be combined with an iterative "stress period" identification approach to guide efficient decision-making considering both average and high-risk weather conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗