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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Spatially Dependent Seismic Wavefield Scattering from an Underground Chemical Explosion: Analysis of the Source Physics Experiment Dry Alluvium Geology Large- N Array

Explosion sources have been observed to generate significant shear‐wave energy despite their isotropic nature. To investigate this phenomenon, we conduct an analysis of the seismic data collected as part of the Source Physics Experiment (SPE): Dry Alluvium Geology (DAG) and investigate the generation of shear‐wave energy via scattering. The data were produced by three underground chemical explosions and consist of three‐component seismograms, which were recorded by the DAG Large‐N array. Synthetic tests suggest that for the DAG experiments, small‐scale stochastic heterogeneities, defined as features with correlation lengths of 10–100s of meters, are more effective than large‐scale geologic structure (scales >1–10 km) at reproducing the scattering of explosion generated wavefields observed at DAG. We analyze the seismic data for spatially variable ratios between transversely and radially polarized seismic energy, and then estimate the mean free path of P and S waves. All analyses are conducted within a frequency band of 5–50 Hz. The ratio of transversely to radially polarized energy is the highest in the east and west portion of the Large‐N array. In addition, the magnitude of the estimated S‐wave mean free path is shorter in the eastern portion of the Large‐N array. This variation indicates that the eastern area of the DAG array is where more scattering is occurring, suggesting azimuthal dependence of P‐to‐P and P‐to‐S scattering. Furthermore, this azimuthal dependence of P‐to‐S scattering can have implications for explosion discrimination based on spectral ratios of seismic wave types, because the general assumption is that explosions do not generate shear‐wave energy. Synthetic tests modeling only larger‐scale geologic structure had lower transversely polarized energy (only four stations showing a transversely to radially polarized energy ratio greater than 1) and fewer stations (<10) displaying shorter (<300 m) mean free paths than what was observed in the DAG data results.

Darrh, Andréa Nicole↗
DOE OSTI2021

Adversarial sampling of unknown and high-dimensional conditional distributions

Many engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample elements from unknown high-dimensional spaces with possibly millions of degrees of freedom. While there exist methods able to sample elements from probability density functions (PDF) with known shapes, several approximations need to be made when the distribution is unknown. In this paper the sampling method, as well as the inference of the underlying distribution, are both handled with a data-driven method known as generative adversarial networks (GAN), which trains two competing neural networks to produce a network that can effectively generate samples from the training set distribution. In practice, it is often necessary to draw samples from conditional distributions. When the conditional variables are continuous, only one (if any) data point corresponding to a particular value of a conditioning variable may be available, which is not sufficient to estimate the conditional distribution. This work handles this problem using an a priori estimation of the conditional moments of a PDF. Herein, two approaches, stochastic estimation, and an external neural network are compared for computing these moments; however, any preferred method can be used. The algorithm is demonstrated in the case of the deconvolution of a filtered turbulent flow field. It is shown that all the versions of the proposed algorithm effectively sample the target conditional distribution with minimal impact on the quality of the samples compared to state-of-the-art methods. Additionally, the procedure can be used as a metric for the diversity of samples generated by a conditional GAN (cGAN) conditioned with continuous variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Why Are 5-Thioglycopyranosyl Donors More Axially Selective than their Glycopyranosyl Counterparts? A Low and Variable Temperature NMR Spectroscopy and Computational Study

5-Thioglycopyranosyl donors differ in reactivity and selectivity from simple glycopyranosyl donors. An extensive study has been conducted on the nature and stability of the reactive intermediates generated on the activation of per-O-acetyl and per-O-methyl 5-thioglucopyranosyl donors and the corresponding glucopyranosyl donors. Variable temperature nuclear magnetic resonance (NMR) studies with per-O-methylated or per-O-acetyl glycosyl sulfoxides and trichloroacetimidates on activation with trifluoromethanesulfonic anhydride or trimethylsilyl triflate are reported. These show that following initial adduct formation with the promoter conversion of the 5-thioglucopyranosyl donors to the 5-thioglucopyranosyl triflates requires higher temperatures than that of the glucopyranosyl donors to the glucopyranosyl triflates. It is demonstrated that neighboring group participation is a less important phenomenon for the peracetylated thioglucosyl donors than for the peracetylated glucosyl donors. A simple thiocarbenium ion was generated by protonation of 2,3-dihydro-4H-thiopyran at low temperature and characterized by NMR spectroscopy. However, the corresponding 5-thioglucopyranosyl thenium ions were not observed in any of the NMR studies of the 5-thiopyranosyl donors: the electron-withdrawing C–O bonds around the thiopyranoside core discourage thiocarbenium ion formation, just as they discourage oxocarbenium ion formation. Density functional theory (DFT) calculations reveal the tetrahydrothiopyranyl thiocarbenium ion to be approximately 2.5 kcal/mol lower in energy than the corresponding tetrahydropyranyl oxocarbenium ion relative to the corresponding covalent triflates. However, the computations reveal a 5.8 kcal/mol activation barrier for conversion of the tetrahydrothiopyranyl triflate to the thiocarbenium ion, while formation of the oxocarbenium ion–triflate ion pair from tetrahydropyranyl triflate requires only 2.6 kcal·mol –1 . Overall, the greater axial selectivity of 5-thioglycopyranosyl donors compared to analogous glycopyranosyl donors derives from (i) the lower kinetic reactivity necessitating higher reaction temperatures, (ii) the greater stability of the thiocarbenium ion over the oxocarbenium ion facilitating equilibration under thermodynamic conditions, (iii) the greater magnitude of the anomeric effect in the 5-thiosugars, and (iv) decreased neighboring group participation in the per-esterified 5-thiosugars.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Building Energy Systems as Behind-the-Meter Resources for Grid Services: Intelligent load control and transactive control and coordination

To mitigate the impacts of climate change, significant reductions in emissions from all sectors of the economy are needed. The electricity generation sector has embarked on an ambitious plan to include renewable generation as part of its decarbonization efforts, and many cities and states are mandating all-electric buildings. While renewable resources will reduce emissions, they are not dispatchable, they vary temporally, and their generation is uncertain. Under these conditions, traditional approaches to managing grid reliability, where supply follows demand, will not be efficient and may not be cost-effective. Further, there is a more efficient alternative for balancing the supply–demand imbalance and for absorbing variability and uncertainty of renewable energy using distributed energy resources (DERs) as opposed to reserve generation. Because buildings consume more than 75% of total U.S. annual electricity consumption, behind-the-meter (BTM) DERs have a load flexibility of 77 GW of power and 90 GWh of virtual energy storage capacity nationwide (Kalsi, 2017). Therefore, some portion of the supply–demand imbalance can be met by these DERs at a lower cost compared to business-as-usual solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Frequency and time multiplexing for multiphoton state generation

One of the primary challenges with photonic quantum information processing is that single-photon states are created and heralded probabilistically, rather than being created on demand. One solution to this problem is multiplexing , which is attempting probabilistic generation of a single photon at multiple locations, times, or frequencies, and switching one successfully generated photon into an output mode. Motivated by the development of frequency-based photonic quantum information processing, we consider multiplexing using frequency and time as our options to use to get many photon generation opportunities. We devise an approach for generating multiphoton states, with photons populating multiple frequency modes in the same spatiotemporal mode. This method uses a variable-length optical delays to manipulate the temporal mode of the photons, and spaced fiber Bragg grating (FBG) reflectors to jointly manipulate the frequency and temporal modes of the photons. Experimental progress toward implementing this multiplexing scheme is proceeding along 2 fronts, each with different ways to achieve the variable-length optical delay. First, we will implement this scheme using a free-space optical quantum memory with multiple discretely adjustable free-space delays. For this implementation method, I calculate multiphoton generation rates, accounting for loss, that are realistically achievable with commercially available hardware. This work will appear in a theory paper, currently in preparation. Second, we will implement this multiplexing scheme in an integrated manner: the variable-length optical delay will happen on-chip using a long-lifetime Q-switchable Fabry-Perot cavity. The joint manipulation of the frequency and temporal modes of the photons will still happen off-chip in fiber with FBG reflectors. I report on experimental progress in constructing integrated Q-switchable Fabry-Perot cavities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Mixture density network estimation of continuous variable maximum likelihood using discrete training samples

Abstract Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$ θ given a set of observables $${\mathbf {x}}$$ x . In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\theta }$$ θ . In such situations, a number of performance-limiting issues arise which can result in biased estimates. We demonstrate the usage of MDNs for parameter estimation, discuss the origins of the biases, and propose a corrective method for each issue.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Meteoric 10Be Flux Calibration Data for the East River Watershed, Colorado, USA

This data package contains tabular and geospatial data used to quantify and model meteoric beryllium-10 fluxes in the East River watershed, Colorado, USA. The tabular component includes calibration-site data from five glacial moraine sites and includes environmental variables used to evaluate spatial controls on meteoric 10Be delivery, including elevation, mean annual precipitation (MAP), mean snow depth, and mean snow water equivalent (SWE). These site-level data were used to compare observed fluxes with environmental gradients across the watershed and to evaluate the effects of erosion correction on flux estimates. The package also includes supporting slope and curvature values used to assess topographic inputs to the erosion analysis. A second component of the data package contains updated manuscript tables and regression outputs used to summarize the relationships between meteoric 10Be flux and environmental predictors. These tables include meteoric 10Be sample information and AMS results, site-level environmental values, site-level meteoric 10Be inventory and flux values, watershed-averaged predicted fluxes, soil bulk density measurements, fine-fraction values, soil pH measurements, and regression statistics including slope, intercept, coefficient of determination, and p-value. The regression products include both standard linear regressions and regressions in which the intercept is constrained to pass through zero, and they support the analyses presented in the companion manuscript. Together, these tabular files provide the numerical basis for the manuscript tables and the regression-based interpretation of meteoric 10Be flux variability in a snow-dominated mountain watershed. The geospatial component of the package consists of GeoTIFF raster files used to generate the map products presented in Figures 2 and 6 of the companion manuscript. These rasters represent watershed-scale spatial layers for environmental variables and regression-based predictions of meteoric 10Be flux. This dataset contains comma-separated values files (.csv), Microsoft Excel files (.xlsx), GeoTIFF raster files (.tif), and upporting metadata files, including CSV data dictionaries and readme text files (.csv, .txt). The tabular files can be opened with standard spreadsheet software, and the raster files can be viewed and analyzed in GIS software such as ArcGIS Pro or QGIS. Together, these files document the numerical and spatial datasets used to calibrate and predict meteoric 10Be delivery in the East River watershed.

East River↗
DOE OSTI2023

Considering the Variability of Soiling in Long-Term PV Performance Forecasting: Preprint

This study presents the development of a methodology for evaluating the variability associated with soiling on long-term PV forecasting. Independent engineering firms typically build P50 forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions, or Pvalues, are constructed through a Monte Carlo analysis that includes various factors such as: satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within Pvalues. In this work soiling information from 16 sites in the U.S. Southwest are combined with 20 years of rainfall data to generate 20 years of energy production with soiling losses and then subsequently generate Pvalues. The results show that the spread of Pvalues (P1-P99) can increase from 0-13% when interannual soiling variability is included.

interannual variability↗
DOE OSTI2024

Evaluation of Global Climate Models for Use in Energy Analysis

The interplay between energy, climate, and weather is becoming more complex due to increasing contributions of renewable energy generation, energy storage, electrified end uses, and the increasing frequency of extreme weather events. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and subjective process. In this work, we assess datasets from various global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We present evaluations of their skills with respect to the historical climate and comparisons of their future projections of climate change for two climate change scenarios. We present the results for different climatic and energy system regions and include interactive figures in the accompanying software repository. Previous work has presented similar GCM evaluations, but none have presented variables and metrics specifically intended for comprehensive energy systems analysis including impacts on energy demand, thermal cooling, hydropower, water availability, solar energy generation, and wind energy generation. We focus on GCM output meteorological variables that directly affect these energy system components including the representation of extreme values that can drive grid resilience events. The objective of this work is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and scenarios in subsequent work.

14 SOLAR ENERGY↗
DOE OSTI2023

Improving the Representation of Hydropower in Production Cost Models

One of the challenges of a high-renewable, low-carbon system is that grid operators will no longer be able to count on fossil-fired generation should wind miss its forecast or modeling errors create shortage issues, and this change has operational and reliability implications that affect variable renewable energy integration. Hydropower can help in that it can provide many of the services that have been traditionally provided by fossil generation. However, getting the most from hydropower can be challenging in that its capabilities vary with time and are affected by other water users' needs, two complexities that are not well represented in most of today's production cost models (PCMs). This work is an early step in addressing these gaps and making the most of hydropower's capabilities.

cost model↗
DOE OSTI2024

Aerogel-derived nickel-iron oxide catalysts for oxygen evolution reaction in alkaline media

Anion exchange membrane water electrolyzers (AEMWEs) can generate hydrogen with a pure water feed using noble metal-free catalysts. Here, the development of highly active and stable catalysts for oxygen evolution reaction (OER) is required for improving performance of AEMWEs systems. Ni-Fe (oxy)hydroxides show high OER catalytic activity in alkaline media, but typically have low surface area. In this work, we investigate a series of Ni-Fe oxides with high surface area and disordered morphology, obtained using an aerogel synthesis method. We evaluate the impact of different synthesis variables on the OER activity and demonstrate that heat treatment at high temperatures generates more ordered structure, resulting in a decrease in OER activity. Advanced characterization reveals that maintaining highly disordered and porous structure of the aerogel is essential to achieving high OER activity, as it enables the formation of highly OER-active lamellar structures of the catalyst.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microgrid design and multi-year dispatch optimization under climate-informed load and renewable resource uncertainty

Microgrids are an increasingly popular solution to provide energy resilience in response to increasing grid dependency and the growing impacts of climate change on grid operations. However, existing microgrid models do not currently consider the uncertain and long-term impacts of climate change when determining a set of design and operational decisions to minimize long-term costs or meet a resilience threshold. In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load, and (iii) population and electrification trends on load growth. Additionally, we develop a two-stage stochastic programming extension of an existing microgrid design and dispatch optimization model to obtain uncertainty-informed and climate-resilient energy system decisions that minimizes long-term costs. Use of sample average approximation to validate our two case studies illustrates that the proposed methodology produces high-quality solutions that add resilience to systems with existing backup generation while reducing expected long-term costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ML Pipeline (Machine Learning Pipeline) [SWR-22-35]

The Machine Learning Pipeline (ML Pipeline) allows the user to fit a model to predict a dependent variable, y, based on a feature set, X. ML Pipeline gives the ability to automatically generate features with its Feature Engineering function, automatically select the most important features with its Feature Selection function, fit the model with its Fit function, and evaluate the model with its Evaluate function. ML Pipeline's functionality can not only help the user predict values for the desired variable it also gives the user a better understanding of which features are important and how effective the model is. ML Pipeline accomplishes all this, while remaining simple to use and interpret.

Schiek, Andrew↗
DOE OSTI2024

Antigen‐binding fragments with improved crystal lattice packing and enhanced conformational flexibility at the elbow region as crystallization chaperones

It has been shown previously that a set of three modifications—termed S1, Crystal Kappa, and elbow—act synergistically to improve the crystallizability of an antigen-binding fragment (Fab) framework. Here, we prepared a phage-displayed library and performed crystallization screenings to identify additional substitutions—located near the heavy-chain elbow region—which cooperate with the S1, Crystal Kappa, and elbow modifications to increase expression and improve crystallizability of the Fab framework even further. One substitution (K141Q) supports the signature Crystal Kappa-mediated Fab:Fab crystal lattice packing interaction. Another substitution (E172G) improves the compatibility of the elbow modification with the Fab framework by alleviating some of the strain incurred by the shortened and bulkier elbow linker region. A third substitution (F170W) generates a split-Fab conformation, resulting in a powerful crystal lattice packing interaction comprising the biological interaction interface between the variable heavy and light chain domains. In sum, we have used K141Q, E172G, and F170W substitutions—which complement the S1, Crystal Kappa, and elbow modifications—to generate a set of highly crystallizable Fab frameworks that can be used as chaperones to enable facile elucidation of Fab:antigen complex structures by x-ray crystallography.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Freshwater Flux Variability Lengthens the Period of the Low-Frequency AMOC Variability

Atlantic Meridional Overturning Circulation (AMOC) exhibits interdecadal to multidecadal variability, yet the extent to which the surface freshwater flux (FWF) variability affects the AMOC variability remains unclear. This study isolates the contribution of FWF variability in modulating AMOC through a partially coupled experiment, in which the FWF effect from the atmosphere and sea ice are disabled. It is demonstrated that the FWF effect can remarkably enhance the persistence of positive salinity anomalies in the Labrador Sea, and sustain the dense water formation that was initiated by the cold temperature anomalies. Therefore, the FWF variability serves as a positive feedback on AMOC and lengthens the period of the AMOC oscillation. Further lead-lag regressions of atmospheric and oceanic variables onto the AMOC time series illuminate that the persistent salinity anomalies are generated through two pathways: i) the phase lag between temperature and salinity anomalies in the Labrador Sea; and ii) a downstream propagation of extra high-salinity anomaly along the East Greenland Current, due to the reduced sea ice melting flux associated with an atmosphere forcing over the southern Greenland tip.

58 GEOSCIENCES↗
DOE OSTI2021

Vertical variation of turbulent entrainment mixing processes in marine stratocumulus clouds using high-resolution digital holography

Marine stratocumulus clouds contribute signi?cantly to the Earth’s radiation budget due to their extensive coverage and high albedo. Yet, subgrid variability in cloud properties such as aerosol concentration, droplet number and precipitation rates lead to considerable errors in global climate models. While these clouds usually have small vertical ex-tent, turbulent entrainment-mixing and precipitation can generate signi?cant variations in droplet number, size and relative dispersion with altitude. In this paper, we analyze turbulent entrainment-mixing processes and the variability in cloud microphysical properties as a function of height within a warm marine stratocumulus cloud layer over the Eastern North Atlantic. We use high resolution airborne holographic measurements and compare them with local turbulence measurements. We ?nd that entrainment-mixing is primarily inhomogeneous near cloud top leading to larger droplet sizes and homogeneous near cloud base leading to smaller droplet sizes. Further analysis of Damk¨ohler number measurements are able to explain the mixing mechanisms at di?erent cloud heights, reinforcing the importance of turbulent mixing and microphysical time scales in deter-mining cloud microphysics.

Desai, Neel↗
DOE OSTI2024

ESM data downscaling: a comparison of super-resolution deep learning models

Abstract Climate projections at fine spatial resolutions are required to conduct accurate risk assessment for critical infrastructure and design adaptation planning. Generating these projections using advanced Earth system models (ESM) requires significant computational resources. To address this issue, various statistical downscaling techniques have been introduced to generate fine-resolution data from coarse-resolution simulations. In this study, we evaluate and compare five deep learning-based downscaling techniques, namely, super-resolution convolutional neural networks, fast super-resolution convolutional neural network ESM, efficient sub-pixel convolutional neural network, enhanced deep residual network (EDRN), and super-resolution generative adversarial network (SRGAN). These techniques are applied to a dataset generated by the Energy Exascale Earth System Model (E3SM), focusing on key surface variables such as surface temperature, shortwave heat flux, and longwave heat flux. Models are trained and validated using paired fine-resolution (0.25 $$^{\circ }$$ ∘ ) and coarse-resolution (1 $$^{\circ }$$ ∘ ) monthly data obtained from a 9-year simulation. Next, blind testing is performed using monthly data obtained from two different years outside of the training and validation set. To evaluate the efficiency of each technique, different statistical metrics are used, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The results show that EDRN outperforms other algorithms in terms of PSNR, SSIM, and MSE, but struggles to capture fine-scale features in the data. In contrast, SRGAN, a generative model that uses perceptual loss, excels in capturing fine details at boundaries and internal structures, resulting in lower LPIPS than other methods.

Pawar, Nikhil M. (ORCID:0000000211613289)↗