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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dynamic Frequency Regulation Improvement in Hydropower-Hybrid System using Variational Mode Decomposition

Providing frequency response, especially fast frequency response such as Red D in the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market, is challenging for many generation plants to deliver on their own. If they have adequate flexibility, hydropower plants are typically able to provide slower regulation support (e.g., Reg A in PJM), but do not respond fast enough to provide Reg D. The ability to provide Reg D would improve their revenue because this service is typically more valuable. This work presents a control approach to use hydropower, battery, and ultracapacitor systems to provide fast regulation in a way that uses the response contribution of each asset. In particular, the proposed control architecture applies a variational mode decomposition (VMD) technique on the incoming Reg D signal to extract multiple dynamic-regulation components with non-overlapping frequencies. With the response-speed dependent alignment, these regulation components are fed to the hydrogenator and hybrid energy storage system (HESS). The paper evaluates the proposed approach on a direct-current (DC)-coupled active system by computing several performance measures and analyzing sensitivity based on HESS component proportional capacities. The results reveal that the proposed VMD-based signal conditioning performs well and that optimizing the sizing of the battery and ultracapacitor components further enhances performance.

13 HYDRO ENERGY↗
DOE OSTI2025

Improved Guarantees for Optimal Nash Equilibrium Seeking and Bilevel Variational Inequalities

We consider a class of hierarchical variational inequality (VI) problems that subsumes VI-constrained optimization and several other problem classes, including the optimal solution selection problem and the optimal Nash equilibrium (NE) seeking problem. Our main contribution is threefold. (i) We consider bilevel VIs with monotone and Lipschitz continuous mappings and devise a single-timescale iteratively regularized extragradient method, named IR-EG 𝚖,𝚖 . We improve the existing iteration complexity results for addressing both bilevel VI and VI-constrained convex optimization problems. (ii) Under the strong monotonicity of the outer-level mapping, we develop a method named IR-EG 𝚜,𝚖 and derive faster guarantees than those in (i). We also study the iteration complexity of this method under a constant regularization parameter. These results appear to be new for both bilevel VIs and VI-constrained optimization. (iii) To our knowledge, complexity guarantees for computing the optimal NE in nonconvex settings do not exist. Motivated by this lacuna, we consider VI-constrained nonconvex optimization problems and devise an inexactly projected gradient method, named IPR-EG, where the projection onto the unknown set of equilibria is performed using IR-EG 𝚜,𝚖 with a prescribed termination criterion and an adaptive regularization parameter. We obtain new complexity guarantees in terms of a residual map and an infeasibility metric for computing a stationary point. Here, we validate the theoretical findings using preliminary numerical experiments for computing the best and the worst NEs.

bilevel optimization↗
DOE OSTI2022

Intra-canopy leaf trait variation facilitates high LAI and compensatory growth in a clonal woody encroaching shrub in the tallgrass prairie

This data package contains the data for Tooley et al. (2022) Intra-canopy leaf trait variation facilitates high leaf area index and compensatory growth in a clonal woody encroaching shrub. The purpose of this project was to evaluate the leaf ecophysiological traits of Cornus drummondii C.A. Mey to better understand how C. drummondii maximizes photosynthetic rates across large gradients of light availability in its short, dense canopies. We also evaluate how C. drummondii modifies its allocation of leaf traits in response to grassland disturbance. High LAI canopies and a compensatory growth response to disturbance are two key factors contributing to the success of C. drummondii and other woody species responsible for the encroachment of grasslands.The data package contains ecophysiological measurements, including leaf morphological, physiological, and chemical traits; photosynthetic measurements, leaf carbon and nitrogen stable isotopes, leaf area index, and photosynthetically active radiation. All measurements were taken vertically across canopies of the clonal woody-encroaching shrub Cornus drummondii under browsed, grazed, and control (unbrowsed and ungrazed) conditions.Files:SeasonalMeasurements_Archive.csv - Data for all seasonal measurements used in the study.LeafPhotoSeasonalRaw_Archive.zip - zip file containing raw data from an li-6400 for each sampling period in the study.LAI_Archive.xlsx - Data for LAI Measurements.ACiandLightCurveResults_Archive.xlsx - Contains parameters extracted from A-ci and Light Response curves along with the morphological and chemical traits for each leaf.ACiRaw Archive.xlsx - Raw data from Li-6400 for A-ci response curvesLightCurves_Raw.zip - zip file containing raw data for Li-6400 light response curves. Excel file format: "IslandID"-"Depth"-lc.xlsx (or .csv)DataCollectionMethods.docx - file containing the methods of data collection, processing, and QA/QC for all data files. File also contains a list of excel file headers, abbreviations, and their units.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Inverse Analysis with Variational Autoencoders: A Comparison of Shallow and Deep Networks

Inverse problems are applied to determine unknown properties by matching observational data with a physical model that often takes many parameters as input. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented involving regularization with a technique known as a variational autoencoder (VAE), which is trained to map a high-dimensional parameter space with a complex structure to a low-dimensional latent space with a simple structure. We apply this approach to unconditioned realizations of the parameters (heterogeneous hydraulic fields) for a hydrogeological inverse problem. Two types of hydraulic conductivity fields are used to evaluate the characterization for the different levels of heterogeneity complexity of the physical inputs. This approach keeps the computational cost of generating the training data low. The reason is unconditioned realizations neither rely on the observational data used to perform the inverse analysis nor require any groundwater flow model forward runs. In addition, this approach applies regularization on a low-dimensional latent space from the VAE and increases optimization efficiency through automatic differentiation. Furthermore, two different neural network (NN) structures are tested for their utility in using the VAE for inverse analysis. The performance of a deep, convolutional neural network strongly depends on the dimensionality of the latent space, which requires tuning. In contrast, a shallow, dense neural network provides consistently accurate characterization without tuning. Furthermore, our approach evaluates the advantages of the shallow, dense neural network over the deep, convolutional one and enables future application to a wide range of inverse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CONCURRENT, CONDENSED STEIN VARIATIONAL GRADIENT DESCENT FOR UNCERTAINTY QUANTIFICATION OF NEURAL NETWORKS

In this work, we propose a Stein variational gradient descent (SVGD) method to concurrently sparsify, train, and provide uncertainty quantification (UQ) of a complexly parameterized model, such as a neural network (NN). It employs a graph reconciliation and condensation process to reduce complexity and increase similarity in the Stein ensemble of parameterizations. Therefore, the proposed concurrent, condensed SVGD (ccSVGD) method can provide UQ on parameters, not just outputs. Furthermore, the parameter reduction speeds up the convergence of the Stein gradient descent as it reduces the combinatorial complexity by aligning and differentiating the sensitivity to parameters. These properties are demonstrated with an illustrative example and an application to a mechanical response representation problem in solid mechanics.

42 ENGINEERING↗
DOE OSTI2022

Natural variation in growth and leaf ion homeostasis in response to salinity stress in Panicum hallii

Soil salinity can negatively impact plants growth, development and fitness. Natural plant populations restricted to coastal environments may evolve in response to saline habitats and therefore provide insights into the process of salinity adaptation. We investigated the growth and physiological responses of coastal and inland populations of Panicum hallii to experimental salinity treatments. Coastal genotypes demonstrated less growth reduction and superior ion homeostasis compared to the inland genotypes in response to saline conditions, supporting a hypothesis of local adaptation. We identified several QTL associated with the plasticity of belowground biomass, leaf sodium and potassium content, and their ratio which underscores the genetic variation present in this species for salinity responses. Genome-wide transcriptome analysis in leaf and root tissue revealed tissue specific overexpression of genes including several cation transporters in the coastal genotype. These transporters mediate sodium ion compartmentalization and potassium ion retention and thus suggests that maintenance of ionic homeostasis of the coastal genotypes might be due to the regulation of these ion transporters. These findings contribute to our understanding of the genetics and molecular mechanisms of salinity adaptation in natural populations, and widens the scope for genetic manipulation of these candidate genes to design plants more resilient to climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference

A multiple input multiple output (MIMO) power line communication (PLC) model for industrial facilities was developed that uses the physics of a bottom-up model but can be calibrated like top-down models. The PLC model considers 4-conductor cables (three-phase conductors and a ground conductor) and has several load types, including motor loads. The model is calibrated to data using mean field variational inference with a sensitivity analysis to reduce the parameter space. The results show that the inference method can accurately identify many of the model parameters, and the model is accurate even when the network is modified.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

One of the fundamental assumptions of compressive sensing (CS) is that a signal can be reconstructed from a small number of samples by solving an optimization problem with the appropriate regularization term. Two standard regularization terms are the L1 norm and the total variation (TV) norm. We present a comparison of CS reconstruction results based on these two approaches in the context of chemical detection, and we demonstrate that optimization based on the L1 norm outperforms optimization based on the TV norm. Our comparison is driven by CS sampling, reconstruction, and chemical detection in two real-world datasets: the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Both datasets contain the release of a chemical simulant such as glacial acetic acid, triethyl phosphate, and sulfur hexafluoride. For chemical detection we use the adaptive coherence estimator (ACE) and bulk coherence, and we propose algorithmic ACE thresholds to define the presence or absence of a chemical of interest in both un-compressed data cubes and reconstructed data cubes. The un-compressed data cubes provide an approximate ground truth. We demonstrate that optimization based on either the L1 norm or TV norm results in successful chemical detection at a compression rate of 90%, but we show that L1 optimization is preferable. We present quantitative comparisons of chemical detection on reconstructions from the two methods, with an emphasis on the number of pixels with an ACE value above the threshold.

Hyperspectral imagery, L1-norm, total variation, A↗
DOE OSTI2021

Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems

We develop data-driven methods for incorporating physical information for priors to learn parsimonious representations of nonlinear systems arising from parameterized PDEs and mechanics. Our approach is based on Variational Autoencoders (VAEs) for learning nonlinear state space models from observations. We develop ways to incorporate geometric and topological priors through general manifold latent space representations. We investigate the performance of our methods for learning low dimensional representations for the nonlinear Burgers equation and constrained mechanical systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Data assimilation for burnup distribution of PWR with three-dimensional variational algorithm and artificial neutral network

In this paper, a data-assimilation method has been proposed and applied for the burnup distribution of PWR. The burnup distribution is significant to the safety and economy of the reactor, as it is essential for the fuel-reloading design and optimization. Due to the burnup distribution cannot be measured directly during the reactor operation, the numerical simulation is widely applied to determine the burnup distribution. However, there is a deviation between the numerical simulation and the actual core due to some unavoidable factors, such as component manufacturing deviation, uneven flow distribution and so on. These differences would induce the errors to the simulation values of power distributions and hence to the burnup distribution. To address this problem, a data-assimilation method for the burnup distribution has been proposed with the application of power-distribution measurements. In our research, the three-dimensional variational (3DVAR) algorithm was applied for burnup-distribution calibration and the artificial neutral network (ANN) was applied to establish the relation between power distribution and corresponding burnup distribution. As engineering verification, the proposed data-assimilation method has been applied to the CNP1000 PWR operated in China. The numerical results indicated that the burnup-distribution errors can be reduced notably, as the maximum value of relative errors for power distribution can be reduced from 5.35% to 3.96%. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Transitional Structures with Continuous Variations in Atomic Positions from Anatase to Rutile Improve Photocatalytic Activity

Abstract TiO 2 polymorphs have distinct properties that are widely employed in various applications. However, mechanisms of transformations between these polymorphs are not fully understood, especially at atomic scale, inhibiting advancing the design and application of the transitional phases. Here, based on results from semi‐in situ transmission electron microscopy, density functional theory, and X‐ray photoemission experiments, a physical picture of transitional structures is discovered, in which continuous variations in atomic positions form along a previously unreported anatase‐to‐rutile phase transformation path of [010] A –to–[] R and (004) A –to– (011) R . These gradient structures give rise to continuous band bending, which promotes electron‐hole separation and inhibits their recombination across the bulk of the particles, leading to a large functionally active volume fraction and resulting in high photoactivity. These findings suggest that interphase matter based on extended gradient structures can be designed to advance new functions not achievable using abrupt interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ending the Chase for a Perfect Binder: Role of Surface Chemistry Variation and its Influence on Silicon Anodes

In this work we examine the influence of the type of silicon source material (two milled and two plasma enhanced chemical vapor deposition) on the electrochemical performance of silicon-based anodes for Li-ion batteries. The four representative Si sources were previously identified in literature but have never been compared side by side, which is critical to understanding the wide variation in performance reported for silicon-based electrodes. These particles display unique surface features associated with surface oxides and residual precursor species (Si-N, Si-C, Si-H) which impact processability by changing interactions within the electrode slurry and the resulting electrochemical performance. These properties should be taken into consideration when utilizing “off-the-shelf” Si powders for investigations and would be good variables to explore to understand why certain chemistries display suitable performance, while others “don't work.”

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Diel variation in CO 2 flux is substantial in many lakes

Lakes play a significant role in the global carbon cycle, acting as sources and sinks of carbon dioxide (CO 2 ). In situ measurements of CO 2 flux (FCO 2 ) from lakes have generally been collected during daylight, despite indications of significant diel variability. This introduces bias when scaling up to whole-lake annual aquatic carbon budgets. We conducted an international sampling program to ascertain the extent of diel variation in FCO 2 across lakes. We sampled 21 lakes over 41 campaigns and measured FCO 2 at 4-h intervals over a full diel cycle. Rates of FCO 2 ranged from −3.16 to 4.39 mmol m −2 h −1 . Integrated over a day, FCO 2 ranged from −381.68 to 878.49 mg C m −2 d −1 (mean = 76.54) across campaigns. We identified three characteristic diel patterns in FCO 2 related to trophic status and show that for half of the campaigns, daily flux estimates were biased by > 50% if based on a single (daytime) measurement.

de Eyto, Elvira [Marine Institute, Mayo (Ireland)]↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2023

Combinatorial Growth of Vertically Aligned Nanocomposite Thin Films for Accelerated Exploration in Composition Variation

Combinatorial growth is capable of creating a compositional gradient for thin film materials and thus has been adopted to explore composition variation mostly for metallic alloy thin films and some dopant concentrations for ceramic thin films. This study uses a combinatorial pulsed laser deposition method to successfully fabricate two‐phase oxide–oxide vertically aligned nanocomposite (VAN) thin films of La 0.7 Sr 0.3 MnO 3 (LSMO)‐NiO with variable composition across the film area. The LSMO‐NiO compositional gradient across the film alters the two‐phase morphology of the VAN through varying nanopillar size and density. Additionally, the magnetic anisotropy and magnetoresistance properties of the nanocomposite thin films increase with increasing NiO composition. This demonstration of a combinatorial method for VAN growth can increase the efficiency of nanocomposite thin film research by allowing all possible compositions of thin film materials to be explored in a single deposition.

36 MATERIALS SCIENCE↗
DOE OSTI2021

CdTe‐Based Solar Cells with Variations in Mg Concentration in the MgZnO Emitter

The optimal fraction of Mg incorporation in sputter-deposited Mg X Zn 1−X O (MZO) emitters for thin-film CdTe-based solar cells is evaluated by varying it over a range of x from 0 to 0.35. This range allows a variation in the conduction band offset from −0.1 eV (cliff like) to +0.32 eV (spike like). Here, a maximum efficiency of 18.5% for cells with the bilayer CdSeTe/CdTe absorber occurs at x = 0.15, which corresponds to a spike-like band offset near 0.2 eV, as confirmed by X-ray photoelectron spectroscopy. In addition, good cell performance is seen over a fairly broad range of x extending from 0.1 to 0.25. The MZO optical bandgap increases with the Mg fraction, consistent with an increasing conduction band offset. Temperature-dependent current−voltage measurements and time-resolved photoluminescence show improvement in the emitter/absorber interface with the incorporation of Mg. Capacitance−voltage measurements show that the depletion region extends further into the absorber with more Mg, and X-ray diffraction confirms a change from a hexagonal-dominant crystal structure toward zinc blende at x = 0.35.

14 SOLAR ENERGY↗
DOE OSTI2022

What can surface wind observations tell us about interannual variation in wind energy output?

The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are ~25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (< 25 km) wind plants and compare annual estimated generation, based on a function of surface wind speed observations, to annual recorded generation. Researchers rely on this publicly available surface data because measurements co-located with wind plants are generally considered proprietary. Our analysis addresses a research gap: interannual variation in observed surface wind speeds is rarely compared to observed data at wind plant locations and turbine heights. We find that despite its common use for this purpose, generation estimates based on publicly available surface observational data provide a poor proxy for interannual variability in recorded wind generation. These findings suggest that caution is generally needed when researchers use surface wind speed measurements to investigate long-term wind energy trends.

17 WIND ENERGY↗
DOE OSTI2021

Real-time gravitational replicas: formalism and a variational principle

This work is the first step in a two-part investigation of real-time replica wormholes. Here we study the associated real-time gravitational path integral and construct the variational principle that will define its saddle-points. We also describe the general structure of the resulting real-time replica wormhole saddles, setting the stage for construction of explicit examples. These saddles necessarily involve complex metrics, and thus are accessed by deforming the original real contour of integration. However, the construction of these saddles need not rely on analytic continuation, and our formulation can be used even in the presence of non-analytic boundary-sources. Furthermore, at least for replica- and CPT-symmetric saddles we show that the metrics may be taken to be real in regions spacelike separated from a so-called ‘splitting surface’. This feature is an important hallmark of unitarity in a field theory dual.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗