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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 109 records · Page 6
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DOE OSTI2023

Distribution System Model Calibration for GMLC 3.3.3 "Incipient Failure Identification for Common Grid Asset Classes" - Project Summary

Distribution system model calibration is a key enabling task for incipient failure identification within the distribution system. This report summarizes the work and publications by Sandia National Laboratories on the GMLC project titled “Incipient Failure Identification for Common Grid Asset Classes”. This project was a joint effort between Sandia National Laboratories, Lawrence Livermore National Laboratory, National Energy Technology Laboratory, and Oak Ridge National Laboratory. The included work covers distribution system topology identification, transformer groupings, phase identification, regulator and tap position estimation, and the open-source release and implementation of the developed algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Blockchain for Fault-Tolerant Grid Operations Version 2.0

This report explores the potential of distributed ledger technology (DLT) as a transformative tool to enhance fault-tolerant operations in electrical distribution systems. Leveraging DLT's core attributes, including an immutable decentralized ledger, distributed consensus mechanisms, and state replication capabilities, this study focuses on three critical use cases. A central aspect of this research centers on the utilization of a consensus-driven ledger, providing actors within the system, such as distributed resources, with access to a reliable data repository. This empowers these actors to collaborate effectively and make informed decisions, all securely recorded on the blockchain. The first use case concentrates on data configuration, utilizing mathematical criteria---particularly, the chi-squared test for gross error detection---to identify trustworthy sensors for advanced decision-making. Building upon this foundation of trust, the second use case, topology identification, accurately determines circuit breaker states, unveiling the distribution network's topology. Ultimately, the third use case leverages this trust to execute switching actions, reconfiguring feeders and restoring power to disconnected customers after fault events. The concept of trust serves as a cornerstone in this approach, marking a departure from traditional fault location, isolation, and service restoration (FLISR) methods. Additionally, the blockchain-based architecture introduces decentralization, empowering disconnected areas to make autonomous decisions, even when communication with a central control center is disrupted. The primary contributions of this report are twofold: (1) a novel approach for evaluating distribution system voltage areas while preserving data ownership and (2) the implementation of interactions between distribution network areas using the actor model. Unlike the previous sequential approach for evaluating the area connection voltages, which required a radial network topology, this study's area model reduction enables a more versatile approach. The area model reduction addresses issues of prolonged data waiting times and multiple points of failure within the previous approach. Notably, the presented evaluation for the reduced network model area connection reveals a significant increase in the differences in voltage magnitudes. Simulation and evaluation of area agents across four distinct cases elucidate the area-level interaction behavior during a fault event. Simulations demonstrate that the proposed distributed FLISR (DFLISR) approach can successfully restore service to an affected area. Varying message delays and message loss probabilities in each simulation case underscore their impacts on restoration times, ranging from 3 min and 32 s to 6 min and 19 s. In contrast, power is not restored in an area in one of our simulation cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Special Section on Local and Distributed Electricity Markets

Driven by the goals of clean energy and zero carbon emissions, the power industry is undergoing significant transformations. The rapid growth of diverse distributed energy resources (DERs) at grid edge such as rooftop photovoltaics (PVs) and electric vehicles is transforming the traditional centralized power grid management to a decentralized, bottom-up, and localized control paradigm. Establishing local and distribution-level electricity markets provides an effective solution to managing large amounts of small-scale DERs. New regulations such as the recent FERC Order 2222 in the U.S. open the door to DERs in the wholesale markets. Through coordinating the local and distribution-level markets with the transmission-level wholesale market, the DERs and prosumers can trade energy and flexibility locally with each other and meanwhile provide energy, flexibility and ancillary services to the bulk power grid. During this transition, there are many new technical challenges to address, calling for innovative ideas and interdisciplinary research in this promising direction. Advanced information and communication technologies (ICT) are needed, as a key enabler, for the development and practical implementation of local and distribution electricity markets. Research into local and distribution markets is strongly interdisciplinary, involving the state of the art in power engineering, economics, and digital/information technology. A broad spectrum of contributors from universities, industry, research laboratories and policy makers is sought to develop and present solutions and technologies that will facilitate and advance practical applications and implementations of local and distribution-level electricity markets to uncover the values of DERs.

distributed energy resources↗
DOE OSTI2022

Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction

Here, we propose GLassoformer, a novel and efficient transformer architecture leveraging group Lasso regularization to reduce the number of queries of the standard self-attention mechanism. Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers. On the power grid post-fault voltage prediction task, GLasso-former shows remarkably better prediction than many existing benchmark algorithms in terms of accuracy and stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

SiC-Based 5-kV Universal Modular Soft-Switching Solid-State Transformer (M-S4T) for Medium-Voltage DC Microgrids and Distribution Grids

Medium-voltage DC (MVDC) grids are attractive for electric aircraft and ship power systems, battery energy storage system (BESS), fast charging electric vehicle (EV), etc. Such EV or BESS applications need isolated bidirectional MVDC to LVDC or LVAC converters. However, the existing Si-based solutions cannot fulfill the requirements of a high-efficiency and robust converter for MVDC grids. This paper presents a 5 kV SiC-based universal modular solid-state transformer (SST). This universal current-source SST can interface either a LVAC or LVDC grid with a MVDC grid in single-stage power conversion, while the conventional dual active bridge (DAB) converter needs an additional inverter. The proposed SST module using 3.3 kV SiC MOSFETs and diodes is bidirectional and can serve as a building block in series or parallel for higher-voltage higher-power systems. The topology of each module is based on the soft-switching solid-state transformer (S4T) with reduced conduction loss, which features reduced EMI through controlled dv/dt, and high efficiency with full-range ZVS for main devices and ZCS for auxiliary devices. Operation principle of the modular S4T (M-S4T), capacitor voltage balancing control between the cascaded modules, design of components including a medium-voltage (MV) medium-frequency transformer (MFT) to realize a 50 kVA 5 kV DC to 600 V DC or 480 V AC M-S4T are presented. Importantly, the MV MFT prototype achieves very low leakage inductance (0.13%) and 15 kV insulation with coaxial cables and nanocrystalline cores. Here, the proposed universal modular SST is compared against the DAB solution and verified with DC-DC and DC-AC simulation and 4 kV experimental results. Significantly, the MV experimental results of a modular DC transformer with each module at MVDC are rarely covered in the literature and reported for the first time.

14 SOLAR ENERGY↗
DOE OSTI2021

A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems

In this note, we present a novel data-based approach to investigate the non-Gaussian stochastic distribution control problem. As the motivation of this note, the existing methods have been summarised regarding to the drawbacks, for example, neural network weights training for unknown stochastic distribution and so on. To overcome these disadvantages, a new transformation for dynamic probability density function is given by kernel density estimation using interpolation. Based upon this transformation, a representative model has been developed while the stochastic distribution control problem has been transformed into an optimisation problem. Then, data-based direct optimisation and identification-based indirect optimisation have been proposed. In addition, the convergences of the presented algorithms are analysed and the effectiveness of these algorithms has been evaluated by numerical examples. In summary, the contributions of this note are as follows: 1) a new data-based probability density function transformation is given; 2) the optimisation algorithms are given based on the presented model; and 3) a new research framework is demonstrated as the potential extensions to the existing stochastic distribution control.

42 ENGINEERING↗
DOE OSTI2023

Parallel Grid-Forming Inverter-Driven Black Start: Power-Hardware-in-the-Loop Validation: Preprint

With power systems encountering increasing deployment levels of inverter-based resources (IBRs), system restoration using grid-forming (GFM) IBRs has gained attention. Engineered to establish grid voltages in the absence of a stiff grid, black-start-capable GFM IBRs are expected to enhance power system resilience by playing a critical role in bottom-up system restoration. This paper experimentally studies the feasibility of the novel approach through power-hardware-in-the-loop (PHIL) testing and validation with a commercial GFM inverter. The PHIL test setup demonstrates an inverter-driven black start of a 5-MW unbalanced distribution feeder where two GFM inverters collectively black start the feeder: one is a commercial hardware GFM inverter interacting through the PHIL interface, and the other is a software GFM emulated by a real-time simulation with full electromagnetic transient (EMT) models. Both GFM inverters are equipped with negative-sequence voltage control to suppress the voltage imbalance resulting from the unbalanced loading, allowing us to study the dynamic interactions between the GFMs with their control parameters unknown. To evaluate the dynamic behavior of the GFM inverters under the entire black-start process, the EMT model of the distribution system details the transformer and motor dynamics to emulate their inrush and startup behaviors. It abstracts conventional grid-connected inverter dynamics, i.e., grid-following inverters. Oscilloscope measurements of the hardware GFM inverter are also presented for critical steps. Takeaways for further study and field deployment are provided.

grid-forming inverter↗
DOE OSTI2022

Neutron diffusion calculation in heterogeneous geometry based on local/global iteration using proper orthogonal decomposition

This study newly proposes a heterogeneous core calculation method based on local/global iteration using proper orthogonal decomposition (POD). By using the singular value decomposition (SVD) and the low-rank approximation, appropriate POD bases for expanding the neutron flux can be obtained from snapshot data of the neutron flux obtained by fine mesh calculations. By projection using the POD bases, the dimension of the target equation (e.g., discretized neutron diffusion equation) can be dramatically reduced. In the proposed method, POD is effectively applied to each single assembly calculation (local calculation). Furthermore, using the local/global iteration, the effective neutron multiplication factor and the neutron flux distribution in the whole core geometry can be obtained by combining the numerical results of the local calculation for each fuel assembly and the global calculation for the whole core. As a feasibility study, the proposed method is applied to a one-dimensional heterogeneous core analysis, and the accuracy is investigated by changing the total number of POD bases. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Universal Polarization Transformations: Spatial Programming of Polarization Scattering Matrices Using a Deep Learning‐Designed Diffractive Polarization Transformer

Abstract Controlled synthesis of optical fields having nonuniform polarization distributions presents a challenging task. Here, a universal polarization transformer is demonstrated that can synthesize a large set of arbitrarily‐selected, complex‐valued polarization scattering matrices between the polarization states at different positions within its input and output field‐of‐views (FOVs). This framework comprises 2D arrays of linear polarizers positioned between isotropic diffractive layers, each containing tens of thousands of diffractive features with optimizable transmission coefficients. After its deep learning‐based training, this diffractive polarization transformer can successfully implement N i N o = 10 000 different spatially‐encoded polarization scattering matrices with negligible error, where N i and N o represent the number of pixels in the input and output FOVs, respectively. This universal polarization transformation framework is experimentally validated in the terahertz spectrum by fabricating wire‐grid polarizers and integrating them with 3D‐printed diffractive layers to form a physical polarization transformer. Through this set‐up, an all‐optical polarization permutation operation of spatially‐varying polarization fields is demonstrated, and distinct spatially‐encoded polarization scattering matrices are simultaneously implemented between the input and output FOVs of a compact diffractive processor. This framework opens up new avenues for developing novel devices for universal polarization control and may find applications in, e.g., remote sensing, medical imaging, security, material inspection, and machine vision.

Optical neural networks↗
DOE OSTI2024

Intelligent, grid-friendly, modular extreme fast charging system with solid-state DC protection

The development of electric vehicle (EV) charging infrastructure is crucial for the widespread adoption of electric transportation. However, implementing such infrastructure is a complex task that requires consideration of factors such as space limitations, adherence to industry standards, grid capacity, and other technical and policy issues. This project seeks to create a framework for the efficient design of compact medium voltage (MV) extreme fast charging (XFC) stations for EVs. The station design involves the use of a solid-state transformer (SST) that connects to the MV distribution network, delivering power to a shared DC bus. This innovative approach eliminates the need for a step-down transformer to provide low-voltage service by connecting directly to the MV distribution network. Eliminating the low-frequency transformer not only reduces the system footprint and losses but also eliminates inrush currents during grid black-start. Additionally, placing power electronics directly on the distribution system allows for high-bandwidth filtering and power factor correction. The inclusion of a shared DC bus enables multiple charging dispensers and DC storage/generation units to connect, forming a DC microgrid. This setup facilitates power sharing with minimal conversion stages. The project showcases a DC distribution network protected by intelligent solid-state (SS) DC circuit breakers (DCCB) capable of isolating the smallest section of the faulted circuit much faster than existing mechanical solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. Here, the deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Advances in catalytic production processes of biomass-derived vinyl monomers

Plastic industries currently source the majority of monomers from crude oil substances. Although we have witnessed a significant interest of companies in the utilization of sustainable feedstock materials for bio-based compound synthesis in the past decade, the transition to photosynthetic or chemosynthetic plant-based production in a circular carbon economy is largely slow due to complex biomass processing, costs and related reaction factors. The upgrade of the separated platform chemicals with a deeper supporting understanding of processes, models and a root–cause analysis about the underlying distribution challenges of engineered transformation mechanisms, catalyzed conversion pathways and selectivity would be beneficial to advance applied scientific development, bio-refining and manufacturing output amount. This review provides a summary, assessment and perspective for three important polymer-forming vinyl molecules, i.e. acrylic acid, methacrylic acid and styrene. These provide a backbone to produce acrylates, polystyrene, resins, rubbers, protective surface coatings, adhesives, textiles and other obtained copolymers. A succinct analytical overview on the thermo-catalytic intermediate routes for property-wise drop-in alternatives is presented. Sugars, acrolein, allyl alcohol, ethylbenzene, glycerol, 3-hydroxypropionic acid, isobutene, itaconic acid and lactic acid are considered as the main starting reactants. Catalysts span mixed metal oxides, silicates, native or impregnated zeolite frameworks (HBEA, HZSM, and MFI) and hetero-poly acids as well as homogeneous base hydroxides or platinum group metals, supported on carbon, alumina and sulfates. Finally, the article concludes with a brief state sum-up of the results, topics and opportunities for systematic future research or scaling.

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

GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Rapidity-dependent spin decomposition of the nucleon

We revisit the two-dimensional Fourier transform of generalized parton distributions (GPDs) at nonzero skewness. At 𝜂 = 0 it reduces to the standard impact-parameter density, while at 𝜂 ≠ 0 it is an off-forward amplitude that we interpret as a genuine parton–nucleon correlation. Its overall strength (the transverse-plane integral of the density) is fixed by the GPD at the kinematic point 𝑡 =−𝑐 𝜂 =−4⁢𝜂 2 ⁢𝑚$^{2}_{𝑁}$/(1 − 𝜂 2 ) and decreases monotonically with the rapidity gap Δ⁢𝑦 = ln⁡[(1+𝜂)/(1−𝜂)] = 2 artanh⁡(𝜂). This rapidity dependence implies rapidity-modified Ji identities that connect helicity, orbital, and total angular momenta of the correlation in closed form. To quantify these effects, we construct leading-twist quark and gluon GPDs in a string-based conformal framework: conformal moments are parametrized by linear open- and closed-string Regge trajectories with slopes constrained by parton distribution functions (PDFs), hadron/glueball spectroscopy, and form-factor data, and GPDs are reconstructed over the full (𝑥,𝜂,𝑡) domain by Mellin-Barnes inversion with next-to-leading order evolution. We find qualitative agreement (and fair quantitative agreement within quoted uncertainties) for several moments and selected nonsinglet 𝑥-space channels at 𝜇 = 2 GeV when compared with lattice QCD, while we also identify channels with visible tension and discuss likely sources (PDF priors and 𝑡-slope systematics).

Gauge-gravity dualities↗
DOE OSTI2025

Fast calculation of diffraction patterns from an ensemble of aligned molecules

We report an algorithm to calculate electron diffraction patterns for molecules with anisotropic angular distribution, which is significantly faster than existing methods. The algorithm uses a transform to convert the molecular orientation distribution, which is a function of three Euler angles, to the atom-pair distribution functions which depend on the polar and azimuthal angles. The diffraction signal can then be calculated from the atom-pair distributions. We demonstrate the computation method numerically by calculating electron diffraction patterns for a symmetric top molecule (trifluoroiodomethane) and an asymmetric top molecule (formaldehyde) and show that it reduces the calculation time by approximately two orders of magnitude compared to the standard brute-force method. Here, the method can also be applied to the calculation of x-ray diffraction patterns.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Inference for Nonparanormal Partial Correlation via Regularized Rank-Based Nodewise Regression

Abstract Partial correlation is a common tool in studying conditional dependence for Gaussian distributed data. However, partial correlation being zero may not be equivalent to conditional independence under non-Gaussian distributions. In this paper, we propose a statistical inference procedure for partial correlations under the high-dimensional nonparanormal (NPN) model where the observed data are normally distributed after certain monotone transformations. The NPN partial correlation is the partial correlation of the normal transformed data under the NPN model, which is a more general measure of conditional dependence. We estimate the NPN partial correlations by regularized nodewise regression based on the empirical ranks of the original data. A multiple testing procedure is proposed to identify the nonzero NPN partial correlations. The proposed method can be carried out by a simple coordinate descent algorithm for lasso optimization. It is easy-to-implement and computationally more efficient compared to the existing methods for estimating NPN graphical models. Theoretical results are developed to show the asymptotic normality of the proposed estimator and to justify the proposed multiple testing procedure. Numerical simulations and a case study on brain imaging data demonstrate the utility of the proposed procedure and evaluate its performance compared to the existing methods. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

97 MATHEMATICS AND COMPUTING↗