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DOE OSTI2024

Deep Learning Image Segmentation for Atmospheric Rivers

Abstract The identification of atmospheric rivers (ARs) is crucial for weather and climate predictions as they are often associated with severe storm systems and extreme precipitation, which can cause large impacts on society. This study presents a deep learning model, termed ARDetect, for image segmentation of ARs using ERA5 data from 1960 to 2020 with labels obtained from the TempestExtremes tracking algorithm. ARDetect is a convolutional neural network (CNN)-based U-Net model, with its structure having been optimized using automatic hyperparameter tuning. Inputs to ARDetect were selected to be the integrated water vapor transport (IVT) and total column water (TCW) fields, as well as the AR mask from TempestExtremes from the previous time step to the one being considered. ARDetect achieved a mean intersection-over-union (mIoU) rate of 89.04% for ARs, indicating its high accuracy in identifying these weather patterns and a superior performance than most deep learning–based models for AR detection. In addition, ARDetect can be executed faster than the TempestExtremes method (seconds vs minutes) for the same period. This provides a significant benefit for online AR detection, especially for high-resolution global models. An ensemble of 10 models, each trained on the same dataset but having different starting weights, was used to further improve on the performance produced by ARDetect, thus demonstrating the importance of model diversity in improving performance. ARDetect provides an effective and fast deep learning–based model for researchers and weather forecasters to better detect and understand ARs, which have significant impacts on weather-related events such as floods and droughts.

Galea, Daniel↗

NASA NTRS1992

Reduction of blocking effects for the JPEG baseline image compression standard

Transform coding has been chosen for still image compression in the Joint Photographic Experts Group (JPEG) standard. Although transform coding performs superior to many other image compression methods and has fast algorithms for implementation, it is limited by a blocking effect at low bit rates. The blocking effect is inherent in all nonoverlapping transforms. This paper presents a technique for reducing blocking while remaining compatible with the JPEG standard. Simulations show that the system results in subjective performance improvements, sacrificing only a marginal increase in bit rate.

Zweigle, Gregary C.↗

NASA NTRS1995

A description of the solar wind-magnetosphere coupling based on nonlinear filters

A nonlinear filtering method is introduced for the study of the solar wind -- magnetosphere coupling and related to earlier linear techniques. The filters are derived from the magnetospheric state, a representation of the magnetospheric conditions in terms of a few global variables, here the auroral electrojet indices. The filters also couple to the input, a representation of the solar wind variables, here the rectified electric field. Filter-based iterative prediction of the indices has been obtained for up to 20 hours. The prediction is stable with respect to perturbations in the initial magnetospheric state; these decrease exponentially at the rate of 30/min. The performance of the method is examined for a wide range of parameters and is superior to that of other linear and nonlinear techniques. In the magnetospheric state representation the coupling is modeled as a small number of nonlinear equations under a time-dependent input.

Vassiliadis, D.↗

DOE OSTI2021

Revealing the Nanostructure of Mesoporous Fuel Cell Catalyst Supports for Durable, High-Power Performance

Achieving high power performance and durability with low Pt loadings are critical challenges for proton exchange membrane fuel cells. PtCo catalysts developed on new carbon black supports show promise by simultaneously providing good oxygen reduction kinetics and local oxygen transport. We investigate the role of nanoscale morphology in the performance of these catalysts supported on accessible (HSC-e and HSC-f) and conventional (Ketjen Black) porous carbons using 3D electron tomography, nitrogen sorption, and electrochemical performance measurements. We find that the accessible porous carbons have hollow interiors with mesopores that are larger and more numerous than conventional porous carbons. However, mesopore-sized openings (>2 nm width) are too rare to account for significant oxygen transport. Instead we propose the primary oxygen transport pathway into the interior is through 1–2 nm microporous channels permeating the carbon. The increased mesoporosity in the accessible porous carbons results in a shorter diffusion pathlength through constrictive, tortuous micropores in the support shell leading to lower local oxygen transport resistance. In durability testing, the accessible porous carbons show faster rates of electrochemical surface area loss, likely from fewer constrictive pores that would mitigate coarsening, but maintain superior high current density performance at end of test from the improved local oxygen transport.

Electrochemistry↗

DOE OSTI2021

Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo

As an important Markov chain Monte Carlo (MCMC) method, the stochastic gradient Langevin dynamics (SGLD) algorithm has achieved great success in Bayesian learning and posterior sampling. Furthermore, SGLD typically suffers from a slow convergence rate due to its large variance caused by the stochastic gradient. In order to alleviate these drawbacks, we leverage the recently developed Laplacian smoothing technique and propose a Laplacian smoothing stochastic gradient Langevin dynamics (LS-SGLD) algorithm. We prove that for sampling from both log-concave and non-log-concave densities, LS-SGLD achieves strictly smaller discretization error in 2-Wasserstein distance, although its mixing rate can be slightly slower. Experiments on both synthetic and real datasets verify our theoretical results and demonstrate the superior performance of LS-SGLD on different machine learning tasks including posterior sampling, Bayesian logistic regression, and training Bayesian convolutional neural networks.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1984

On the passage of high-level pulsed radio frequency interference through a nonlinear satellite transponder

Attention is given to the uncoded bit error rate (BER) performance of a satellite communications system whose modulation scheme is binary PSK and whose transponder contains an arbitrary amplitude nonlinearity, all in the presence of high level pulsed radio frequency interference (RFI). A general approach is presented for direct BER evaluations, in contrast to other approaches which may employ SNR suppression factors. The computed results are based on arbitrarily specified RFI scenarios, in the presence of hard limiter, clipper, or blanker amplitude nonlinearities. Performance curves demonstrate the superiority of an appropriately chosen blanker when the RFI environment is most severe. The results obtained also pertain to the sensitivity of performance to the information bit rate, signal power variations, and the ratio of CW to noise content. The CW effects are found to be the most severe.

Weinberg, A.↗

NASA NTRS2000

Electrolytes for Li-Ion Cells in Low Temperature Applications

Prototype AA-size lithium-ion cells have been demonstrated to operate effectively at temperatures as low as -30 to -40 C. These improvements in low temperature cell performance have been realized by the incorporation of ethylene carbonate-based electrolytes which possess low melting, low viscosity cosolvents, such as methyl acetate, ethyl acetate, gamma-butyrolactone, and ethyl methyl carbonate. The cells containing a 0.75M LiPF6 EC+DEC+DMC+EMC (1:1:1:1) electrolyte displayed the best performance at -30 C (> 90% of the room temperature capacity at approximately C/15 rate), whereas, at -40 C the cells with the 0.75M LiPF6 EC+DEC+DMC+MA (1:1:1:1) and 0.75M LiPF6 EC+DEC+DMC+EA (1:1:1:1) electrolytes showed superior performance.

Smart, M. C.↗

DOE OSTI2020

A Nonlocal-Gradient Descent Method for Inverse Design in Nanophotonics

Local-gradient-based optimization approaches lack nonlocal exploration abilityrequired for escaping from local minima when searching non-convex landscapes.A directional Gaussian smoothing (DGS) approach was recently proposed in [29]and used to define a truly nonlocal gradient, referred to as the DGS gradient, inorder to enable nonlocal exploration in high-dimensional black-box optimization.Promising results show that replacing the traditional local gradient with the nonlocalDGS gradient can significantly improve the performance of gradient-based methodsin optimizing highly multi-modal loss functions. However, the current DGS methodis designed for unbounded and uncontrained optimization problems, making itinapplicable to real-world engineering optimization problems where the tuningparameters are often bounded and the loss function is usually constrained byphysical processes. In this work, we propose to extend to the DGS approachto the constrained inverse design framework in order to find better optima ofmulti-modal loss functions. A series of adaptive strategies for smoothing radiusand learning rate updating are developed to improve the computational efficiencyand robustness. Our methodology is demonstrated by an example of designing ananoscale wavelength demultiplexer, and shows superior performance compared tothe state-of-the-art approaches. By incorporating volume constraints, the optimizeddesign achieves an equivalently high performance but significantly reduces theamount of material usage.

Bi, Sirui↗

NASA NTRS1979

Development of aircraft lavatory compartments with improved fire resistance characteristics. Phase 2: Sandwich panel resin system development

A NASA-funded program is described which aims to develop a resin system for use in the construction of lavatory wall panels, sidewall panels, and ceiling panels possessing flammability, smoke and gas emission, and toxicity (FS&T) characteristics superior to the existing epoxy resin. Candidate resins studied were phenolic, polyimide, and bismaleimide. Based on the results of a series of FS&T as well as mechanical and aesthetic property tests, a phenolic resin was chosen as the superior material. Material and process specifications covering the phenolic resin based materials were prepared and a method of rating sandwich panel performance was developed.

Anderson, R. A.↗

NASA NTRS1991

Emerging standards for still image compression: A software implementation and simulation study

The software implementation is described of an emerging standard for the lossy compression of continuous tone still images. This software program can be used to compress planetary images and other 2-D instrument data. It provides a high compression image coding capability that preserves image fidelity at compression rates competitive or superior to most known techniques. This software implementation confirms the usefulness of such data compression and allows its performance to be compared with other schemes used in deep space missions and for data based storage.

Pollara, F.↗

DOE OSTI2022

Sodium trithiocarbonate cathode for high-performance sodium–sulfur batteries

The high abundance and low cost of sodium and sulfur make room-temperature sodium-sulfur (RT Na-S) batteries an attractive technology compared to the current lithium-ion batteries for large-scale grid-storage applications. However, the commercialization of RT Na-S batteries is impeded by the slow kinetics of Na-S chemistry, severe sodium polysulfide shuttling, and uncontrollable growth of dendritic Na. Herein, sodium trithiocarbonate (Na 2 CS 3 ) is applied as a cathode material to facilitate concurrent improvement in both electrodes, leading to a high-rate performance with extended cycle life. The conductive characteristic of the carbon-sulfur resonance bond provides fast ion and electron transfer throughout the cathode, resulting in superior electrochemical reactivity. At the cathode, the presence of Na2CS3 forms an oligomer-structured layer to suppress the dissolution and shuttling of active materials. Meanwhile, when small portions of Na 2 CS 3 intermediates migrate to the anode, a stable solid electrolyte interphase (SEI) layer with uniform Na-ion flux is formed, enabling improved Na stripping and plating performance. A series of electrochemical and materials characterization, accompanied by density functional theory calculations demonstrate that Na 2 CS 3 is a promising candidate to realize high-rate performance long cycle life RT Na-S batteries.

25 ENERGY STORAGE↗

NASA NTRS1984

The annular flow electrothermal ramjet

The annular flow, electrothermal, plug ramjet is examined as a possible means of achieving rapid projectile acceleration to velocities for such applications as direct launch of spacebound payloads. The performance of this ramjet operating with hydrogen propellant is examined for cases where this working fluid is treated: (1) as a perfect gas, and (2) as a gas that is allowed to dissociate and ionize and then recombine with finite reaction rates in the nozzle. Performance results for these cases are compared to the performance of a conventional ramjet operating with perfect gas hydrogen propellant. The performance of the conventional ramjet is superior to that of the annular flow, electrothermal ramjet. However, it is argued that the mechanical complexities associated with conventional ramjet operation are difficult to attain, and for this reason the annular flow, electrothermal ramjet is more desirable as a launch system. Models are presented which describe both electrothermal plug ramjet and conventional ramjet operation, and it is shown that for a given flight velocity there is a rate of heat addition per unit propellant mass for which ramjet operation is optimized.

Shaw, B. D.↗

DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗

DOE OSTI2024

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

14 SOLAR ENERGY↗

NASA NTRS1986

An advanced Ni-Cd battery cell design

The evolution of an advanced Ni-Cd space battery cell design continues to prove very promising. High oxygen/hydrogen gas recombination rates (currently up to a C/5 charge rate) and increased electrolyte activation level tolerance (currently up to 5.6 grams Ah of positive capacity) were demonstrated by test. A superior performance, extended life battery cell offering advantages should soon be available for mission applications

Miller, L.↗

DOE OSTI2026

Interface-engineered (CrMnTiZnCo) 3 O 4 @polypyrrole nano-hybrids for superior lithium storage

High-entropy oxides (HEO) have emerged as promising anode materials for lithium-ion batteries (LIBs) due to their high theoretical specific capacity. However, their practical application is hindered by several challenges, including significant volume expansion, electrode pulverization, and substantial irreversible capacity loss during initial cycles. To address these limitations, this study designed a novel core-shell composite material, denoted as HEO@PPy, which consists of a (CrMnTiZnCo) 3 O 4 -based HEO core and a polypyrrole (PPy) shell. This composite demonstrates remarkable electrochemical performance: it maintains a specific capacity of 1090.1 mAh/g after 100 cycles at 100 mA/g and retains 521.8 mAh/g after 1000 cycles at 1 A/g, highlighting its superior cycling stability. Furthermore, it exhibits excellent rate capability, delivering a capacity of 372.1 mAh/g even at a high current density of 5 A/g. These findings confirm that the strategic compositional and structural design of HEOs, combined with hybridization with conductive polymers like PPy, provides a viable pathway for developing advanced anode materials for next-generation, high-performance lithium-ion batteries.

25 ENERGY STORAGE↗

DOE OSTI2021

Excellent ballistic impact resistance of Al 0.3 CoCrFeNi multi-principal element alloy with unique bimodal microstructure

Multi-principal element alloys represent a new paradigm in structural alloy design with superior mechanical properties and promising ballistic performance. Here, the mechanical response of Al 0.3 CoCrFeNi alloy, with unique bimodal microstructure, was evaluated at quasistatic, dynamic, and ballistic strain rates. The microstructure after quasistatic deformation was dominated by highly deformed grains. High density of deformation bands was observed at dynamic strain rates but there was no indication of adiabatic shear bands, cracks, or twinning. The ballistic response was evaluated by impacting a 12 mm thick plate with 6.35 mm WC projectiles at velocities ranging from 1066 to 1465 m/s. The deformed microstructure after ballistic impact was dominated by adiabatic shear bands, shear band induced cracks, microbands, and dynamic recrystallization. The superior ballistic response of this alloy compared with similar Al x CoCrFeNi alloys was attributed to its bimodal microstructure, nano-scale L1 2 precipitation, and grain boundary B2 precipitates. Deformation mechanisms at quasistatic and dynamic strain rates were primarily characterized by extensive dislocation slip and low density of stacking faults. Deformation mechanisms at ballistic strain rates were characterized by grain rotation, disordering of the L1 2 phase, and high density of stacking faults.

36 MATERIALS SCIENCE↗

NASA NTRS1978

High-speed communication detector characterization by bit error rate measurements

Performance data taken on several candidate high data rate laser communications photodetectors is presented. Measurements of bit error rate versus signal level were made in both a 1064 nm system at 400 Mbps and a 532 nm system at 500 Mbps. RCA silicon avalanche photodiodes are superior at 1064 nm, but the Rockwell hybrid 3-5 avalanche photodiode preamplifiers offer potentially superior performance. Varian dynamic crossed field photomultipliers are superior at 532 nm, however, the RCA silicon avalanche photodiode is a close contender.

Green, S. I.↗