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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Nonlinear Least Squares Phasor Estimation Algorithm with a Trust Metric

The paper presents a separable nonlinear least squares (NLLS)-based approach for estimation of fundamental-frequency phasors from sampled point-on-wave measurements. An analytical connection is established between the NLLS cost function and the discrete Fourier transform (DFT)-based periodogram of the input signal. This periodogram-based interpretation of the cost function offers an intuitive and easy-to-implement solution for the frequency estimate. Using the residual error of the NLLS-fit, the paper also presents an insightful measure for ascertaining the quality of the phasor estimates and their validity, especially for data windows containing signal transients.

Chatterjee, Kaustav↗
DOE OSTI2022

Design Methodologies for Integrated Quantum Frequency Processors

We report frequency-encoded quantum information offers intriguing opportunities for quantum communications and networking, with the quantum frequency processor paradigm—based on electro-optic phase modulators and Fourier-transform pulse shapers—providing a path for scalable construction of quantum gates. Yet all experimental demonstrations to date have relied on discrete fiber-optic components that occupy significant physical space and impart appreciable loss. In this article, we introduce a model for the design of quantum frequency processors comprising microring resonator-based pulse shapers and integrated phase modulators. We estimate the performance of single and parallel frequency-bin Hadamard gates, finding high fidelity values that extend to frequency bins with relatively wide bandwidths. By incorporating multi-order filter designs as well, we explore the limits of tight frequency spacings, a regime extremely difficult to obtain in bulk optics. Overall, our model is general, simple to use, and extendable to other material platforms, providing a much-needed design tool for future frequency processors in integrated photonics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Field-Aged Glass/Backsheet and Glass/Glass PV Modules: Encapsulant Degradation Comparison

Ethylene vinyl acetate (EVA) is the predominant encapsulant in crystalline-silicon photovoltaic (PV) modules; however, its degradation is a subject of major concern, which causes significant power loss under field conditions. This article presents a comparison of EVA degradation in field-aged PV modules with glass/backsheet (G/B) and glass/glass (G/G) architectures. Modulelevel characterization included UV fluorescence imaging and I-V measurements. Material analytical techniques, including colorimetry, differential scanning calorimetry, thermogravimetric analysis, Fourier transform infrared spectroscopy, and Raman spectroscopy, were performed to correlate the module performance parameters with EVA material properties. An intense EVA discoloration in G/G modules was observed, which was corroborated by higher module Isc and Pmax degradation rates compared with its counterpart G/B modules. Higher power degradation was accompanied by a significant increase in EVA crosslinking, vinyl acetate content, yellowness index, and presence of functional groups containing unsaturated moieties that are linked to degradation products of photothermal reaction, and a higher decrease in the degree of crystallinity. The absence of a polymeric backsheet in hermetically sealed G/G modules, which restricts photobleaching and enhances the entrapment of volatile acetic acid and other degradation by-products, plays a major role in causing higher EVA degradation in G/G modules. This article concludes that EVA might have been a good choice of an encapsulant for the G/B modules over the decades, but it may prove to be an inappropriate choice for the G/G modules because of potential degassing, corrosion, and/or discoloration issues. Ionomers or polyester-based encapsulants like polyolefins could be best suited for G/G modules as it appears to be a current trend in the industry.

14 SOLAR ENERGY↗
DOE OSTI2021

A Comparison of Emerging Nonfluoropolymer-Based Coextruded PV Backsheets to Industry-Benchmark Technologies

As the photovoltaic (PV) industry is rapidly expanding around the world, there has been an increasing interest in extending the lifespan of PV modules. Concern has also emerged regarding the recyclability of modules and their component materials, including fluoropolymer-based backsheets. Laminated polyethylene-terephthalate (PET) core backsheets have traditionally been used in the PV industry, but new, coextruded polyolefin (PO) backsheets show promise as an improved alternative. In this work, minimodule and coupon samples of seven different backsheets (made of layers including contemporary PET and fluoropolymers, novel PO, and polyamide materials) were run through hygrometric- or UV photolytic-accelerated aging to identify and better understand each material's degradation modes and the backsheets' field reliability. In addition to the artificial aging, the natural weathering methods used in this article are described. The comprehensive set of chemical, mechanical, and structural characterizations at intermittent read points in this article is presented, including: visual appearance and color; gloss; mechanical tensile testing; I-V performance; electroluminescence (EL) imaging; dielectric breakdown; Fourier-transform infrared-chemical structure; X-ray-polymer structure; and differential scanning calorimetry-crystalline content. After 4000 h of aging, a strong correlation occurs between initial physical characteristics (mechanical tensile test) and operating performance (EL and I-V characteristics).

14 SOLAR ENERGY↗
DOE OSTI2022

Development of Fixtures and Methods to Assess the Durability of Balance of Systems Components

The degradation of photovoltaic (PV) balance of systems (BoS) components is not well studied, but the consequences include offline modules, strings, and inverters; system shutdown; arc faults; and fires. A utility provider experienced a ~30% failure rate in their power transfer chain, originally attributed to branch connectors. Field-failed specimen assemblies were, therefore, examined, consisting of cable connector, branch connector, and discrete fuse components. In this study, unused field-vintage specimens are examined using a benchtop prototype fixture to identify the most influential environmental stressors on BoS components as well as the effect of external mechanical perturbation. The prototype fixture was used to develop a perturbation capability for future use in the combined-accelerated stress testing chamber. The benchtop experiments were also used to develop the in-situ data acquisition of specimen current, voltage, and temperature. A significant increase in operating temperature (~100 °C from ~40 °C) and a different failure mode (arcing at the metal pins rather than overheating of the fuse filament) were observed promptly once periodic mechanical perturbation was applied. The current at failure was decreased from 35 A (measured for static specimens, with failure occurring in the fuses) to 15 A (for tests with mechanical perturbation, with failure at the male/female metal pin connection). After initial examination using X-ray computed tomography, the external plastic was machined away from failed specimens to allow for failure analysis, including the extraction of the internal convolute springs for morphological examination (optical and electron microscopy). Chemical composition analysis included energy-dispersive X-ray spectroscopy, differential scanning calorimetry, and Fourier transform infrared spectroscopy.

14 SOLAR ENERGY↗
DOE OSTI2024

Investigating the Crosslinking, Degradation, and Adhesion Behavior of Photovoltaic Encapsulants Under Thermal Accelerated Aging

Degradation of photovoltaic (PV) module encapsulant characteristics that lead to mechanical embrittlement and delamination remains a cause of failure in solar installations. A multiscale reliability model connecting the encapsulant mechanical and fracture properties to the degraded molecular structure and interfacial bonding to adjacent solar cell and glass substrates was previously published. The model, developed primarily for poly(ethylene-co-vinyl acetate) acetate (EVA) encapsulants, remains to be experimentally validated. Determining the degradation and crosslinking kinetics of alternative encapsulants, such as polyolefin elastomer (POE) and EVA/POE/EVA composites (EPE), can generalize the model. In this work, we subject fully cured EVA, POE, and EPE encapsulants to accelerated thermal aging to determine how high temperatures impact reaction kinetics. An increase in gel content (crosslinking) and decrease in crystallinity of the encapsulants under hot-aerobic (90 degrees C, 22% RH) and hot-anaerobic (90 degrees C, sealed in N 2 air) aging were observed, even in the absence of UV and crosslinking initiators. Fourier transform infrared spectroscopy (FTIR)-attenuated total reflectance analysis showed insignificant encapsulant degradation, demonstrating the critical role of UV and moisture in accelerating degradation. Adhesion testing performed on coupon-level specimens (cell/encapsulant/glass laminates) showed decreases in adhesion energy, Gc , from 5000 h of hot-dry (90 degrees C, ~1% RH) and hot-humid (90 degrees C, 60% RH) aging. POE coupons demonstrated the best stability, followed by EPE then EVA. For EVA and POE, hot-humid aged coupons experienced a larger decrease in Gc due to enhanced hydrolytic degradation. Hot-dry aging condition demonstrated that thermal degradation of the interface could be significant even if the encapsulant experiences negligible degradation in the absence of UV and elevated humidity.

accelerated aging↗
DOE OSTI2023

On the Investigation of Phase Fault Classification in Power Grid Signals: A Case Study for Support Vector Machines, Decision Tree and Random Forest

In monitoring the power grid, an ability to differentiate between fault types is essential to ensuring electrical safety. Accordingly, this study introduces a fault detection and classification method by considering different machine learning (ML) and feature extraction (FE) methods combinations. Specifically, the proposed method is established in two classification layers; the first layer determines the fault, and the second layer distinguishes the type of fault. Based on the proposed system model, this study seeks to determine the influential data attributes in a power grid signal using FE methods, including fast Fourier transform, power spectral density (PSD), auto-correlation, and wavelet transform (WT). A cross-comparison of the effectiveness of the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) is also performed to accomplish the classification layers of the proposed method. The designed algorithm is analyzed under the various combinations of FE and ML methods, and outcomes are presented by considering the trade-off between computational complexity and prediction accuracy. The results reveal that the RF-based ML algorithm shows the most accurate classification performance with PSD, and the most time-saving of the models is the DT WT. Also, SVM emerges superior on a subsequent test of the simulated models on real-world signals.

Galbraith, Kelli↗
DOE OSTI2024

CNN-Based Phase Fault Classification in Real and Simulated Power Systems Data

This study proposes a convolutional neural network (CNN)–based two-step phase fault detection and identification method to classify anomalies in the power grid signal. Specifically, the first step checks the fault’s existence and determines the need for the second step. Subsequently, in the case of anomalies in the power grid signal, the second step identifies the type of fault, including line-to-line, single-line-to-ground, double-line-to-ground, and triple-line. Accordingly, the CNN architecture is both designed for the classification layers and trained with simulated data. To provide maximum prediction accuracy with minimum processing time, this study investigates the combinations of various feature extraction (FE) techniques, such as fast Fourier transform (FFT), amplitude and phase (AP), auto-correlation function, power spectral density, and wavelet transform (WT). Consequently, simulated and real-world results demonstrate that the proposed two-step method outperforms conventional one-step techniques, with the best performance obtained by using the combination of AP-AP, AP-WT, FFT-AP, and FFT-WT–based FE methods.

Alaca, Ozgur↗
DOE OSTI2020

Correlation of Advanced Accelerated Stress Testing with Polyamide-Based Photovoltaic Backsheet Field-Failures

Cracking of polyamide (PA)-based photovoltaic (PV) backsheet materials has been widely reported for field-aged modules. Failure was not detected by conventional accelerated stress tests (ASTs), which lacked the necessary combination of stress factors and/or factor sequences. PA-based AAA backsheet cracking has since been reproduced through combined and sequential stress testing. Planar- and cross-sectional-optical microscopy as well as Fourier-transform Infrared Spectroscopy (FTIR), have been used to elucidate the mechanical and chemical changes which lead to failure of the backsheet. Field-aged backsheet samples demonstrating failure in various climates (including locations in China and Italy) are also analyzed. Through the analysis, a comparison is made between the different stress testing protocols and the field-aged samples to validate relevance of the advanced stress tests. It is shown that the changes induced through combined-accelerated stress testing (C-AST) were most representative of changes induced by the field, supporting the relevance of C-AST and providing validity for the test protocol.

14 SOLAR ENERGY↗
DOE OSTI2021

Evaluating Non-Fluoropolymer-Based Co-Extruded Backsheets Using Combined-Accelerated Stress Testing and Materials Forensics

Co-extruded, non-fluoropolymer backsheet films for photovoltaic (PV) modules have gained popularity in recent years based on their cost competitiveness. However, their viability has been thrown into question as a result of widespread, early-life field-failures of some materials, particularly the polyamide (PA)-based “AAA” backsheet product. Failure to detect weaknesses in those earlier products could be due, in part, to insufficient quality testing practices. New testing protocols have recently been developed to better evaluate such materials. Here we show the testing results for a non-fluoropolymer, co-extruded PA-based backsheet film, which demonstrates greater durability than AAA and some commercial fluoropolymers. Using material characterization techniques including Fourier-transform infrared spectroscopy (FTIR), wide-angle x-ray scattering (WAXS), differential scanning calorimetry (DSC), X-ray photoelectron spectroscopy (XPS), elongation-to-break (ETB), and optical microscopy we perform a comprehensive failure analysis to better understand the materials weaknesses. This analysis can ultimately inform how best to improve the material and extend its lifetime. This work serves as a demonstration that some materials should not be discounted due to a single poorly designed product and that more appropriate qualification testing with subsequent materials analysis can be used to develop better materials.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

A Weakly Supervised Machine Learning Procedure for Magnet Quench Diagnostics

Voltage taps remain the standard and reliable diagnostic tool for detecting quenches in superconducting magnets. However, they identify a quench only at the time of voltage rise and do not provide information on earlier physical precursors. In this work, we investigate whether acoustic emission data can reveal precursor activity that occurs before conventional voltage detection using machine learning techniques. We introduce an event selection method and a weakly supervised machine learning procedure to learn data-driven criteria for identifying potential acoustic precursors to quenches. Two Convolutional Neural Network (CNN) architectures are trained: one on acoustic sensor events from our selection procedure and one on the Fast Fourier Transforms (FFTs) of these events. Both networks are trained iteratively using confidence-weighted loss functions to associate certain subsets of training data with a precursor label. We evaluate the performance of these models by examining the time distribution of events classified as potential precursors relative to the quench onset. Results indicate that the proposed approach can possibly distinguish acoustic emission events occurring closer to the quench from earlier acoustic activity during ramping, suggesting the potential for flagging quench precursors in acoustic data.

Khan, Maira [Fermilab] (ORCID:0009000891602387)↗
DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Generic Multi-Frequency Modelling of Converter-Connected Renewable Energy Generators Considering Frequency and Sequence Couplings

Frequency and sequence couplings can compromise the trustworthiness of multi-frequency models for converter-based systems. There have been effective attempts to address the couplings mainly by linearized averaged models. Only a few studies have been conducted on practical optimization of such models with enormous matrices and experimental results. This paper provides a generic theory for coupling patterns and proposes a multi-frequency modelling method to detect and address only the main couplings in the sequence domain for converter-connected renewable energy generators. The proposed generic model is based on empirical tests using small-signal perturbations and adopting Fourier transform on the switching converter response. The proposed theory and modelling methodology are verified using a 7MVA grid emulator for voltage perturbation tests on a 2MVA photo-voltaic converter. Accordingly, the couplings can exist in more generic forms, including multiples of perturbation and fundamental frequencies. To the best of our knowledge, the patterns with the multiples of the perturbation frequency have been overlooked in the literature. Furthermore, the mirror frequency concept is valid for all coupling patterns and is included in the proposed model. Besides, the proposed patterns and the environment noise levels have been practical criteria for selecting the main couplings.

frequency and sequence couplings↗
DOE OSTI2020

Eddy Current Loss Model Unifying the Effects of Reaction Field and Non-Homogeneous 3-D Magnetic Field

In this article, a new analytical method is proposed to estimate eddy currents inside linear conductive materials. The novel closed-form formulation takes into account the effects of both conductor reaction field that dominates at high frequencies as well as the spatially non-homogeneous nature of the magnetic field penetrating the material. Although the model is suitable for most kinds of permanent magnet machines, it is particularly useful for Surface-mounted Permanent Magnet (SPM) machines where both of these phenomena are prominent. The numerical implementation of the proposed model, which consists of a combination of the method of images and 4-D Fourier transform, is presented. The model is then validated against 2-D and 3-D finite element models (FEMs) for a simple magnetic circuit, showing good agreement. Finally, the eddy current magnet loss in an SPM synchronous machine is evaluated and results are discussed.

Sahu, Raj↗
DOE OSTI2025

Analog In-Memory Computing for the Synthetic Aperture Radar Polar Format Algorithm

As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. Here, in this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.

Analog computing↗
DOE OSTI2024

Time series methods for the analysis of soundscapes and other cyclical ecological data

Biodiversity monitoring has entered an era of ‘big data’, exemplified by a near-continuous collection of sounds, images, chemical and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analysing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underlying, cyclic behaviour of the soundscape for each site, we show how one can identify circadian patterns in acoustic activity. Soundscapes in the dry season had a complex diel cycle, requiring multiple harmonics to represent daily variation, while in the wet season there was less variance attributable to the daily cyclic patterns. Our framework can be applied to most continuous, or near-continuous ecological data collected at a fine temporal resolution, allowing ecologists to explore patterns of temporal autocorrelation at multiple levels for biologically meaningful trends. Such methods will become indispensable as biological big data are used to understand the impact of anthropogenic pressures on biodiversity and to inform efforts to mitigate them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Root traits of perennial C 4 grasses contribute to cultivar variations in soil chemistry and species patterns in particulate and mineral‐associated carbon pool formation

Abstract Recent studies have indicated that the C 4 perennial bioenergy crops switchgrass ( Panicum virgatum ) and big bluestem ( Andropogon gerardii ) accumulate significant amounts of soil carbon (C) owing to their extensive root systems. Soil C accumulation is likely driven by inter‐ and intraspecific variability in plant traits, but the mechanisms that underpin this variability remain unresolved. In this study we evaluated how inter‐ and intraspecific variation in root traits of cultivars from switchgrass (Cave‐in‐Rock, Kanlow, Southlow) and big bluestem (Bonanza, Southlow, Suther) affected the associations of soil C accumulation across soil fractions using stable isotope techniques. Our experimental field site was established in June 2008 at Fermilab in Batavia, IL. In 2018, soil cores were collected (30 cm depth) from all cultivars. We measured root biomass, root diameter, specific root length, bulk soil C, C associated with coarse particulate organic matter (CPOM) and fine particulate organic matter plus silt‐ and clay‐sized fractions, and characterized organic matter chemical class composition in soil using high‐resolution Fourier‐transform ion cyclotron resonance mass spectrometry. C 4 species were established on soils that supported C 3 grassland for 36 years before planting, which allowed us to use differences in the natural abundance of stable C isotopes to quantify C 4 plant‐derived C. We found that big bluestem had 36.9% higher C 4 plant‐derived C compared to switchgrass in the CPOM fraction in the 0–10 cm depth, while switchgrass had 60.7% higher C 4 plant‐derived C compared to big bluestem in the clay fraction in the 10–20 cm depth. Our findings suggest that the large root system in big bluestem helps increase POM‐C formation quickly, while switchgrass root structure and chemistry build a mineral‐bound clay C pool through time. Thus, both species and cultivar selection can help improve bioenergy management to maximize soil carbon gains and lower CO 2 emissions.

60 APPLIED LIFE SCIENCES↗