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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Evaluation of COTS Electronics by Power Spectrum Analysis and Multivariate Data Analysis

Power spectrum analysis (PSA) is a fast, non-destructive, sensitive method for examining commercial off-the-shelf ( COTS ) electronic components. These features make PSA attractive for both component screening and surveillance in support of component reliability efforts. Current analysis methods limit the utility of PSA due to the need to manually examine the results of analysis to identify anomalous parts. This study demonstrates the development and application of a workflow to automate the screening of COTS electronic components. Further, this study demonstrates the use of multivariate algorithms to assess aging of Zener diodes. These workflows can be readily extended to other components, combining the benefits of PSA and multivariate analysis to screen and evaluate COTS electronic components.

42 ENGINEERING↗
DOE OSTI2022

CTAP REPORT. Commercialization of Power Spectrum Analysis (PSA) Technology

Power Spectrum Analysis (PSA) is a Sandia-developed, non-intrusive, electrical technique that captures distinct frequency-domain signatures of microelectronics devices using an innovative, unconventional biasing scheme (off-normal biasing). PSA can identify subtle differences in devices and is applicable in various areas such as device screening, counterfeit identification, reliability assurance, and trust authentication. From October 2020 to April 2021, Sandia worked with entrepreneurs from a new start-up company, Chiplytics, to commercialize PSA technology through NNSA-sponsored FedTech Program. In September 2021, Sandia received funding through Covid-19 Technical Assistance Program (CTAP) to provide technical assistance to Chiplytics for commercialization. Under the CTAP Statement of Work, Sandia was tasked with providing technical assistance to Chiplytics in PSA pilot testing for Naval Surface Warfare Center (NSWC) at Crane and other pilot participants. Sandia was also tasked with assisting Chiplytics in hardware development and evaluation of Chiplytics prototype system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series

Abstract In this article we propose an extension of singular spectrum analysis for interval‐valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set‐valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.

de Carvalho, Miguel↗
DOE OSTI2025

Neural network-based model of galaxy power spectrum: fast full-shape galaxy power spectrum analysis

ABSTRACT We present a neural network-based emulator for the galaxy redshift-space power spectrum that enables several orders of magnitude acceleration in the galaxy clustering parameter inference, while preserving 3$\sigma$ accuracy better than 0.5 per cent up to $k_{\mathrm{max}}$ = 0.25 $\, h\text{Mpc}^{-1}$ within Lambda-cold dark matter ($\Lambda$CDM) and around 0.5 per cent $w_0$–$w_a$CDM. Our surrogate model only emulates the galaxy bias-invariant terms of one-loop perturbation theory predictions, these terms are then combined analytically with galaxy bias terms, counter-terms, and stochastic terms in order to obtain the non-linear redshift-space galaxy power spectrum. This allows us to avoid any galaxy bias prescription in the training of the emulator, which makes it more flexible. Moreover, we include the redshift $z \in [0,1.4]$ in the training which further avoids the need for re-training the emulator. We showcase the performance of the emulator in recovering the cosmological parameters of $\Lambda$CDM by analysing the suite of 25 AbacusSummit simulations that mimic the Dark Energy Spectroscopic Instrument luminous red galaxies at $z=0.5$ and 0.8, together as the emission line galaxies at $z=0.8$. We obtain similar performance in all cases, demonstrating the reliability of the emulator for any galaxy sample at any redshift in $0 \lt z \lt 1.4$. We will make our emulator public at github repository.

Trusov, Svyatoslav (ORCID:0000000224146720)↗
DOE OSTI2023

Development of a Low-Cost, Sensitivity-Optimized Fluorescence Sensor for Visible Spectrum Analysis

This paper presents an economically viable, sensitivity-optimized fluorescence sensor for detecting low concentrations of fluorophores in the visible spectrum range. The sensor employs a deep ultraviolet (DUV) 275-nm LED and a dichroic mirror to establish a co-axial optical path for excitation and emission. Instead of utilizing a general-purposed spectrometer optimized for high spectral resolution, the sensor strikes a balance between resolution and sensitivity by using an appropriate aperture size and optimized optical design tailored for the spectral characteristics of fluorophores. Here, the manuscript demonstrates measurements of ppb-level rare earth element (REE) samples containing terbium (Tb), europium (Eu), dysprosium (Dy), and samarium (Sm) in aqueous solutions. Through balanced design that optimizes collection of fluorescence emission without losing spectral information, this work shows the feasibility of developing a low-cost, compact, and highly sensitive fluorescence sensors with improved detection sensitivity when compared to bench-top commercial spectrofluorometers at far higher costs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

21 cm Power Spectrum Analysis of North Celestial Pole Observations with the Tianlai Dish Pathfinder Array

The Tianlai Dish Pathfinder Array (TDPA) is a radio interferometer designed to test techniques for 21 cm intensity mapping in the post-reionization Universe as a means of measuring large-scale cosmic structure. Using nine nights of observations targeting the North Celestial Pole field, totaling approximately 107 hr of integration time, we analyze data in the frequency range 700–800 MHz (corresponding to redshift z ∼ 0.9). We do the data format conversion, radio frequency interference flagging, calibration, imaging and point source subtraction, and foreground removal via Singular Value Decomposition. The spherically averaged power spectrum Δ 2 (k) is obtained. Furthermore, this work successfully establishes and validates a comprehensive data analysis framework for the TDPA. We identify key improvements including sky model refinement, increased integration time, and pipeline optimization that will enable future detection of the 21 cm signal through auto-correlation and cross-correlation with optical galaxy surveys.

cosmology: large-scale structure of universe↗
DOE OSTI2024

Complex multicomponent spectrum analysis with Deep Neural Network

Here, in this paper, we present the use of deep neural networks to estimate physical parameters from complex optical emission spectra of the D β /H β transition. Specifically, we focus on estimating the radio frequency electric field vector of the lower hybrid wave and isotope ratio within the scrape-off-layer plasma of the WEST tokamak. Fitting the spectral data using a traditional non-linear least squares analysis requires many free parameters and is computationally expensive, rendering the data unusable for real-time control. By implementing relatively small neural networks, the physical parameters can be directly extracted from the spectral data with reasonable accuracy in a few milliseconds. The deep neural network prediction can serve as input for a reduced model using least-squares fitting or for real-time control. We show that deep neural networks can be an effective tool for analyzing complex multicomponent spectra, providing a speedup of more than 10 5 times compared to least residual analysis, with an accuracy of 0.5% for the isotope ratio, and 0.09 kV/cm and 0.38 kV/cm for the RF radial and poloidal electric field respectively.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The Simons Observatory: impact of bandpass, polarization angle and calibration uncertainties on small-scale power spectrum analysis

We study the effects due to mismatches in passbands, polarization angles, and temperature and polarization calibrations in the context of the upcoming cosmic microwave background experiment Simons Observatory (SO). Using the SO multi-frequency likelihood, we estimate the bias and the degradation of constraining power in cosmological and astrophysical foreground parameters assuming different levels of knowledge of the instrumental effects. We find that incorrect but reasonable assumptions about the values ofall the systematics examined here can have significant effects on cosmologicalanalyses, hence requiring marginalization approaches at the likelihood level.When doing so, we find that the most relevant effect is due to bandpass shifts. When marginalizing over them, the posteriors of parameters describing astrophysical microwave foregrounds (such as radio point sources or dust) get degraded, while cosmological parameters constraints are not significantly affected.Marginalization over polarization angles with up to 0.25$^{°}$ uncertainty causes an irrelevant bias ≲ 0.05 σ in all parameters.Marginalization over calibration factors in polarization broadens the constraints on the effective number of relativistic degrees of freedom N$_{eff}$ by a factor 1.2, interpreted here as a proxy parameter for non standard model physics targeted by high-resolution CMB measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra

A method of supervised machine learning-based spectrum analysis information, using a neural network trained with spectrum information, to identify a specified feature of a given material, a system for supervised machine learning-based spectrum analysis, and a method of training a neural network to analyze spectrum data. The method of supervised machine learning-base spectrum analysis comprises inputting into the neural network spectrum data obtained from a sample of the given material; and the neural network processing the spectrum data, in accordance with the training of the neural network, and outputting one or more values for the specified feature of the sample of the material. In an embodiment, the training set of data includes x-ray absorption spectroscopy data for the given material. In an embodiment, the training set of data includes electron energy loss spectra (EELS) data.

Frenkel, Anatoly↗
DOE OSTI2020

Cyclic Prefix Direct Sequence Spread Spectrum Capacity Analysis

Cyclic Prefix Direct Sequence Spread Spectrum (CP-DSSS) is a novel waveform that has the potential to solve 5G objectives such as ultra reliable low latency communication (URLLC) and massive machine Type Communication (mMTC). This paper analyzes the capacity of the CP-DSSS waveform in comparison with Orthogonal Frequency Domain Multiplexing OFDM). CP-DSSS can be optimized to achieve the same capacity as OFDM when optimized by the water-filling algorithm. A significant advantage for CP-DSSS is that this capacity can be achieved with all symbols being transmitted with the same effective rate. As a result, stronger forward error correction codes can be used in a CP-DSSS implementation compared to an OFDM implementation with resource block constraints. In addition, the applicability of CP-DSSS as a waveform for a secondary network operating in the same frequency band as the primary network is discussed.

5G and Beyond Communications↗
DOE OSTI2021

Atomic Layer Deposition of Nanolayered Carbon Films

In this paper, carbon thin films were grown using the plasma-enhanced atomic layer deposition (PE-ALD). Methane (CH4) was used as the carbon precursor to grow the carbon thin film. The grown film was analyzed by the high-resolution transmission electron micrograph (TEM), X-ray photoelectron spectroscopy (XPS) analysis, and Raman spectrum analysis. The analyses show that the PE-ALD-grown carbon film has an amorphous structure. It was found that the existence of defective sites (nanoscale holes or cracks) on the substrate of copper foil could facilitate the formation of nanolayered carbon films. The mechanism for the formation of nanolayered carbon film in the nanoscale holes was discussed. This finding could be used for the controlled growth of nanolayered carbon films or other two-dimensional nanomaterials while combining with modern nanopatterning techniques.

Xiao, Zhigang (ORCID:0000000215950565)↗