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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 OSTI2017

Frequency spectrum analysis code for nuclear noise based on improved wavelet algorithm - 337

The core of the narrow flow channel generally faced the problem of local bubble boiling with the temperature rise of the nuclear fuel element, it is necessary and meaningful to answer and deal with the problem on the technical level. Reactor noise analysis begins with the study of the physical characteristics of the core, after nearly 30 years of research and development it has become a powerful tool in the auxiliary monitoring of the power reactor operating. By using this noise frequency spectrum analysis technology, the reactor dynamic parameter such as prompt neutron decay constant can be measured. Also, through such system, the frequency characteristics of reactor can be real-time monitored, which is helpful for safety operation and fault diagnosis. Spectrum analysis technology in the Fourier analysis era is now barely available, the reason is that the local spectral characteristics of this technology are not obvious, but this defect can be greatly improved by wavelet analysis technique, however, the traditional wavelet analysis will introduce the problem of spectral confusion. To solve the problem, based on the improved wavelet algorithm, frequency spectrum analysis code for nuclear noise is developed,from the perspective of fine spectral analysis, the local boiling accident of the coolant can be prejudged and avoid. Based on the analysis and test of experimental data,The results show that the calculation ability of the code is in accordance with the expected, it can be applied in the operation of research reactor. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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 OSTI2019

Determining moisture content of Traditional Chinese Medicines using a near-infrared LED-based moisture content sensor with spectrum analysis

In Traditional Chinese Medicine (TCM), moisture content (MC) inspection using voltage level or a lock-in amplifier, with a features-based near-infrared light-emitting diode sensor is possible. Abundant absorption response in a wide frequency range and information in both the time-domain and frequency-domain can be obtained, with the square excitation method. However, planeness, heterogeneous particle sizes, and measurement noise negatively affect the time-domain feature during MC inspection. In this work, spectrum analysis (SA) for feature extraction is investigated, to precisely measure the MC of Dutong pills. The principal components for low and high frequencies, are aimed at counteracting the dimensional methods, which are used for supplying the lower dimensional features. The SA experimental results demonstrate that the low frequency components, ranging from the base to the 3rd harmonic components, have a high linear correlation with the MC. The calibration and validation experimental results reveal that the MC can be a monotonic function about the SA amplitudes or first principal component (PC1), with the proposed sensor. This method has the potential to be used for gauging automatic, in situ MCs, particularly for quality control and Process Analytical Technology in TCM. Future research opportunities include improving accuracy and application of the method under field conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Full-Spectrum-Analysis Isotope ID

FSAIsotopeID analyzes gamma ray spectra to identify radioactive isotopes (radionuclides). The algorithm fits the entire spectrum with combinations of pre-computed templates for a comprehensive set of radionuclides with varying thicknesses and compositions of shielding materials. The isotope identification algorithm is suitable for the analysis of spectra collected by gamma-ray sensors ranging from medium-resolution detectors, such a NaI, to high-resolution detectors, such as HPGe. In addition to analyzing static measurements, the isotope identification algorithm is applied for the radiation search applications. The search subroutine maintains a running background spectrum that is passed to the isotope identification algorithm, and it also selects temporal integration periods that optimize the responsiveness and sensitivity. Gain stabilization is supported for both types of applications.

Mitchell, Dean J.↗
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 OSTI2018

Comparative Study of Finite Element Methods Using the Time-Accuracy-Size(TAS) Spectrum Analysis

We present a performance analysis appropriate for comparing algorithms using different numerical discretizations. By taking into account the total time-to-solution, numerical accuracy with respect to an error norm, and the computation rate, a cost-benefit analysis can be performed to determine which algorithm and discretization are particularly suited for an application. This work extends the performance spectrum model in for interpretation of hardware and algorithmic tradeoffs in numerical PDE simulation. As a proof-of-concept, popular finite element software packages are used to illustrate this analysis for Poisson’s equation.

97 MATHEMATICS AND COMPUTING↗
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↗