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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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NASA NTRS1975

Stellar refraction - A tool to monitor the height of the tropopause from space

Calculations of stellar refraction for a setting or rising star as viewed from a spacecraft show that the tropopause is a discernible feature in a plot of refraction vs time. The height of the tropopause is easily obtained from such a plot. Since the refraction suffered by the starlight appears to be measurable with some precision from orbital altitudes, this technique is suggested as a method for remotely monitoring the height of the tropopause. Although limited to nighttime measurements, the method is independent of supporting data or model fitting and easily lends itself to on-line data reduction.

Schuerman, D. W.↗

NASA NTRS1988

Laser Doppler anemometry

The material in this NASA TM is to appear as a chapter on Laser Doppler Anemometry (LDA) in the AGARDograph entitled, A Survey of Measurements and Measuring Techniques in Rapidly Distorted Compressible Turbulent Boundary Layers. The application of LDA (specifically, the dual-beam, burst-counter approach) to compressible flows is discussed. Subjects treated include signal processing, particle light scattering and tracking, data reduction and sampling bias, and three-dimensional measurements.

Johnson, Dennis A.↗

NASA NTRS1992

Atmospheric effects on the NDVI - Strategies for its removal

The compositing technique used to derive global vegetation index (NDVI) from the NOAA AVHRR radiances reduces the residual effect of water vapor and aerosol on the NDVI. The reduction in the atmospheric effect is shown using a comprehensive measured data set for desert conditions, and a simulation for grass with continental aerosol. A statistical analaysis of the probability of occurrence of aerosol optical thickness and precipitable water vapor measured in different climatic regimes is used for this simulation. It is concluded that for a long compositing period (e.g., 27 days), the residual aerosol optical thickness and precipitable water vapor are usually too small to be corrected. For a 9-day compositing, the residual average aerosol effect may be about twice the correction uncertainty. For Landsat TM or Earth Observing System Moderate Resolution Imaging Spectrometer (EOS-MODIS) data, the newly defined atmospherically resistant vegetation index (ARVI) is more promising than possible direct atmospheric correction schemes, except for heavy desert dust conditions.

Kaufman, Y. J.↗

NASA NTRS2006

Small Hot Jet Acoustic Rig Validation

The Small Hot Jet Acoustic Rig (SHJAR), located in the Aeroacoustic Propulsion Laboratory (AAPL) at the NASA Glenn Research Center in Cleveland, Ohio, was commissioned in 2001 to test jet noise reduction concepts at low technology readiness levels (TRL 1-3) and develop advanced measurement techniques. The first series of tests on the SHJAR were designed to prove its capabilities and establish the quality of the jet noise data produced. Towards this goal, a methodology was employed dividing all noise sources into three categories: background noise, jet noise, and rig noise. Background noise was directly measured. Jet noise and rig noise were separated by using the distance and velocity scaling properties of jet noise. Effectively, any noise source that did not follow these rules of jet noise was labeled as rig noise. This method led to the identification of a high frequency noise source related to the Reynolds number. Experiments using boundary layer treatment and hot wire probes documented this noise source and its removal, allowing clean testing of low Reynolds number jets. Other tests performed characterized the amplitude and frequency of the valve noise, confirmed the location of the acoustic far field, and documented the background noise levels under several conditions. Finally, a full set of baseline data was acquired. This paper contains the methodology and test results used to verify the quality of the SHJAR rig.

Brown, Cliff↗

NASA NTRS2016

NASA Hybrid Wing Aircraft Aeroacoustic Test Documentation Report

This report summarizes results of the Hybrid Wing Body (HWB) N2A-EXTE model aeroacoustic test. The N2A-EXTE model was tested in the NASA Langley 14- by 22-Foot Subsonic Tunnel (14x22 Tunnel) from September 12, 2012 until January 28, 2013 and was designated as test T598. This document contains the following main sections: Section 1 - Introduction, Section 2 - Main Personnel, Section 3 - Test Equipment, Section 4 - Data Acquisition Systems, Section 5 - Instrumentation and Calibration, Section 6 - Test Matrix, Section 7 - Data Processing, and Section 8 - Summary. Due to the amount of material to be documented, this HWB test documentation report does not cover analysis of acquired data, which is to be presented separately by the principal investigators. Also, no attempt was made to include preliminary risk reduction tests (such as Broadband Engine Noise Simulator and Compact Jet Engine Simulator characterization tests, shielding measurement technique studies, and speaker calibration method studies), which were performed in support of this HWB test. Separate reports containing these preliminary tests are referenced where applicable.

Heath, Stephanie L.↗

DOE OSTI2018

AUTOMATIC LIMIT SURFACE SEARCH FOR PWR TRANSIENTS BY RELAP5-3D/RAVEN CODES

Deterministic safety analysis of nuclear power plants can be improved by applying modern algorithms and codes exploiting machine learning and parallel computing. In this paper we show the application of some of these advanced methodologies for the determination of limit surfaces. A limit surface is an n-dimensional surface describing the plant status, e.g. identifying, as function of selected plant parameters, the boundaries between failed and safe conditions for the core fuel. Using the RELAP5-3D system thermal-hydraulic code coupled to the RAVEN statistical-analysis code, we show how to apply machine-learning algorithms (near-neighbor, support-vector machine, etc.) for determining relevant limit surfaces for PWR station-blackout and loss of offsite power. As a first step, a set of training simulations is run to sample the nuclear power plant responses. Those calculations are used to train a Reduced-Order-Model (ROM), which RAVEN then uses for determining an approximated limit surface. Verification of the guessed limit surface is then automatically performed and calculation is terminated depending by the chosen convergence criteria. The combination of High Performance Computing (HPC) and ROMs allow a remarkable reduction of the time required for the surface determination. RAVEN performs also the automatic data collection of the output of the numerous runs, thus easing the workload of the safety analyst. Finally, using RAVEN “Ensamble Forward” sampling technique, we show how to inform the limit surface calculations with some of the code and input uncertainties.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗

NASA NTRS1985

Digital fringe reduction techniques applied to the measurement of three-dimensional transonic flow fields

A system for digital inteferogram evaluation based on an image-processing system connected to a host computer has been implemented. The system supports one- and two-dimensional interferogram evaluations. Interferograms are digitized, enhanced, and segmented. The fringe coordinates are extracted, and the fringes are represented as polygon data structures. Fringe numbering and fringe interpolation modules are implemented. The system supports editing and interactive features as well as graphic visualization. An application of the system to the evaluation of double exposure interferograms from the transonic flow field around a helicopter blade and the reconstruction of the 3-D flow field are given.

Becker, F.↗

DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗

DOE OSTI2018

Evidence for an Electronic State at the Interface between the SnO 2 Core and the TiO 2 Shell in Mesoporous SnO 2 /TiO 2 Thin Films

A comparative spectroelectrochemical study of micron thick mesoporous thin films composed of TiO 2 and SnO 2 nanocrystallites was undertaken to identify the location and identity of electronic state(s) in core/shell SnO 2 /TiO 2 films that have emerging applications in energy science. The mesoporous TiO 2 and SnO 2 films were synthesized by sol–gel techniques, and the core/shell material was created by atomic layer deposition of a TiO 2 shell on a SnO 2 core. Electrochemical reduction of these materials in aqueous electrolytes resulted in a blue shift of the fundamental VB → CB absorbance as well as a broad absorbance across the visible region. For SnO 2 , three unique spectra were required to model the potential dependent data while only one unique spectrum was needed to model the corresponding data for SnO 2 /TiO 2 and TiO 2 . Significantly, standard addition of the spectra measured for SnO 2 and TiO 2 did not model that for SnO 2 /TiO 2 . In other words, upon reduction of SnO 2 /TiO 2 there was no spectroscopic evidence for electrons residing in the SnO 2 core or in the TiO 2 shell. Instead, the appearance of a single potential-independent absorption spectrum was observed and attributed to electrons present within an interfacial region between the core and the shell. The charge extraction technique was used to determine that the density of SnO 2 /TiO 2 states was more similar to that of TiO 2 than SnO 2 . The potential onset followed the trend SnO 2 /TiO 2 > SnO 2 > TiO 2 , with TiO 2 closest to the vacuum level and SnO 2 /TiO 2 the furthest away. The implications of these results for solar energy conversion are discussed.

14 SOLAR ENERGY↗

DOE OSTI2018

Active Subspaces for Wind Plant Surrogate Modeling

Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces technique to UQ of plant power output with respect to uncertainty in turbine axial induction factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogate-based UQ approaches tractable for utility-scale wind plants.

17 WIND ENERGY↗

NASA NTRS1998

A New Approach to Overcoming Spatial Aliasing in Structural Damage Detection

Aircraft, reusable launch vehicles,unmanned aircraft, and other advanced structures are being built using lightweight composite materials/metals with design safety factors as low as 1.25. These advanced structures operate in uncertain and severe environments and are susceptible to damage such as delamination, fiber/matrix damage, hydrothermal strain in composite materials, and fatigue and cracking in metals. To ensure human safety and load-bearing integrity these structures must be inspected to detect and locate often invisible damage and faults before they become catastrophic. Conventional methods of non-destructive evaluation sometimes miss significant damage and are time consuming and expensive to perform. In contrast, vibrometry or vibration signature techniques are a global method of structural integrity monitoring that potentially can efficiently detect damage on large structures, including damage that is away from sensor locations, and in the interior of structures. However, a barrier problem in damage detection using vibration measurements is the need to measure the vibration response at a large number of points on the structure. Typically, model reduction or expansion procedures such as Guyan reduction or dynamic expansion are attempted to overcome the problem of insufficient measurements or spatial aliasing. These approaches depend on using information from the healthy model, and thus put error into the reduction used to represent the damaged structure. In this paper, a Frequency Response Function technique is used to detect damage to a fixed-free beam. The technique uses measured frequency response functions from the healthy structure as reference data, and then monitors vibration measurements during the life of the structure to detect damage. In an analytical simulation using a finite-element model of a beam, damage was located using only sparse measurements because the technique uses both rotation and translation measurements from the damaged structure.

Schulz, Mark J.↗

NASA NTRS2000

Recent Cycle Time Reduction at Langley Research Center

The NASA Langley Research Center (LaRC) has been engaged in an effort to reduce wind tunnel test cycle time in support of Agency goals and to satisfy the wind tunnel testing needs of the commercial and military aerospace communities. LaRC has established the Wind Tunnel Enterprise (WTE), with goals of reducing wind tunnel test cycle time by an order of magnitude by 2002, and by two orders of magnitude by 2010. The WTE also plans to meet customer expectations for schedule integrity, as well as data accuracy and quality assurance. The WTE has made progress towards these goals over the last year with a focused effort on technological developments balanced by attention to process improvements. This paper presents a summary of several of the WTE activities over the last year that are related to test cycle time reductions at the Center. Reducing wind tunnel test cycle time, defined here as the time between the freezing of loft lines and delivery of test data, requires that the relationship between high productivity and data quality assurance be considered. The efforts have focused on all of the drivers for test cycle time reduction, including process centered improvements, facility upgrades, technological improvements to enhance facility readiness and productivity, as well as advanced measurement techniques. The application of internet tools and computer modeling of facilities to allow a virtual presence of the customer team is also presented.

Kegelman, Jerome T.↗

DOE OSTI2025

Systematic characterization of unknown compounds via dimensionality reduction of time series

Analysis of ambient aerosols provides valuable insight into particle sources and formation chemistry. However, due to the complexity of atmospheric data and the dynamic nature of aerosol composition, a substantial fraction of data often become discarded by conventional analysis methods. Furthermore, a large fraction of chemical species within those data are unidentifiable due to a lack of matching spectral information, resulting in suboptimal characterization of chemical composition. Previous work has demonstrated techniques for cataloging analytes in a chromatographic dataset by deconvolution of mass spectra, but integration of these analytes throughout a large dataset remains time consuming. Here, we present a method to automatically identify an ion for quantitation for single-ion chromatogram based peak fitting and integration, enabling comprehensive integration of analytes with minimal user interaction. The resulting time series are clustered with a machine-learning based dimensionality reduction technique to systematically investigate the underlying characteristics of the categorized analytes and gain new insights into the chemical composition and physicochemical properties of the unidentifiable analytes. We apply these methods to existing atmospheric datasets collected in Manacapuru, Brazil during the GoAmazon2014/5 campaign to identify new analytes and interpret their variability and transformations in the atmosphere. The analysis results generate 408 time series from cataloged analytes of interest, and the clustering of those time series with spherical k-means results in 8 distinct clusters. We find the analytes form clusters based on their distinct physicochemical properties, demonstrating the method’s ability to systematically identify and selectively filter contaminants and instrumental analytes and characterize the unidentifiable analytes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1973

Digital processing of radiographic images

Some techniques are presented and the software documentation for the digital enhancement of radiographs. Both image handling and image processing operations are considered. The image handling operations dealt with are: (1) conversion of format of data from packed to unpacked and vice versa; (2) automatic extraction of image data arrays; (3) transposition and 90 deg rotations of large data arrays; (4) translation of data arrays for registration; and (5) reduction of the dimensions of data arrays by integral factors. Both the frequency and the spatial domain approaches are presented for the design and implementation of the image processing operation. It is shown that spatial domain recursive implementation of filters is much faster than nonrecursive implementations using fast fourier transforms (FFT) for the cases of interest in this work. The recursive implementation of a class of matched filters for enhancing image signal to noise ratio is described. Test patterns are used to illustrate the filtering operations. The application of the techniques to radiographic images of metallic structures is demonstrated through several examples.

Bond, A. D.↗

DOE OSTI2022

A study of in situ reduction of MoO 3 to MoO 2 by X-ray Photoelectron Spectroscopy

Results from X-ray Photoelectron Spectroscopy (XPS) of molybdenum oxide samples are presented to elucidate how Mo (VI) oxide evolves to Mo (IV) oxide upon heating of a MoO 3 sample. XPS data analysis techniques based on manipulation of spectra treated as vectors are shown and allow insights into intermediate phases of Mo oxide which suggest how the original oxide changes under the influence of heat but also supports an interpretation of as-received tetravalent Mo powders as multivalent materials. In particular, several new spectral components were observed and assigned to the Magnéli phase as well as MoO 3 domains that have been modified by the presence of X-rays or temperature. Finally, these assignments comprise a new method of data processing where sample modification is used to inform XPS data interpretation and differ from previous work where the linear relationship of the binding energy of molybdenum oxides has been used.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1969

Technique for predicting the thermal expansion coefficients of cryogenic metallic alloys

Series of measurements on the thermal expansion coefficients of several aerospace alloys and standard materials establish relationships between related alloys that would aid in predicting their thermal expansion reliability. Thermal expansion data are also necessary for the reduction of electrical resistivity measurements of those same materials.

Clark, A. F.↗

NASA NTRS1984

Development of flight testing techniques

A list of students involved in research on flight analysis and development is given along with abstracts of their work. The following is a listing of the titles of each work: Longitudinal stability and control derivatives obtained from flight data of a PA-30 aircraft; Aerodynamic drag reduction tests on a box shaped vehicle; A microprocessor based anti-aliasing filter for a PCM system; Flutter prediction of a wing with active aileron control; Comparison of theoretical and flight measured local flow aerodynamics for a low aspect ratio fin; In flight thrust determination on a real time basis; A comparison of computer generated lift and drag polars for a Wortmann airfoil to flight and wind tunnel results; and Deep stall flight testing of the NASA SGS 1-36.

Sandlin, D. R.↗