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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Discovering the desirable alleles contributing to the lignocellulosic biomass traits in saccharum germplasm collections for energy cane improvement

Phenotyping Methods: The accessions (which includes 21 taxa and 1,177 accessions) in the World Collection of Sugarcane and Related Grasses (WCSRG) was evaluated for the following traits: arenchyma, internode length and diameter, pubescence, pith, Brix, stalk number and fiber. A core of 300 accessions that included each species in the World Collection was selected by using the Maximization Strategy in MStrat software. Results: The core had a higher diversity rating than random selections of 300 accessions. The Shannon–Weaver Diversity Index scores of the core and whole collection were similar indicating that the majority of the diversity was captured by the core collection. The ranges and medians between the core and WCSRG were similar; only two of the trait medians were not significant at P = 0.05 using the non-parametric Wilcoxon method and the coincidence rate (CR % = 96.2) was high (>80) indicating that extreme values were retained. Thus, the phenotypic diversity of these traits in the WCSRG was well represented by the core collection. Associations Methods: Genotypic and phenotypic data were collected for 1002 accessions of the WCSRG including 209 SSR markers. Association analysis was performed using both General Linear (GLM) and Maximum Likelihood (MLM) models. Different core collections with 300 accessions each were selected based on genotypic, phenotypic and combined data based on the Maximization Strategy in MStrat software. Results: A major portion of the genotyping involving SNPs is being conducted by Dr. Jianping Wang of the University of Florida under the DOE award DE-FG 02-11ER 65214 and the genotypic and phenotypic associations will be reported separately next year. In the current, study forty one and seventeen markers were found to be associated with traits using the GLM and MLM methods respectively including associations with arenchyma, internode length and diameter, pubescence, pith, and Sugar Cane Yellow Leaf Virus. The data indicates that each of the cores and the World Collection are similar to each other genotypically and phenotypically, but the core that was selected using only genotypic data was significantly different phenotypically. This indicates that there is not enough association between the genotypic and phenotypic diversity as to select using only genotypic diversity and get the full phenotypic diversity. Core Collection: Creation and Phenotyping Methods: To evaluate this germplasm for breeding purposes, a representative diversity panel selected from the WCSRG of approximately 300 accessions was planted at Canal Point, FL in three replications. These accessions were measured for stalk height and stalk number multiple times throughout the growing season and Brix and fresh biomass during harvest in 2013 and, stalk height, stalk number, stalk diameter, internode length, Brix and fresh and dry biomass was determined in the ratoon crop harvest in 2014. Results: In correlations of multiple measurements, there were higher correlations for early measurements of stalk number and stalk height with harvest traits like Brix and fresh weight. Hybrids had higher fresh mass and Brix while Saccharum spontaneum had higher stalk number and dry mass. The heritability of hybrid mass traits was lower in the ratoon crop. According to the principal component analysis, the diversity panel was divided into two groups. One group had accessions with high stalk number and high dry biomass like S. spontaneum and the other groups contained accessions with higher Brix and fresh biomass like S. officinarum. Mass traits correlated with each other as expected but hybrids had lower correlations between fresh and dry mass. Stalk number and the mass traits correlated with each other except in S. spontaneum and hybrids in the first ratoon. There were 110 accessions not significantly different in Brix from the commercial sugarcane checks including 10 S. spontaneum accessions. There were 27 dry and 6 fresh mass accessions significantly higher than the commercial sugarcane checks. Core Collection: Fiber analysis Methods: A biomass sample was taken from each accession then shredded and dried. Fiber analysis was then performed on each sample. The acetyl groups, acid insoluble lignin, acid soluble lignin, arabinan, glucan, holocellulose, total lignin, structural ash, and xylan were quantified on a % fiber basis and nonstructural ash on a % total basis. Results: There were significant, but not large differences between species for holocellulose, lignin, acetyl, acid soluble lignin, nonstructural ash, and glucan. For each trait, S. spontaneum had significantly more holocellulose, glucan, lignin, and nonstructural ash and less acetyl and acid soluble lignin than the other species. In all populations, glucan and was positively correlated with holocellulose were positively correlated and glucan and and holocellulose were negatively correlated with lignin. In hybrids, internode length correlated positively with holocellulose and nonstructural ash and negatively with lignin. The heritability estimates for each of the fiber component traits is low indicating that environment is an important factor in fiber composition. Principal component analysis indicated that a large amount of diversity exists within each of the species.

09 BIOMASS FUELS↗
DOE OSTI2018

ToF-SIMS characterization of glyoxal surface oxidation products by hydrogen peroxide: A comparison between dry and liquid samples

As one of the simplest volatile organic compounds (VOCs), glyoxal and its oxidation products were considered to be important precursors to aqueous secondary organic aerosol (aqSOA) formation. Herein, we analyzed products from glyoxal oxidation by hydrogen peroxide in dry and liquid samples using time-of-flight secondary ion mass spectrometry (ToF-SIMS). ToF-SIMS spectra and spectral principal component analysis (PCA) were used to investigate surface oxidation products. Dry samples were prepared on clean silicon wafers. Liquid samples consisting of glyoxal and hydrogen peroxide (H2O2) were introduced to a vacuum compatible microfluidic reactor prior to UV illumination or dark aging followed by in situ liquid SIMS analysis. A number of reaction products were observed in both dry and liquid samples; different oligomers and carboxylic acids could be formed depending on reaction conditions. In addition, hydrolyzed products were observed in the liquid samples, but not in the dry samples. Although dry samples reveal some products of the aqueous process, they are not fully representative as results from those of the aqueous samples. Our findings suggest that the ability to characterize the liquid surface reaction products provides more realistic information of the reaction products associated with aqSOA formation in the atmosphere. Meanwhile, the high mass resolution spectra from the dry sample SIMS measurement are helpful to identify oxidation products in the liquid samples.

, Aqueous Secondary Organic Aerosol, Sample Prepar↗
DOE OSTI2016

Planck 2015 results: XIV. Dark energy and modified gravity

For this research, we study the implications of Planck data for models of dark energy (DE) and modified gravity (MG) beyond the standard cosmological constant scenario. We start with cases where the DE only directly affects the background evolution, considering Taylor expansions of the equation of state w(a), as well as principal component analysis and parameterizations related to the potential of a minimally coupled DE scalar field. When estimating the density of DE at early times, we significantly improve present constraints and find that it has to be below ~2% (at 95% confidence) of the critical density, even when forced to play a role for z < 50 only. We then move to general parameterizations of the DE or MG perturbations that encompass both effective field theories and the phenomenology of gravitational potentials in MG models. Lastly, we test a range of specific models, such as k-essence, f(R) theories, and coupled DE. In addition to the latest Planck data, for our main analyses, we use background constraints from baryonic acoustic oscillations, type-Ia supernovae, and local measurements of the Hubble constant. We further show the impact of measurements of the cosmological perturbations, such as redshift-space distortions and weak gravitational lensing. These additional probes are important tools for testing MG models and for breaking degeneracies that are still present in the combination of Planck and background data sets. All results that include only background parameterizations (expansion of the equation of state, early DE, general potentials in minimally-coupled scalar fields or principal component analysis) are in agreement with ΛCDM. Finally, when testing models that also change perturbations (even when the background is fixed to ΛCDM), some tensions appear in a few scenarios: the maximum one found is ~2σ for Planck TT+lowP when parameterizing observables related to the gravitational potentials with a chosen time dependence; the tension increases to, at most, 3σ when external data sets are included. It however disappears when including CMB lensing.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

PROPERTIES OF CARBON–OXYGEN WHITE DWARFS FROM MONTE CARLO STELLAR MODELS

We investigate properties of carbon–oxygen white dwarfs with respect to the composite uncertainties in the reaction rates using the stellar evolution toolkit, Modules for Experiments in Stellar Astrophysics (MESA) and the probability density functions in the reaction rate library STARLIB. These are the first Monte Carlo stellar evolution studies that use complete stellar models. Focusing on 3 M{sub ⊙} models evolved from the pre main-sequence to the first thermal pulse, we survey the remnant core mass, composition, and structure properties as a function of 26 STARLIB reaction rates covering hydrogen and helium burning using a Principal Component Analysis and Spearman Rank-Order Correlation. Relative to the arithmetic mean value, we find the width of the 95% confidence interval to be ΔM{sub 1TP} ≈ 0.019 M{sub ⊙} for the core mass at the first thermal pulse, Δt{sub 1TP} ≈ 12.50 Myr for the age, Δlog(T{sub c}/K) ≈ 0.013 for the central temperature, Δlog(ρ{sub c}/g cm{sup −3}) ≈ 0.060 for the central density, ΔY{sub e,c} ≈ 2.6 × 10{sup −5} for the central electron fraction, ΔX{sub c}({sup 22}Ne) ≈ 5.8 × 10{sup −4}, ΔX{sub c}({sup 12}C) ≈ 0.392, and ΔX{sub c}({sup 16}O) ≈ 0.392. Uncertainties in the experimental {sup 12}C(α,γ){sup 16}O, triple-α, and {sup 14}N(p,γ){sup 15}O reaction rates dominate these variations. We also consider a grid of 1–6 M{sub ⊙} models evolved from the pre main-sequence to the final white dwarf to probe the sensitivity of the initial–final mass relation to experimental uncertainties in the hydrogen and helium reaction rates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

First study of reionization in tilted flat and untilted non-flat dynamical dark energy inflation models

We examine the effects of dark energy dynamics and spatial curvature on cosmic reionization by studying reionization in tilted spatially flat and untilted non-flat XCDM and $\phi$CDM dynamical dark energy inflation models that best fit the Planck 2015 cosmic microwave background (CMB) anisotropy and a large compilation of non-CMB data. We carry out a detailed statistical study, based on a principal component analysis and a Markov chain Monte Carlo analysis of a compilation of lower-redshift reionization data to estimate the uncertainties in the cosmological model reionization histories. We find that, irrespective of the nature of dark energy, there are significant differences between the reionization histories of the spatially flat and non-flat models. Although both the flat and non-flat models can accurately match the low-redshift ( z ≲ 6) reionization observations, there is a clear discrepancy between high-redshift ( z > 7) Lyman-α emitter data and the predictions from non-flat models. This is solely due to the fact that the non-flat models have a significantly larger electron scattering optical depth, τ el , compared to the flat models, which requires an extended and much earlier reionization scenario supported by more high-redshift ionizing sources in the non-flat models. Non-flat models also require strong redshift evolution in the photon escape fraction, which can become unrealistically high (≳1) at some redshifts. However, τ el is about 0.9-σ lower in the tilted flat ΛCDM model when the new Planck 2018 data are used and this reduction will partially alleviate the tension between the non-flat model predictions and the data.

Astronomy & Astrophysics↗
DOE OSTI2015

Background model systematics for the Fermi GeV excess

The possible gamma-ray excess in the inner Galaxy and the Galactic center (GC) suggested by Fermi-LAT observations has triggered a large number of studies. It has been interpreted as a variety of different phenomena such as a signal from WIMP dark matter annihilation, gamma-ray emission from a population of millisecond pulsars, or emission from cosmic rays injected in a sequence of burst-like events or continuously at the GC. We present the first comprehensive study of model systematics coming from the Galactic diffuse emission in the inner part of our Galaxy and their impact on the inferred properties of the excess emission at Galactic latitudes 2° < |b| < 20° and 300 MeV to 500 GeV. We study both theoretical and empirical model systematics, which we deduce from a large range of Galactic diffuse emission models and a principal component analysis of residuals in numerous test regions along the Galactic plane. We show that the hypothesis of an extended spherical excess emission with a uniform energy spectrum is compatible with the Fermi-LAT data in our region of interest at 95% CL. Assuming that this excess is the extended counterpart of the one seen in the inner few degrees of the Galaxy, we derive a lower limit of 10.0° (95% CL) on its extension away from the GC. We show that, in light of the large correlated uncertainties that affect the subtraction of the Galactic diffuse emission in the relevant regions, the energy spectrum of the excess is equally compatible with both a simple broken power-law of break energy E{sub break} = 2.1 ± 0.2 GeV, and with spectra predicted by the self-annihilation of dark matter, implying in the case of b-bar b final states a dark matter mass of m{sub χ}=49{sup +6.4}{sub −5.4} GeV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Machine Learning-Based Investigation of the Associations between Residential Power Consumption and Weather Conditions

Machine learning feature selection analyses were conducted to understand the impact of various weather attributes on power electricity demand and its components in the northwest United States. Load profiles data were obtained from the Northwest Energy Efficiency Alliance (NEEA) and processed to yield hourly load profiles with the same temporal resolution as the weather conditions data obtained from the National Oceanic and Atmospheric Administration (NOAA) for representative weather stations. The data were divided into five seasons; for each season, for each load component, the influences of weather factors were evaluated and quantified using cross-correlation, principal component analysis, and mutual information evaluation; then predictive models were developed based on the ranked/screened factors using the regression tree (RT) approach. After multi-fold cross-validation, the optimal complexity/depth of the regress tree models is found to vary for different load components, and the prediction accuracy using RT and weather data can be higher than 80% for heating and refrigeration, or lower than 30% for vent and cooking. The study provides guidance on improvement of load profile generation, or even serves as a standalone predictive tool for approximating load profiles in balance authorities or climate zones where load profile data is absent.

load composite, load profiling, NEEA, mutual infor↗
DOE OSTI2014

A limited-angle intrafraction verification (LIVE) system for radiation therapy

Purpose: Currently, no 3D or 4D volumetric x-ray imaging techniques are available for intrafraction verification of target position during actual treatment delivery or in-between treatment beams, which is critical for stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments. This study aims to develop a limited-angle intrafraction verification (LIVE) system to use prior information, deformation models, and limited angle kV-MV projections to verify target position intrafractionally. Methods: The LIVE system acquires limited-angle kV projections simultaneously during arc treatment delivery or in-between static 3D/IMRT treatment beams as the gantry moves from one beam to the next. Orthogonal limited-angle MV projections are acquired from the beam's eye view (BEV) exit fluence of arc treatment beam or in-between static beams to provide additional anatomical information. MV projections are converted to kV projections using a linear conversion function. Patient prior planning CT at one phase is used as the prior information, and the on-board patient volume is considered as a deformation of the prior images. The deformation field is solved using the data fidelity constraint, a breathing motion model extracted from the planning 4D-CT based on principal component analysis (PCA) and a free-form deformation (FD) model. LIVE was evaluated using a 4D digital extended cardiac torso phantom (XCAT) and a CIRS 008A dynamic thoracic phantom. In the XCAT study, patient breathing pattern and tumor size changes were simulated from CT to treatment position. In the CIRS phantom study, the artificial target in the lung region experienced both size change and position shift from CT to treatment position. Varian Truebeam research mode was used to acquire kV and MV projections simultaneously during the delivery of a dynamic conformal arc plan. The reconstruction accuracy was evaluated by calculating the 3D volume percentage difference (VPD) and the center of mass (COM) difference of the tumor in the true on-board images and reconstructed images. Results: In both simulation and phantom studies, LIVE achieved substantially better reconstruction accuracy than reconstruction using PCA or FD deformation model alone. In the XCAT study, the average VPD and COM differences among different patient scenarios for LIVE system using orthogonal 30° scan angles were 4.3% and 0.3 mm when using kV+BEV MV. Reducing scan angle to 15° increased the average VPD and COM differences to 15.1% and 1.7 mm. In the CIRS phantom study, the VPD and COM differences for the LIVE system using orthogonal 30° scan angles were 6.4% and 1.4 mm. Reducing scan angle to 15° increased the VPD and COM differences to 51.9% and 3.8 mm. Conclusions: The LIVE system has the potential to substantially improve intrafraction target localization accuracy by providing volumetric verification of tumor position simultaneously during arc treatment delivery or in-between static treatment beams. With this improvement, LIVE opens up a new avenue for margin reduction and dose escalation in both fractionated treatments and SRS and SBRT treatments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: An intertechnique and interinstitutional study

Purpose: To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution. Methods: Forty-four Tomotherapy intensity modulate radiotherapy plans of HN cases (Tomo-IMRT) from Institution A were included in the study. A different patient group of 53 HN fixed gantry IMRT (FG-IMRT) plans was selected from Institution B. The analyzed OARs included the parotid, larynx, spinal cord, brainstem, and submandibular gland. Two major groups of anatomical features were considered: the volumetric information and the spatial information. The volume information includes the volume of target, OAR, and overlapped volume between target and OAR. The spatial information of OARs relative to PTVs was represented by the distance-to-target histogram (DTH). Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. Two regression models, one for Tomotherapy plan and one for IMRT plan, were built independently. The accuracy of intratreatment-modality model prediction was validated by a leave one out cross-validation method. The intertechnique and interinstitution validations were performed by using the FG-IMRT model to predict the OAR dosimetry of Tomo-IMRT plans. The dosimetry of OARs, under the same and different institutional preferences, was analyzed to examine the correlation between the model prediction and planning protocol. Results: Significant patient anatomical factors contributing to OAR dose sparing in HN Tomotherapy plans have been analyzed and identified. For all the OARs, the discrepancies of dose indices between the model predicted values and the actual plan values were within 2.1%. Similar results were obtained from the modeling of FG-IMRT plans. The parotid gland was spared in a comparable fashion during the treatment planning of two institutions. The model based on FG-IMRT plans was found to predict the median dose of the parotid of Tomotherapy plans quite well, with a mean error of 2.6%. Predictions from the FG-IMRT model suggested the median dose of the larynx, median dose of the brainstem and D2 of the brainstem could be reduced by 10.5%, 12.8%, and 20.4%, respectively, in the Tomo-IMRT plans. This was found to be correlated to the institutional differences in OAR constraint settings. Re-planning of six Tomotherapy patients confirmed the potential of optimization improvement predicted by the FG-IMRT model was correct. Conclusions: The authors established a mathematical model to correlate the anatomical features and dosimetric indexes of OARs of HN patients in Tomotherapy plans. The model can be used for the setup of patient-specific OAR dose sparing goals and quality control of planning results. The institutional clinical experience was incorporated into the model which allows the model from one institution to generate a reference plan for another institution, or another IMRT technique.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Structuring Nutrient Yields throughout Mississippi/Atchafalaya River Basin Using Machine Learning Approaches

To minimize the eutrophication pressure along the Gulf of Mexico or reduce the size of the hypoxic zone in the Gulf of Mexico, it is important to understand the underlying temporal and spatial variations and correlations in excess nutrient loads, which are strongly associated with the formation of hypoxia. This study’s objective was to reveal and visualize structures in high-dimensional datasets of nutrient yield distributions throughout the Mississippi/Atchafalaya River Basin (MARB). For this purpose, the annual mean nutrient concentrations were collected from thirty-three US Geological Survey (USGS) water stations scattered in the upper and lower MARB from 1996 to 2020. Eight surface water quality indicators were selected to make comparisons among water stations along the MARB over the past two decades. Principal component analysis (PCA) was used to comprehensively evaluate the nutrient yields across thirty-three USGS monitoring stations and identify the major contributing nutrient loads. The results showed that all samples could be analyzed using two main components, which accounted for 81.6% of the total variance. The PCA results showed that yields of orthophosphate (OP), silica (SI), nitrate–nitrites (NO 3 -NO 2 ), and total suspended sediment (TSS) are major contributors to nutrient yields. It also showed that land-planted crops, density of population, domestic and industrial discharges, and precipitation are fundamental causes of excess nutrient loads in MARB. These factors are of great significance for the excess nutrient load management and pollution control of the Mississippi River. It was found that the average nutrient yields were stable within the sub-MARB area, but the large nitrogen yields in the upper MARB and the large phosphorus yields in the lower MARB were of great concern. t-distributed stochastic neighbor embedding (t-SNE) revealed interesting nonlinear and local structures in nutrient yield distributions. Clustering analysis (CA) showed the detailed development of similarities in the nutrient yield distribution. Moreover, PCA, t-SNE, and CA showed consistent clustering results. This study demonstrated that the integration of dimension reduction techniques, PCA, and t-SNE with CA techniques in machine learning are effective tools for the visualization of the structures of the correlations in high-dimensional datasets of nutrient yields and provide a comprehensive understanding of the correlations in the distributions of nutrient loads across the MARB.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Fixed Eigenvector Analysis of Thermographic NDE Data

Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. This paper will discuss an alternative method of analysis that has been developed where a predetermined set of eigenvectors is used to process the thermal data from both reinforced carbon-carbon (RCC) and graphiteepoxy honeycomb materials. These eigenvectors can be generated either from an analytic model of the thermal response of the material system under examination, or from a large set of experimental data. This paper provides the details of the analytic model, an overview of the PCA process, as well as a quantitative signal-to-noise comparison of the results of performing both conventional PCA and fixed eigenvector analysis on thermographic data from two specimens, one Reinforced Carbon-Carbon with flat bottom holes and the second a sandwich construction with graphite-epoxy face sheets and aluminum honeycomb core.

Cramer, K. Elliott↗
NASA NTRS2013

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: 1. an EOF Approach to the Spatial-Temporal Variability of Aerosol Optical Depth Using Multiple Remote Sensing Data Sets

Many remote sensing techniques and passive sensors have been developed to measure global aerosol properties. While instantaneous comparisons between pixel-level data often reveal quantitative differences, here we use Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis, to demonstrate that satellite-derived aerosol optical depth (AOD) data sets exhibit essentially the same spatial and temporal variability and are thus suitable for large-scale studies. Analysis results show that the first four EOF modes of AOD account for the bulk of the variance and agree well across the four data sets used in this study (i.e., Aqua MODIS, Terra MODIS, MISR, and SeaWiFS). Only SeaWiFS data over land have slightly different EOF patterns. Globally, the first two EOF modes show annual cycles and are mainly related to Sahara dust in the northern hemisphere and biomass burning in the southern hemisphere, respectively. After removing the mean seasonal cycle from the data, major aerosol sources, including biomass burning in South America and dust in West Africa, are revealed in the dominant modes due to the different interannual variability of aerosol emissions. The enhancement of biomass burning associated with El Niño over Indonesia and central South America is also captured with the EOF technique.

variability↗
DOE OSTI2016

Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

load forecast↗
DOE OSTI2019

Anomaly detection in scientific data using joint statistical moments

We propose an anomaly detection method for multi-variate scientific data based on analysis of high-order joint moments. Using kurtosis as a reliable measure of outliers, we suggest that principal kurtosis vectors, by analogy to principal component analysis (PCA) vectors, signify the principal directions along which outliers appear. The inception of an anomaly, then, manifests as a change in the principal values and vectors of kurtosis. Obtaining the principal kurtosis vectors requires decomposing a fourth order joint cumulant tensor for which we use a simple, computationally less expensive approach that involves performing a singular value decomposition (SVD) over the matricized tensor. We demonstrate the efficacy of this approach on synthetic data, and develop an algorithm to identify the occurrence of a spatial and/or temporal anomalous event in scientific phenomena. The algorithm decomposes the data into several spatial sub-domains and time steps to identify regions with such events. Feature moment metrics, based on the alignments of the principal kurtosis vectors, are computed at each sub-domain and time step for all features to quantify their relative importance towards the overall kurtosis in the data. Accordingly, spatial and temporal anomaly metrics for each sub-domain are proposed using the Hellinger distance of the feature moment metric distribution from a suitable nominal distribution. Finally, we apply the algorithm to two turbulent auto-ignition combustion cases and demonstrate that the anomaly metrics reliably capture the occurrence of auto-ignition in relevant spatial sub-domains at the right time steps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Hyperspectral imaging of minerals in the longwave infrared: the use of laboratory directional-hemispherical reference measurements for field exploration data

Hyperspectral Imaging (HSI) continues to grow as a method for remote detection of vegetation, materials, minerals, and pure chemicals. We have used a longwave infrared (7.7 - 11.8 µm) imaging spectrometer in a static outdoor experiment to collect HSI data from 24 minerals and background materials to determine the efficacy with which HSI can remotely detect and distinguish both pure minerals and mineral mixtures at three solar angles (25°, 35°, and 45° relative to ground) as well as varying backgrounds and other sample effects. Measurements were obtained separately for the minerals and materials mounted directly both on a bare plywood board and a plywood board coated with aluminum foil: 19 powders (3 mixtures and 16 pure mineral powders) held in polyethylene bottle lids as well as 5 samples in rock form were taped directly to the boards. The primary goal of the experiment was to demonstrate that a longwave infrared (LWIR) library of solids and minerals collected as directional-hemispherical reflectance spectra in the laboratory can be used directly for HSI field identification. Prior to the experiment, all 24 mineral/inorganic samples were measured in the laboratory using a Fourier transform infrared spectrometer (FTIR) equipped with a gold-coated IR integrating sphere; the spectra were assimilated as part of a larger reference library of 21 pure minerals, 3 mixtures, and the polyethylene lid. Principal components analysis (PCA) with mean-centering was used in an exploratory analysis of the HSI images and showed that for the aluminum coated board, the first principal component captured the difference between signal that resembled a blackbody and the highly reflective aluminum background. In contrast, principal components PC 2, 3, and 4 were able to discriminate the materials including phosphates, sulfates, silicates, carbonates, and the mixtures. Results from generalized least squares target detection clearly showed that laboratory reference spectra of minerals could be utilized as targets with high fidelity for field detection, both for pure and mixed samples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

State space parameterization of explosive eigenvalues during autoignition

Explosive modes such as ignition and extinction are characterized by an eigenvalue of the chemical Jacobian matrix with positive real part, representing the transient instability of chain-branching chemistry and thermal feedback. Formation and eigen-decomposition of the Jacobian matrix are expensive operations whose cost increases cubically with chemical mechanism size. As an alternative to directly computing the eigenvalues of the Jacobian, we explore principal component analysis (PCA) along with nonlinear regression as a methodology to parameterize the eigenvalues by state variables (or linear combinations thereof). We evaluate this modeling strategy using homogeneous autoignition data on two different applications: pseudotransient continuation (Ψtc)-based ODE solvers and chemical explosive mode analysis (CEMA). Results indicate that the PCA-based parameterization of the eigenvalues appears feasible for Ψtc solvers in autoignition calculations over a range of temperatures and pressures. Our results also show that eigenvalue models are capable of tracking sharp discontinuities (such as ignition or extinction) in the eigenvalue for computational flame diagnostics such as CEMA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

System and method for automated extraction of high resolution structural dynamics from video

A method for extracting vibrational modes of a structure includes: receiving a plurality of video frames, each of the video frames including a plurality of pixels; decomposing each of the video frames on a plurality of spatial scales in accordance with complex steerable pyramid filters to obtain a filter response for each of the spatial scales; computing a plurality of local phases of the pixels of each frame; removing a temporal mean from each frame to obtain a plurality of factored vibration motion functions; performing principal component analysis on the factored vibration motion functions to obtain principal components; blind source separating the principal components to compute a plurality of modal coordinates; computing frequency and damping ratios in accordance with the modal coordinates; and outputting the computed frequency and damping ratios.

42 ENGINEERING↗