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NASA NTRS1993

Fast structural design and analysis via hybrid domain decomposition on massively parallel processors

A hybrid domain decomposition framework for static, transient and eigen finite element analyses of structural mechanics problems is presented. Its basic ingredients include physical substructuring and /or automatic mesh partitioning, mapping algorithms, 'gluing' approximations for fast design modifications and evaluations, and fast direct and preconditioned iterative solvers for local and interface subproblems. The overall methodology is illustrated with the structural design of a solar viewing payload that is scheduled to fly in March 1993. This payload has been entirely designed and validated by a group of undergraduate students at the University of Colorado using the proposed hybrid domain decomposition approach on a massively parallel processor. Performance results are reported on the CRAY Y-MP/8 and the iPSC-860/64 Touchstone systems, which represent both extreme parallel architectures. The hybrid domain decomposition methodology is shown to outperform leading solution algorithms and to exhibit an excellent parallel scalability.

Farhat, Charbel↗

NASA NTRS1997

Finding corner point correspondence from wavelet decomposition of image data

A time efficient algorithm for image registration between two images that differ in translation is discussed. The algorithm is based on coarse-fine strategy using wavelet decomposition of both the images. The wavelet decomposition serves two different purposes: (1) its high frequency components are used to detect feature points (corner points here) and (2) it provides coarse-to-fine structure for making the algorithm time efficient. The algorithm is based on detecting the corner points from one of the images called reference image and computing corresponding points from the other image called test image by using local correlations using 7x7 windows centered around the corner points. The corresponding points are detected at the lowest decomposition level in a search area of about 11x11 (depending on the translation) and potential points of correspondence are projected onto higher levels. In the subsequent levels the local correlations are computed in a search area of no more than 3x3 for refinement of the correspondence.

Manohar, Mareboyana↗

NASA NTRS2000

Differential Scanning Calorimetry of Volatile-Bearing Iron Minerals Under Mars-Like Pressures: New Insights Into Energetics and Mechanisms of Thermal Decomposition

In this study, we have examined the thermal decomposition of two volatile-bearing iron minerals, in particular, lepidocrocite (gamma-FeOOH) and siderite (FeCO3), under Mars-like pressures. Both minerals exhibit different enthalpic events during their decomposition at reduced atmospheric pressures when compared to those at ambient pressure. These differences in energetics are related to the mechanisms of thermal decomposition at Mars-like pressures. Such knowledge regarding the thermal stability of volatile-bearing minerals on Mars will provide useful information for planetary surface processes.

Lin, I.-C.↗

NASA NTRS2001

A New Method for Nonlinear and Nonstationary Time Series Analysis: The Empirical Mode Decomposition Method

A new method for analyzing nonlinear and nonstationary data has been developed. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert Spectrum. Classical nonlinear system models are used to illustrate the roles played by the nonlinear and nonstationary effects in the energy-frequency-time distribution.

Huang, Norden E.↗

NASA NTRS2003

A Domain Decomposition Parallelization of the Fast Marching Method

In this paper, the first domain decomposition parallelization of the Fast Marching Method for level sets has been presented. Parallel speedup has been demonstrated in both the optimal and non-optimal domain decomposition case. The parallel performance of the proposed method is strongly dependent on load balancing separately the number of nodes on each side of the interface. A load imbalance of nodes on either side of the domain leads to an increase in communication and rollback operations. Furthermore, the amount of inter-domain communication can be reduced by aligning the inter-domain boundaries with the interface normal vectors. In the case of optimal load balancing and aligned inter-domain boundaries, the proposed parallel FMM algorithm is highly efficient, reaching efficiency factors of up to 0.98. Future work will focus on the extension of the proposed parallel algorithm to higher order accuracy. Also, to further enhance parallel performance, the coupling of the domain decomposition parallelization to the G(sub 0)-based parallelization will be investigated.

Herrmann, M.↗

NASA NTRS2005

Phlogopite Decomposition, Water, and Venus

Venus is a hot and dry planet with a surface temperature of 660 to 740 K and 30 parts per million by volume (ppmv) water vapor in its lower atmosphere. In contrast Earth has an average surface temperature of 288 K and 1-4% water vapor in its troposphere. The hot and dry conditions on Venus led many to speculate that hydrous minerals on the surface of Venus would not be there today even though they might have formed in a potentially wetter past. Thermodynamic calculations predict that many hydrous minerals are unstable under current Venusian conditions. Thermodynamics predicts whether a particular mineral is stable or not, but we need experimental data on the decomposition rate of hydrous minerals to determine if they survive on Venus today. Previously, we determined the decomposition rate of the amphibole tremolite, and found that it could exist for billions of years at current surface conditions. Here, we present our initial results on the decomposition of phlogopite mica, another common hydrous mineral on Earth.

Johnson, N. M.↗

NASA NTRS1999

Computer implemented empirical mode decomposition method, apparatus and article of manufacture

A computer implemented physical signal analysis method is invented. This method includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Huang, Norden E.↗

NASA NTRS2002

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Huang, Norden E.↗

NASA NTRS2003

Computer implemented empirical mode decomposition method apparatus, and article of manufacture utilizing curvature extrema

A computer implemented physical signal analysis method is includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals based on local extrema and curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Huang, Norden Eh↗

NASA NTRS2004

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

Huang, Norden E.↗

NASA NTRS2009

Combustion of Organic Molecules by the Thermal Decomposition of Perchlorate Salts: Implications for Organics at the Mars Phoenix Scout Landing Site

The Mars 2007 Phoenix Scout Mission successfully landed on May 25, 2008 and operated on the northern plains of Mars for 150 sols. The primary mission objective was to study the history of water and evaluate the potential for past and present habitability in Martian arctic ice-rich soil [1]. Phoenix landed near 68 N latitude on polygonal terrain created by ice layers that are a few centimeters under loose soil materials. The Phoenix Mission is assessing the potential for habitability by searching for organic molecules in the ice or icy soils at the landing site. Organic molecules are necessary building blocks for life, although their presence in the ice or soil does not indicate life itself. Phoenix searched for organic molecules by heating soil/ice samples in the Thermal and Evolved-Gas Analyzer (TEGA, [2]). TEGA consists of 8 differential scanning calorimeter (DSC) ovens integrated with a magnetic-sector mass spectrometer with a mass range of 2-140 daltons [2]. Endothermic and exothermic reactions are recorded by the TEGA DSC as samples are heated from ambient to ~1000 C. Evolved gases, including any organic molecules and their fragments, are simultaneously measured by the mass spectrometer during heating. Phoenix TEGA data are still under analysis; however, no organic fragments have been identified to date in the evolved gas analysis (EGA). The MECA Wet Chemistry Lab (WCL) discovered a perchlorate salt in the Phoenix soils and a mass 32 peak evolved between 325 and 625 C for one surface sample dubbed Baby Bear [3]. The mass 32 peak is attributed to evolved O2 generated during the thermal decomposition of the perchlorate salt. Perchlorates are very strong oxidizers when heated, so it is possible that organic fragments evolved in the temperature range of 300-600 C were combusted by the O2 released during the thermal decomposition of the perchlorate salt. The byproduct of the combustion of organic molecules is CO2. There is a prominent release of CO2 between 200-600 C for several of the Phoenix soils analyzed by TEGA. This low temperature release of CO2 might be any combination of 1) desorption of adsorbed CO2, 2) thermal decomposition of Fe- and Mg-carbonates, and 3) combustion of organic molecules [2].

Ming, D.W.↗

NASA NTRS2009

Thermal Decomposition of an Impure (Roxbury) Siderite: Relevance to the Presence of Chemically Pure Magnetite Crystals in ALH84001 Carbonate Disks

The question of the origin of nanophase magnetite in Martian meteorite ALH84001 has been widely debated for nearly a decade. Golden et al. have reported producing nearly chemically pure magnetite from thermal decomposition of chemically impure siderite [(Fe, Mg, Mn)CO3]. This claim is significant for three reasons: first, it has been argued that chemically pure magnetite present in the carbonate disks in Martian meteorite ALH84001 could have formed by the thermal decomposition of the impure carbonate matrix in which they are embedded; second, the chemical purity of magnetite has been previously used to identify biogenic magnetite; and, third, previous studies of thermal decomposition of impure (Mg,Ca,Mn)-siderites, which have been investigated under a wide variety of conditions by numerous researchers, invariably yields a mixed metal oxide phase as the product and not chemically pure magnetite. The explanation for this observation is that these siderites all possess the same crystallographic structure (Calcite; R3c) so solid solutions between these carbonates are readily formed and can be viewed on an atomic scale as two chemically different but structurally similar lattices.

McKay, D.S.↗

NASA NTRS2011

Isothermal Decomposition of Hydrogen Peroxide Dihydrate

We present a new method of growing pure solid hydrogen peroxide in an ultra high vacuum environment and apply it to determine thermal stability of the dihydrate compound that forms when water and hydrogen peroxide are mixed at low temperatures. Using infrared spectroscopy and thermogravimetric analysis, we quantified the isothermal decomposition of the metastable dihydrate at 151.6 K. This decomposition occurs by fractional distillation through the preferential sublimation of water, which leads to the formation of pure hydrogen peroxide. The results imply that in an astronomical environment where condensed mixtures of H2O2 and H2O are shielded from radiolytic decomposition and warmed to temperatures where sublimation is significant, highly concentrated or even pure hydrogen peroxide may form.

Loeffler, M. J.↗

NASA NTRS2013

A Structural Model Decomposition Framework for Systems Health Management

Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.

Roychoudhury, Indranil↗

NASA NTRS2009

Synthesis, Decomposition and Characterization of Fe and Ni Sulfides and Fe and CO Nanoparticles for Aerospace Applications

We describe several related studies where simple iron, nickel, and cobalt complexes were prepared, decomposed, and characterized for aeronautics (Fischer-Tropsch catalysts) and space (high-fidelity lunar regolith simulant additives) applications. We describe the synthesis and decomposition of several new nickel dithiocarbamate complexes. Decomposition resulted in a somewhat complicated product mix with NiS predominating. The thermogravimetric analysis of fifteen tris(diorganodithiocarbamato)iron(III) has been investigated. Each undergoes substantial mass loss upon pyrolysis in a nitrogen atmosphere between 195 and 370 C, with major mass losses occurring between 279 and 324 C. Steric repulsion between organic substituents generally decreased the decomposition temperature. The product of the pyrolysis was not well defined, but usually consistent with being either FeS or Fe2S3 or a combination of these. Iron nanoparticles were grown in a silica matrix with a long-term goal of introducing native iron into a commercial lunar dust simulant in order to more closely simulate actual lunar regolith. This was also one goal of the iron and nickel sulfide studies. Finally, cobalt nanoparticle synthesis is being studied in order to develop alternatives to crude processing of cobalt salts with ceramic supports for Fischer-Tropsch synthesis.

Cowen, Jonathan E.↗

NASA NTRS2015

The Relative Importance of Methanogenesis in the Decomposition of Organic Matter in Northern Peatlands

Using an isotope-mass balance approach and assuming the equimolar production of CO2 and CH4 from methanogenesis (e.g., anaerobic decomposition of cellulose), we calculate that the proportion of total CO2 production from methanogenesis varies from 37 to 83% across a variety of northern peatlands. In a relative sense, methanogenesis was a more important pathway for decomposition in bogs (80 +/- 13% of CO2 production) than in fens (64 +/- 5.7% of CO2 production), but because fens contain more labile substrates they may support higher CH4 production overall. The concentration of CO2 produced from methanogenesis (CO2-meth) can be considered equivalent to CH4 concentration before loss due to ebullition, plant-mediated transport, or diffusion. Bogs produced slightly less CO2-meth than fens (2.9 +/- 1.3 and 3.7 +/- 1.4 mmol/L, respectively). Comparing the quantity of CH4 present to CO2-meth, fens lost slightly more CH4 than bogs (89 +/- 2.8% and 82 +/- 5.3%, respectively) likely due to the presence of vascular plant roots. In collapsed permafrost wetlands, bog moats produced half the amount of CO2-meth (0.8 +/- 0.2mmol/L) relative to midbogs (1.6 +/- 0.6 mmol/L) and methanogenesis was less important (42 +/- 6.6% of total CO2 production relative to 55 +/- 8.1%).We hypothesize that the lower methane production potential in collapsed permafrost wetlands occurs because recently thawed organic substrates are being first exposed to the initial phases of anaerobic decomposition following collapse and flooding. Bog moats lost a comparable amount of CH4 as midbogs (63 +/- 7.0% and 64 +/- 9.3%).

bacteria↗

NASA NTRS2019

Applicability of Spectral Decomposition by Varimax-Rotated, Principal Component Analysis to the Surface Biology and Geology (SBG) VNIR Mission Concept

Cyanobacterial and Harmful Algal Blooms (CyanoHABs) are a growing concern in coastal and inland waters. But, spectral interference from multiple constituents in optically complex waters can hamper application of remote sensing using traditional image processing methods. The Kent State University (KSU) spectral decomposition method can be applied to multispectral and hyperspectral remote sensing images (e.g. HICO and the NASA Glenn HSI2) to partition and identify signals related to cyanobacteria, algae, pigment degradation products and suspended sediment in each pixel. Fundamental to the use of remote sensing data is the ability to extract independent signals from correlated hyperspectral VNIR data cubes. The Kent State University varimax-rotated, principal component analysis method (VPCA) is important to integrate into the SBG VNIR mission concept because it provides greater specificity, a software-based SNR boost relative to hardware performance, and can assist with Cal/Val, Modeling and Applications. We present examples of the hyperspectral application of the KSU VPCA method with relevance to SBG. The information extracted by VPCA can be validated spectrally or spatially with laboratory and/or in situ sensors, which capture spatial or time series of information at discrete points within remote sensing images. Comparisons show hyperspectral sensors extract more components than multispectral ones, but more independent information can be extracted from multispectral sensors by VPCA than traditional band ratio approaches. The spectral decomposition method is capable of enhancing the signal to noise ratio (SNR) of the NASA Glenn, second-generation hyperspectral imager by a factor of 7x to 20x, with a spectral reproducibility of ±3%. The spectral decomposition method, when compared against existing remote sensing monitoring methods exhibits both greater specificity and a lower detection limit. The method has been validated with multispectral images in Lake Erie to quantify the Microcystis CyanoHAB and from the Indian River Lagoon, Florida to quantify the Brown Tide resulting from A. lagunesnsis. Field operations in the Western Basin of Lake Erie were conducted using a bbe Fluoroprobe to collect vertical profiles and horizontal tows along a transect from the Toledo to the Detroit Lighthouse during coincident satellite overpasses. Extraction of pixel values from the MODIS Aqua sensor yields agreement between in situ field and lab-based measures of cyanobacterial, cryptophyte, diatoms and green algae, suspended sediment and pigment degradation products with R2>0.8.

hyperspectral↗

NASA NTRS2021

A Phenomenon-Based Decomposition of Model-Based Estimates of Boreal Winter ENSO Variability

Climate models are now routinely being used to simulate and predict climate variability on time scales ranging from sub-seasonal to seasonal and longer. As such, there are now long histories of such simulations and predictions spanning multiple decades and multiple ensemble members, both of which are crucial for separating climate signal from climate noise. A key focus of such runs has been the El Niño-Southern Oscillation (ENSO), spurred by recent improvements in our ability to predict such events, though questions remain as to how well climate models do beyond simply always predicting the “canonical” atmospheric response to an ENSO event—something simple statistical models already do reasonably well. This is a critical issue that needs addressing, given the importance of event-to-event differences for predicting regional impacts of ENSO teleconnections, and the need to justify the expense of running sophisticated climate models. Unfortunately, current diagnostic tools are not well suited for quantifying the different sources of variability associated with specific phenomena such as ENSO. More generally, while much effort has focused on addressing model bias, less has been done to address errors in second moment statistics—an issue whose importance is gaining increased attention particularly as we build climate prediction systems capable of taking advantage of forecasts of opportunity—a capability that requires reliable estimates of forecast uncertainty. In this report, we outline a phenomenon-based statistical decomposition of climate variance(in essence a detailed variance budget)that is specifically tailored to address the above questions by separating the variability (both the signal and noise) into that tied to the long-term average impact of a particular phenomenon(the composite mean) and the event-to-event(E2E) variability about the composite mean. In addition, we provide related decompositions of the correlations that allow us to quantify how much of the agreement with observations (the skill) comes from the composite mean as opposed to from the E2Evariability. As an example, we present the results of such a decomposition for ENSO based on simulations with the GEOS atmospheric general circulation model (AGCM), with a focus on the monthly mean impacts over North America during boreal winter(December –March). Here we take advantage of existing GEOS AGCM simulations that were produced as companion simulations to MERRA-2for the period 1980-2016. Comparisons are made throughout with MERRA-2.

ENSO↗