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At least 379 records · Page 21

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NASA NTRS1982

Science support for the atmospheric trace molecule spectroscopy (ATMOS) experiment

The experiment is a satellite solar occultation interferometer spectrometer experiment under development for flight on Spacelab 3. Launch is expected in March 1985. ATMOS will provide high spectral resolution (0.02/cm) and high spatial resolution (2 km from Shuttle altitude) data over the spectral range from 2 micrometers to 16 micrometers. This region of the spectrum is rich in absorption features of major and minor trace gases which are critical to the understanding of the chemistry and physics of the Earth's upper atmosphere. The broad objectives of the ATMOS experiment are to: identify and measure, on a global scale, the volume mixing ratios of known and newly discovered molecular species; determine vertical profiles for these species; and provide spectral data on the atmosphere for use in optimizing the design of future instruments to monitor specific gases.

Rinsland, C. P.↗

DOE OSTI2024

The Dark Energy Survey: Cosmology Results with ∼1500 New High-redshift Type Ia Supernovae Using the Full 5 yr Data Set

Abstract We present cosmological constraints from the sample of Type Ia supernovae (SNe Ia) discovered and measured during the full 5 yr of the Dark Energy Survey (DES) SN program. In contrast to most previous cosmological samples, in which SNe are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being an SN Ia, we find 1635 DES SNe in the redshift range 0.10 < z < 1.13 that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality z > 0.5 SNe compared to the previous leading compilation of Pantheon+ and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints, we combine the DES SN data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025 < z < 0.10. Using SN data alone and including systematic uncertainties, we find Ω M = 0.352 ± 0.017 in flat ΛCDM. SN data alone now require acceleration ( q 0 < 0 in ΛCDM) with over 5 σ confidence. We find ( Ω M , w ) = ( 0.264 − 0.096 + 0.074 , − 0.80 − 0.16 + 0.14 ) in flat w CDM. For flat w 0 w a CDM, we find ( Ω M , w 0 , w a ) = ( 0.495 − 0.043 + 0.033 , − 0.36 − 0.30 + 0.36 , − 8.8 − 4.5 + 3.7 ) , consistent with a constant equation of state to within ∼2 σ . Including Planck cosmic microwave background, Sloan Digital Sky Survey baryon acoustic oscillation, and DES 3 × 2pt data gives (Ω M , w ) = (0.321 ± 0.007, −0.941 ± 0.026). In all cases, dark energy is consistent with a cosmological constant to within ∼2 σ . Systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified SN analyses.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics

Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulating phenomenological constitutive laws that can accurately capture the observed material response. However, even though neural network-based constitutive laws have been shown to generalize proficiently, the generated representations are not easily interpretable due to their high number of trainable parameters. Sparse regression approaches exist that allow for obtaining interpretable expressions, but the user is tasked with creating a library of model forms which by construction limits their expressiveness to the functional forms provided in the libraries. Here, in this work, we propose to train regularized physics-augmented neural network-based constitutive models utilizing a smoothed version of $L^0$-regularization. This aims to maintain the trustworthiness inherited by the physical constraints, but also enables interpretability which has not been possible thus far on any type of machine learning-based constitutive model where model forms were not assumed a priori but were actually discovered. During the training process, the network simultaneously fits the training data and penalizes the number of active parameters, while also ensuring constitutive constraints such as thermodynamic consistency. We show that the method can reliably obtain interpretable and trustworthy constitutive models for compressible and incompressible hyperelasticity, yield functions, and hardening models for elastoplasticity, using synthetic and experimental data. This work aims to set a new paradigm for interpretable machine learning models in the broad area of solid mechanics where low and limited data is available along with prior knowledge of physical constraints that the learned maps need to obey. This paradigm can potentially be extended to a broader spectrum of scientific exploration.

Data-driven constitutive models↗

NASA NTRS2005

Revealing Relationships among Relevant Climate Variables with Information Theory

The primary objective of the NASA Earth-Sun Exploration Technology Office is to understand the observed Earth climate variability, thus enabling the determination and prediction of the climate's response to both natural and human-induced forcing. We are currently developing a suite of computational tools that will allow researchers to calculate, from data, a variety of information-theoretic quantities such as mutual information, which can be used to identify relationships among climate variables, and transfer entropy, which indicates the possibility of causal interactions. Our tools estimate these quantities along with their associated error bars, the latter of which is critical for describing the degree of uncertainty in the estimates. This work is based upon optimal binning techniques that we have developed for piecewise-constant, histogram-style models of the underlying density functions. Two useful side benefits have already been discovered. The first allows a researcher to determine whether there exist sufficient data to estimate the underlying probability density. The second permits one to determine an acceptable degree of round-off when compressing data for efficient transfer and storage. We also demonstrate how mutual information and transfer entropy can be applied so as to allow researchers not only to identify relations among climate variables, but also to characterize and quantify their possible causal interactions.

Knuth, Kevin H.↗

NASA NTRS2004

Grid Technology as a Cyberinfrastructure for Delivering High-End Services to the Earth and Space Science Community

Grid technology consists of middleware that permits distributed computations, data and sensors to be seamlessly integrated into a secure, single-sign-on processing environment. In &is environment, a user has to identify and authenticate himself once to the grid middleware, and then can utilize any of the distributed resources to which he has been,panted access. Grid technology allows resources that exist in enterprises that are under different administrative control to be securely integrated into a single processing environment The grid community has adopted commercial web services technology as a means for implementing persistent, re-usable grid services that sit on top of the basic distributed processing environment that grids provide. These grid services can then form building blocks for even more complex grid services. Each grid service is characterized using the Web Service Description Language, which provides a description of the interface and how other applications can access it. The emerging Semantic grid work seeks to associates sufficient semantic information with each grid service such that applications wii1 he able to automatically select, compose and if necessary substitute available equivalent services in order to assemble collections of services that are most appropriate for a particular application. Grid technology has been used to provide limited support to various Earth and space science applications. Looking to the future, this emerging grid service technology can provide a cyberinfrastructures for both the Earth and space science communities. Groups within these communities could transform those applications that have community-wide applicability into persistent grid services that are made widely available to their respective communities. In concert with grid-enabled data archives, users could easily create complex workflows that extract desired data from one or more archives and process it though an appropriate set of widely distributed grid services discovered using semantic grid technology. As required, high-end computational resources could be drawn from available grid resource pools. Using grid technology, this confluence of data, services and computational resources could easily be harnessed to transform data from many different sources into a desired product that is delivered to a user's workstation or to a web portal though which it could be accessed by its intended audience.

Hinke, Thomas H.↗

NASA NTRS2011

Next-Generation Aura/OMI NO2 and SO2 Products

The measurement of both SO2 and NO2 gases are recognized as an essential component of atmospheric composition missions. We describe current capabilities and limitations of the operational Aura/OMI NO2 and SO2 data that have been used by a large number of researchers. Analyses of the data and validation studies have brought to light a number of areas in which these products can be expanded and improved. Major improvements for new NASA standard (SP) NO2 product include more accurate tropospheric and stratospheric column amounts, along with much improved error estimates and diagnostics. Our approach uses a monthly NO2 climatology based on the NASA Global Modeling Initiative (GMI) chemistry-transport model and takes advantage of OMI data from cloudy scenes to find clean areas where the contribution from the trap NO2 column is relatively small. We then use a new filtering, interpolation and smoothing techniques for separating the stratospheric and tropospheric components of NO2, minimizing the influence of a priori information. The new algorithm greatly improves the structure of stratospheric features relative to the original SP. For the next-generation OMI SO2 product we plan to implement operationally the offline iterative spectral fitting (ISF) algorithm and re-process the OMI Level-2 SO2 dataset using a priori SO2 and aerosol profiles, clouds, and surface reflectivity appropriate for observation conditions. This will improve the ability to detect and quantify weak tropospheric SO2 loadings. The new algorithm is validated using aircraft in-situ data during field campaigns in China (2005 and 2008) and in Maryland (Frostburg, 2010 and DISCOVER-AQ in July 2011). The height of the SO2 plumes will also be estimated for high SO2 loading cases (e.g., volcanic eruptions). The same SO2 algorithm will be applied to the data from OMPS sensor to be launched on NPP satellite later this year. The next-generation NO2 and SO2 products will provide critical information (e.g., averaging kernels) for evaluation of chemistry-transport models, for data assimilation, and to impose top-down constraints on the SO2 and NO2 emission sources.

Krotkov, Nickolay↗

DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗

NASA NTRS1982

Solar analysis of solar-constant monitoring package (SMM)

The activity cavity radiometer irradiance monitor is supplying the first high precision data on solar total irradiance at the Earth. Thee classes of variability were discovered: large variations of amplitudes up to 0.2%; small high frequency variations in the form of a continuum in the periodogram, extending up to the Nyquist frequency; and sharp spikes at frequencies corresponding to the individual p modes already known from radial velocity measurements. The observed variations (up to 0.3%, on time scales of several days) were identified with sunspot darkness. The data analysis is expected to give information about the solar interior, as well as about the solar input to the terrestrial climate.

Hudson, H. S.↗

NASA NTRSDate not supplied

Towards a program of record of inland water quality: Exploiting present and heritage multispectral sensors for maximum information extraction

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This research exploits recent advancements in bio-optical modeling, cloud computing, and machine learning to enhance our capacity to leverage present and heritage satellite data. Recent research suggests that sensors with low spectral resolution, such as Sentinel 2 and Landsat missions, contain enough hidden spectral variation which can be exploited using data-driven approaches. The availability of three decades of archival imagery will open doors to discover global trends of eutrophication and increased cyanobacteria dominance and provide valuable insight to the development of predictive methodologies. Preliminary efforts in synthetic emulation of global natural inland waters will be discussed and contextualized against satellite radiometric measurement uncertainty, satellite data product uncertainties and causal signal ambiguity over the visible wavelength range, supported by high quality field and image data for selected inland aquatic sites. Insights on water quality estimation via data-driven machine learning models versus matrix inversions will be discussed, and how we can exploit spectral-spatial relationships in high spatial resolution data. A cross-sensor synergistic approach with detailed uncertainty analysis based on optical water types, will allow for unprecedented global snapshots of fine scale ecological dynamics of inland waters.

Inland↗

DOE OSTI2024

Venturing into Unexplored Phase Space: Synthesis, Structure, and Properties of MgCo 3 B 2 Featuring a Rumpled Kagomé Network

MgCo 3 B 2 , a novel ternary boride in a previously unexplored phase space, was synthesized using the hydride route. In situ powder X-ray diffraction and DFT calculations aided in the discovery of this compound, whose structure was then determined by single-crystal X-ray diffraction. Like the closely related CeCo 3 B 2 , MgCo 3 B 2 crystallizes in centrosymmetric space group P6/mmm (a = 4.883(2) Å, c = 2.926(2) Å at 210 K, Z = 1). Unlike CeCo 3 B 2 , however, it adopts a disordered structure that features a rumpled Kagomé network of Co atoms, and Mg atoms fill the channels of a Co–B framework. Although the structural disorder leads to motifs that are similar to those observed in MgNi 3 B 2 and other related ternary borides, no evidence of an ordered superstructure was found by single-crystal X-ray diffraction or high-resolution powder X-ray diffraction. In the case of CeCo 3 B 2 , boron atoms occupy the center of regular Co 6 trigonal prisms; in MgCo 3 B 2 , boron atoms are shifted from the center of the prism to form B–B dimers with roughly the same length as those found in MgNi 3 B 2 . Magnetic susceptibility data exhibit an unusual temperature dependence that cannot be convincingly modeled by the modified Curie–Weiss equation, consistent with DFT calculations predicting a nonmagnetic ground state. Intrinsic susceptibility at 300 K is 1.42 × 10 –3 emu/mol Oe, which is comparable to that of paramagnetic YCo 3 B 2 and CeCo 3 B 2 with a similar structure and composition. Here, this study showcases the efficacy of combining several methodologies to discover new solids in unexplored phase spaces. This approach includes in situ PXRD data to monitor reactions of precursors upon heating, a diffusion-enhanced synthesis method, and DFT assessment of compound stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

In-Depth Proteome Profiling of the Hippocampus of LDLR Knockout Mice Reveals Alternation in Synaptic Signaling Pathway

The low-density lipoprotein receptor (LDLR) is a major apolipoprotein receptor that regulates cholesterol homeostasis. LDLR deficiency is associated with cognitive impairment by the induction of synaptopathy in the hippocampus. Despite the close relationship between LDLR and neurodegenerative disorders, proteomics research for protein profiling in the LDLR knockout (KO) model remains insufficient. Therefore, understanding LDLR KO-mediated differential protein expression within the hippocampus is crucial for elucidating a role of LDLR in neurodegenerative disorders. In this study, we conducted first-time proteomic profiling of hippocampus tissue from LDLR KO mice using tandem mass tag (TMT)-based MS analysis. LDLR deficiency induces changes in proteins associated with the transport of diverse molecules, and activity of kinase and catalyst within the hippocampus. Additionally, significant alterations in the expression of components in the major synaptic pathways were found. Furthermore, these synaptic effects were verified using a data-independent acquisition (DIA)-based proteomic method. In conclusion, our data will serve as a valuable resource for further studies to discover the molecular function of LDLR in neurodegenerative disorders.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Constituent model of light hybrid meson decays

A model of light hybrid mesons and their strong decays is developed. The model employs a gluonic quasiparticle to describe low energy gluodynamics and uses the QCD Hamiltonian in Coulomb gauge to guide the construction of states and decay amplitudes. We compute the partial widths of the twelve low lying isovector and vector hybrids. Implications of these results on hybrid searches are also made, with the chief conclusions being that direct observation of the vector states will be difficult, that a hybrid π ( 1800 ) has distinctive decay characteristics, a narrow η ( 1900 ) hybrid should exist, an η 1 ( 1750 ) should be sought, and that the exotic nature of J P C = 2 − + hybrid mesons should be discernible with sufficient data. We argue that the isovector π 2 hybrid has been discovered, giving a total of four possible hybrid mesons, π 1 ( 1600 ) , η 1 ( 1855 ) , π ( 1800 ) , and π 2 ( 2360 ) , which appear to be filling out the low lying hybrid supermultiplet in the expected fashion. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

NASA NTRS1973

Short and long periodic atmospheric variations between 25 and 200 km

Previously collected data on atmospheric pressure, density, temperature and winds between 25 and 200 km from sources including Meteorological Rocket Network data, ROBIN falling sphere data, grenade release and pitot tube data, meteor winds, chemical release winds, satellite data, and others were analyzed by a daily difference method and results on the distribution statistics, magnitude, and spatial structure of gravity wave and planetary wave atmospheric variations are presented. Time structure of the gravity wave variations were determined by the analysis of residuals from harmonic analysis of time series data. Planetary wave contributions in the 25-85 km range were discovered and found to have significant height and latitudinal variation. Long period planetary waves, and seasonal variations were also computed by harmonic analysis. Revised height variations of the gravity wave contributions in the 25 to 85 km height range were computed. An engineering method and design values for gravity wave magnitudes and wave lengths are given to be used for such tasks as evaluating the effects on the dynamical heating, stability and control of spacecraft such as the space shuttle vehicle in launch or reentry trajectories.

Justus, C. G.↗

NASA NTRS2002

Structural Acoustic Response of a Shape Memory Alloy Hybrid Composite Panel (Lessons Learned)

This study presents results from an effort to fabricate a shape memory alloy hybrid composite (SMAHC) panel specimen and test the structure for dynamic response and noise transmission characteristics under the action of thermal and random acoustic loads. A method for fabricating a SMAHC laminate with bi-directional SMA reinforcement is described. Glass-epoxy unidirectional prepreg tape and Nitinol ribbon comprise the material system. Thermal activation of the Nitinol actuators was achieved through resistive heating. The experimental hardware required for mechanical support of the panel/actuators and for establishing convenient electrical connectivity to the actuators is presented. Other experimental apparatus necessary for controlling the panel temperature and acquiring structural acoustic data are also described. Deficiency in the thermal control system was discovered in the process of performing the elevated temperature tests. Discussion of the experimental results focuses on determining the causes for the deficiency and establishing means for rectifying the problem.

Turner, Travis L.↗

NASA NTRS2004

Verification of Java Programs using Symbolic Execution and Invariant Generation

Software verification is recognized as an important and difficult problem. We present a norel framework, based on symbolic execution, for the automated verification of software. The framework uses annotations in the form of method specifications an3 loop invariants. We present a novel iterative technique that uses invariant strengthening and approximation for discovering these loop invariants automatically. The technique handles different types of data (e.g. boolean and numeric constraints, dynamically allocated structures and arrays) and it allows for checking universally quantified formulas. Our framework is built on top of the Java PathFinder model checking toolset and it was used for the verification of several non-trivial Java programs.

Pasareanu, Corina↗

NASA NTRS2005

Inductive Learning Approaches for Improving Pilot Awareness of Aircraft Faults

Neural network flight controllers are able to accommodate a variety of aircraft control surface faults without detectable degradation of aircraft handling qualities. Under some faults, however, the effective flight envelope is reduced; this can lead to unexpected behavior if a pilot performs an action that exceeds the remaining control authority of the damaged aircraft. The goal of our work is to increase the pilot s situational awareness by informing him of the type of damage and resulting reduction in flight envelope. Our methodology integrates two inductive learning systems with novel visualization techniques. One learning system, the Inductive Monitoring System (IMS), learns to detect when a simulation includes faulty controls, while two others, Inductive Classification System (INCLASS) and multiple binary decision tree system (utilizing C4.5), determine the type of fault. In off-line training using only non-failure data, IMS constructs a characterization of nominal flight control performance based on control signals issued by the neural net flight controller. This characterization can be used to determine the degree of control augmentation required in the pitch, roll, and yaw command channels to counteract control surface failures. This derived information is typically sufficient to distinguish between the various control surface failures and is used to train both INCLASS and C4.5. Using data from failed control surface flight simulations, INCLASS and C4.5 independently discover and amplify features in IMS results that can be used to differentiate each distinct control surface failure situation. In real-time flight simulations, distinguishing features learned during training are used to classify control surface failures. Knowledge about the type of failure can be used by an additional automated system to alter its approach for planning tactical and strategic maneuvers. The knowledge can also be used directly to increase the pilot s situational awareness and inform manual maneuver decisions. Our multi-modal display of this information provides speech output to issue control surface failure warnings to a lesser-used communication channel and provides graphical displays with pilot-selectable !eve!s of details to issues additional information about the failure. We also describe a potential presentation for flight envelope reduction that can be viewed separately or integrated with an existing attitude indicator instrument. Preliminary results suggest that the inductive approach is capable of detecting that a control surface has failed and determining the type of fault. Furthermore, preliminary evaluations suggest that the interface discloses a concise summary of this information to the pilot.

Spikovska, Lilly↗

NASA NTRS2010

Thermal Performance of Cryogenic Multilayer Insulation at Various Layer Spacings

Multilayer insulation (MLI) has been shown to be the best performing cryogenic insulation system at high vacuum (less that 10 (exp 3) torr), and is widely used on spaceflight vehicles. Over the past 50 years, many investigations into MLI have yielded a general understanding of the many variables that are associated with MLI. MLI has been shown to be a function of variables such as warm boundary temperature, the number of reflector layers, and the spacer material in between reflectors, the interstitial gas pressure and the interstitial gas. Since the conduction between reflectors increases with the thickness of the spacer material, yet the radiation heat transfer is inversely proportional to the number of layers, it stands to reason that the thermal performance of MLI is a function of the number of layers per thickness, or layer density. Empirical equations that were derived based on some of the early tests showed that the conduction term was proportional to the layer density to a power. This power depended on the material combination and was determined by empirical test data. Many authors have graphically shown such optimal layer density, but none have provided any data at such low densities, or any method of determining this density. Keller, Cunnington, and Glassford showed MLI thermal performance as a function of layer density of high layer densities, but they didn't show a minimal layer density or any data below the supposed optimal layer density. However, it was recently discovered that by manipulating the derived empirical equations and taking a derivative with respect to layer density yields a solution for on optimal layer density. Various manufacturers have begun manufacturing MLI at densities below the optimal density. They began this based on the theory that increasing the distance between layers lowered the conductive heat transfer and they had no limitations on volume. By modifying the circumference of these blankets, the layer density can easily be varied. The simplest method of determining the thermal performance of MLI at cryogenic temperature is by boil-off calorimetry. Several blankets were procured and tested at various layer densities at the Cryogenics Test Laboratory at Kennedy Space Center. The densities that the blankets were tested over covered a wide range of layer densities including the analytical minimum. Several of the blankets were tested at the same insulation thickness while changing the layer density (thus a different number of reflector layers). Optimizing the layer density of multilayer insulation systems for heat transfer would remove a layer density from the complex method of designing such insulation systems. Additional testing was performed at various warm boundary temperatures and pressures. The testing and analysis was performed to simplify the analysis of cryogenic thermal insulation systems. This research was funded by the National Aeronautics and Space Administration's Exploration Technology Development Program's Cryogenic Fluid Management Project

Johnson, Wesley Louis↗

NASA NTRS2011

Symbolically Modeling Concurrent MCAPI Executions

Improper use of Inter-Process Communication (IPC) within concurrent systems often creates data races which can lead to bugs that are challenging to discover. Techniques that use Satisfiability Modulo Theories (SMT) problems to symbolically model possible executions of concurrent software have recently been proposed for use in the formal verification of software. In this work we describe a new technique for modeling executions of concurrent software that use a message passing API called MCAPI. Our technique uses an execution trace to create an SMT problem that symbolically models all possible concurrent executions and follows the same sequence of conditional branch outcomes as the provided execution trace. We check if there exists a satisfying assignment to the SMT problem with respect to specific safety properties. If such an assignment exists, it provides the conditions that lead to the violation of the property. We show how our method models behaviors of MCAPI applications that are ignored in previously published techniques.

Fischer, Topher↗