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At least 253 records · Page 14

NASA Tech Briefs, July 2011

Topics covered include: 1) Collaborative Clustering for Sensor Networks; 2) Teleoperated Marsupial Mobile Sensor Platform Pair for Telepresence Insertion Into Challenging Structures; 3) Automated Verification of Spatial Resolution in Remotely Sensed Imagery; 4) Electrical Connector Mechanical Seating Sensor; 5) In Situ Aerosol Detector; 6) Multi-Parameter Aerosol Scattering Sensor; 7) MOSFET Switching Circuit Protects Shape Memory Alloy Actuators; 8) Optimized FPGA Implementation of Multi-Rate FIR Filters Through Thread Decomposition; 9) Circuit for Communication Over Power Lines; 10) High-Efficiency Ka-Band Waveguide Two-Way Asymmetric Power Combiner; 11) 10-100 Gbps Offload NIC for WAN, NLR, and Grid Computing; 12) Pulsed Laser System to Simulate Effects of Cosmic Rays in Semiconductor Devices; 13) Flight Planning in the Cloud; 14) MPS Editor; 15) Object-Oriented Multi Disciplinary Design, Analysis, and Optimization Tool; 16) Cryogenic-Compatible Winchester Connector Mount and Retaining System for Composite Tubes; 17) Development of Position-Sensitive Magnetic Calorimeters for X-Ray Astronomy; 18) Planar Rotary Piezoelectric Motor Using Ultrasonic Horns; 19) Self-Rupturing Hermetic Valve; 20) Explosive Bolt Dual-Initiated from One Side; 21) Dampers for Stationary Labyrinth Seals; 22) Two-Arm Flexible Thermal Strap; 23) Carbon Dioxide Removal via Passive Thermal Approaches; 24) Polymer Electrolyte-Based Ambient Temperature Oxygen Microsensors for Environmental Monitoring; 25) Pressure Shell Approach to Integrated Environmental Protection; 26) Image Quality Indicator for Infrared Inspections; 27) Micro-Slit Collimators for X-Ray/Gamma-Ray Imaging; 28) Scatterometer-Calibrated Stability Verification Method; 29) Test Port for Fiber-Optic-Coupled Laser Altimeter; 30) Phase Retrieval System for Assessing Diamond Turning and Optical Surface Defects; 31) Laser Oscillator Incorporating a Wedged Polarization Rotator and a Porro Prism as Cavity Mirror; 32) Generic, Extensible, Configurable Push-Pull Framework for Large-Scale Science Missions; 33) Dynamic Loads Generation for Multi-Point Vibration Excitation Problems; 34) Optimal Control via Self-Generated Stochasticity; 35) Space-Time Localization of Plasma Turbulence Using Multiple Spacecraft Radio Links; 36) Surface Contact Model for Comets and Asteroids; 37) Dust Mitigation Vehicle; 38) Optical Coating Performance for Heat Reflectors of the JWST-ISIM Electronic Component; 39) SpaceCube Demonstration Platform; 40) Aperture Mask for Unambiguous Parity Determination in Long Wavelength Imagers; 41) Spaceflight Ka-Band High-Rate Radiation-Hard Modulator; 42) Enabling Disabled Persons to Gain Access to Digital Media; 43) Cytometer on a Chip; 44) Principles, Techniques, and Applications of Tissue Microfluidics; and 45) Two-Stage Winch for Kites and Tethered Balloons or Blimps.

Source record↗

Measurement of G-phase volume fraction and number density in duplex stainless steels using transmission electron microscopy

Duplex stainless steels (DSS) have a high toughness and strength due the presence of both austenitic and ferritic phases. These alloys have had limited use in power production applications due to thermal embrittlement caused by spinodal decomposition and development of G-phase precipitates in the ferrite. Lean grade DSS alloys (e.g., 2101, 2003) may offer improved thermal stability due to the reduction of Cr- and Ni-equivalent elements when compared to standard grade compositions (e.g., 2205, 2209). The abundance of the G-phase was measured in five duplex stainless steels, three wrought alloys (2101, 2003, 2205) and their matching filler metals (2101-w, 2209-w), after aging at 427 °C for 1000 h and 10,000 h. The G-phase volume fraction, number density, size, and precipitate spacing were found using quantitative analysis of transmission electron microscopy dark field images and the composition of G-phase precipitates on other clusters were characterized with atom probe tomography (APT). In the welded alloys, the G-phase was found to develop rapidly, relative to the wrought material. A positive correlation was found between the nickel equivalent composition of the alloy and the G-phase volume fraction. The alloys 2205, 2209, and 2101-w, which are higher in Cr and Ni, all showed significant G-phase precipitation, further strengthening the hypothesis that lean grade DSS alloys are more thermally stable against precipitation in the ferrite. Electron diffraction showed a secondary phase present in the 2101 wrought alloy at 10,000 h, but it was not crystallographically consistent with the G-phase; APT showed the presence of nanoclusters rich in both nickel and copper for this alloy. In conclusion, no secondary phases or clusters were found in 2003 after 10,000 h of aging, so it may be a candidate alloy for applications that require long-life times at high operating temperatures.

36 MATERIALS SCIENCE↗

An Overview of Starfish: A Table-Centric Tool for Interactive Synthesis

Engineering is an interactive process that requires intelligent interaction at many levels. My thesis [1] advances an engineering discipline for high-level synthesis and architectural decomposition that integrates perspicuous representation, designer interaction, and mathematical rigor. Starfish, the software prototype for the design method, implements a table-centric transformation system for reorganizing control-dominated system expressions into high-level architectures. Based on the digital design derivation (DDD) system a designer-guided synthesis technique that applies correctness preserving transformations to synchronous data flow specifications expressed as co- recursive stream equations Starfish enhances user interaction and extends the reachable design space by incorporating four innovations: behavior tables, serialization tables, data refinement, and operator retiming. Behavior tables express systems of co-recursive stream equations as a table of guarded signal updates. Developers and users of the DDD system used manually constructed behavior tables to help them decide which transformations to apply and how to specify them. These design exercises produced several formally constructed hardware implementations: the FM9001 microprocessor, an SECD machine for evaluating LISP, and the SchemEngine, garbage collected machine for interpreting a byte-code representation of compiled Scheme programs. Bose and Tuna, two of DDD s developers, have subsequently commercialized the design derivation methodology at Derivation Systems, Inc. (DSI). DSI has formally derived and validated PCI bus interfaces and a Java byte-code processor; they further executed a contract to prototype SPIDER-NASA's ultra-reliable communications bus. To date, most derivations from DDD and DRS have targeted hardware due to its synchronous design paradigm. However, Starfish expressions are independent of the synchronization mechanism; there is no commitment to hardware or globally broadcast clocks. Though software back-ends for design derivation are limited to the DDD stream-interpreter, targeting synchronous or real-time software is not substantively different from targeting hardware.

Tsow, Alex↗

Experimental investigation of turbulent wake flows in a helically wrapped rod bundle in presence of localized blockages

In nuclear sodium fast reactors, bundles of rods are tightly packed into a triangular lattice, enclosed in a hexagonal duct, and each pin is spirally wrapped with a thin wire. Flow blockages can potentially impact the local flow characteristics and heat transfer mechanisms in the bundle due to its small subchannel size. The effects of the blockage on the flow structures and heat transfer mechanisms are important aspects that require an accurate investigation. In this study, the flow-field characteristics in the vicinity of a blockage located in the exterior subchannel of rod bundles with helically wrapped wires were experimentally investigated. The velocity fields in the exterior subchannel were acquired by applying matched-index-of-refraction and time-resolved particle image velocimetry (TR-PIV) techniques for Reynolds numbers of Re 1 = 4000 and Re 2 = 17 000, i.e., equivalent to Rew 1 = 19 600 and Rew 2 = 83 200, respectively, based on the blockage width. The results from the TR-PIV measurements revealed an arch-shaped vortex with a large flow recirculation and a pair of counter-rotating vortices in the wake region downstream of the blockage, which is commonly observed in the wake flow of bluff bodies. The relative lateral distance and angle between the two vortices decreased when the Reynolds numbers increased. Profiles of maximum turbulence intensity along the shear layers illustrated the transition process including the growth, peak, and decay along the flow direction. From the spectral analysis of the turbulent velocities extracted at points along the shear layer, the Strouhal numbers ( St ) representing the vortex shedding frequency were found to be St = 0.25 and St = 0.56 for the left and right shear layers, respectively. Characteristics of shear layers generated by the blockage in the exterior subchannel were investigated via the two-point cross correlation of fluctuating velocities. The patiotemporal cross correlations of turbulent velocities, computed at points in the region where the left shear layer exhibited rolling effects and vortex breakdowns, were considerably wider and longer. The convection velocity U c was estimated to be ~0.82 U m to 0.93 U m . Proper orthogonal decomposition (POD) analysis was applied to the instantaneous velocity fields to extract the statistically dominant flow structures. It was found that POD modes 2–3 and 4–5 formed the pair modes when the corresponding POD temporal coefficients depicted sinusoidal shapes and exhibited nearly circular orbits in the phase space. Spectral analysis of the POD temporal coefficients confirmed the vortex shedding frequencies detected in the analysis of turbulent velocities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Automatic Generation of Algorithms for the Statistical Analysis of Planetary Nebulae Images

Analyzing data sets collected in experiments or by observations is a Core scientific activity. Typically, experimentd and observational data are &aught with uncertainty, and the analysis is based on a statistical model of the conjectured underlying processes, The large data volumes collected by modern instruments make computer support indispensible for this. Consequently, scientists spend significant amounts of their time with the development and refinement of the data analysis programs. AutoBayes [GF+02, FS03] is a fully automatic synthesis system for generating statistical data analysis programs. Externally, it looks like a compiler: it takes an abstract problem specification and translates it into executable code. Its input is a concise description of a data analysis problem in the form of a statistical model as shown in Figure 1; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Internally, however, it is quite different: AutoBayes derives a customized algorithm implementing the given model using a schema-based process, and then further refines and optimizes the algorithm into code. A schema is a parameterized code template with associated semantic constraints which define and restrict the template s applicability. The schema parameters are instantiated in a problem-specific way during synthesis as AutoBayes checks the constraints against the original model or, recursively, against emerging sub-problems. AutoBayes schema library contains problem decomposition operators (which are justified by theorems in a formal logic in the domain of Bayesian networks) as well as machine learning algorithms (e.g., EM, k-Means) and nu- meric optimization methods (e.g., Nelder-Mead simplex, conjugate gradient). AutoBayes augments this schema-based approach by symbolic computation to derive closed-form solutions whenever possible. This is a major advantage over other statistical data analysis systems which use numerical approximations even in cases where closed-form solutions exist. AutoBayes is implemented in Prolog and comprises approximately 75.000 lines of code. In this paper, we take one typical scientific data analysis problem-analyzing planetary nebulae images taken by the Hubble Space Telescope-and show how AutoBayes can be used to automate the implementation of the necessary anal- ysis programs. We initially follow the analysis described by Knuth and Hajian [KHO2] and use AutoBayes to derive code for the published models. We show the details of the code derivation process, including the symbolic computations and automatic integration of library procedures, and compare the results of the automatically generated and manually implemented code. We then go beyond the original analysis and use AutoBayes to derive code for a simple image segmentation procedure based on a mixture model which can be used to automate a manual preproceesing step. Finally, we combine the original approach with the simple segmentation which yields a more detailed analysis. This also demonstrates that AutoBayes makes it easy to combine different aspects of data analysis.

Fischer, Bernd↗

Parametric model-order-reduction development for unsteady convection

A time-averaged error indicator with POD- h Greedy is developed to drive parametric model order reduction (pMOR) for 2D unsteady natural convection in a high-aspect ratio slot parameterized with the Prandtl number, Rayleigh number, and slot angle with respect to the gravity. The error indicator is extended to accommodate the energy equation and Leray regularization. Despite being two-dimensional and laminar, the target flow regime presents several challenges: 1) there is a bifurcation in the angle parameter space; 2) the solution can be multivalued, even at steady state; and 3) the solution exhibits spatio-temporal chaos at several points in the parameter space. The authors explore several reduced-order models (ROMs) and demonstrate that Leray-regularized Galerkin ROMs provide a robust solution approach for this class of flows. They further demonstrate that error-indicated pMOR can efficiently predict several QOIs, such as mean flow, mean Nusselt number and mean turbulent kinetic energy, even in the presence of a bifurcation. Finally, they show that spatio-temporal chaos can lead to lack of reproducibility in both the full-order model and the reduced-order model and that the variance in the full-order model provides a lower bound on the pMOR error in these cases.

leray regularization↗

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗

FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica

Numerical simulations of Greenland and Antarctic ice sheets involve the solution of large-scale highly nonlinear systems of equations on complex shallow geometries. This work is concerned with the construction of Schwarz preconditioners for the solution of the associated tangent problems, which are challenging for solvers mainly because of the strong anisotropy of the meshes and wildly changing boundary conditions that can lead to poorly constrained problems on large portions of the domain. In this study, two-level generalized Dryja--Smith--Widlund (GDSW)--type Schwarz preconditioners are applied to different land ice problems, i.e., a velocity problem, a temperature problem, as well as the coupling of the former two problems. We employ the message passing interface (MPI)--parallel implementation of multilevel Schwarz preconditioners provided by the package FROSch (fast and robust Schwarz) from the Trilinos library. The strength of the proposed preconditioner is that it yields out-of-the-box scalable and robust preconditioners for the single physics problems. To the best of our knowledge, this is the first time two-level Schwarz preconditioners have been applied to the ice sheet problem and a scalable preconditioner has been used for the coupled problem. The preconditioner for the coupled problem differs from previous monolithic GDSW preconditioners in the sense that decoupled extension operators are used to compute the values in the interior of the subdomains. Additionally, several approaches for improving the performance, such as reuse strategies and shared memory OpenMP parallelization, are explored. In our numerical study we target both uniform meshes of varying resolution for the Antarctic ice sheet as well as nonuniform meshes for the Greenland ice sheet. We present several weak and strong scaling studies confirming the robustness of the approach and the parallel scalability of the FROSch implementation. Among the highlights of the numerical results are a weak scaling study for up to 32,K processor cores (8,K MPI ranks and 4 OpenMP threads) and 566,M degrees of freedom for the velocity problem as well as a strong scaling study for up to 4,K processor cores (and MPI ranks) and 68,M degrees of freedom for the coupled problem.

97 MATHEMATICS AND COMPUTING↗

Image Segmentation, Registration, Compression, and Matching

A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity/topology components of the generated models. The highly efficient triangular mesh compression compacts the connectivity information at the rate of 1.5-4 bits per vertex (on average for triangle meshes), while reducing the 3D geometry by 40-50 percent. Finally, taking into consideration the characteristics of 3D terrain data, and using the innovative, regularized binary decomposition mesh modeling, a multistage, pattern-drive modeling, and compression technique has been developed to provide an effective framework for compressing digital elevation model (DEM) surfaces, high-resolution aerial imagery, and other types of NASA data.

Yadegar, Jacob↗

The Uranian aurora and its relationship to the magnetosphere

About 32 h of Voyager Ultraviolet Spectrometer (UVS) observations of Uranus H2 band airglow emission (875 less than or equal to lambda less than or equal to 1115 A) have been analyzed using the singular value decomposition (SVD) approach to inversion, producing an intensity map showing aurora at both magnetic poles. An H Lyman alpha aurora may also be present but is difficult to separate from scattered solar and local interstellar medium components. SVD analysis of variance shows that the intensity estimate is significantly larger than the error estimate over both Uranographic poles and part of the equatorial region, fortuitously including both magnetic polar regions. The Goddard Space Flight Center Q(sub 3) magnetic field model correctly predicts that the aurora should be larger in area and emit more power at the weaker N magnetic pole than at the stronger S magnetic pole. However, the auroral emissions are quite localized in magnetic longitude and so do not form complete auroral ovals. The brightest auroral emission at each magnetic pole is confined to a range of approximately 90 deg of magnetic longitude centered on the magnetotail direction, at moderate magnetic L parameter (5 less than or equal to L less than or equal to 10), but some emission at each pole is distributed over a range of more than 180 deg of longitude. The magnetic longitudes of the aurora are completely inconsistent with the 'windshield wiper' effect for either ions or electrons, indicating that some other effect, such as rapid depletion of the population of precipitating particles of highly localized strong pitch-angle diffusion, may be acting to localize emission. The low apparent L of the precipitating particles indicates that their energies may be less than or equal to 10 keV. Hence magnetospheric convection is likely to be important, and thus particles exciting the aurora may not remain on constant L shells. The precipitating particles may be a relatively low-energy population at high L that is heated to aurora-exciting energy by adiabatic compression during convection to low L. We estimate that the total auroral power output at H Lyman alpha and shorter wavelengths is about 3 x 10(exp 9) to 7 x 10(exp 9) W, requiring about 10 times that much power for excitation.

Herbert, Floyd↗

Simultaneous Measurements of Noncommuting Observables: Positive Transformations and Instrumental Lie Groups

We formulate a general program for describing and analyzing continuous, differential weak, simultaneous measurements of noncommuting observables, which focuses on describing the measuring instrument autonomously, without states. The Kraus operators of such measuring processes are time-ordered products of fundamental differential positive transformations, which generate nonunitary transformation groups that we call instrumental Lie groups. The temporal evolution of the instrument is equivalent to the diffusion of a Kraus-operator distribution function, defined relative to the invariant measure of the instrumental Lie group. This diffusion can be analyzed using Wiener path integration, stochastic differential equations, or a Fokker-Planck-Kolmogorov equation. This way of considering instrument evolution we call the Instrument Manifold Program. We relate the Instrument Manifold Program to state-based stochastic master equations. We then explain how the Instrument Manifold Program can be used to describe instrument evolution in terms of a universal cover that we call the universal instrumental Lie group, which is independent not just of states, but also of Hilbert space. The universal instrument is generically infinite dimensional, in which case the instrument’s evolution is chaotic. Special simultaneous measurements have a finite-dimensional universal instrument, in which case the instrument is considered principal, and it can be analyzed within the differential geometry of the universal instrumental Lie group. Principal instruments belong at the foundation of quantum mechanics. We consider the three most fundamental examples: measurement of a single observable, position and momentum, and the three components of angular momentum. As these measurements are performed continuously, they converge to strong simultaneous measurements. For a single observable, this results in the standard decay of coherence between inequivalent irreducible representations. For the latter two cases, it leads to a collapse within each irreducible representation onto the classical or spherical phase space, with the phase space located at the boundary of these instrumental Lie groups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Quantifying VOC-Reaction Tracers, Ozone Production, and Continuing Aerosol Production Rates in Urban and Far-Downwind Atmospheres

We have found a surprisingly informative decomposition of the complex question of smoggy ozone production (basically, [HO2] in a more locally determined field of [NO]) in the process of linked investigations of modestly smoggy Eastern North America (by NASA aircraft, July 2004) and rather polluted Flushing, NYC (Queens College, July, 2001). In both rural and very polluted situations, we find that a simple contour graph parameterization of the local principal ozone production rate can be estimated using only the variables [NO] and j(sub rads) [HCHO]: Po(O3) = c (j(sub rads) [HCHO])(sup a) [HCHO](sup b). Here j(sub rads) is the photolysis of HCHO to radicals, presumably capturing many harder-UV photolytic processes and the principle ozone production is that due to HO2; mechanisms suggest that ozone production due to RO2 is closely correlated, often suggesting a limited range of different proportionality factors. The method immediately suggests a local interpretation for concepts of VOC limitation and NOx limitation. We believe that the product j(sub rads) [HCHO] guages the oxidation rate of observed VOC mixtures in a way that also provides [HO2] useful for the principle ozone production rate k [HO2] [NO], and indeed, all ozone chemical production. The success of the method suggests that dominant urban primary-HCHO sources may transition to secondary plume-HCHO sources in a convenient way. Are there other, simple, near-terminal oxidized VOC's which help guage ozone production and aerosol particle formation? Regarding particles, we report on, to the extent NASA Research resources allow, on appealing relationships between far-downwind (Atlantic PBL) HCHO and very fine aerosol (including sulfate. Since j(sub rads) [HCHO] provides a time-scale, we may understand distant-plume particle production in a more quantitative manner. Additionally we report on a statistical search in the nearer field for relationships between glyoxals (important near-terminal aromatic and isoprene reaction products) and aerosol production, looking for VOC's that might be most implicated. All three variables j(sub rads), [HCHO], and [NO] are relatively easily measured in widespread air pollution monitoring networks, and all are deducible form space-borne observations, though estimation of [NO] from [NO2] (the species observable from space) may require care. We report also on airborne and surface observations of HCHO, suggesting that concentrated (urban) and more diffuse (forest) sources may be distinguishable from space. The use of the 3.58 micron microwindow for HCHO remote sensing should allow much sharper resolution of HCHO than the UV. UV sensing requires large and expensive instruments, but even these seem justified since formaldehyde is so informative.

Chatfield, Robert↗

FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica

Numerical simulations of Greenland and Antarctic ice sheets involve the solution of large-scale highly nonlinear systems of equations on complex shallow geometries. This work is concerned with the construction of Schwarz preconditioners for the solution of the associated tangent problems, which are challenging for solvers mainly because of the strong anisotropy of the meshes and wildly changing boundary conditions that can lead to poorly constrained problems on large portions of the domain. Here, two-level GDSW (Generalized Dryja–Smith–Widlund) type Schwarz preconditioners are applied to different land ice problems, i.e., a velocity problem, a temperature problem, as well as the coupling of the former two problems. We employ the MPI-parallel implementation of multi-level Schwarz preconditioners provided by the package FROSch (Fast and Robust Schwarz)from the Trilinos library. The strength of the proposed preconditioner is that it yields out-of-the-box scalable and robust preconditioners for the single physics problems. To our knowledge, this is the first time two-level Schwarz preconditioners are applied to the ice sheet problem and a scalable preconditioner has been used for the coupled problem. The pre-conditioner for the coupled problem differs from previous monolithic GDSW preconditioners in the sense that decoupled extension operators are used to compute the values in the interior of the sub-domains. Several approaches for improving the performance, such as reuse strategies and shared memory OpenMP parallelization, are explored as well. In our numerical study we target both uniform meshes of varying resolution for the Antarctic ice sheet as well as non uniform meshes for the Greenland ice sheet are considered. We present several weak and strong scaling studies confirming the robustness of the approach and the parallel scalability of the FROSch implementation. Among the highlights of the numerical results are a weak scaling study for up to 32 K processor cores (8 K MPI-ranks and 4 OpenMP threads) and 566 M degrees of freedom for the velocity problem as well as a strong scaling study for up to 4 K processor cores (and MPI-ranks) and 68 M degrees of freedom for the coupled problem.

58 GEOSCIENCES↗

An optimized index of human cardiovascular adaptation to simulated weightlessness

Prolonged exposure to weightlessness is known to produce a variety of cardiovascular changes, some of which may influence the astronaut's performance during a mission. In order to find a reliable indicator of cardiovascular adaptation to weightlessness, we analyzed data from nine male subjects after a 24-hour period of normal activity and after a period of simulated weightlessness produced by two hours in a launch position followed by 20 hours of 6 degrees head-down tilt plus pharmacologically induced diuresis (furosemide). Heart rate, arterial pressure, thoracic fluid index, and radial flow were analyzed. Autoregressive spectral estimation and decomposition were used to obtain the spectral components of each variable from the subjects in the supine position during pre- and post-simulated weightlessness. We found a significant decrease in heart rate power and an increase in thoracic fluid index power in the high frequency region (0.2-0.45 Hz) and significant increases in radial flow and arterial pressure powers in the low frequency region (<0.2 Hz) in response to simulated weightlessness. However, due to the variability among subjects, any single variable appeared limited as a dependable index of cardiovascular adaptation to weightlessness. The backward elimination algorithm was then used to select the best discriminatory features from these spectral components. Fisher's linear discriminant and Bayes' quadratic discriminant were used to combine the selected features to obtain an optimal index of adaptation to simulated weightlessness. Results showed that both techniques provided improved discriminant performance over any single variable and thus have the potential for use as an index to track adaptation and prescribe countermeasures to the effects of weightlessness.

NASA Discipline Number 14-10↗

Thermal Dehydration of Aluminum (Oxy)hydroxides on Fuel Cladding Material - 20200

The aluminum cladding of research-reactor fuel undergoes general corrosion with resulting formation of adherent aluminum (oxy)hydroxide films during in-reactor and post-discharge exposure to water under various conditions and temperatures. These (oxy)hydroxides contain chemically-bound water that poses challenges for extended dry storage due to the risk of thermal or radiolytic decomposition releasing free water and/or hydrogen and oxygen gases. This study comprised laboratory experiments of thermal drying behavior of aluminum (oxy)hydroxides to identify approaches for reducing bound water on fuel cladding prior to sealed dry storage. Effective drying strategies for adherent (oxy)hydroxides on fuel cladding will improve the safety of dry storage by mitigating potential avenues for additional corrosion and/or generation of flammable gases inside the storage canister. This work is part of a broader investigation to address knowledge gaps and technical data needs for dry storage of aluminum-clad spent nuclear fuel (ASNF), which included investigation of (oxy)hydroxide formation on aluminum alloy substrates immersed in water, characterization of service-grown films from ASNF, and radiolytic yield of hydrogen from (oxy)hydroxide powders and films. The current work comprises experimental thermal dehydration of aluminum trihydroxides (bayerite and gibbsite) characteristic of low-temperature (<80 deg. C) corrosion in water and aluminum oxyhydroxide (boehmite) characteristic of high temperature (>80 deg. C) corrosion in water. First, commercially produced (oxy)hydroxide powders (boehmite and gibbsite) were tested via thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The effort aimed to identify or confirm key temperature ranges/thresholds for the thermal decomposition reactions, as well as impacts of ramp rate and hold times, for drying of isolated, high-surface-area (oxy)hydroxides. The information gleaned from powder tests was used to guide subsequent drying tests on adherent (oxy)hydroxide films grown on aluminum alloy substrates. TGA was used to analyze small samples of the aluminum coupons with adherent (oxy)hydroxide films. Specimens were characterized both pre- and post-drying using X-ray diffraction (XRD) to determine the film composition and scanning electron microscopy (SEM) to determine its morphology. Drying of adherent (oxy)hydroxide films was anticipated to pose additional complications relative to drying of (oxy)hydroxide powders. Anticipated challenges include achieving sufficient drying of the (oxy)hydroxide at low-enough temperature to avoid melting or other undesirable phase changes in the aluminum substrate and overcoming transport limitations of water through the thickness of a dense film or through a tortuous pore structure to reach the outer surface of the film. Inn addition, dehydration of (oxy)hydroxides significantly changes the film density, which may lead to substantial alterations in the morphology, including potential cracking and spalling of the film. TGA/DSC tests of gibbsite powders resulted in successful conversion to boehmite or alumina, depending on the maximum temperature reached. At low ramp rates (≤5 deg. C/min), the conversion to boehmite occurred around 300 deg. C (210-340 deg. C). XRD confirmed that boehmite was the only phase detected after tests reaching 450 deg. C. The transition to alumina occurred around 510 deg. C (470-550 deg. C), with XRD detecting only alumina after tests reaching at least 600 deg. C. Boehmite powders dehydrated to alumina at about 400 deg. C (330-460 deg. C) for coarse (77-μm particle diameter) powder and about 490 deg. C (420-520 deg. C) for fine (0.7-μm particle diameter) powder. For all powders tested, the maximum percent mass loss after drying to high temperature slightly exceeded the theoretical mass loss for complete dehydration of the stoichiometric (oxy)hydroxide to alumina, which is likely attributable to physisorbed water in the powder. Drying of a thick (∼8.6 μm), predominantly bayerite adherent film displayed a dramatic change in film morphology, with the initially continuous trihydroxide film cracking into sections on the order of 50 μm square and partially delaminating. The outermost layer of the film completely faked of in some regions. The layer exposed under the spalled oxide also displayed prominent cracking, with spacing on the order of 10 μm, but this surface layer appeared to remain completely adhered to the aluminum substrate. TGA showed mass losses per unit surface area up to ∼1.0 mg/cm{sup 2}. XRD characterization of the remaining oxide was inconclusive, with no crystalline phases detected. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

CLINICAL DECISION SUPPORT: PATH TO FUNCTIONAL REQUIREMENTS

Long-duration, deep-space exploration missions present significant challenges to crew health and performance. These challenges include the individual and combined effects of microgravity, radiation exposure, isolation, limited resources (mass, volume, power, data and crew time), limited options for evacuation and those associated with delayed or constrained communications, all of which demand greater crew autonomy. Specifically, as the communication delays intensify the further we explore space, the unqualified need for Earth-independent medical operations focused on autonomous diagnosis, treatment and prevention will be key to mission continuation and success. To augment the requisite knowledge, skills and abilities (KSAs) of a time-constrained crew operating under stressful conditions, combatting fatigue, and facing a potential medical crisis, a robust clinical decision support system (CDSS) is a probable solution that would facilitate, guide and inform Earth-independent medical operations, while assisting crewmembers through various clinical presentations. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) is expanding the boundaries of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit. ExMC is actively identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses gap Medical-701 within the Inflight Medical Conditions risk: “Enhance medical capabilities within an exploration medical system.” Though mass, volume, and power will face increasing constraints, the projected computational capabilities of spacecraft systems will increase exponentially as information technology continues to advance this decade and beyond. Hence, data, software and computational resources will play an essential and synergistic role in maintaining crew health, wellness and performance in deep space missions. The focus of the CDS project is to develop recommended requirements for an in-vehicle CDSS that acts as a ‘virtual assistant’ for delivering optimal health, performance and medical care during exploration missions. The CDSS is envisioned as an integrated, software-based tool deployed on a laptop computer or handheld device. The CDSS will assist the crew and ground support when interacting with knowledge/databases (e.g. records, pharmacy, schedule), instrumentation (e.g. imaging, physiological monitoring devices), and habitat (e.g. wellness system, task performance system) and vehicle systems (e.g. environmental system, communication system). In addition, the human interface will employ a context-based approach that accounts for the crew’s situation. Thus, extraneous and clinically/operationally non-relevant information are reduced to avoid an increase in cognitive load. The framework of an ideal spaceflight CDSS is to include core and advanced analytical features that incorporate work from collaborators yet maintain a flexible platform for integrating new technology in the future. In fiscal year 2021 (FY21), the CDS project identified requirements through two primary mechanisms: (i) the development of software implementation prototypes and (ii) the application of systems engineering processes. The CDS project developed and tested a series of increasingly complex system prototypes that were based on use cases derived from the CDSS concept of operations (ConOps). These software implementations yielded insights on CDSS functionality as well as lessons learned that provided the initial requirements for CDSS capability. By applying a systems engineering (SE) approach, medical scenarios provided in the ConOps and the use cases for software implementation underwent functional decomposition to identify CDSS functionality. Also, systems-based modeling language (SysML) tools such as activity diagrams were developed from the same ConOps and use cases to identify CDSS functionality. The lessons learned from software implementation defined both specific requirements and broad areas of requirements. Within these defined broad requirement areas, further analysis of the SE products identified specific capability that resulted in the final functional requirements. In summary, the software prototypes, functional decomposition of the ConOps and use cases, and SysML diagrams provided the basis for the CDSS requirements developed in FY21. In the upcoming year, these requirements will be refined for their final ExMC baseline review in latter FY22.

clinical decision support↗

Clinical Decision Support: Path to Functional Requirements

Long-duration, deep-space exploration missions present significant challenges to crew health and performance. These challenges include the individual and combined effects of microgravity, radiation exposure, isolation, limited resources (mass, volume, power, data and crew time), limited options for evacuation and those associated with delayed or constrained communications, all of which demand greater crew autonomy. Specifically, as the communication delays intensify the further we explore space, the unqualified need for Earth-independent medical operations focused on autonomous diagnosis, treatment and prevention will be key to mission continuation and success. To augment the requisite knowledge, skills and abilities (KSAs) of a time-constrained crew operating under stressful conditions, combatting fatigue, and facing a potential medical crisis, a robust clinical decision support system (CDSS) is a probable solution that would facilitate, guide and inform Earth-independent medical operations, while assisting crewmembers through various clinical presentations. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) is expanding the boundaries of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit. ExMC is actively identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses gap Medical-701 within the Inflight Medical Conditions risk: “Enhance medical capabilities within an exploration medical system.” Though mass, volume, and power will face increasing constraints, the projected computational capabilities of spacecraft systems will increase exponentially as information technology continues to advance this decade and beyond. Hence, data, software and computational resources will play an essential and synergistic role in maintaining crew health, wellness and performance in deep space missions. The focus of the CDS project is to develop recommended requirements for an in-vehicle CDSS that acts as a ‘virtual assistant’ for delivering optimal health, performance and medical care during exploration missions. The CDSS is envisioned as an integrated, software-based tool deployed on a laptop computer or handheld device. The CDSS will assist the crew and ground support when interacting with knowledge/databases (e.g. records, pharmacy, schedule), instrumentation (e.g. imaging, physiological monitoring devices), and habitat (e.g. wellness system, task performance system) and vehicle systems (e.g. environmental system, communication system). In addition, the human interface will employ a context-based approach that accounts for the crew’s situation. Thus, extraneous and clinically/operationally non-relevant information are reduced to avoid an increase in cognitive load. The framework of an ideal spaceflight CDSS is to include core and advanced analytical features that incorporate work from collaborators yet maintain a flexible platform for integrating new technology in the future. In fiscal year 2021 (FY21), the CDS project identified requirements through two primary mechanisms: (i) the development of software implementation prototypes and (ii) the application of systems engineering processes. The CDS project developed and tested a series of increasingly complex system prototypes that were based on use cases derived from the CDSS concept of operations (ConOps). These software implementations yielded insights on CDSS functionality as well as lessons learned that provided the initial requirements for CDSS capability. By applying a systems engineering (SE) approach, medical scenarios provided in the ConOps and the use cases for software implementation underwent functional decomposition to identify CDSS functionality. Also, systems-based modeling language (SysML) tools such as activity diagrams were developed from the same ConOps and use cases to identify CDSS functionality. The lessons learned from software implementation defined both specific requirements and broad areas of requirements. Within these defined broad requirement areas, further analysis of the SE products identified specific capability that resulted in the final functional requirements. In summary, the software prototypes, functional decomposition of the ConOps and use cases, and SysML diagrams provided the basis for the CDSS requirements developed in FY21. In the upcoming year, these requirements will be refined for their final ExMC baseline review in latter FY22.

Clinical decision support↗

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗