Results for “Gradient information” — Page 33

NASA NTRS1999

Ocean Turbulence I: One-Point Closure Model Momentum and Heat Vertical Diffusivities

Since the early forties, one-point turbulence closure models have been the canonical tools used to describe turbulent flows in many fields. In geophysics, Mellor and Yamada applied such models using the 1980 state-of-the art. Since then, no improvements were introduced to alleviate two major difficulties: 1) closure of the pressure correlations, which affects the correct determination of the critical Richardson number Ri(sub cr) above which turbulent mixing is no longer possible and 2) the need to express the non-local third-order moments (TOM) in terms of lower order moments rather than via the down-gradient approximation as done thus far, since the latter seriously underestimates the TOMs. Since 1) and 2) are still being dealt with adjustable parameters which weaken the credibility of the models, alternative models, not based on turbulence modeling, have been suggested. The aim of this paper is to show that new information, partly derived from the newest 2-point closure model discussed, can be used to solve these shortcomings. The new one-point closure model, which in its simplest form is algebraic and thus simple to implement, is first shown to reproduce a variety of data. Then, it is used in a Ocean-General Circulation Model (O-GCM) where it reproduces well a large variety of ocean data. While phenomenological models are specifically tuned to ocean turbulence, the present model is not. It is first tested against laboratory data on stably stratified flows and then used in an O-GCM. It is more general, more predictive and more resilient, e.g., it can incorporate phenomena like wave-breaking at the surface, salinity diffusivity, non-locality, etc. One important feature that naturally comes out of the new model is that the predicted Richardson critical value Ri(sub cr) is Ri (sub cr approx. = 1) in agreement with both Large Eddy Simulations (LES) and empirical evidence while all previous models predicted Ri (sub cr approx. = 0.2) which led to a considerable underestimate of the extent of turbulent mixing and thus to an incorrect mixed layer depth. The predicted temperature and salinity profiles (vs. depth) are presented and compared with those of the Kolmogorov-Petruvsky-Piskunuv (KPP) model and Levitus data.

Canuto, V. M.↗

NASA NTRS2001

Operational Cloud Detection in GEOS Imagery

The bispectral spatial coherence approach for the detection and masking of clouds is revisited with modifications for operational applications to Geostationary Operational Environmental Satellite (GOES) Imager and Sounder data. The approach applies an edge detection algorithm to a "difference" image to identify the edges or contours of the clouds. The difference image is generated as the difference between the 3.9 and 11 micrometer channels of the GOES sensors. This difference image better highlights cloud edges than either image alone and draws upon the emissivity differences in the various channels. The cloud contours are "filled-in" by referencing the thermal gradients in the 11-micrometer channel or difference image associated with the cloud edge. This paper and associate poster will present a preliminary validation of the bispectral spatial coherence approach and will compare the cloud masks generated with this approach to the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) operational cloud products.

Jedlovec, Gary J.↗

NASA NTRS2011

Messengers from the Early Solar System - Comets as Carriers of Cosmic Information

Viewed from a cosmic perspective, Earth is a dry planet yet its oceans are enriched in deuterium by a large factor relative to nebular hydrogen. Can comets have delivered Earth s water? The question of exogenous delivery of water and organics to Earth and other young planets is of critical importance for understanding the origin of Earth s water, and for assessing the possible existence of exo-planets similar to Earth. Strong gradients in temperature and chemistry in the proto-planetary disk, coupled with dynamical models, imply that comets from the Oort Cloud and Kuiper Disk reservoirs should have diverse composition. The primary volatiles in comets (ices native to the nucleus) provide the preferred metric, and taxonomies based on them are now beginning to emerge [1, 2, 3]. The measurement of cosmic parameters such as the nuclear spin temperatures for H2O, NH3, and CH4, and of enrichment factors for isotopologues (D/H in water and hydrogen cyanide, N-14/N-15 in CN and hydrogen cyanide) provide additional important tests for the origin of cometary material.

Mumma, Michael J.↗

NASA NTRS1976

Snow and ice surfaces measured by the Nimbus 5 microwave spectrometer

The 22.2- and 31.4-GHz channels of the microwave spectrometer on board the Nimbus 5 earth observatory satellite provide information about the global distribution and character of various types of snow and ice. Observations for the winter and summer of 1973 are presented for both polar regions. Well-defined spectral signatures are found for snow, sea ice, and land ice in Greenland and Antarctica. A simple model with subsurface temperature gradients in a lossy homogeneous dielectric does not account for the observations; internal scattering effects appear to play a dominant role.

Kunzi, K. F.↗

NASA NTRS1999

Thermal-Mechanical Stability of Single Crystal Oxide Refractive Concentrators for High-Temperature Solar Thermal Propulsion

Single crystal oxides such as yttria-stabilized zirconia (Y2O3-ZrO2), yttrium aluminum garnet (Y3Al5O12, or YAG), magnesium oxide (MgO) and sapphire (Al2O3) are candidate refractive secondary concentrator materials for high temperature solar propulsion applications. However, thermo-mechanical reliability of these components in severe thermal environments during the space mission sun/shade transition is of great concern. Simulated mission tests are important for evaluating these candidate oxide materials under a variety of transient and steady-state heat flux conditions, and thus provide vital information for the component design. In this paper, a controlled heat flux thermal shock test approach is established for the single crystal oxide materials using a 3.0 kW continuous wave CO2 laser, with a wavelength 10.6 micron. Thermal fracture behavior and failure mechanisms of these oxide materials are investigated and critical temperature gradients are determined under various temperature and heating conditions. The test results show that single crystal sapphire is able to sustain the highest temperature gradient and heating-cooling rate, and thus exhibit the best thermal shock resistance, as compared to the yttria-stabilized zirconia, yttrium aluminum garnet, and magnesium oxide.

Zhu, Dongming↗

NASA NTRS1999

Thermal-Mechanical Stability of Single Crystal Oxide Refractive Concentrators for High-Temperature Solar Thermal Propulsion

Single crystal oxides such as yttria-stabilized zirconia (Y2O3-ZrO2), yttrium aluminum garnet (Y3Al5O12, or YAG), magnesium oxide (MgO) and sapphire (Al2O3) are candidate refractive secondary concentrator materials for high temperature solar propulsion applications. However, thermo-mechanical reliability of these components in severe thermal environments during the space mission sun/shade transition is of great concern. Simulated mission tests are important for evaluating these candidate oxide materials under a variety of transient and steady-state heat flux conditions, and thus provide vital information for the component design. In this paper, a controlled heat flux thermal shock test approach is established for the single crystal oxide materials using a 3.0 kW continuous wave CO2 laser, with a wavelength 10.6 micron. Thermal fracture behavior and failure mechanisms of these oxide materials are investigated and critical temperature gradients are determined under various temperature and heating conditions. The test results show that single crystal sapphire is able to sustain the highest temperature gradient and heating-cooling rate, and thus exhibit the best thermal shock resistance, as compared to the yttria-stabilized zirconia, yttrium aluminum garnet and magnesium oxide.

Zhu, Dongming↗

NASA NTRS2002

Splashing Droplets

Current data on droplet breakup is scarce for the sizes and velocities typical of practical applications such as in spray combustion processes and coating processes. While much more representative of practical applications, the small spatial scales and rapid time-scales prevent detailed measurement of the internal fluid dynamics and liquid property gradients produced by impinging upon surfaces. Realized through the extended spatial and temporal scales afforded by a microgravity environment, an improved understanding of drop breakup dynamics is sought to understand and ultimately control the impingement dynamics of droplets upon surfaces in practical situations. The primary objective of this research will be to mark the onset of different 'splashing modes' and to determine their temperature, pressure and angle dependence for impinging droplets representative of practical fluids. In addition, we are modeling the evolution of droplets that do not initially splash but rather undergo a 'fingering' evolution observed on the spreading fluid front and the transformation of these fingers into splashed products. An example of our experimental data is presented below. These images are of Isopar V impacting a mirror-polished surface. They were acquired using a high-speed camera at 1000 frames per second. They show the spreading of a single droplet after impact and ensuing finger instabilities. Normal gravity experimental data such as this will guide low gravity measurements in the 2.2 second drop tower and KC-135 aircraft as available. Presently we are in the process of comparing the experimental data of droplet shape evolution to numerical models, which can also capture the internal fluid dynamics and liquid property gradients such as produced by impingement upon a heated surface. To-date isothermal numerical data has been modeled using direct numerical simulations of representative splashing droplets. The data obtained so far indicates that the present model describes well the droplet wall interactions to a point in time just before splash. Additional information is included in the original extended abstract.

VanderWal, Randall L.↗

DOE OSTI2025

Bias-Variance Trade-Off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs

Physics-Informed Neural Networks (PINNs) have triggered a paradigm shift in scientific computing, leveraging mesh-free properties and robust approximation capabilities. While proving effective for low-dimensional partial differential equations (PDEs), the computational cost of PINNs remains a hurdle in high-dimensional scenarios. This is particularly pronounced when computing high-order and high-dimensional derivatives in the physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian noise for stochastic smoothing of the original neural net model, enabling the use of Monte Carlo methods for derivative approximation, which eliminates the need for costly automatic differentiation. Despite its computational efficiency, especially in the approximation of high-dimensional derivatives, RS-PINN introduces biases in both loss and gradients, negatively impacting convergence, especially when coupled with stochastic gradient descent (SGD) algorithms. We present a comprehensive analysis of biases in RS-PINN, attributing them to the nonlinearity of the Mean Squared Error (MSE) loss as well as the intrinsic nonlinearity of the PDE itself. We propose tailored bias correction techniques, delineating their application based on the order of PDE nonlinearity. The derivation of an unbiased RS-PINN allows for a detailed examination of its advantages and disadvantages compared to the biased version. Specifically, the biased version has a lower variance and runs faster than the unbiased version, but it is less accurate due to the bias. To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version. In addition to methodological contributions, we present an enhanced implementation of RS-PINN. Extensive experiments on diverse high-dimensional PDEs, including Fokker-Planck, Hamilton-Jacobi-Bellman (HJB), viscous Burgers’, Allen-Cahn, and Sine-Gordon equations, illustrate the bias-variance trade-off and highlight the effectiveness of the hybrid RS-PINN. Empirical guidelines are provided for selecting biased, unbiased, or hybrid versions, depending on the dimensionality and nonlinearity of the specific PDE problem.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2005

Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery

The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Evaluation Studies↗

DOE OSTI2026

Microstructure prediction for Ti-22Al-25Nb in laser powder bed fusion

This work presents a physics-informed framework for predicting solidification morphology and defect susceptibility in additively manufactured Ti–22Al–25Nb across a broad processing space. The framework integrates solidification microstructure selection (SMS) analysis with a single-track defect-based printability map to establish a unified methodology linking processing parameters to both interfacial morphology and manufacturability. Thermal gradients G and solidification rates R are first computed using the Thermo-Calc Additive Manufacturing (TC-AM) module, a finite-interface-dissipation (FID) phase-field (PF) model coupled with CALPHAD method is then employed to systematically distinguish planar and dendritic regimes as functions of $G$ and $R$. By superimposing the printability map onto the morphology projections, a comprehensive process–structure framework is obtained. Across most processing conditions, the predicted microstructure is predominantly dendritic, while planar growth emerges only under selected laser power $P$ and scan speed $v$ combinations. In addition to morphology classification, the framework quantifies the dendritic area fraction and introduces a width-based morphology descriptor to characterize the spatial extent of planar/dendritic regions within the melt pool. It provides mechanistic insight into the interplay between solidification physics and defect formation, offering practical guidance for parameter selection and microstructural control in Ti–22Al–25Nb additive manufacturing (AM).

36 MATERIALS SCIENCE↗

DOE OSTI2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

42 ENGINEERING↗

DOE OSTI2021

Assessing biogeographic survey gaps in bacterial diversity knowledge: A global synthesis of freshwaters

Freshwaters account for 0.8% of Earth's surface area, yet support >10% of known plant and animal species making them disproportionately biodiverse. Modern molecular techniques have begun to reveal microbial diversity, but application of these approaches to address global microbial biogeography is relatively unknown in freshwaters. Our aim was to identify gaps in microbial data coverage along climatic and landscape disturbance gradients and among terrestrial biomes and hydrographic regions for all freshwater ecosystems and three freshwater habitat types: lakes and reservoirs (lentic); streams and rivers (lotic); and wetlands. We reviewed literature on microbial diversity in freshwaters surveyed using 16S ribosomal RNA sequencing which identify microbial taxa. We georeferenced survey locations and used a geographic information system to identify and map gaps in survey coverage using open-source data for climate, landscape disturbance, terrestrial biomes, and freshwater ecoregions. In our study, we compiled 3,425 georeferenced survey locations reported from 963 studies. Streams were surveyed most frequently (60.8% of survey locations), followed by lakes (33.5%) and wetlands (5.6%). Surveys were concentrated in North America, central and western Europe, and Southeast Asia; 35% of freshwater ecoregions were surveyed at least once across freshwater habitat types, whereas 23%, 23%, and 12% were surveyed at least once for lentic, lotic, and wetland habitat types, respectively. The climatic gap analysis indicated coverage is high for temperate regions but lacking in the tropics and Arctic, particularly for wetland ecosystems. Our assessment revealed high climatic coverage of freshwater microbial diversity knowledge, but expansive ecoregional gaps attributable to biased sampling near research institutions in North America, western Europe, and China. Future surveys should target ecoregions in Africa, South America, Central Asia, Australia, and Antarctica. An essential next step will be to curate and disseminate sequencing efforts to facilitate the study of processes driving global diversity patterns.

16S rRNA↗

NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗

NASA NTRS1986

Analysis and forecast experiments incorporating satellite soundings and cloud and water vapor drift wind information

A system for assimilating conventional meteorological data and satellite-derived data in order to produce four-dimensional gridded data sets of the primary atmospheric variables used for updating limited area forecast models is described. The basic principles of a data assimilation scheme as proposed by Lorenc (1984) are discussed. The design of the system and its incremental assimilation cycles are schematically presented. The assimilation system was tested using radiosonde, buoy, VAS temperature, dew point, gradient wind data, cloud drift, and water vapor motion data. The rms vector errors for the data are analyzed.

Goodman, Brian M.↗

DOE OSTI2020

Machine learning-based prediction of enzyme substrate scope: Application to bacterial nitrilases

Predicting the range of substrates accepted by an enzyme from its amino acid sequence is challenging. Although sequenc- and structure-based annotation approaches are often accurate for predicting broad categories of substrate specificity, they generally cannot predict which specific molecules will be accepted as substrates for a given enzyme, particularly within a class of closely related molecules. Combining targeted experimental activity data with structural modeling, ligand docking, and physicochemical properties of proteins and ligands with various machine learning models provides complementary information that can lead to accurate predictions of substrate scope for related enzymes. Here we describe such an approach that can predict the substrate scope of bacterial nitrilases, which catalyze the hydrolysis of nitrile compounds to the corresponding carboxylic acids and ammonia. Each of the four machine learning models (logistic regression, random forest, gradient-boosted decision trees, and support vector machines) performed similarly (average ROC = 0.9, average accuracy = ~82%) for predicting substrate scope for this dataset, although random forest offers some advantages. Finally, this approach is intended to be highly modular with respect to physicochemical property calculations and software used for structural modeling and docking.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Dynamic realization of emergent high-dimensional optical vortices

The dimensionality of vortical structures has recently been extended beyond two dimensions, providing additional topological complexity and robustness for high-capacity information processing and turbulence control. The generation of high-dimensional vortical structures has mostly been demonstrated in classical systems through the complex interference of fluidic, acoustic, or electromagnetic waves. However, natural materials rarely support three- or higher-dimensional vortical structures and their physical interactions. Here, we experimentally demonstrate a high-dimensional gradient thickness optical cavity (GTOC) in which the optical coupling of planar metal-dielectric multilayers implements topological interactions across multiple dimensions. At non-trivial topological phases, high-dimensional GTOC induces high-dimensional vortical structures in generalized parameter space in three, four dimensions, and beyond. These emergent high-dimensional vortical structures are observed under electro-optic tomography as optical vortex dynamics in two-dimensional real-space, employing the optical thicknesses of dielectric layers as synthetic dimensions. Our findings hold significant promise for emulating high-dimensional physics and developing active topological photonic devices.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Conditional filamentation as an adaptive trait of bacteria and its ecological significance in soils

Bacteria can regulate cell morphology in response to environmental conditions, altering their physiological and metabolic characteristics to improve survival. Conditional filamentation, in which cells suspend division while continuing lateral growth, is a strategy with a range of adaptive benefits. Here, we review the causes and consequences of conditional filamentation with respect to bacterial physiology, ecology and evolution. We describe four major benefits from conditional filamentation: stress tolerance, surface colonization, gradient spanning and the facilitation of biotic interactions. Adopting a filamentous growth habit involves fitness trade-offs which are also examined. We focus on the role of conditional filamentation in soil habitats, where filamentous morphotypes are highly prevalent and where environmental heterogeneity can benefit a conditional response. Here, to illustrate the use of information presented in our review, we tested the conditions regulating filamentation by the forest soil isolate Paraburkholderia elongata 5N T . Filamentation by P. elongata was induced at elevated phosphate concentrations, and was associated with the accumulation of intracellular polyphosphate, highlighting the role of filamentation in a phosphate-solubilizing bacterium. Conditional filamentation enables bacteria to optimize their growth and metabolism in environments that are highly variable, a trait that can impact succession, symbioses, and biogeochemistry in soil environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Toroidal polar topology in strained ferroelectric polymer

Polar topological texture has become an emerging research field for exotic phenomena and potential applications in reconfigurable electronic devices. Here, we report toroidal topological texture self-organized in a ferroelectric polymer, poly(vinylidene fluoride-ran-trifluoroethylene) [P(VDF-TrFE)], that exhibits concentric topology with anticoupled chiral domains. The interplay among the elastic, electric, and gradient energies results in continuous rotation and toroidal assembly of the polarization perpendicular to polymer chains, whereas relaxor behavior is induced along polymer chains. Such toroidal polar topology gives rise to periodic absorption of polarized far-infrared (FIR) waves, enabling the manipulation of the terahertz wave on a mesoscopic scale. Our observations should inform design principles for flexible ferroic materials toward complex topologies and provide opportunities for multistimuli conversions in flexible electronics.

36 MATERIALS SCIENCE↗