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Results for “Feature similarity measurement”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probing interfacial momentum closures in two-phase bubbly flow with machine learning-aided methods

Computational fluid dynamics (CFD) approach has already reached a high level of maturity for single-phase flows, however the development of closure models for two-phase flow requires additional attention. Multiphase CFD (M-CFD) methods resolve the conservation equations for mass, momentum and energy while differing in the approaches and strategies adopted in the physical closure models. The most widely adopted framework for M-CFD is the Eulerian-Eulerian two-fluid approach which assumes that all phases are co-existing inside each computational cell. For each fluid, the full set of conservation equations is solved; therefore, each fluid has a different velocity field. For adiabatic two-phase flow, the mechanisms of the interfacial momentum transfer are modeled by the interfacial forces representing different physical mechanisms. One of the crucial issues in the development and application of two-fluid model is the understanding of the interfacial momentum closures which determines the bubble distribution and migration behaviors. Dedicated experiments are performed to support the physical understanding and drive the closures’ development. However, limitations exist due to the uncertainties in the experimental measurement and the simplified analytical assumptions which have difficulties on representing the complex non-linear flow fields. In this paper, a data-driven approach, Feature Similarity Measurement (FSM), is developed and proposed to resolve the challenges of modeling the interfacial forces closures. Case study is performed with two-phase flow scenarios where the high-fidelity experimental data is available. Within the Eulerian-Eulerian two-fluid framework, only momentum equations for gas and liquid phases are solved and reduced-order interfacial momentum closures are aided with FSM. Predictions of void fraction and velocity fields are analyzed and demonstrate the potential of machine learning-driven interfacial forces closures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. In this work, by defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

DEMONSTRATION OF A DATA-DRIVEN PHYSICS-BASED APPROACH FOR COMPUTATIONALLY EFFICIENT CFD PREDICTION OF TWO-PHASE BUBBLY FLOW

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale physics-guided data-driven approach, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

42 ENGINEERING↗
DOE OSTI2020

Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1988

Toolmaker's Microscope With Video Monitor

Display accessories increase resolution and flexibility of use. Toolmaker's microscope equipped with video monitor, auxiliary lighting, and high-resolution readout devices enables noncontacting measurements of tiny slots, indentations, and similar features on parts. Measures places difficult or impossible to reach by mechanical means.

Ahmed, Arif S.↗
DOE OSTI2023

A Comparison of Solidification Structures and Submicroscale Cellular Segregation in Rapidly Solidified Stainless Steels Produced via Two-Piston Splat Quenching and Laser Powder Bed Fusion

Fusion-based additive manufacturing techniques leverage rapid solidification (RS) conditions to create parts with complex geometries, unique microscale/nanoscale morphological features, and elemental segregation. Three custom composition stainless steel alloys with varying chromium equivalence to nickel equivalence ratio (Cr eq /Ni eq ) between 1.53 and 1.95 were processed using laser powder bed fusion (LPBF) and/or two-piston splat quenching (SQ) to produce solidification rates estimated between 0.4 and 0.8 m/s. Both scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were utilized to collect high-resolution images, electron backscatter diffraction (EBSD) phase identification, and measure cellular segregation. Similar features were observed in both LPBF and SQ samples including phase and microstructure, nanoscale oxide particles, cell size, and segregation behavior. However, dislocation pileup was observed along the cell boundaries only in the LPBF austenite solidified microstructure. Targeted adjustment of the SQ feedstock Cr and Ni concentrations, within the ASTM A240 specification for 316L resulted in no observable impact on the cell size, oxide particle size, or magnitude of segregation. Also, the amount of Ni segregation in the ferrite solidified microstructures did not significantly differ, regardless of Cr/Ni eq or processing technique. Here, SQ is demonstrated as capable of simulating RS rates and microstructures similar to LPBF for use as an alternative screening tool for new RS alloy compositions.

42 ENGINEERING↗
NASA NTRS1989

Tropospheric aerosol backscatter background mode at CO2 wavelengths

A comparison is made between three climatologies of backscatter measurements in the troposphere and lower stratosphere at CO2 wavelengths. These were obtained from several locations using ground-based and airborne lidar systems. All three measurement sets show similar features, specifically, a high frequency of occurrence of low backscatter over a limited range of values in the middle and upper atmosphere (the 'background mode'). This background mode is important for the design and performance simulation of the prospective satellite sensors that rely on atmospheric aerosols as scattering targets.

Rothermel, Jeffry↗
NASA NTRS2004

Registration and Fusion of Multiple Source Remotely Sensed Image Data

Earth and Space Science often involve the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, global coverage of an area at multiple resolutions, map updating or validation of new instruments, as well as integration of data provided by multiple instruments carried on multiple platforms, e.g. in spacecraft constellations or fleets of planetary rovers. Our focus is on developing methods to perform fast, accurate and automatic image registration and fusion. General methods for automatic image registration are being reviewed and evaluated. Various choices for feature extraction, feature matching and similarity measurements are being compared, including wavelet-based algorithms, mutual information and statistically robust techniques. Our work also involves studies related to image fusion and investigates dimension reduction and co-kriging for application-dependent fusion. All methods are being tested using several multi-sensor datasets, acquired at EOS Core Sites, and including multiple sensors such as IKONOS, Landsat-7/ETM+, EO1/ALI and Hyperion, MODIS, and SeaWIFS instruments. Issues related to the coregistration of data from the same platform (i.e., AIRS and MODIS from Aqua) or from several platforms of the A-train (i.e., MLS, HIRDLS, OMI from Aura with AIRS and MODIS from Terra and Aqua) will also be considered.

LeMoigne, Jacqueline↗
NASA NTRS2002

Real-Time Integrity Monitoring of Stored Geo-Spatial Data Using Forward-Looking Remote Sensing Technology

Terrain Awareness and Warning Systems (TAWS) and Synthetic Vision Systems (SVS) provide pilots with displays of stored geo-spatial data (e.g. terrain, obstacles, and/or features). As comprehensive validation is impractical, these databases typically have no quantifiable level of integrity. This lack of a quantifiable integrity level is one of the constraints that has limited certification and operational approval of TAWS/SVS to "advisory-only" systems for civil aviation. Previous work demonstrated the feasibility of using a real-time monitor to bound database integrity by using downward-looking remote sensing technology (i.e. radar altimeters). This paper describes an extension of the integrity monitor concept to include a forward-looking sensor to cover additional classes of terrain database faults and to reduce the exposure time associated with integrity threats. An operational concept is presented that combines established feature extraction techniques with a statistical assessment of similarity measures between the sensed and stored features using principles from classical detection theory. Finally, an implementation is presented that uses existing commercial-off-the-shelf weather radar sensor technology.

Young, Steven D.↗
NASA NTRS2003

Using X-band Weather Radar Measurements to Monitor the Integrity of Digital Elevation Models for Synthetic Vision Systems

Synthetic Vision Systems (SVS) provide pilots with displays of stored geo-spatial data representing terrain, obstacles, and cultural features. As comprehensive validation is impractical, these databases typically have no quantifiable level of integrity. Further, updates to the databases may not be provided as changes occur. These issues limit the certification level and constrain the operational context of SVS for civil aviation. Previous work demonstrated the feasibility of using a realtime monitor to bound the integrity of Digital Elevation Models (DEMs) by using radar altimeter measurements during flight. This paper describes an extension of this concept to include X-band Weather Radar (WxR) measurements. This enables the monitor to detect additional classes of DEM errors and to reduce the exposure time associated with integrity threats. Feature extraction techniques are used along with a statistical assessment of similarity measures between the sensed and stored features that are detected. Recent flight-testing in the area around the Juneau, Alaska Airport (JNU) has resulted in a comprehensive set of sensor data that is being used to assess the feasibility of the proposed monitor technology. Initial results of this assessment are presented.

Young, Steve↗
DOE OSTI2020

Metalorganic chemical vapor deposition of ZnGeN 2 films on GaN: effects of cation stoichiometry on surface morphology and crystallinity

Novel optoelectronic device designs based on the heterostructures of III-N and II-IV-N 2 are promising to advance device performance significantly. For example, by utilizing InGaN-ZnGeN 2 quantum well (QW) structures instead of pure InGaN, the band structure engineering in the QW active region can lead to improved electron-hole wavefunction overlap, and thus enhance the radiative efficiency for photon generation. These novel heterostructures have great potential to address the current challenge of low quantum efficiency in InGaN QW based light emitting diodes emitting in green and beyond. The materials development of ZnGeN 2 is still at an early stage as compared to the much matured GaN material system. In an ideal octet rule preserving ordered structure of ZnGeN 2 , every N atom is coordinated by exactly two Zn and two Ge atoms. However, local violation of octet rule can be caused by non-ideal coordination of N by the cations. The disordered structure is thermodynamically less favorable but can still be achieved, for example, in kinetics-limited growth regime. The ordered ZnGeN 2 has a bandgap very close to that of GaN (~3.4 eV) and a lattice mismatch of <0.1% with GaN. Interestingly, the valence band of ZnGeN 2 has been predicted to be ~1 eV above that of GaN, which has inspired novel designs for high efficiency light emitters. In this work, we investigated the metalorganic chemical vapor deposition (MOCVD) of ZnGeN 2 films on GaN/c-sapphire templates. Diethylzinc (DEZn), germane (GeH4) and ammonia were used as the precursors for Zn, Ge and N, respectively. A systematic study was conducted to investigate the cation stoichiometry as a function of growth temperature (TG), total reactor pressure (P) and DEZn/GeH 4 molar flow rate ratio (RII/IV). Under the investigated growth window, the Zn/(Zn+Ge) composition in the films, determined from energy dispersive X-ray spectroscopy, decreased monotonically with increase in TG but increased with increase in P and RII/IV. Atom probe tomography data did not indicate the presence of any secondary phases such as Zn 3 N 2 or Ge 3 N 4 . The surface morphology and crystallinity of the grown films had strong correlation with the Zn/(Zn+Ge) composition. The scanning electron microscopy images showed that the near-stoichiometric films have planar surfaces whereas Zn-rich films had crystallites on their surface and the Zn-poor films had faceted surface. Scanning transmission electron microscopy (STEM) imaging revealed that the Zn-rich and Zn-poor films have columnar and filament-like morphology, respectively, whereas the near-stoichiometric films have continuous film-like cross-sectional morphology. TEM nano-diffraction patterns as well as X-ray diffraction 2θ-ω scan profiles indicate that the near stoichiometric films are single crystalline. Nano-diffraction pattern of the stoichiometric films resembled that of a disordered ZnGeN 2 structure. Room temperature Raman spectra of near-stoichiometric films showed only the phonon density of states like features of a cation disordered ZnGeN 2 . Cathodoluminescence and photoluminescence spectra measured at different temperatures had similar features with peak emission wavelength at ~ 2 eV. In conclusion, the stoichiometry of ZnGeN 2 films can be widely tuned by tuning the MOCVD growth parameters. The surface morphology and the crystallinity of the films were found to have strong correlation with the Zn/(Zn+Ge) composition. The stoichiometric ZnGeN 2 films grown on GaN were demonstrated with uniform surface morphology and high crystalline quality. The results from this work will provide pathway to implement ZnGeN 2 in device structures.

Karim, Md Rezaul↗
NASA NTRS2006

Direct Measurement of Wave Kernels in Time-Distance Helioseismology

Solar f-mode waves are surface-gravity waves which propagate horizontally in a thin layer near the photosphere with a dispersion relation approximately that of deep water waves. At the power maximum near 3 mHz, the wavelength of 5 Mm is large enough for various wave scattering properties to be observable. Gizon and Birch (2002,ApJ,571,966)h ave calculated kernels, in the Born approximation, for the sensitivity of wave travel times to local changes in damping rate and source strength. In this work, using isolated small magnetic features as approximate point-sourc'e scatterers, such a kernel has been measured. The observed kernel contains similar features to a theoretical damping kernel but not for a source kernel. A full understanding of the effect of small magnetic features on the waves will require more detailed modeling.

Duvall, T. L., Jr.↗
NASA NTRS1976

The 35-D X-ray profile of Her X-1

Substantial X-ray emission as reported by Ariel 5 from Her X-l midway through the extended low-state portion of its 35-day cycle is examined. A similar feature during another cycle measured from UHURU satellite is also examined. It is proposed that this feature should occur regularly, consistent with some models for the mass accretion disc which have been reported in the literature. The results of the analysis of 500 days of Ariel-5 All-Sky Monitor data are reported which support the view that the feature occurs regularly in the 35-day cycle.

Holt, S. S.↗
DOE OSTI2024

Special Nuclear Material Hold-up Measurement

Facilities that process special nuclear material (SNM) generally have a variety of equipment, shielding, and nearby radiation sources, which can pose challenges when performing nondestructive analysis (NDA) within the facility, as shown in Figure 1. In this figure, the left image features two individuals performing a holdup measurement of a large duct at a posting, utilizing a detector probe attached to a yardstick to reduce some aspect of the measurement uncertainty. Note that the distance to the holdup within the pipe from the posting cannot be accounted for. The right image similarly features several individuals guiding a holdup measurement of a ducting pipe several meters overhead, by attaching a detector probe to a long handle and lining that up with a posting on the outside of the pipe. SNM processing begets SNM holdup, and this difficulty with accurate NDA can result in unaccounted accumulation of SNM holdup within a process or area. Normal SNM processing operations can accumulate holdup within filters, pumps, pipes, ducts, other equipment, and facility support systems, as demonstrated by Figure 2. It is important for deactivation and decommissioning (D&D) gloveboxes and their ancillary equipment, material accountability, criticality safety, facility operations, waste management, radiation safety, and security to accurately characterize the location, composition, and quantity of this holdup to minimize these associated risks and potential diversion pathways. Current NDA techniques for assay of holdup are expensive, time consuming, and have high uncertainty.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Adaptively remeshed multiphysical modeling of resistance forge welding with experimental validation of residual stress fields and measurement processes

Welding processes used in the production of pressure vessels impart residual stresses in the manufactured component. Computational modeling is critical to predicting these residual stress fields and understanding how they interact with notches and flaws to impact pressure vessel durability. Here, in this work, we present a finite element model for a resistance forge weld and validate it using laboratory measurements. Extensive microstructural changes, near-melt temperatures, and large localized deformations along the weld interface pose significant challenges to Lagrangian finite element modeling. The proposed modeling approach overcomes these roadblocks in order to provide a high-fidelity simulation that can predict the residual stress state in the manufactured pressure vessel; a rich microstructural constitutive model accounts for material recrystallization dynamics, a frictional-to-tied contact model is coordinated with the constitutive model to represent interfacial bonding, and adaptive remeshing is employed to alleviate severe mesh distortion. An interrupted-weld approach is applied to the simulation to facilitate comparison to displacement measures. Several techniques are employed for residual stress measurement in order to validate the finite element model: neutron diffraction, the contour method, and the slitting method. Model-measurement comparisons are supplemented with detailed simulations that reflect the configurations of the residual-stress measurement processes themselves. The model results show general agreement with experimental measurements, and we observe some similarities in the features around the weld region. Factors that contribute to model-measurement differences are identified. Finally, we conclude with some discussion of the model development and residual stress measurement strategies, including how to best leverage the efforts put forth here for other weld problems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Resilience of the Aurivillius structure upon La and Cr doping in a Bi 5 Ti 3 FeO 15 multiferroic

Here, combining the experimental techniques of high-resolution X-ray diffraction, magnetometry, specific heat measurement, and X-ray photoelectron, Raman and dielectric spectroscopy techniques, we have studied the influence of La and Cr doping on the crystal structure and magnetism of the room temperature Aurivillius multiferroic Bi 5 Ti 3 FeO 15 by investigating the physical properties of (Bi 4 La)Ti 3 FeO 15 and Bi 5 Ti 3 (Fe 0.5 Cr 0.5 )O 15 . The parent (Bi 5 Ti 3 FeO 15 ) and the doped ((Bi 4 La)Ti 3 FeO 15 and Bi 5 Ti 3 (Fe 0.5 Cr 0.5 )O 15 ) compounds crystallize in the A2 1 am space group, which is confirmed through our analysis of high-resolution synchrotron X-ray diffraction data obtained on phase-pure polycrystalline powders. We determined the oxidation states of the metal atoms in the studied compounds as Fe 3+ , Ti 4+ , Cr 3+ , and La 3+ through the analysis of X-ray photoelectron spectroscopy data. The magnetic susceptibilities of the three compounds are marked by the absence of a long-range ordered ground state, but dominated by superparamagnetic clusters with dominant antiferromagnetic interactions. This signature of short-range magnetism is also seen in specific heat as a low temperature enhancement which is suppressed upon the application of external magnetic fields up to 8 T. Our dielectric spectroscopy experiments showed that the three studied compounds display similar features in the dielectric constant measured as a function of frequency. However, upon doping La at the Bi site, the width of the ferroelectric hysteresis loop increases for (Bi 4 La)Ti 3 FeO 15 compared to that of the parent compound Bi 5 Ti 3 FeO 15 , and with Cr doping, Bi 5 Ti 3 (Fe 0.5 Cr 0.5 )O 15 becomes a leaky dielectric. The resilience of the Aurivillius crystal structure towards doping of La at the Bi site and Cr at the Fe site is clearly seen in the bulk properties of magnetic susceptibility, specific heat and the average crystal structure. The relevance of changes in the local structure is evident from our Raman spectroscopy and X-ray pair distribution function studies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Topographic and nanomechanical mapping of plutonium surfaces

Atomic force microscopy of gallium-stabilized δ-phase plutonium coupons has been used to map their topography and qualitative mechanical character, including Derjaguin-Muller-Toporov modulus, adhesion, deformation, and dissipation. Initial topographical results demonstrate that the plutonium surface is considerably rough and heterogeneous. Qualitative nanomechanical mapping revealed distinct striation patterns that are not observed in the topography images. Further investigation of the striations in the nanomechanical modes of imaging suggest that they are not measurement artifacts, or plutonium specific features. Similar investigations of stainless steel surfaces established a strong correlation between the mechanical working of the surface (i.e. cutting and grinding) and the appearance of the striations. Reimaging of the plutonium surface after moderate oxidation occurring within an inert Ar atmosphere (~1 ppm O 2 ) over the course of 13 months revealed a diminished striation pattern. It is suggested that surface stress/strain induced by the mechanical working of the metal surfaces results in the patterns of variable modulus, adhesion, deformation, and dissipation, and that these patterns are affected by the chemical changes and aging of the plutonium surface.

36 MATERIALS SCIENCE↗