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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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13 records

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NASA NTRS2018

Combining Sparsity DEM Inversions with Event Tracking for AIA Data

We introduce a modified version of the ASGARD code (Automated Selection and Grouping of events in A/A Regional Data). Originally written to detect and group brightenings ("events") in the AIA EUV channels, it now includes the sparsity DEM inversion method and instead detects emission measure enhancements in different temperature bins. Ultimately, the goal is to automatically determine heating and cooling rates in different coronal structures.

Bethge, Christian↗

NASA NTRSDate not supplied

Optimizing spectroheliogram deconvolution methods : MaGIXS - A case study

Over the past five years, new methods to reconstruct spectrally pure maps of the Sun from spectroheliogram data have emerged. In particular, with the rebirth of long-abandoned slitless imaging spectroscopy, one may obtain both spatial and spectral information over a large field of view simultaneously. Yet, depending on the size of the extended source combined with the extent of spectral dispersion, there will be locations in the focal plane where spectral lines from different spatial locations overlap and must be deconvolved. An unfolding method has been successfully developed and demonstrated on the recent rocket flight MaGIXS, which observed several strong emission lines (9 to 30$\AA$) from different portions of two active regions. In the work we are going to present, we conduct a systematic investigation of the parameters that controls and optimizes the inversion method to unfold overlappogram data. We also demonstrate a derived method of the inversion that does not require previous assumptions on the thermal and ionization equilibrium and abundance state of the plasma.

Spectroheliogram↗

NASA NTRS2018

Vegetation Height Estimate in Rice Fields Using Single Polarization TanDEM-X Science Phase Data

This study presents the retrieval of rice paddy height using single polarization (single-pol) interferometric SAR (InSAR) data by means of model-based height inversion without an external DEM. A total of eight TanDEM-X (TDX) scenes were used and the TDX images were; acquired using a large cross-track baseline configuration during the TDX Science Phase (June–August 2015). A single-pol inversion approach for a flooded rice field is proposed and evaluated over the Buan test site in South Korea. A novel approach is adopted for the estimation of the ground (i.e., water level) interferometric phase within a flooded rice paddy from TDX data acquired during a period of early rice growth. It is consequently possible to apply the model-based inversion algorithm to the single-pol InSAR data. Rice height maps during the rice growth cycle presented and validated by field measurements. The results demonstrated the high performance of the inversion with a correlation coefficient of 0.78 and an RMSE of 0.10 m. The proposed methodology will be useful to monitor rice plants and to predict a gross rice yield, along with dual and fully polarimetric interferometric SAR data.

Lee, Seung-Kuk↗

NASA NTRS2014

On Characterizing Particle Shape

It is well known that particle shape affects flow characteristics of granular materials, as well as a variety of other solids processing issues such as compaction, rheology, filtration and other two-phase flow problems. The impact of shape crosses many diverse and commercially important applications, including pharmaceuticals, civil engineering, metallurgy, health, and food processing. Two applications studied here include the dry solids flow of lunar simulants (e.g. JSC-1, NU-LHT-2M, OB-1), and the flow properties of wet concrete, including final compressive strength. A multi-dimensional generalized, engineering method to quantitatively characterize particle shapes has been developed, applicable to both single particle orientation and multi-particle assemblies. The two-dimension, three dimension inversion problem is also treated, and the application of these methods to DEM model particles will be discussed. In the case of lunar simulants, flow properties of six lunar simulants have been measured, and the impact of particle shape on flowability - as characterized by the shape method developed here -- is discussed, especially in the context of three simulants of similar size range. In the context of concrete processing, concrete construction is a major contributor to greenhouse gas production, of which the major contributor is cement binding loading. Any optimization in concrete rheology and packing that can reduce cement loading and improve strength loading can also reduce currently required construction safety factors. The characterization approach here is also demonstrated for the impact of rock aggregate shape on concrete slump rheology and dry compressive strength.

Ennis, Bryan J.↗

DOE OSTI2021

The divergence of nearby trajectories in soft-sphere DEM

The n-body instability is investigated with the soft-sphere discrete element method. The divergence of nearby trajectories is quantified by the dynamical memory time. Using the inverse proportionality between the dynamical memory time and the largest Lyapunov exponent, the soft-sphere discrete element method results are compared to previous hard-sphere molecular dynamics data for the first time. Good agreement is observed at low concentrations and the degree of instability is shown to increase asymptotically with increasing spring stiffness. At particle concentrations above 30%, the soft-sphere Lyapunov exponents increase faster than the corresponding hard-sphere data. Finally, this paper concludes with a demonstration of how this case study may be used in conjunction with regression testing and code verification activities.

42 ENGINEERING↗

DOE OSTI2018

MFIX-DEM Phi: Performance and Capability Improvements Towards Industrial Grade Open-source DEM Framework with Integrated Uncertainty Quantification

Two major challenges hinder the effective use and adoption of multiphase computational fluid dynamics tools by the industry. The first is the need for significant computational resources, which is inversely proportional to the accuracy of solutions due to computational intensity of the algorithms. The second barrier is assessing the prediction credibility and confidence in the simulation results. In this project, a multi-tiered approach has been proposed under four broad activities to overcome these challenges while addressing all of the objectives outlined in FOA-0001238 through Phases 1 and 2 of the project. The present report consists of the results for only Phase 1, which was the funded performance period. From the start the project, all of the objectives outlined in FOA were addressed through four major activity tasks in an integrated and balanced fashion to improve adoption of MFIX suite of solvers for industrial use. The first task aimed to improve the performance of MFIX-DEM specifically targeting to acquire the peak performance on Intel Xeon and Xeon Phi based systems, which are expected to be one of the primary high-performance computing platforms both affordable and available for the industrial users in the next two to five years. However, due to a number of changes in course of the project, the scope of the performance improvements related task was significantly reduced to avoid duplicate work. Hence, more emphasis was placed on the other three tasks as discussed below. The second task aimed at physical modeling enhancements through implementation of polydispersity capability and validation of heat transfer models in MFIX. An extended verification and validation (V&V) study was performed for the new polydispersity feature implemented in MFIX-DEM both for granular and coupled gas-solid flows. The features of the polydispersity capability and results for an industrially relevant problem were disseminated through journal papers (one published and one under review at the time of writing of the final technical report). As part of the validation efforts, another industrially relevant problem of interest based on rotary drums was studied for several modes of heat transfer and results were presented in conferences. Third task was aimed towards an important and unique contribution of the project, which was to develop a unified uncertainty quantification framework by integrating MFIX-DEM with a graphical user interface (GUI) driven uncertainty quantification (UQ) engine, i.e., MFIX-GUI and PSUADE. The goal was to enable a user with only modest knowledge of statistics to effectively utilize the UQ framework offered with MFIX-DEM Phi to perform UQ analysis routinely. For Phase 1, a proof-of-concept demonstration of the proposed framework was completed and shared. Direct industry involvement was one of the key virtues of this project, which was performed through forth task. For this purpose, even at the proposal stage, the project team received strong interest in the proposed capabilities from two major corporations, which were further expanded throughout Phase 1 and a new collaboration with another major corporation from chemical industry was also initiated. The level of interest received and continued collaboration for the project during Phase 1 clearly shows the relevance and potential impact of the project for the industrial users.

42 ENGINEERING↗

NASA NTRS2014

Polarimetric SAR Interferometry Evaluation in Mangroves

TanDEM-X (TDX) enables to generate an interferometric coherence without temporal decorrelation effect that is the most critical factor for a successful Pol-InSAR inversion, as have recently been used for forest parameter retrieval. This paper presents mangrove forest height estimation only using single-pass/single-baseline/dual-polarization TDX data by means of new dual-Pol-InSAR inversion technique. To overcome a lack of one polarization in a conventional Pol- InSAR inversion (i.e. an underdetermined problem), the ground phase in the Pol-InSAR model is directly estimated from TDX interferograms assuming flat underlying topography in mangrove forest. The inversion result is validated against lidar measurement data (NASA's G-LiHT data).

Mangrove↗

DOE OSTI2023

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

15 GEOTHERMAL ENERGY↗

DOE OSTI2018

Classification of particle height in a hopper bin from limited discharge data using convolutional neural network models

Hopper bin discharge processes are ubiquitous in many industrial applications. The preponderance of previous studies of this system has been focused on the forward discharge process given an initial particle packing configuration. Motivated by the needs in reaction design and optimization, we develop here an inverse reconstruction procedure that enables one to obtain the particle height information in the initial packing configuration in the hopper bin from limited discharge dynamics data and particle characteristics. Our procedure is based on convolutional neural network (CNN) models, which take experimentally measurable particle residence time, diameter and density as input, and provide a classification of particle height in the initial packing as output. Using MFIX-DEM simulations with enhanced physical modeling capabilities, we generate extensive discharge data for hoppers containing two distinct solid phase particles with four distinct classes of initial packing geometries. The CNN reconstruction models are subsequently trained and tested using the discharge data. Here, we find that the reconstruction (i.e., particle height classification) accuracy strongly depends on the initial packing geometry. For hopper-specific CNN models exclusively trained using discharge data from a single hopper, the classification can attain an accuracy up to almost 90%. The accuracy decreases for generic reconstruction models trained using a collection of discharge data for multiple hoppers. Finally, we apply the CNN model to accurately reconstruct a hopper containing four distinct solid phases with a layered configuration to further demonstrate its utility.

42 ENGINEERING↗

DOE OSTI2020

Plate motion in sheared granular fault system

Plate motion near the fault gouge layer, and the elastic interplay between the gouge layer and the plate under stick-slip conditions, is key to understanding the dynamics of sheared granular fault systems. Here, a two-dimensional implementation of the combined finite-discrete element method (FDEM), which merges the finite element method (FEM) and the discrete element method (DEM), is used to explicitly simulate a sheared granular gouge fault system. In this work we focus on investigating the influence of normal load, driving shear velocity and plate stiffness on the velocities and displacements in the direction parallel to the shear direction (x-direction) measured at locations on the upper and lower plates just adjacent to the gouge. The simulations show that during slip phases the magnitudes of the measured velocities on the upper and lower plates are proportional to the normal load and may be inversely proportional to the square root of the plate's shear modulus. Whereas, the driving shear velocity does not show distinct influence on the measured velocities. Additionally, large slip velocities are generally associated with large macroscopic friction coefficient drops. For the models subjected to smaller normal loads, larger shear velocities and with stiffer shear plates, the same magnitude of slip velocity could cause a larger drop of macroscopic friction coefficient. During stick phases, the velocities of the upper and lower plates are respectively slightly greater and slightly smaller than half of the driving shear velocity and are both in the same direction of shear. The shear strain rate of the gouge is calculated from this velocity difference between the upper and lower plate during stick phases and thus the gouge effective shear modulus can be calculated. The results show that the gouge effective shear modulus increases proportionally with normal load, while the influence of shear velocity and plate stiffness on gouge effective shear modulus is minor. The simulations address the dynamics of a laboratory-scale fault gouge system and may aid in revealing the complexities of earthquake frictional dynamics.

58 GEOSCIENCES↗

DOE OSTI2019

Wetness index based on landscape position and topography ($\mathrm{WILT}$): Modifying TWI to reflect landscape position

Water and land resource management planning benefits greatly from accurate prediction and understanding of the spatial distribution of wetness. The topographic wetness index (TWI) was conceived to predict relative surface wetness, and thus hydrologic responsiveness, across a watershed based on the assumption that shallow slope-parallel flow is a major driver of the movement and distribution of soil water. The index has been extensively used in modeling of landscape characteristics responsive to wetness, and some studies have shown the TWI performs well in landscapes where interflow is a dominant process. However, groundwater flow dominates the hydrology of low-slope landscapes with high subsurface conductivities, and the TWI assumptions are not likely to perform well in such environments. For groundwater dominated systems, we propose a hybrid wetness index (Wetness Index based on Landscape position and Topography, WILT) that inversely weights the upslope contributing area by the distance to the nearest surface water feature and the depth to groundwater. When explicit depth to groundwater data are not available, height above and separation from surface water features can act as surrogates for proximity to groundwater. Here, the resulting WILT map provides a more realistic spatial distribution of relative wetness across a low-slope Coastal Plain landscape as demonstrated by improved prediction of hydric soils, depth to groundwater, nitrogen and carbon concentrations in the A horizon of the soil profile, and sensitivity to DEM scale.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Geometric Scale-up Experiments on Fluidization of Geldart B Glass Beads

The objective of this work is to provide a valuable database from controlled experiments for validating computational models. Recently, coarse-grained techniques such as particle-in-cell (PIC) or coarse-grained discrete element modeling (DEM) have gained popularity due to their computational efficiency while modeling large-scale systems; however, the influence of model parameters and their sensitivities at different geometric scales and flow conditions remain to be analyzed. These datasets are critical for the multiphase flow research community to assess predictive capability of modeling techniques as well as elucidate the hydrodynamic behavior in these systems. This study performed fluidization experiments using three different test sections with internal diameters of 2.5, 4, and 6 in. The operating conditions, bed material, and range of flow velocities at the inlet were constant in all the units, which were not hydrodynamically scaled. Glass beads having a Sauter Mean Diameter of 332 μm were used. Superficial velocity was varied from 2.97 to 5.35 times the minimum fluidization velocity and the initial static bed height was 0.1524 m. The order in which the experiments were performed was randomized and replicates were included to provide uncertainty in measurements. Statistics of differential pressure and bed height from these tests were reported. Future plans include validating PIC methodology in the open-source software, MFiX (Multiphase Flow with Interphase Exchanges) using results from this study. This could further be extended to determine optimal model parameters using inverse techniques such as deterministic calibration or Bayesian inference.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Geometric Scale-up Experiments on Fluidization of Geldart B Glass Beads

42 ENGINEERING↗