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NASA NTRS1994

The use of the AOA TTR-4P GPS receiver in operation at the BIPM for real-time restitution of GPS time

The Global Positioning System is an outstanding tool for the dissemination of time. Using mono-channel C/A-code GPS time receivers, the restitution of GPS time through the satellite constellation presents a peak-to-peak discrepancy of several tens of nanoseconds without SA but may be as high as several hundreds of nanoseconds with SA. As a consequence, civil users are more and more interested in implementing hardware and software methods for efficient restitution of GPS time, especially in the framework of the project of a real-time prediction of UTC (UTCp) which could be available in the form of time differences (UTCp - GPS time). Previous work, for improving the real-time restitution of GPS time with SA, to the level obtained without SA, focused on the implementation of a Kalman filter based on past data and updated at each new observation. An alternative solution relies upon the statistical features of the noise brought about by SA; it has already been shown that the SA noise is efficiently reduced by averaging data from numerous satellites observed simultaneously over a sufficiently long time. This method was successfully applied to data from a GPS time receiver, model AOA TTR-4P, connected to the cesium clock kept at the BIPM. This device, a multi-channel, dual frequency, P-code GPS time receiver, is one of the first TTR-4P units in operation in a civil laboratory. Preliminary comparative studies of this new equipment with conventional GPS time receivers are described in this paper. The results of an experimental restitution of GPS time, obtained in June 1993, are also detailed: 3 to 6 satellites were observed simultaneously with a sample interval of 15 s, an efficient smoothing of SA noise was realized by averaging data on all observed satellites over more than 1 hour. When the GPS system is complete in 1994, 8 satellites will be observable continuously from anywhere in the world and the same level of uncertainty will be obtained using a shorter averaging time.

Thomas, Claudine↗

NASA NTRS1994

Multifractal characterizations of nonstationarity and intermittency in geophysical fields: Observed, retrieved, or simulated

Geophysical data rarely show any smoothness at any scale, and this often makes comparison with theoretical model output difficult. However, highly fluctuating signals and fractal structures are typical of open dissipative systems with nonlinear dynamics, the focus of most geophysical research. High levels of variability are excited over a large range of scales by the combined actions of external forcing and internal instability. At very small scales we expect geophysical fields to be smooth, but these are rarely resolved with available instrumentation or simulation tools; nondifferentiable and even discontinuous models are therefore in order. We need methods of statistically analyzing geophysical data, whether measured in situ, remotely sensed or even generated by a computer model, that are adapted to these characteristics. An important preliminary task is to define statistically stationary features in generally nonstationary signals. We first discuss a simple criterion for stationarity in finite data streams that exhibit power law energy spectra and then, guided by developments in turbulence studies, we advocate the use of two ways of analyzing the scale dependence of statistical information: singular measures and qth order structure functions. In nonstationary situations, the approach based on singular measures seeks power law behavior in integrals over all possible scales of a nonnegative stationary field derived from the data, leading to a characterization of the intermittency in this (gradient-related) field. In contrast, the approach based on structure functions uses the signal itself, seeking power laws for the statistical moments of absolute increments over arbitrarily large scales, leading to a characterization of the prevailing nonstationarity in both quantitative and qualitative terms. We explain graphically, step by step, both multifractal statistics which are largely complementary to each other. The geometrical manifestations of nonstationarity and intermittency, 'roughness' and 'sparseness', respectively, are illustrated and the associated analytical (differentiability and continuity) properties are discussed. As an example, the two techniques are applied to a series of recent measurements of liquid water distributions inside marine stratocumulus decks; these are found to be multifractal over scales ranging from approximately 60 m to approximately 60 km. Finally, we define the 'mean multifractal plane' and show it to be a simple yet comprehensive tool with many applications including data intercomparison, (dynamical or stochastic) model and retrieval validations.

Davis, Anthony↗

NASA NTRS2000

[Compton GRO Cycle 7]

The goals of this project were to use the improved location accuracy of combined BATSE/Ulysses-IPN gamma-ray burst (GRB) data to search for small-angle clustering within the burst population and to search for possible correlations between bursts and catalogs of known astrophysical objects. The research was completed in several phases due to the length of time needed to process the basic BATSE/Ulysses data and to develop the appropriate analysis algorithms. In the first phase, BATSE/Ulysses data were gathered from their respective sources and processed to yield, final BATSE burst catalog locations and final BATSE/Ulysses IPN timing annuli. The final data are published. In the next phase, traditional statistical analysis techniques were used to search a subset of the data for small-angle clustering and. correlations, and to produce upper limits. Next, the unique analysis techniques and software described in the proposal were developed and,applied to the final set of data (corresponding to the 4B catalog). The final search did not find any significant self-clustering or correlations with supernovae, AGN, or near by galaxy clusters. The investigation ruled-out earlier findings that indicated (with marginal significance) such results, and. provided stringent limits on the allowable fraction of bursts that are self-clustered and associated with selected known objects. An added benefit of this research came in 1998 when it was suggested that some gamma-ray bursts may be associated with supernova (SN) explosions. We responded rapidly and applied the tools of this investigation to study this possibility. Using combined BATSE/Ulysses data, we were able to completely rule out any association between bright bursts and known supernovae, thereby questioning the validity of any GRB/SN association. If a GRB/SN association exists, it must be only with a small fraction of weak bursts.

Kippen, R. Marc↗

NASA NTRS1999

Modeling of polychromatic attenuation using computed tomography reconstructed images

This paper presents a procedure for estimating an accurate model of the CT imaging process including spectral effects. As raw projection data are typically unavailable to the end-user, we adopt a post-processing approach that utilizes the reconstructed images themselves. This approach includes errors from x-ray scatter and the nonidealities of the built-in soft tissue correction into the beam characteristics, which is crucial to beam hardening correction algorithms that are designed to be applied directly to CT reconstructed images. We formulate this approach as a quadratic programming problem and propose two different methods, dimension reduction and regularization, to overcome ill conditioning in the model. For the regularization method we use a statistical procedure, Cross Validation, to select the regularization parameter. We have constructed step-wedge phantoms to estimate the effective beam spectrum of a GE CT-I scanner. Using the derived spectrum, we computed the attenuation ratios for the wedge phantoms and found that the worst case modeling error is less than 3% of the corresponding attenuation ratio. We have also built two test (hybrid) phantoms to evaluate the effective spectrum. Based on these test phantoms, we have shown that the effective beam spectrum provides an accurate model for the CT imaging process. Last, we used a simple beam hardening correction experiment to demonstrate the effectiveness of the estimated beam profile for removing beam hardening artifacts. We hope that this estimation procedure will encourage more independent research on beam hardening corrections and will lead to the development of application-specific beam hardening correction algorithms.

NASA Discipline Musculoskeletal↗

NASA NTRS2004

Launch Vehicle Propulsion Parameter Design Multiple Selection Criteria

The optimization tool described herein addresses and emphasizes the use of computer tools to model a system and focuses on a concept development approach for a liquid hydrogen/liquid oxygen single-stage-to-orbit system, but more particularly the development of the optimized system using new techniques. This methodology uses new and innovative tools to run Monte Carlo simulations, genetic algorithm solvers, and statistical models in order to optimize a design concept. The concept launch vehicle and propulsion system were modeled and optimized to determine the best design for weight and cost by varying design and technology parameters. Uncertainty levels were applied using Monte Carlo Simulations and the model output was compared to the National Aeronautics and Space Administration Space Shuttle Main Engine. Several key conclusions are summarized here for the model results. First, the Gross Liftoff Weight and Dry Weight were 67% higher for the design case for minimization of Design, Development, Test and Evaluation cost when compared to the weights determined by the minimization of Gross Liftoff Weight case. In turn, the Design, Development, Test and Evaluation cost was 53% higher for optimized Gross Liftoff Weight case when compared to the cost determined by case for minimization of Design, Development, Test and Evaluation cost. Therefore, a 53% increase in Design, Development, Test and Evaluation cost results in a 67% reduction in Gross Liftoff Weight. Secondly, the tool outputs define the sensitivity of propulsion parameters, technology and cost factors and how these parameters differ when cost and weight are optimized separately. A key finding was that for a Space Shuttle Main Engine thrust level the oxidizer/fuel ratio of 6.6 resulted in the lowest Gross Liftoff Weight rather than at 5.2 for the maximum specific impulse, demonstrating the relationships between specific impulse, engine weight, tank volume and tank weight. Lastly, the optimum chamber pressure for Gross Liftoff Weight minimization was 2713 pounds per square inch as compared to 3162 for the Design, Development, Test and Evaluation cost optimization case. This chamber pressure range is close to 3000 pounds per square inch for the Space Shuttle Main Engine.

Shelton, Joey Dewayne↗

NASA NTRS2005

Thermal and Mechanical Property Characterization of the Advanced Disk Alloy LSHR

A low solvus, high refractory (LSHR) powder metallurgy disk alloy was recently designed using experimental screening and statistical modeling of composition and processing variables on sub-scale disks to have versatile processing-property capabilities for advanced disk applications. The objective of the present study was to produce a scaled-up disk and apply varied heat treat processes to enable full-scale demonstration of LSHR properties. Scaled-up disks were produced, heat treated, sectioned, and then machined into specimens for mechanical testing. Results indicate the LSHR alloy can be processed to produce fine and coarse grain microstructures with differing combinations of strength and time-dependent mechanical properties, for application at temperatures exceeding 1300 F.

Gabb, Timothy P.↗

NASA NTRS2010

MG Isotopic Measurement of FIB-Isolated Presolar Silicate Grains

The majority of presolar oxide and silicate grains are ascribed to origins in low-mass red giant and asymptotic giant branch (AGB) stars based on their O isotopic ratios. However, a minor population of these grains (< 10%) has O isotopic ratios incompatible with these sources. Two principle alternative sources are higher-than-solar metallicity (Z) stars or, more likely, supernovae (SN) [1-3]. These rare (Group 4) grains [3] are characterized by enrichments in O-18, and typically also enrichments in O-17. An even rarer subset of grains with extremely large enrichments in O-17 and smaller depletions in O-18 were suggested to come from binary star systems [2]. To establish the origins of these isotopically unusual grains, it is necessary to examine isotopic systems in addition to O. Presolar silicates offer several elements diagnostic of their stellar sources and nuclear processes, including O, Si, Mg, Fe and Ca. However, the database for minor element isotopic compositions in silicates is seriously lacking. To date only two silicate grains have been analyzed for Mg [4] or Fe [5]. One major complicating factor is their small size (average ~230 nm), which greatly limits the number of measurements that can be performed on any one grain and makes it more difficult to obtain statistically relevant data. This problem is compounded because the grains are identified among isotopically solar silicates, which contribute a diluting signal in isotopic measurements [1]. Thus, relatively small isotopic anomalies are missed due to this dilution effect. By applying focused ion beam (FIB) milling, we obtain undiluted Mg isotopic ratios of isolated rare presolar silicate grains to investigate their sources.

Messenger, Scott R.↗

NASA NTRS2012

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

Classification↗

NASA NTRS2018

Remote Sensing, Uncertainty Quantification, and a Theory of Data Systems; Workshop Report

The purpose of the workshop was to invite statisticians, applied mathematicians, computer scientists, data system architects, experts in remote sensing technology, and Climate and Earth System scientists to review, discuss, and plan research on issues related to large-scale, efficient analysis of distributed data using spatial statistical methods. Our motivation in organizing this event was to catalyze interchange among experts on the fast-emerging problem of analysis of distributed data. As part of SAMSI's 2017-2018 Program on Mathematical and Statistical Methods for Climate and the Earth System, a Working Group on Remote Sensing was established to address statistical and mathematical research problems in the analysis of remote sensing data. The Working Group has five subgroups: 1) Spatial Retrieval Methodology (the so-called \Spatial-X" subgroup); 2) Spatial Analysis for Hyperspectral Data (the so-called \Spatial-Y" subgroup); 3) Emulators for Complex Forward Models; 4) Optimization for Remote Sensing Retrievals; and 5) Theory of Data Systems (ToDS). The ToDS subgroup spent the first half of this academic year formulating a framework in which to consider the joint problem of a) optimizing statistical methods for environments where data are distributed and too large to move to a central location, and b) the design of data system infrastructures within which to implement those statistical methods. To x ideas, the Workshop focused on spatial statistical methods. To date there are many new spatial statistical methods designed with massive data sets in mind, in the literature. However, very few have been implemented for remote sensing data, and none have been implemented in operational settings like those used by NASA and NOAA. A major impediment to their use in these cases is that the data are not only massive, but are stored in different physical locations. These data must be brought together in some way in order to estimate spatial covariance functions, but moving data to a central location for analysis is tedious at best and impossible at worst. Some remote data reduction is almost certainly necessary, but how much? What are the consequences for inference? The fundamental issue underlying these questions is how to navigate the trade-space between costs and uncertainty in the estimates or inferences that are ultimately produced.

Braverman, Amy↗

NASA NTRS2019

POLARRIS: A POLArimetric Radar Retrieval and Instrument Simulator

This paper introduces a synthetic polarimetric radar simulator and retrieval package, POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS), for evaluating cloud‐resolving models (CRMs). POLARRIS is composed of forward (POLARRIS‐f) and inverse (retrieval and diagnostic) components (iPOLARRIS) to generate not only polarimetric radar observables (Zh, Zdr, Kdp, ρhv) but also radar‐consistent geophysical parameters such as hydrometeor identification, vertical velocity, and rainfall rates retrieved from CRM data. To demonstrate its application and uncertainties, POLARRIS is applied to simulations of a mesoscale convective system over the Southern Great Plains on 23 May 2011, using the Weather Research and Forecasting model with both spectral bin microphysics (SBM) and the Goddard single‐moment bulk 4ICE microphysics. Statistical composites reveal a significant dependence of simulated polarimetric observables (Zdr, Kdp) on the assumptions of the particle axis ratio (oblateness) and orientation angle distributions. The simulated polarimetric variables differ considerably between the SBM and 4ICE microphysics in part due to the differences in their ice particle size distributions as revealed by comparisons with aircraft measurements. Regardless of these uncertainties, simulated hydrometeor identification distributions overestimate graupel and hail fractions, especially from the simulation with SBM. To minimize uncertainties in forward model, the particle shape and orientation angle distributions of frozen particles should be predicted in a microphysics scheme in addition to the size distributions and particle densities.

Toshi Matsui↗

NASA NTRS2022

Effects of Debulking on the Fiber Microstructure and Void Distribution in Carbon Fiber Reinforced Plastics

Carbon Fiber Reinforced Plastics (CFRPs) are widely used due to their high stiffness to weight ratios. A common process manufacturers use to increase the strength to weight ratio is debulking. Debulking is the process of compacting a dry fibrous reinforcement prior to resin infusion. This process is meant to decrease the average inter-fiber distance, effectively increasing the fiber volume fraction of the sample. While this process is widely understood macroscopically its effects on fibrous microstructures have not yet been well characterized. The aim of this work is to compare the microstructures of three CFRP laminates, varying only the debulking step in the manufacturing process. High resolution serial sections of all three laminates were taken for analysis. Using these scans, the fiber positions were reconstructed. Statistical descriptors such as local fiber and void volume fractions, fiber orientation, and void distribution and morphology were then generated for each sample. Fiber clusters present within the material were identified and analyzed for each level of debulking applied. Using these descriptors, the effects of debulking on the morphology and organization of the composite microstructure was evaluated.

carbon fiber↗

DOE OSTI2020

Low Count and Background Radionuclides Analysis - 20488

The US Department of Energy (DOE) is often faced with the need to evaluate radionuclides at low concentrations. When site sample data are likely to be close to threshold activity concentrations of interest, then the means by which the radiochemical analysis is performed and reported is critical. This situation can occur when differentiating from zero (presence/absence) for radionuclides that do not occur naturally, close comparison with environmental background for naturally occurring radionuclides, close comparison with a risk- or dose-based threshold concentrations of interest, or even comparisons across studies. There are several analytical issues that are of concern, but the two that appear to cause incorrect decisions to be made most often involve establishing detection limits and subtracting ambient background conditions in the laboratory. These issues are not critical when radionuclide activity concentrations are large relative to thresholds of concern, but they seem to be poorly understood when it matters. When the comparisons are important and are likely to be close to a threshold of interest, then the general contract with the analytical laboratories needs to be changed so that the right or appropriate data are obtained. The concern is that important decisions are made incorrectly more often as greater scrutiny is placed on DoE's radionuclide cleanup or monitoring decisions by the public and other stakeholders. Examples are presented of problems that have been observed for different projects, both within and outside the realm of DOE and NRC remediation and radioactive waste disposal problems, and solutions are offered that should lead to better data from which important decisions need to be made. The first example is from Los Alamos National Laboratory (LANL) and involves radionuclide concentrations in soil and rock beneath LANL's Material Disposal Area (MDA) G. An initial review of the data led to a conclusion that americium and plutonium are a long way present beneath MDA G. A more thorough review of the data that accounted properly for ambient background and the detection limits that had been established led to the opposite conclusion. Another example is from the Nevada National Security Site where tritium results from one of the wells were unexpectedly high. Proper understanding and analysis of ambient background led to the conclusion that the increased concentrations were not so obvious, and that a different contract with the analytical laboratory was needed to provide more appropriate data to support a better determination. Other examples are used from regulatory review of projects in Nevada, where background levels and secular equilibrium for naturally occurring radionuclides are not established correctly because of analytical issues. The same basic issues have also been found to create difficulties analyzing historical data from the West Valley Demonstration Project. There is evidence in the data that the apparent lack of secular equilibrium where it is expected to exist is related to ambient background subtraction or other analytical issues. A final example is presented for analysis of Tc-99 in samples of depleted uranium. In this case, two different studies that were performed only three months apart provide quite different results. The US Environmental Protection Agency (EPA) established the data quality objectives (DQO) process in the mid-1980's to establish decision performance criteria for data collection. EPA guidance (EPA G-4, for example) clearly distinguishes between DQOs and measurement performance objectives (MQOs) that should be addressed for laboratory analysis of samples. The language of DQOs and MQOs has become confused over time it seems, and the subsequent effects seem to include a lack of attention to decision performance and a routine approach to measurement quality. In order to better address radionuclide sample analysis when the concentrations are close to thresholds of concern, which might be zero for some radionuclides, background for others, and risk-based thresholds for yet others, it is important that routine laboratory analysis methods are adjusted, and that the project team and the laboratory work closely together to ensure that the data meets the MQO requirements of laboratory analysis and reporting of results, and that the MQOs effectively support project-specific DQOs. This basic approach will be applied in Los Alamos in the coming year to the collection of moisture data from underneath MDA T that will be analyzed for americium and neptunium isotopes. Proper understanding of the radiochemistry methods and reporting, and of appropriate statistical methods is critical to the success of such projects, ensuring that the right decisions are made. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

NASA NTRS1993

Application of the target decomposition theorem to a polarimetric random media model

With advances in polarimetric radar measurements of land surfaces, the need for understanding the underlying scattering mechanisms and dominant target features has become the focus of many studies. In particular, the maximum use of the polarimetric information to identify and/or separate parameters related to the surface features such as vegetation thickness, structure, water content, and soil surface characteristics will enhance the possibility of using polarimetric radars for monitoring the earth's surface from space. In this paper, Cloude's decomposition theorem is applied to a polarimetric random media model to simulate the radar measurements of vegetated canopies. The vegetated canopies are modeled as a three layer discrete random medium with leaves and branches in the first layer, tree trunks in the second layer, and a half space of homogeneous ground with rough interface as the bottom layer. The distorted born approximation (DBA) has been used to compute full Mueller matrix of the canopy, using canonical dielectric objects such as thin discs and cylinders as leaves, branches, and trunks, respectively. The Mueller matrix and the derived covariance matrix contain information on the second order statistics of radar signals at various polarizations from the canopy. To decompose the covariance matrix to its constituent targets, the eigenvalues and eigenvectors of the covariance matrix are computed in terms of the physical parameters of the canopy. In addition, each eigenvector explicitly shows the scattering mechanisms such as odd and even reflections in the canopy. Cloude's decomposition theorem is applied using the Pauli spin matrices as a basis and an expression for the degree of disorder or the entropy for the vegetated surface is found. Then, the physical parameters estimated from in situ measurements are used in the random media to obtain realistic covariance matrices. As a result, the sensitivity of the eigenvalue spectrums and the coefficients resulting from the target decomposition theorem to the physical parameters of the canopy are examined and the possible use of Cloude's theorem to estimate vegetation and soil parameters is discussed.

Saatchi, Sasan S.↗

DOE OSTI2024

Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2008

A Goddard Multi-Scale Modeling System with Unified Physics

Numerical cloud resolving models (CRMs), which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that CRMs agree with observations in simulating various types of clouds and cloud systems from different geographic locations. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that Numerical Weather Prediction (NWP) and regional scale model can be run in grid size similar to cloud resolving model through nesting technique. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a szrper-parameterization or multi-scale modeling -framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign can provide initial conditions as well as validation through utilizing the Earth Satellite simulators. At Goddard, we have developed a multi-scale modeling system with unified physics. The modeling system consists a coupled GCM-CRM (or MMF); a state-of-the-art weather research forecast model (WRF) and a cloud-resolving model (Goddard Cumulus Ensemble model). In these models, the same microphysical schemes (2ICE, several 3ICE), radiation (including explicitly calculated cloud optical properties), and surface models are applied. In addition, a comprehensive unified Earth Satellite simulator has been developed at GSFC, which is designed to fully utilize the multi-scale modeling system. A brief review of the multi-scale modeling system with unified physics/simulator and examples is presented in this article.

Tao, W.K.↗

DOE OSTI2025

Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km‐Scale Simulations

Abstract Global kilometer‐scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model‐observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi‐model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter‐model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process‐oriented diagnostics to guide future model development.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Phase-field model of precipitation processes with coherency loss

A phase-field model is proposed to simulate coherency loss coupled with microstructure evolution. A special field variable is employed to describe the degree of coherency loss of each particle and its evolution is governed by a Ginzburg-Landau type kinetic equation. For the sake of computational efficiency, a flood-fill algorithm is introduced that can drastically reduce the required number of field variables, which allows the model to efficiently simulate a large number of particles sufficient for characterizing their statistical features during Ostwald ripening. The model can incorporate size dependence of coherency loss, metastability of coherent particles, and effectively incorporate the underlying mechanisms of coherency loss by introducing a so-called differential energy criterion. The model is applied to simulate coarsening of Al 3 Sc precipitates in aluminum alloy and comprehensively compared with experiments. Our results clearly show how the particle size distribution is changed during coherency loss and affects the coarsening rate.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Strain fields in twisted bilayer graphene

Van der Waals heteroepitaxy allows deterministic control over lattice mismatch or azimuthal orientation between atomic layers to produce long-wavelength superlattices. The resulting electronic phases depend critically on the superlattice periodicity and localized structural deformations that introduce disorder and strain. In this study we used Bragg interferometry to capture atomic displacement fields in twisted bilayer graphene with twist angles < 2°. Nanoscale spatial fluctuations in twist angle and uniaxial heterostrain were statistically evaluated, revealing the prevalence of short-range disorder in moiré heterostructures. By quantitatively mapping strain tensor fields, we uncovered two regimes of structural relaxation and disentangled the electronic contributions of constituent rotation modes. Further, we found that applied heterostrain accumulates anisotropically in saddle-point regions, generating distinctive striped strain phases. Our results establish the reconstruction mechanics underpinning the twist-angle-dependent electronic behaviour of twisted bilayer graphene and provide a framework for directly visualizing structural relaxation, disorder and strain in moiré materials.

36 MATERIALS SCIENCE↗