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DOE OSTI2023

Understanding the Formation of Complex Phases: The Case of FeSi 2

One of the fundamental goals of materials science is to understand and predict the formation of complex phases. In this study, FeSi 2 is considered as an illustration of complex phase formation. Although Fe and Si both crystallize with a simple structure, namely, body-centered cubic (bcc A2) and diamond (A4) structures, respectively, it is rather intriguing to note the existence of two complex structures in the Si-rich part of the phase diagram around FeSi 2 : α-FeSi 2 at high temperatures (HT) with a slight iron-deficient structure and β-FeSi2 (also referred to as Fe 3 Si 7 ) at low temperatures (LT). We re-analyze the geometry of these two phases and rely on approximant phases that make the relationship between these two phases simple. To complete the analysis, we also introduce a surrogate of the C16 phase that is observed in FeGe 2 . We clearly identify the relationship that exists between these three approximant phases, corroborated by a ground-state analysis of the Ising model for describing ordering that takes place between the transition metal element and the “vacancies”. This work is further supported by ab initio electronic structure calculations based on density functional theory in order to investigate properties and transformation paths. Finally, extension to other alloys, including an entire class of alloys, is discussed.

36 MATERIALS SCIENCE↗

NASA NTRS2001

Development of a Space Radiation Monte-Carlo Computer Simulation Based on the FLUKE and Root Codes

The radiation environment in space is a complex problem to model. Trying to extrapolate the projections of that environment into all areas of the internal spacecraft geometry is even more daunting. With the support of our CERN colleagues, our research group in Houston is embarking on a project to develop a radiation transport tool that is tailored to the problem of taking the external radiation flux incident on any particular spacecraft and simulating the evolution of that flux through a geometrically accurate model of the spacecraft material. The output will be a prediction of the detailed nature of the resulting internal radiation environment within the spacecraft as well as its secondary albedo. Beyond doing the physics transport of the incident flux, the software tool we are developing will provide a self-contained stand-alone object-oriented analysis and visualization infrastructure. It will also include a graphical user interface and a set of input tools to facilitate the simulation of space missions in terms of nominal radiation models and mission trajectory profiles. The goal of this project is to produce a code that is considerably more accurate and user-friendly than existing Monte-Carlo-based tools for the evaluation of the space radiation environment. Furthermore, the code will be an essential complement to the currently existing analytic codes in the BRYNTRN/HZETRN family for the evaluation of radiation shielding. The code will be directly applicable to the simulation of environments in low earth orbit, on the lunar surface, on planetary surfaces (including the Earth) and in the interplanetary medium such as on a transit to Mars (and even in the interstellar medium). The software will include modules whose underlying physics base can continue to be enhanced and updated for physics content, as future data become available beyond the timeframe of the initial development now foreseen. This future maintenance will be available from the authors of FLUKA as part of their continuing efforts to support the users of the FLUKA code within the particle physics community. In keeping with the spirit of developing an evolving physics code, we are planning as part of this project, to participate in the efforts to validate the core FLUKA physics in ground-based accelerator test runs. The emphasis of these test runs will be the physics of greatest interest in the simulation of the space radiation environment. Such a tool will be of great value to planners, designers and operators of future space missions, as well as for the design of the vehicles and habitats to be used on such missions. It will also be of aid to future experiments of various kinds that may be affected at some level by the ambient radiation environment, or in the analysis of hybrid experiment designs that have been discussed for space-based astronomy and astrophysics. The tool will be of value to the Life Sciences personnel involved in the prediction and measurement of radiation doses experienced by the crewmembers on such missions. In addition, the tool will be of great use to the planners of experiments to measure and evaluate the space radiation environment itself. It can likewise be useful in the analysis of safe havens, hazard migration plans, and NASA's call for new research in composites and to NASA engineers modeling the radiation exposure of electronic circuits. This code will provide an important complimentary check on the predictions of analytic codes such as BRYNTRN/HZETRN that are presently used for many similar applications, and which have shortcomings that are more easily overcome with Monte Carlo type simulations. Finally, it is acknowledged that there are similar efforts based around the use of the GEANT4 Monte-Carlo transport code currently under development at CERN. It is our intention to make our software modular and sufficiently flexible to allow the parallel use of either FLUKA or GEANT4 as the physics transport engine.

Pinsky, L. S.↗

NASA NTRS2001

A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation

Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.

Kim, Kyu-Myong↗

NASA NTRS2013

Towards Bridging the Gaps in Holistic Transition Prediction via Numerical Simulations

The economic and environmental benefits of laminar flow technology via reduced fuel burn of subsonic and supersonic aircraft cannot be realized without minimizing the uncertainty in drag prediction in general and transition prediction in particular. Transition research under NASA's Aeronautical Sciences Project seeks to develop a validated set of variable fidelity prediction tools with known strengths and limitations, so as to enable "sufficiently" accurate transition prediction and practical transition control for future vehicle concepts. This paper provides a summary of selected research activities targeting the current gaps in high-fidelity transition prediction, specifically those related to the receptivity and laminar breakdown phases of crossflow induced transition in a subsonic swept-wing boundary layer. The results of direct numerical simulations are used to obtain an enhanced understanding of the laminar breakdown region as well as to validate reduced order prediction methods.

Choudhari, Meelan M.↗

NASA NTRS2005

Intelligent Systems: Terrestrial Observation and Prediction Using Remote Sensing Data

NASA has made science and technology investments to better utilize its large space-borne remote sensing data holdings of the Earth. With the launch of Terra, NASA created a data-rich environment where the challenge is to fully utilize the data collected from EOS however, despite unprecedented amounts of observed data, there is a need for increasing the frequency, resolution, and diversity of observations. Current terrestrial models that use remote sensing data were constructed in a relatively data and compute limited era and do not take full advantage of on-line learning methods and assimilation techniques that can exploit these data. NASA has invested in visualization, data mining and knowledge discovery methods which have facilitated data exploitation, but these methods are insufficient for improving Earth science models that have extensive background knowledge nor do these methods refine understanding of complex processes. Investing in interdisciplinary teams that include computational scientists can lead to new models and systems for online operation and analysis of data that can autonomously improve in prediction skill over time.

Coughlan, Joseph C.↗

DOE OSTI2022

Role of Sea‐Surface Salinity in Simulating Historical Decadal Variations of Atlantic Meridional Overturning Circulation in a Coupled Climate Model

Abstract Extending climate prediction from seasonal to decadal timescales requires realistic initialization of not only upper ocean heat content but also the Atlantic meridional overturning circulation (AMOC). However, it remains a major challenge to realistically initialize AMOC in a coupled system while also maintaining a balanced atmosphere‐ocean initial state. This study demonstrates the feasibility of generating fully coupled historical states with realistic AMOC variability. Employing a forced ocean—sea‐ice (FOSI) model simulation as the “truth,” we show reproducibility of key features of historical AMOC decadal variability in a fully coupled model by restoring sea‐surface salinity, in addition to sea‐surface temperature restoring widely used in seasonal prediction. The atmospheric state of the restored coupled model solution is much closer to that of the free coupled simulation than to the observations used in FOSI, pointing to potential advantages of using this approach for initializing decadal predictions with reduced ocean initialization shock.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models

Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering toolboxes that relates microstructures to homogenized materials properties and establishes the structure–property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. Here in this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304 L, Tantalum, and Cantor high-entropy alloy.

304L stainless steel↗

DOE OSTI2021

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

Saturn's ionosphere: Inferred electron densities

During the two Voyager encounters with Saturn, radio bursts were detected which appear to have originated from atmospheric lightning storms. Although these bursts generally extended over frequencies from as low as 100 kHz to the upper detection limit of the instrument, 40 MHz, they often exhibited a sharp but variable low frequency cutoff below which bursts were not detected. We interpret the variable low-frequency extent of these bursts to be due to the reflection of the radio waves as they propagate through an ionosphere which varies with local time. We obtain estimates of electron densities at a variety of latitude and local time locations. These compare well with the dawn and dusk densitis measured by the Pioneer 11 Voyager Radio Science investigations, and with model predictions for dayside densities. However, we infer a two-order-of-magnitude diurnal variation of electron density, which had not been anticipated by theoretical models of Saturn's ionosphere, and an equally dramatic extinction of ionospheric electron density by Saturn's rings.

Kaiser, M. L.↗

NASA NTRS1984

Saturn's ionosphere - Inferred electron densities

During the two Voyager encounters with Saturn, radio bursts were detected which appear to have originated from atmospheric lightning storms. Although these bursts generally extended over frequencies from as low as 100 kHz to the upper detection limit of the instrument, 40 MHz, they often exhibited a sharp but variable low frequency cutoff below which bursts were not detected. We interpret the variable low-frequency extent of these bursts to be due to the reflection of the radio waves as they propagate through an ionosphere which varies with local time. We obtain estimates of electron densities at a variety of latitude and local time locations. These compare well with the dawn and dusk densities measured by the Pioneer 11 Voyager Radio Science investigations, and with model predictions for dayside densities. However, we infer a two-order-of-magnitude diurnal variation of electron density, which had not been anticipated by theoretical models of Saturn's ionosphere, and an equally dramatic extinction of ionospheric electron density by Saturn's rings. Previously announced in STAR as N84-17102

Kaiser, M. L.↗

NASA NTRS2004

Parametric Study for Increasing On-Station Duration via Unconventional Aircraft Launch Approach

The need for better atmospheric predictions is causing the atmospheric science community to look for new ways to obtain longer, higher-resolution measurements over several diurnal cycles. The high resolution, in-situ measurements required to study many atmospheric phenomena can be achieved by an Autonomous Aerial Observation System (AAOS); however, meeting the long on-station time requirements with an aerial platform poses many challenges. Inspired by the half-scale drop test of the deployable Aerial Regional-scale Environmental Survey (ARES) Mars airplane, a study was conducted at the NASA Langley Research Center to examine the possibility of increasing on-station time by launching an airplane directly at the desired altitude. The ARES Mars airplane concept was used as a baseline for Earth atmospheric flight, and parametric analyses of fundamental configuration elements were performed to study their impact on achieving desired on-station time with this class of airplane. The concept involved lifting the aircraft from the ground to the target altitude by means of an air balloon, thereby unburdening the airplane of ascent requirements. The parameters varied in the study were aircraft wingspan, payload, fuel quantity, and propulsion system. The results show promising trends for further research into aircraft-payload design using this unconventional balloon-based launch approach.

Kuhl, Christopher A.↗

NASA NTRS2014

The Use of NASA near Real-time and Archived Satellite Data to Support Disaster Assessment

With support from a NASA's Applied Sciences Program, The Short‐term Prediction Research and Transition (SPoRT) Center has explored a variety of techniques for utilizing archived and near real‐time NASA satellite data to support disaster assessment activities. MODIS data from the NASA Land Atmosphere Near Real‐time Capability for EOS currently provides true color and other imagery for assessment and potential applications including, but not limited to, flooding, fires, and tornadoes. In May 2013, the SPoRT Center developed unique power outage composites using the VIIRS Day/Night Band to represent the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF‐5 tornado that occurred on May 20. Pre‐event imagery provided by the NASA funded Web‐Enabled Landsat Data project offer a basis of comparison for monitoring post-disaster recovery efforts. Techniques have also been developed to generate products from higher resolution imagery from the recently available International Space Station SERVIR Environmental Research and Visualization System instrument. Of paramount importance is to deliver these products to end users expeditiously and in formats compatible with Decision Support Systems (DSS). Delivery techniques include a Tile Map Service (TMS) and a Web Mapping Service (WMS). These mechanisms allow easy integration of satellite products into DSS's, including the National Weather Service's Damage Assessment Toolkit for use by personnel conducting damage surveys. This poster will present an overview of the developed techniques and products and compare the strengths and weaknesses of the TMS and WMS.

McGrath, Kevin M.↗

NASA NTRS2013

Community-wide Validation of Geospace Model Ground Magnetic Field Perturbation Predictions to Support Model Transition to Operations

In this paper we continue the community-wide rigorous modern space weather model validation efforts carried out within GEM, CEDAR and SHINE programs. In this particular effort, in coordination among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), modelers, and science community, we focus on studying the models' capability to reproduce observed ground magnetic field fluctuations, which are closely related to geomagnetically induced current phenomenon. One of the primary motivations of the work is to support NOAA SWPC in their selection of the next numerical model that will be transitioned into operations. Six geomagnetic events and 12 geomagnetic observatories were selected for validation.While modeled and observed magnetic field time series are available for all 12 stations, the primary metrics analysis is based on six stations that were selected to represent the high-latitude and mid-latitude locations. Events-based analysis and the corresponding contingency tables were built for each event and each station. The elements in the contingency table were then used to calculate Probability of Detection (POD), Probability of False Detection (POFD) and Heidke Skill Score (HSS) for rigorous quantification of the models' performance. In this paper the summary results of the metrics analyses are reported in terms of POD, POFD and HSS. More detailed analyses can be carried out using the event by event contingency tables provided as an online appendix. An online interface built at CCMC and described in the supporting information is also available for more detailed time series analyses.

MAGNETIC↗

NASA NTRS2021

PBL Height from AIRS, GPS RO, and MERRA-2 Products in NASA GES DISC and Their 10 Year Seasonal Mean Intercomparison

Within the planetary boundary layer (PBL), surface forcing response, drag, turbulence, and vertical mixing are important processes and play a more critical role here than in the overlying “free atmosphere”. The PBL Height (PBLH) is an important parameter in climate models, weather forecasts, and air quality prediction. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides data processing, archiving, and distribution services for numerous Earth science products. PBLH is a parameter in three products served by GES DISC, which are from the Atmospheric Infrared Sounder (AIRS), the Global Positioning System (GPS) radio occultation (RO) experiment, and the NASA reanalysis product Modern-Era Retrospective analysis for Research and Applications – 2 (MERRA-2). These products have different spatial and temporal resolutions and coverages, and their PBLH definitions are also different. To better serve the PBL research community, we have summarized the specifications of these products. A ten-year seasonal mean intercomparison is also conducted to provide further guidance to users. The intercomparison results show that MERRA-2 has a much shallower PBL than AIRS and GPS RO. An experimental study indicates the different PBLH definition in MERRA-2 caused smaller values of PBLH. The improvement of the water vapor retrieval in AIRS version 7 over version 6 results in the version 7 PBLH agreeing better with GPS RO and MERRA-2 than version 6, especially near the equator and low latitudes.

Feng Ding↗

NASA NTRSDate not supplied

Nearest-Neighbor Machine Learning Feature Selection for Interpretation of Microbial Molecular Signatures from Isotope Ratio Mass Spectrometry Data

Mass spectrometry (MS) promises to be a powerful tool for potential biosignature detection during astrobiological missions on ocean worlds in our solar system. Accurate and generalizable machine learning methods could enhance science return on investment by predicting seawater chemistry and classifying isotopic biosignatures, either as a signature consistent with microbial life (biotic) or as a novelty (unclassified/unique). However, machine learning models are likely to be complex and involve interactions between MS features, making biosignatures difficult to interpret. Feature selection methods provide biological and chemical context that help interpret the mechanisms of machine learning models, but these methods also need the ability to detect complex interactions. Previously, we developed a machine learning feature selection algorithm called nearest-neighbor projected distance regression (NPDR) that has the ability to identify important model features that involve complex interactions and automatically reduce correlation and the dimensionality in a high-dimensional variable space. The standard distance metrics used in NPDR – Manhattan and Euclidean – assume the multivariate data are isotropic, which is often violated in real data due to differences in the covariance between variables. Thus, we extend NPDR to include a random forest distance, and other anisotropic distance metrics, for computing nearest neighbors. We also augment the isotope-ratio MS data with time-series features from the raw MS signal to improve biotic classification. We test NPDR on our novel experimental ocean world seawater analog MS data. We measure isotope fractionations of volatile CO 2 that could be measured in exospheres or plumes. Samples include baseline abiotic conditions using a range of possible seawater chemistry consistent with Europa and Enceladus, and biotic samples that include microbes in these seawaters. We use penalized NPDR with random forest proximity to identify interpretable microbial molecular signatures. We compare features with random forest importance, and we train a classifier that discriminates between biotic and abiotic samples with high accuracy. These ML-trained ocean-world analog MS data could be used to assist in identifying biosignatures during future missions.

geochemistry↗

NASA NTRSDate not supplied

Cloud-Based Time Series Analysis of Extremes: Use Cases and Applications

"Extreme weather events, such as hurricanes, tornadoes, floods, droughts, heatwaves, and blizzards, can cause widespread damage, disrupting ecosystems, agricultural production, and economies. The frequency and intensity of these events have been increasing, likely due to climate change, raising concerns and the need for more accurate analysis and predictions. NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) has migrated its long-term historical datasets, including precipitation data from MERRA-2 reanalysis, GLDAS land data assimilation, and IMERG satellite observations, to the cloud. This cloud-stored data enables scientists and researchers to utilize cloud computing for advanced modeling and forecasting of extreme weather events, eliminating the need to download large datasets. In this presentation, we will provide an overview of the cloud-based data and services managed by GES DISC; demonstrate methods for accessing and analyzing time series data stored in the cloud; and compare results across various datasets to address critical questions related to extreme precipitation. We will present use cases including: 1. Determining the average total precipitation in California during January and February from 2000 to 2024, and identifying anomalous precipitation in 2021. 2. Calculating the 10, 20, 50, and 100-year return periods for maximum daily rainfall based on 25 years of historical precipitation data (2000-2024) for Maryland.

time series↗

DOE OSTI2021

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Abstract Deep learning has emerged as a revolutionary technology for protein residue‐residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning‐based contact predictions have been achieved since then. However, little effort has been put into interpreting the black‐box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention‐based convolutional neural network for protein contact prediction, which consists of two attention mechanism‐based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free‐modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold‐determining residues in proteins. We expect the attention‐based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap .

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2005

Interpreting the International Space Station Microgravity Environment

The International Space Station (ISS) serves as a platform for microgravity research for the foreseeable future. A microgravity environment is one in which the effects of gravity are drastically reduced which then allows physical experiments to be conducted without the overpowering effects of gravity. A physical environment with very low-levels of acceleration and vibration has been accomplished by both the free fall associated with orbital flight and the design of the International Space Station. The International Space Station design has been driven by a long-standing, high-level requirement for a microgravity mode of operation. The Space Acceleration Measurement System has been in operation for nearly four years on the ISS measuring the microgravity environment in support of principal investigators and to characterize the ISS microgravity environment. The Principal Investigator Microgravity Services project functions as a detective to ascertain the source of disturbances seen in the ISS microgravity environment to allow correlation between that environment and experimental data. Payload developers need to predict the microgravity environment that will be imposed upon an experiment and ensure that the science and engineering requirements will be met. The Principal Investigator Microgravity Services project is developing n interactive tool to predict the microgravity environment at science payloads based on user defined operational scenarios. These operations (predictions and post-analyses) allow a researcher to examine the microgravity acceleration levels expected to exist when their experiment is operated and then receive an analysis of the environment which existed during their experiment operations. Presented in this paper will be descriptions of the environment predictive tool and an investigation into a previously unknown disturbance in the ISS microgravity environment.

DeLombard, Richard↗