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NASA NTRS1992

A quasi-Hertzian stress field from an internal source: A possible working model for the Vredefort structure

The Vredefort structure is a large domal feature about 110 km southeast of Johannesburg, South Africa, situated within and almost central to the large intracratonic Witwatersrand Basin. This structure consists of an Archean core of ca. 45 km in diameter, consisting largely of granitic gneiss, surrounded by a collar of metasedimentary and metavolcanic supracrustal rocks of the Dominian Group, Witwatersrand and Ventersdorp Supergroups, and Transvaal Sequence. The interpretation of images of the gravity and magnetic fields over Vredefort has permitted the delineation of several important features of the structure and of its environment. The outline of the collar strata is a prominent feature of both the gravity and the magnetic fields. The Vredefort structure shares this distinctive geometry with other structures (e.g., Manicouagan, Decaturville, Sierra Madera) of debated impact origin. In all these, successively older strata with steep outward dips are encountered while traversing inward to the center of the structure. A further attribute of these structures is the shortening of the outcrop of a particular stratigraphic unit compared to the original perimeter of that unit. To account for the geometric attributes of the Vredefort structure a mechanical scheme is required where there is radial movement of horizontal strata toward, with uplift in, the center of the Vredefort structure. Two models can be proposed: (1) one in which there is a rapid rise and violent disruption of cover rocks in response to expansion of a fluid accumulation; and (2) one in which there is, in contrast, a nonexplosive, quasi-Hertzian stress field resulting from a diapiric process. Both models can accommodate the geometry and structural components of Vredefort.

Antoine, L. A. G.↗

NASA NTRS1986

Temporal features of the refilling of a plasmaspheric flux tube

The refilling of plasmaspheric flux tubes was studied by assuming that the protonosphere provides an ionospheric boundary where the H(+) density can be assumed; the supersonic flow in the flux tube is driven by the depletion of the plasma from the flux tube, while the base density and pressure in the protonosphere remain constant. The time-dependent continuity and momentum equations for the H(+) ions were solved. The electron gas was assumed to obey the Boltzmann law, and the proton gas was assumed to be isothermal. In agreement with the postulate of Banks et al. (1971), it was found that an important feature of the refilling is the formation of a shock pair at the equator; as the shocks propagate toward the ionosphere, the refilling occurs. Depending on the density at the ionospheric boundaries, a fair agreement was found between the refilling rates obtained for L = 6.6 and those from the GEOS 2 observations.

Singh, Nagendra↗

DOE OSTI2024

Comparing models for a unitary black hole S matrix

This paper compares features, challenges, and puzzles of different models for a unitary black hole S -matrix, focusing on both recent nonisometric models, as well as “nonviolent unitarization,” which is based on new quantum interactions of a black hole. As a foundation for comparison, the description of real-time Hawking evolution is first overviewed, including leading effects of gravitational dressing and backreaction. Connection is then made to qubit models for evolution, and some technology is outlined to facilitate their description. Important features of both nonisometric models and nonviolent unitarization are investigated in qubit models, which illustrate essential differences between the respective approaches. The nonisometric models present puzzles for understanding evolution of internal outgoing excitations, which can be excited by interactions such as particle decay. Qubit models for nonviolent unitarization are further developed, and nicely illustrate aspects of that approach. Some remaining questions in generalizing to more complete models for evolution are discussed. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

NASA NTRS2016

Satellite Sea Ice Measurements in the Arctic Ocean

Satellite observations sustained since 1979 have been the primary source of information to reveal Arctic Ocean sea ice extent is diminishing rapidly. Sea ice is also thinning based on historical surface and near-surface records and more recent satellite retrievals. There is a new “normal” environment in the Arctic environment with substantial socio-economic impacts. A key question is: What are the spatial and temporal characteristics of the sea ice thickness distribution throughout the annual cycle, and what is the evolving inter-annual trend? Arctic Ocean sea ice conditions have been determined with passive microwave radiometer, active scatterometer and other satellite instrument measurements recorded by a continuing series of satellites. An important feature of the continuous time series measurements has been the overlap of each new satellite dataset with ongoing measurements so as to provide adequate time intervals for calibration and validation. Satellite instrument diversity has provided both coarse spatial resolution measurements over the entire Arctic Ocean for long-period time series and limited-duration fine spatial resolution data over selected regions for navigation and other applications. Satellite observations of Arctic Ocean sea ice will continue to increase in importance because predictability of sea ice is poor and societal interest is great. Unfortunately, however the sustainability of some critical elements of the current Arctic Ocean satellite measurement suite beyond 2020 remains uncertain, e.g., after the CryoSat-2 and ICESat-2 missions have concluded.

Halpern, David↗

DOE OSTI2022

Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations

This work investigates origins of cation ordering in double perovskites using first-principles theory computations combined with machine learning (ML) and causal relations. We have considered various oxidation states of A, A', B, and B' from the family of transition metal ions to construct a diverse compositional space. A conventional framework employing traditional ML classification algorithms such as Random Forest (RF) coupled with appropriate features including geometry-driven and key structural modes leads to accurate prediction (~98%) of A-site cation ordering. We have evaluated the accuracy of ML models by employing analyses of decision paths, assignments of probabilistic confidence bound, and finally a direct non-Gaussian acyclic structural equation model to investigate causality. Our study suggests that structural modes are crucial for classifying layered, columnar, and rock-salt ordering. The charge difference between A and A' is the most important feature for predicting clear layered ordering, which in turn depends on the B and B' charge separation. We have also designed mathematical relationships with these features to derive energy differences to form clear layered ordering. Here, the trilinear coupling between tilt, in-phase rotation, and A-site antiferroelectric displacement in the Landau free-energy expansion becomes the necessary condition behind formation of A-site cation ordering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1975

A generalized correlation of experimental flat-plate collector performance

A generalized correlation of flat-plate collector performance obtained by outdoor and indoor test methods is presented. This correlation shows that the indoor (simulator) test approach is a special case of the general situation of variable solar conditions. The important feature of the generalized correlation is that it permits a separation of the solar variables (flux, incident angle, etc.) which affect collector performance from the collector parameters (absorptance, transmittance, heat loss, etc.) which also affect collector performance and which are uniquely part of a given collector design. The correlation permits an evaluation of the relative merits of using instantaneous, hourly and daily collector efficiencies in obtaining a good collector correlation. The question of the transient behavior outdoors of a collector is an important part of determining whether to use instantaneous, hourly or daily efficiency values in a correlation approach. Correlation of the experimental performance of collectors allows the following: (1) comparisons of different collector designs, (2) collector performance prediction under conditions that differ from the conditions of the test program, and (3) monitoring performance degradation effects.

Simon, F. F.↗

NASA NTRS1995

Three-dimensional ring current decay model

This work is an extension of a previous ring current decay model. In the previous work, a two-dimensional kinetic model was constructed to study the temporal variations of the equatorially mirroring ring current ions, considering charge exchange and Coulomb drag losses along drift paths in a magnetic dipole field. In this work, particles with arbitrary pitch angle are considered. By bounce averaging the kinetic equation of the phase space density, information along magnetic field lines can be inferred from the equator. The three-dimensional model is used to simulate the recovery phase of a model great magnetic storm, similar to that which occurred in early February 1986. The initial distribution of ring current ions (at the minimum Dst) is extrapolated to all local times from AMPTE/CCE spacecraft observations on the dawnside and duskside of the inner magnetosphere spanning the L value range L = 2.25 to 6.75. Observations by AMPTE/CCE of ring current distributions over subsequent orbits during the storm recovery phase are compared to model outputs. In general, the calculated ion fluxes are consistent with observations, except for H(+) fluxes at tens of keV, which are always overestimated. A newly invented visualization idea, designated as a chromogram, is used to display the spatial and energy dependence of the ring current ion differential flux. Important features of storm time ring current, such as day-night asymmetry during injection and drift hole on the dayside at low energies (less than 10 keV), are manifested in the chromogram representation. The pitch angle distribution is well fit by the function, J(sub o)(1 + Ay(sup n)), where y is sine of the equatorial pitch angle. The evolution of the index n is a combined effect of charge exchange loss and particle drift. At low energies (less than 30 keV), both drift dispersion and charge exchange are important in determining n.

Fok, Mei-Ching↗

NASA NTRS1994

A three-dimensional ring current decay model

This work is an extension of a previous ring current decay model. In the previous work, a two-dimensional kinetic model was constructed to study the temporal variations of the equatorially mirroring ring current ions, considering charge exchange and Coulomb drag losses along drift paths in a magnetic dipole field. In this work, particles with arbitrary pitch angle are considered. By bounce averaging the kinetic equation of the phase space density, information along magnetic field lines can be inferred from the equator. The three-dimensional model is used to simulate the recovery phase of a model great magnetic storm, similar to that which occurred in early February 1986. The initial distribution of ring current ions (at the minimum Dst) is extrapolated to all local times from AMPTE/CCE spacecraft observations on the dawn and dusk sides of the inner magnetosphere spanning the L value range L = 2.25 to 6.75. Observations by AMPTE/CCE of ring current distributions over subsequent orbits during the storm recovery phase are compared to model outputs. In general, the calculated ion fluxes are consistent with observations, except for H+ fluxes at tens of keV, which are always over-estimated. A newly-invented visualization idea, designated as a chromogram, is used to display the spatial and energy dependence of the ring current ion differential flux. Important features of storm-time ring current, such as day-night asymmetry during injection and drift hole on the dayside at low energies (less than 10 keV), are manifested in the chromogram representation. The pitch angle distribution is well fit by the function, j(sub o)(1+Ay(exp n)), where y is sine of the equatorial pitch angle. The evolution of the index n is a combined effect of charge exchange loss and particle drift. At low energies (less than 30 keV), both drift dispersion and charge exchange are important in determining n.

Fok, Mei-Ching↗

DOE OSTI2021

Progress toward favorable landscapes in quantum combinatorial optimization

The performance of variational quantum algorithms relies on the success of using quantum and classical computing resources in tandem. Here, we study how these quantum and classical components interrelate. In particular, we focus on algorithms for solving the combinatorial optimization problem MaxCut, and study how the structure of the classical optimization landscape relates to the quantum circuit used to evaluate the MaxCut objective function. In order to analytically characterize the impact of quantum features on the critical points of the landscape, we consider a family of quantum circuit ansätze composed of mutually commuting elements. We identify multiqubit operations as a key resource and show that overparameterization allows for obtaining favorable landscapes. Namely, we prove that an ansatz from this family containing exponentially many variational parameters yields a landscape free of local optima for generic graphs. However, we further prove that these ansätze do not offer superpolynomial advantages over purely classical MaxCut algorithms. Here, we then present a series of numerical experiments illustrating that noncommutativity and entanglement are important features for improving algorithm performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery

Tropical cyclones are an important cause of forest disturbance, and major storms caused severe structural damage and elevated tree mortality in coastal tropical forests. Model capabilities that can be used to understand post-hurricane forest recovery are still limited. We use a vegetation demography model, the Functionally Assembled Terrestrial Ecosystem Simulator, coupled with the Energy Exascale Earth System Model Land Model (ELM-FATES) to study the processes and the key factors regulating post-hurricane forest recovery. We implemented hurricane-induced forest damage, including defoliation, structural biomass reduction, and tree mortality, performed ensemble model simulations, and used random forest feature importance. For the simulation in the Luquillo Experimental Forest, Puerto Rico, we identified factors controlling the post-hurricane forest recovery, and quantified the sensitivity of key model parameters to the post-hurricane forest recovery. The results indicate a tendency for the Bisley forests to shift toward the light demanding plant functional type (PFT) when the pre-hurricane biomass between the light demanding and shade tolerant PFTs is nearly equal and forests experience hurricane disturbance with mortality >60% for both the two PFTs. Under more realistic conditions where the shade tolerant PFT is initially dominant, mortality >80% is required for a shift toward dominance of the light demanding PFT at Bisley. Hurricane mortality and background mortality are the two major factors regulating post-hurricane forest recovery in simulations. This research improves understanding of the ELM-FATES model behavior associated with hurricane disturbance and provides guidance for dynamic vegetation model development in representing hurricane induced forest damage with varied intensities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Pulse: An Outlier Sensitive Downsampling Algorithm For Timeseries Data

Pulse is a downsampling algorithm for timeseries data. Frequently datasets become so large that visualization tools and web browsers cannot effectively render graphics due to memory constraints. Downsampling algorithms are commonly applied to minimize the quantity of data required to visualize important features or trends in the data, but some datasets are composed by distinct enough features and trends that most existing downsampling algorithms fail to preserve them. Pule was developed to downsample timeseries data for galvanostatic stack test data at the Idaho National Laboratory. These datasets were composed by approximately 4 million records, most of them being extremely uniform. However, during relatively brief time periods when the stack test changes state, for example when the test article is powered on, or a load is added, the data produce sparse asymptotes. No existing downsampling algorithm was capable of preserving the sparse asymptotes in electrolysis stack test data. Instead, we develop a downsampling algorithm that preserves important outliers in data, and otherwise aggressively downsamples uniform data. The algorithm has applications in other domains like seismology, in the measurement of earthquakes, or astronomy, in the measurement of quasars or transit photometry.

Woodruff, Nathan [Idaho National Laboratory (INL),↗

NASA NTRS1993

Cognitive factors associated with immersion in virtual environments

Immersion into the dataspace provided by a computer, and the feeling of really being there or 'presence', are commonly acknowledged as the uniquely important features of virtual reality environments. How immersed one feels appears to be determined by a complex set of physical components and affordances of the environment, and as yet poorly understood psychological processes. Pimentel and Teixeira say that the experience of being immersed in a computer-generated world involves the same mental shift of 'suspending your disbelief for a period of time' as 'when you get wrapped up in a good novel or become absorbed in playing a computer game'. That sounds as if it could be right, but it would be good to get some evidence for these important conclusions. It might be even better to try to connect these statements with theoretical positions that try to do justice to complex cognitive processes. The basic precondition for understanding Virtual Reality (VR) is understanding the spatial representation systems that localize our bodies or egocenters in space. The effort to understand these cognitive processes is being driven with new energy by the pragmatic demands of successful virtual reality environments, but the literature is largely sparse and anecdotal.

Psotka, Joseph↗

DOE OSTI2021

Microfluidic Liquid-Liquid Extraction Chip with Integrated Raman Sensors (Phase I and Phase II Final Report)

As demand for electricity continues to increase worldwide, the world’s nuclear power generating capacity will continue to grow. Nuclear power is the most environmentally benign way of producing electricity on a large scale. The long-term successful use of nuclear power, however, is critically dependent upon adequate and safe processing and disposal of spent nuclear fuels. A very important feature of nuclear energy is that spent fuels can be reprocessed to recover fissile and fertile materials that can then be used as fresh fuel for nuclear power plants. The DOE-NE Fuel Cycle Research and Development (FCR&D) is currently developing nuclear material reprocessing technologies. In a typical nuclear fuel reprocessing system, centrifugal contactors are used as liquid-liquid extraction devices where two immiscible liquids are mixed at high speeds using a rotor, which creates a fine dispersion of droplets of organic phase in an aqueous phase that contains the analyte. Understanding the extraction efficiency at these contactors through modeling and simulation is important in the development of reprocessing technologies and the optimization of current technologies such as the PUREX process. The outcome of this program will be a liquid-liquid microfluidic flow cell chip with embedded spectroscopic sensors for the analysis of extracted analytes. This device will be useful in the optimization of new fuel reprocessing schemes as well as existing reprocessing processes by providing a microfluidic modeling platform to optimize extraction parameters. The Phase I and Phase II work developed a microfluidic chip design that allows integration of Raman and absorption fiber optic probes and successfully demonstrated the feasibility of using Raman and absorption probes to detect key analytes that partition into the aqueous and organic phases.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

The information content of jet quenching and machine learning assisted observable design

Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning techniques to identify important features that distinguish jets produced in heavy-ion collisions from jets produced in proton-proton collisions. We formulate the problem using binary classification and focus on leveraging machine learning in ways that inform theoretical calculations of jet modification: (i) we quantify the information content in terms of Infrared Collinear (IRC)-safety and in terms of hard vs. soft emissions, (ii) we identify optimally discriminating observables that are in principle calculable in perturbative QCD, and (iii) we assess the information loss due to the heavy-ion underlying event and background subtraction algorithms. We illustrate our methodology using Monte Carlo event generators, where we find that important information about jet quenching is contained not only in hard splittings but also in soft emissions and IRC-unsafe physics inside the jet. This information appears to be significantly reduced by the presence of the underlying event. We discuss the implications of this for the prospect of using jet quenching to extract properties of the QGP. Since the training labels are exactly known, this methodology can be used directly on experimental data without reliance on modeling. We outline a proposal for how such an experimental analysis can be carried out, and how it can guide future measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

maslov-group/FUN-PROSE

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Liu, Simon↗

DOE OSTI2023

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Classifying Agnostic Biosignatures using Raman, VNIR, and Elemental Data

How can we use our current wealth of terrestrial data, encompassing biogenic and abiogenic systems, to determine the distinguishing properties of life? SCOBI (Statistical Classification of Biosignature Information) uses machine learning techniques to algorithmically identify combinations of measurements that are “indicative of life”. A set of ~1000 observations, comprising elemental abundance, isotopic fractionation, VNIR reflectance, and (in progress) Raman spectra, have been assembled from existing literature and databases. The observations cover systems classified as “indicative alive” (e.g., cells, vegetation), “indicative non-alive” (e.g., fossils, teeth), “mixed indicative” (e.g., soil, pond water), or “non-indicative” (e.g., rocks, meteorites). VNIR data was preprocessed by linear interpolation from 400-2100 nm and smoothed with a Savitzky-Golay filter. To limit the amount of Earth-biochemistry-specific (non-agnostic) information included, the first five spectral features extracted were number of peaks, number of troughs, mean reflectance, mean peak width, and broadest peak width. To help further emphasize agnostic biosignatures, Earth-specific features such as chlorophylls have been manually flagged so that feature importance with and without them can be compared. Classifiers including k-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented, as was a combination voting classifier. Performance metrics included false positive rates, false negative rates, and AUC with 50-50 test/train splits (Monte Carlo simulations). Key takeaways from this stage, prior to the inclusion of Raman spectra, are (1) the overall success rate of 0.933 AUC was most heavily influenced by the elemental abundance data; and (2) VNIR reflectance had the lowest classification performance with 0.52 AUC (58% of objects correctly classified). The next steps are to complete integration of Raman spectral data and to improve the approach to pre-processing and feature extraction for both types of spectral data, such as automated baseline removal, whole spectrum matching, and dimensionality reduction.

Biosignatures↗

NASA NTRS1995

MODTRAN3: An update and recent validations against airborne high resolution interferometer measurements

MODTRAN, the Moderate Resolution Atmospheric Radiance and Transmittance Model, encompasses all the capabilities of LOWTRAN 7, the widely used 20 cm(exp -1) resolution radiance code, but incorporates a much more sensitive molecular band model with 2 cm(exp -1) resolution. MODTRAN contains many important elements that other band model based radiative transfer codes do not incorporate. It shares with FASCODE: spherical geometry, single and multiple scattering default atmospheric profile descriptors (gases, aerosols, clouds, fogs, and rain), and molecular continua (H2O, CO2, O3, O2, N2). In addition, it can calculate the solar/lunar direct and scattered radiation. MODTRAN3 was released to the general public in November 1994. It has several important features that the previous version, MODTRAN2, does not have. Chloro-fluorocarbon (CFC) and related heavy molecules (whose spectroscopic properties first appear on the HITRAN92 data base as temperature-dependent cross sections) have been incorporated into pseudo-band models, with provision for using both default and user supplied profiles. The addition of SO2 and O2 in the UV, along with upgraded ozone Chappuis bands in the visible is also part of MODTRAN3. An improved multiple scattering algorithm, the DIScrete Ordinate Radiative Transfer (DISORT) has also been incorporated into MODTRAN3. MODTRAN is very fast: simple timing runs of MODTRAN3 vs. FASCOD3 show an improvement of more than a factor of 100 for a typical 500 cm(exp -1) spectral interval and comparable vertical layering. Speed is an important consideration in heating/cooling rates calculations, where a large number of radiative transfer calculations are needed. The MODTRAN3 used in this study is based on HITRAN92, but as mentioned, above, it will be upgraded to HITRAN94 upon its release at the end of 1994. MODTRAN has been adopted by: some researchers in the AVIRIS program as one radiative transfer code to derive surface reflectance from AVIRIS measurements. The accuracy of the code is very important because any errors in the radiative transfer calculation will directly translate into errors in the derived surface reflectance. In this paper, the new solar irradiance calculated by Kurucz, which is adopted in MODTRAN3, will be presented. Recent validations of MODTRAN3 with airborne high resolution interferometer measurements over ocean will be discussed. Good agreeement between model calculations and measurements was achieved.

Anderson, Gail P.↗