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

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At least 1,207 records · Page 67

Machine learning of Kondo physics using variational autoencoders and symbolic regression

In this work, we employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions. Autoencoders are trained to find a low-dimensional, latent space representation that faithfully characterizes each element of the training set, as measured by a reconstruction error. Variational autoencoders, a probabilistic generalization of standard autoencoders, further condition the learned latent space to promote highly interpretable features. In our study, we find that the learned latent variables strongly correlate with well known, but nontrivial, parameters that characterize emergent behaviors in the Anderson impurity model. In particular, one latent variable correlates with particle-hole asymmetry, while another is in near one-to-one correspondence with the Kondo temperature, a dynamically generated low-energy scale in the impurity model. Using symbolic regression, we model this variable as a function of the known bare physical input parameters and "rediscover" the nonperturbative formula for the Kondo temperature. The machine learning pipeline we develop suggests a general purpose approach, which opens opportunities to discover new domain knowledge in other physical systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Modeling the fundamental characteristics and processes of the spacecraft functioning

The fundamental aspects of modeling of spacecraft characteristics by using computing means are considered. Particular attention is devoted to the design studies, the description of physical appearance of the spacecraft, and simulated modeling of spacecraft systems. The fundamental questions of organizing the on-the-ground spacecraft testing and the methods of mathematical modeling were presented.

Bazhenov, V. I.↗

Models of Human Information Requirements: "When Reasonable Aiding Systems Disagree"

Aircraft flight management and Air Traffic Control (ATC) automation are under development to maximize the economy of flight and to increase the capacity of the terminal area airspace while maintaining levels of flight safety equal to or better than current system performance. These goals are being realized by the introduction of flight management automation aiding and operations support systems on the flight deck and by new developments of ATC aiding systems that seek to optimize scheduling of aircraft while potentially reducing required separation and accounting for weather and wake vortex turbulence. Aiding systems on both the flight deck and the ground operate through algorithmic functions on models of the aircraft and of the airspace. These models may differ from each other as a result of variations in their models of the immediate environment. The resultant flight operations or ATC commands may differ in their response requirements (e.g. different preferred descent speeds or descent initiation points). The human operators in the system must then interact with the automation to reconcile differences and resolve conflicts. We have developed a model of human performance including cognitive functions (decision-making, rule-based reasoning, procedural interruption recovery and forgetting) that supports analysis of the information requirements for resolution of flight aiding and ATC conflicts. The model represents multiple individuals in the flight crew and in ATC. The model is supported in simulation on a Silicon Graphics' workstation using Allegro Lisp. Design guidelines for aviation automation aiding systems have been developed using the model's specification of information and team procedural requirements. Empirical data on flight deck operations from full-mission flight simulation are provided to support the model's predictions. The paper describes the model, its development and implementation, the simulation test of the model predictions, and the empirical validation process. The model and its supporting data provide a generalizable tool that is being expanded to include air/ground compatibility and ATC crew interactions in air traffic management.

Corker, Kevin↗

Molecular Assemblies, Genes and Genomics Integrated Efficiently (MAGGIE)

Final report on MAGGIE. We set ambitious goals to model the functions of individual organisms and their community from molecular to systems scale. These scientific goals are driving the development of sophisticated algorithms to analyze large amounts of experimental measurements made using high throughput technologies to explain and predict how the environment influences biological function at multiple scales and how the microbial systems in turn modify the environment. By experimentally evaluating predictions made using these models we will test the degree to which our quantitative multiscale understanding wilt help to rationally steer individual microbes and their communities towards specific tasks. Towards this end we have made substantial progress towards understanding evolution of gene families, transcriptional structures, detailed structures of keystone molecular assemblies (proteins and complexes), protein interactions, biological networks, microbial interactions, and community structure. Using comparative analysis we have tracked the evolutionary history of gene functions to understand how novel functions evolve. One level up, we have used proteomics data, high-resolution genome tiling microarrays, and 5' RNA sequencing to revise genome annotations, discover new genes including ncRNAs, and map dynamically changing operon structures of five model organisms: For Desulfovibrio vulgaris Hildenborough, Pyrococcus furiosis, Sulfolobus solfataricus, Methanococcus maripaludis and Haiobacterium salinarum NROL We have developed machine learning algorithms to accurately identify protein interactions at a near-zero false positive rate from noisy data generated using tagfess complex purification, TAP purification, and analysis of membrane complexes. Combining other genome-scale datasets produced by ENIGMA (in particular, microarray data) and available from literature we have been able to achieve a true positive rate as high as 65% at almost zero false positives when applied to the manually curated training set. Applying this method to the data representing around a quarter of the fraction space for water soluble proteins in D. vulgaris, we obtained 854 reliable pair wise interactions. Further, we have developed algorithms to analyze and assign significance to protein interaction data from bait pull-down experiments and integrate these data with other systems biology data through associative biclustering in a parallel computing environment. We will 'fill-in' missing information in these interaction data using a 'Transitive Closure' algorithm and subsequently use 'Between Commonality Decomposition' algorithm to discover complexes within these large graphs of protein interactions. To characterize the metabolic activities of proteins and their complexes we are developing algorithms to deconvolute pure mass spectra, estimate chemical formula for m/z values, and fit isotopic fine structure to metabolomics data. We have discovered that in comparison to isotopic pattern fitting methods restricting the chemical formula by these two dimensions actually facilitates unique solutions for chemical formula generators. To understand how microbial functions are regulated we have developed complementary algorithms for reconstructing gene regulatory networks (GRNs). Whereas the network inference algorithms cMonkey and Inferelator developed enable de novo reconstruction of predictive models for GRNs from diverse systems biology data, the RegPrecise and RegPredict framework developed uses evolutionary comparisons of genomes from closely related organisms to reconstruct conserved regulons. We have integrated the two complementary algorithms to rapidly generate comprehensive models for gene regulation of understudied organisms. Our preliminary analyses of these reconstructed GRNs have revealed novel regulatory mechanisms and cis-regulatory motifs, as well asothers that are conserved across species. Finally, we are supporting scientific efforts in ENIGMA with data management solutions and by integrating all of the algorithms, software and data into a Knowledgebase. For instance, we have developed the RegPrecise database (http://regprecise.lbl.gov) which represents manually curated sets of regulons laying the basis for automatic annotation of regulatory interactions in closely related species. We are also in the midst of scaling up MicrobesOnline to handle the growing volume of sequence and functional genomics data. Over the last year our efforts have been focused on providing support for additional genomic and functional genomic data types. Similarly, we have developed several visualization tools to help with the exploration of complex systems biology datasets. A case in point is the Gaggle Genome Browser (GGB), which was enhanced with visualizations for plotting peptide detections and protein-DNA binding alongside transcriptome structure, plus the ability to interactively filter by signal intensity or p-value.

54 ENVIRONMENTAL SCIENCES↗

Depolarisation of light scattered by disperse systems of low-dimensional potassium polytitanate nanoparticles in the fundamental absorption band

The results of experimental studies of depolarising properties of disperse systems on the basis of potassium polytitanate nanoplatelets and nanoribbons in the visible and near-UV spectral regions are presented. It is shown that in the fundamental absorption band of the nanoparticle material the increase in the depolarisation factor takes place for the radiation scattered perpendicularly to the direction of the probing beam. For nanoribbons a pronounced peak of depolarisation is observed, which is caused by the essential anisotropy of the particles shape and the peculiarities of the behaviour of the material dielectric function. The empirical data are compared with the theoretical results for 'nanodiscs' and 'nanoneedles' with the model dielectric function, corresponding to that obtained from optical constants of the titanium dioxide dielectric function. (laser biophotonics)

77 NANOSCIENCE AND NANOTECHNOLOGY↗

A comparison of template vs. direct model fitting for redshift-space distortions in BOSS

The growth of large-scale structure, as revealed in the anisotropic of clustering of galaxies in the low redshift Universe at z < 2, provides a stringent test of our cosmological model. The strongest current constraints come from the BOSS and eBOSS surveys, with uncertainties on σ 8 , the amplitude of clustering on an 8 h –1 Mpc scale, of less than 10 per cent. A number of different approaches have been taken to fitting this signal, leading to discrepancies of up to 1σ in the measurements of the amplitude of fluctuations at late times. We compare in some detail two of the leading approaches, one based on fitting a template cosmology whose amplitude and length scales are allowed to float with one based on varying the underlying parameters of a cosmological model directly, when fitting to the BOSS DR12 data. Holding the input data, scale cuts, window functions and modeling framework fixed we are able to isolate the cause of the differences and discuss the implications for future surveys.

redshift surveys↗

{alpha}-decay and fusion phenomena in heavy ion collisions using nucleon-nucleon interactions derived from relativistic mean-field theory

Nucleus-nucleus potentials are determined in the framework of the double-folding model for a new microscopic nucleon-nucleon (NN) interaction relativistic mean field-3-Yukawa (R3Y) derived from the popular relativistic mean-field theory Lagrangian, and the results are compared for the use of Michigan-3-Yukawa (M3Y) effective NN interactions. The double-folding potentials so obtained are further taken up in the context of the preformed cluster model (PCM) of Gupta and collaborators and the barrier penetration model to study respectively the ground-state (g.s.) {alpha}-decay and low-energy fusion reactions. In this paper, using PCM, we deduce empirically the {alpha} preformation probability P{sub 0}{sup {alpha}(emp)} from experimental data on a few g.s. {alpha} decays in the trans-lead region. For fusion reactions, two projectile-target systems {sup 12}C+{sup 208}Pb and {sup 16}O+{sup 208}Pb are selected for calculating the barrier energies as well positions, fusion cross sections ({sigma}{sub fus}), and fusion barrier distribution [D(E{sub c.m.})]. The barrier energies and positions change for the R3Y NN interactions in comparison with those of the M3Y NN interactions. We find that in the {alpha}-decay studies the values of P{sub 0}{sup {alpha}(emp)}(R3Y) are similar to those of P{sub 0}{sup {alpha}(emp)}(M3Y). Further, both NN interactions give similar {sigma}{sub fus} values using the Wong formula specifically when the R3Y NN interaction calculated {sigma}{sub fus} values are reduced by 1.5 times, and the results are in agreement with the experimental data for both the systems, especially for the higher energies. Results for D(E{sub c.m.}) are also quite similar for both choices of NN interaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Determination of the titanium spectral function from ($e,e'p$) data

The E12-14-012 experiment, performed in Jefferson Lab Hall A, has measured the (e, e'p) cross section in parallel kinematics using a natural titanium target. In this paper, we report the analysis of the dataset obtained in different kinematics for our solid natural titanium target. Data were obtained in a range of missing momentum and missing energy between 15 ≲ p m ≲ 250 MeV / c and 12 ≲ E m ≲ 80 MeV, respectively, and using an electron beam energy of 2.2 GeV. We measured the reduced cross section with ~7% accuracy as a function of both missing momentum and missing energy. Furthermore, our Monte Carlo simulation, including both a model spectral function and the effects of final-state interactions, satisfactorily reproduces the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

SPEL: Software tool for Porting E3SM Land Model with OpenACC in a Function Unit Test Framework

Most high-end computers adopt hybrid architecture, porting a large-scale scientific code onto accelerators is necessary. The paper presents a generic method for porting large-scale scientific code onto accelerators using compiler directives within a modularized function unit test platform. We have implemented the method and designed a software tool (SPEL) to port the E3SM Land Model (ELM) onto the GPUs in the Summit computer. SPEL automatically generates GPU-ready test modules for all ELM functions, such as CanopyFlux, SoilTemperature, and EcosystemDynamics. SPEL breaks the ELM into a collection of standalone unit test programs for easy code verification and further performance improvement. We further optimize several ELM test modules with advanced techniques, including memory reduction, reconstructed parallel loops, and asynchronous GPU kernel launch. We hope our study will inspire new toolkit developments that expedite large-scale scientific code porting with compiler directives.

Schwartz, Peter↗

Joint Inversion of Regional Waveform, First-Motion Polarity, and Synthetic Aperture Radar Surface Displacement for the Fourth and Sixth North Korean Declared Nuclear Explosions

Here, this study analyzed the Democratic People’s Republic of Korea’s (DPRK) fourth (DPRK4, 6 January 2016 M w 4.49) and sixth (DPRK6, 7 September 2017 M w 5.2) declared nuclear tests, employing a joint seismic and Interferometric Synthetic Aperture Radar (InSAR) inversion to improve understanding of these events and enhance moment tensor (MT) inversion capabilities. The recent efforts have focused on employing seismic waveform and InSAR geodetic deformation data separately to analyze these and the previous nuclear tests (e.g., Chiang et al., 2018; Myers et al., 2018; Wang et al., 2018). Building upon our previous work (Chi-Durán et al., 2021), we performed a joint regional waveform, first-motion (FM) polarity, and surface displacement inversion, which demonstrated improved source-type discrimination, a revised MT solution with reduced scalar moment uncertainty, and an independently constrained location. In this article, we build on the previous results for DPRK6 by including an analysis using a four-layered velocity model with free-surface topography to compute the near-source static deformation Green’s functions. The model consists of a 50 m basalt layer (⁠V P = 2.07 km/s, V S = 1.2 km/s⁠), a 250 m stratified volcanic deposit layer (⁠V P = 1.73 km/s, V S = 1.0 km/s⁠), a 700 m weathered granodiorite layer (⁠V P = 2.5 km/s, V S = 1.3 km/s⁠⁠), and a granodiorite half-space (⁠V P = 5.35 km/s, V S = 3.09 km/s⁠⁠). The half-space shares the velocity of the regional MDJ2 velocity model (Ford et al., 2010), which has proven effective for waveform inversion in the region. This model considers the range of reported values for various lithologies and weathering effects. Our findings show that using the layered velocity model enhances the recovery of source location and depth for both the explosions by improving fits and reducing uncertainties. The joint inversion also improves source-type discrimination and better constrains the scalar seismic moment necessary for downstream yield estimation.

58 GEOSCIENCES↗

Finite-difference models of ordinary differential equations - Influence of denominator functions

This paper discusses the influence on the solutions of finite-difference schemes of using a variety of denominator functions in the discrete modeling of the derivative for any ordinary differential equation. The results obtained are a consequence of using a generalized definition of the first derivative. A particular example of the linear decay equation is used to illustrate in detail the various solution possibilities that can occur.

Mickens, Ronald E.↗

Model Based Mission Assurance: Emerging Opportunities for Robotic Systems

The emergence of Model Based Systems Engineering (MBSE) in a Model Based Engineering framework has created new opportunities to improve effectiveness and efficiencies across the assurance functions. The MBSE environment supports not only system architecture development, but provides for support of Systems Safety, Reliability and Risk Analysis concurrently in the same framework. Linking to detailed design will further improve assurance capabilities to support failures avoidance and mitigation in flight systems. This also is leading new assurance functions including model assurance and management of uncertainty in the modeling environment. Further, the assurance cases, a structured hierarchal argument or model, are emerging as a basis for supporting a comprehensive viewpoint in which to support Model Based Mission Assurance (MBMA).

Mission Assurance↗

The Auto-Gopher Deep Drill

Subsurface penetration by coring, drilling or abrading is of great importance for a large number of space and earth applications. An Ultrasonic/Sonic Drill/Corer (USDC) has been in development at JPL's Nondestructive Evaluation and Advanced Actuators (NDEAA) lab as an adaptable tool for many of these applications. The USDC uses a novel drive mechanism to transform the high frequency ultrasonic or sonic vibrations of the tip of a horn into a lower frequency sonic hammering of a drill bit through an intermediate free-flying mass. The USDC device idea has been implemented at various scales from handheld drills to large diameter coring devices. A series of computer programs that model the function and performance of the USDC device were developed and were later integrated into an automated modeling package. The USDC has also evolved from a purely hammering drill to a rotary hammer drill as the design requirements increased form small diameter shallow drilling to large diameter deep coring. A synthesis of the Auto-Gopher development is presented in this paper.

Badescu, Mircea↗

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

Rajkumar, T.↗

Cosmology with decaying cosmological constant—exact solutions and model testing

We study dynamics of Λ(t) cosmological models which are a natural generalization of the standard cosmological model (the ΛCDM model). We consider a class of models: the ones with a prescribed form of Λ(t)=Λ{sub bare}+α{sup 2}/t{sup 2}. This type of a Λ(t) parametrization is motivated by different cosmological approaches. We interpret the model with running Lambda (Λ(t)) as a special model of an interacting cosmology with the interaction term −dΛ(t)/dt in which energy transfer is between dark matter and dark energy sectors. For the Λ(t) cosmology with a prescribed form of Λ(t) we have found the exact solution in the form of Bessel functions. Our model shows that fractional density of dark energy Ω{sub e} is constant and close to zero during the early evolution of the universe. We have also constrained the model parameters for this class of models using the astronomical data such as SNIa data, BAO, CMB, measurements of H(z) and the Alcock-Paczyński test. In this context we formulate a simple criterion of variability of Λ with respect to t in terms of variability of the jerk or sign of estimator (1−Ω{sub m},0−Ω{sub Λ,0}). The case study of our model enable us to find an upper limit α{sup 2} < 0.012 (2σ C.L.) describing the variation from the cosmological constant while the LCDM model seems to be consistent with various data.

79 ASTRONOMY AND ASTROPHYSICS↗

THE SPATIAL CLUSTERING OF ROSAT ALL-SKY SURVEY AGNs. II. HALO OCCUPATION DISTRIBUTION MODELING OF THE CROSS-CORRELATION FUNCTION

This is the second paper of a series that reports on our investigation of the clustering properties of active galactic nuclei (AGNs) in the ROSAT All-Sky Survey (RASS) through cross-correlation functions (CCFs) with Sloan Digital Sky Survey (SDSS) galaxies. In this paper, we apply the Halo Occupation Distribution (HOD) model to the CCFs between the RASS broad-line AGNs with SDSS luminous red galaxies (LRGs) in the redshift range 0.16 < z < 0.36 that was calculated in Paper I. In our HOD modeling approach, we use the known HOD of LRGs and constrain the HOD of the AGNs by a model fit to the CCF. For the first time, we are able to go beyond quoting merely a 'typical' AGN host halo mass, M{sub h}, and model the full distribution function of AGN host dark matter halos. In addition, we are able to determine the large-scale bias and the mean M{sub h} more accurately. We explore the behavior of three simple HOD models. Our first model (Model A) is a truncated power-law HOD model in which all AGNs are satellites. With this model, we find an upper limit to the slope ({alpha}) of the AGN HOD that is far below unity. The other two models have a central component, which has a step function form, where the HOD is constant above a minimum mass, without (Model B) or with (Model C) an upper mass cutoff, in addition to the truncated power-law satellite component, similar to the HOD that is found for galaxies. In these two models we find that the upper limits on {alpha} are still below unity, with {alpha} {approx}< 0.95 and {alpha} {approx}< 0.84 for Models B and C, respectively. Our analysis suggests that the satellite AGN occupation increases slower than, or may even decrease with, M{sub h}, in contrast to the satellite HODs of luminosity-threshold samples of galaxies, which, in contrast, grow approximately as (N{sub s}) {proportional_to} M{sup {alpha}}{sub h} with {alpha} {approx} 1. These results are consistent with observations that the AGN fraction in groups and clusters decreases with richness.

79 ASTRONOMY AND ASTROPHYSICS↗

Dielectric function of sol-gel prepared nano-granular zinc oxide by spectroscopic ellipsometry

ZnO thin films have been prepared by sol gel and deposited by spin coating. The dielectric function has been determined by spectroscopic ellipsometry. Ellipsometric spectra are inverted by a direct numerical method without using the standard fitting procedures. The obtained dielectric function presents a broad excitonic effect. The dielectric function is studied using Elliot excitonic theory including exciton plus band-to-band Coulomb interactions with standard Lorentzian broadening. A modification of this model dielectric function with independent bound and unbound exciton contributions is empirically proposed to improve modelling of the band gap excitonic peak.

36 MATERIALS SCIENCE↗

DOE Energy Frontiers Research Center for Heterogeneous Functional Materials; the “HeteroFoaM Center”

Synopsis of five year accomplishments: Devices that convert and store energy are generally made from heterogeneous constituent materials that act and interact to selectively conduct, transport, and separate mass, heat, and charge. Controlling these actions and interactions enables the technical breakthroughs that have made fuel cells, batteries, and solid state membranes, for example, essential parts of our society. In the biological sense, these materials are ‘vascular’ rather than primitive ‘cellular’ materials, in which the arrangements and configurations of the constituents (including their void phases) play essential and definitive roles in their functional capabilities. In 2009 a group of investigators, with lifetime investments of effort in the understanding of heterogeneous materials, recognized that the design of such material systems is not an optimization problem as such. Local interactions of the constituents create “emergent” properties and responses that are not part of the formal set of constituent characteristics, in much the same sense that society and culture is created by the group interactions of the people involved. The design of emergent properties is an open question in all formal science, but for energy materials the lack of this foundation science relegates development tasks to Edisonian trial and error, with anecdotal success and frequent costly failures. That group defined, for the first time, multi-scale heterogeneous functional materials with functional disordered and void phase regions as “HeteroFoaM,” and formed the first multidisciplinary research team to define and codify the foundation science of that material class. The primary goal of the HeteroFoaM Center was, and is, to create and establish the multi-scale fundamental knowledge and related methodology required for the rational and systematic multiphysics design of heterogeneous functional materials and their interfaces and surfaces for applications in energy transformation and storage. The scope of the HeteroFoaM center was focused on the discovery and development of the control science of key phenomena across multiple length scales that create functionality in heterogeneous materials and their structured interfaces, boundaries, and surfaces for applications in energy technologies. The HeteroFoaM Center defined a critical path and established an essential foundation for progress in the field of heterogeneous functional materials. Perhaps the single most important element of progress was the establishment of the capability to design, characterize, and model heterogeneous functional materials at the conformal level, i.e., for a limited set of material systems, the HeteroFoaM team defined how to control the order / disorder at the atomic level, the surfaces, and the interfaces for selected constituent morphologies, and to use multiphysical models to explain the remarkable property variations resulting from that control science for several heterogeneous material systems. For those cases we defined “meso-structures” (at various scales) where the interactive physics of constituent phases acted to create emergent properties, e.g., strongly emergent mixed conductor behavior and ionic transport. The general approach used by this EFRC is shown in Fig. 1. The HeteroFoaM Center created the genre of Heterogeneous Functional Materials with functional surfaces and interfaces (including void phases) called HeteroFoaM as a science platform to enable rational analysis and design of functional material systems by focusing on the meso-interactions that drive emergent response. The team firmly established this approach with over 180 archival publications (see “Publications” section), 7 patent applications, and over 100 invited lectures in 15 countries on this topic, enabled by building a remarkably effective and uniquely coherent research team. Indeed, our team was our principal strength; this problem eluded solution earlier because such a team was not available.

36 MATERIALS SCIENCE↗