Loads Generation Process for Multi-Commercial Supplier Space Station
Create a standardized process to accurately and consistently generate loads from evolving/maturing models, forcing functions, and configuration manifests.
Claire Puccini↗
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Results for “FUNCTIONAL MODELS”
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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Shape Servoing of Deformable Objects Using Model Estimation and Barrier Lyapunov Function
An adaptive shape servoing control method is presented in this article to manipulate a deformable object into a desired shape in 3-D. A finite-point-based representation of the deformable object is used and the deformation Jacobian matrix is approximated using Fourier series basis functions. The unknown parameters of the deformation Jacobian are learned by using the velocity applied to a control point on the object and corresponding change of positions of the points describing the entire object. An integral concurrent learning (ICL)-based parameter update law is designed along with a constrained controller to satisfy the state constraints on the motion of the control point using Barrier Lyapunov function analysis. ICL-based parameter update law uses data history of velocity and corresponding positions of the points along with their current values. An efficient algorithm to update the history stack using singular value maximization is proposed based on the structure of the regressor matrix. Simulations using a physical simulator and experiments using a robot platform are performed to validate the performance of the proposed controller on two different deformable objects.
Vrithik Raj Guthikonda↗
MOdeling charged defects inside density functional theory band gaps: An improved inside-out perspective.
Abstract not provided.
Schultz, Peter Andrew↗
A Polyhedral Finite-Element Formulation using Harmonic Shape Functions with Applications to the Modeling of Multi-Physics Fracture Processes.
Abstract not provided.
Bishop, Joseph E.↗
Flexible Computer Science Infrastructure (FleCSI)
FleCSI is a compile-time configurable framework designed to support multi-physics application development. As such, FleCSI attempts to provide a very general set of infrastructure design patterns that can be specialized and extended to suit the needs of a broad variety of solver and data requirements. Current support includes multi-dimensional mesh topology, mesh geometry, and mesh adjacency information, n-dimensional hashed-tree data structures, graph partitioning interfaces, and dependency closures. FleCSI also introduces a functional programming model with control, execution, and data abstractions that are consistent with both MPI and state-of-the-art task-based runtimes such as Legion and Charm++. The FleCSI abstraction layer provides the developer with insulation from the underlying runtime, while allowing support for multiple runtime systems, including conventional models like asynchronous MPI. The intent is to give developers a concrete set of user-friendly programming tools that can be used now, while allowing flexibility in choosing runtime implementations and optimizations that can be applied to architectures and runtimes that arise in the future. The control and execution models in FleCSI also provide formal nomenclature for describing poorly understood concepts like kernels and tasks.
Bergen, Ben↗
Multi-technology Investigation of the Atomic Structure of Calcium Silicate Hydrates
In this study, synthetic C-S-H samples were investigated to reveal the feature at atomic scale. Rietveld refinement was applied to high resolution X-ray scattering data, yielding the lattice constants of the pseudocrystal structure, as well as the crystallinity along three axes. Near Edge X-ray Absorption Fine Structure (NEXAFS) spectra was collected at calcium L3,2-edge. Evolution of calcium coordination symmetry were studied by investigating spectra characteristics. Pair Distribution Function (PDF) study yields the statistics of atom pair distribution. Coordination number of Ca and Si were obtained by integrating Radial distribution function. Atomic model based on dimeric structure were discussed and compared with experimental data. Synthetic C-S-H samples with increasing Ca/Si ratio exhibit pseudo-crystal structure, resembling Dreierketten configuration similar to natural tobermorite structure. Along c-axis, the repeated structure could not survives two layers in case of low Ca/Si ratio (0.70, 1.05). But in high Ca/Si ratio (1.42) case, the crystallinity along c-axis is much bigger. The coordination number of Ca decreases with increasing Ca/Si ratio. Octahedrally coordinated Ca are observed in sample with Ca/Si ratio of 1.42. Various dimeric models are compared with experimental data. In case of Ca/Si ratio of 1.42, SiO4 tetrahedron chain needs to be shortened in linkage, most probably by substituting bridging SiO4 tetrahedron with CaO6 octahedron. These octahedrons in interlayer space act like pins to join two adjacent layer structures together. The crystallinity is thus increased along c-axis, and average coordination number is therefore reduced. In case of Ca/Si 1.05, crystallinity is low along c-axis since, indicating that not too many Ca ions exist in interlayer space to hold two layers together. Instead, negative charge of end oxygen could be balanced by proton. Ca/Si 0.70 has long tetrahedron chain linkage within layer while the linkage between adjacent layers are not strong, resulting in low crystallinity along c-axis. Neither Ca/Si ratio 0.70 nor 1.42 sample contains any Ca in octahedral symmetry, as indicated by the weak crystal field splitting of NEXAFS spectra.
Atomic Configuration↗
NONPARAMETRIC EMPIRICAL BAYES ESTIMATION THROUGH DECONVOLUTION IN PROBABILISTIC RISK ANALYSIS
The advent of Markov Chain Monte Carlo (MCMC) simulations and availability of open source software made full hierarchical Bayesian analysis tractable and a popular choice in parameter estimation for Probabilistic Risk Assessment (PRA). However, despite its theoretical attractiveness, the hierarchical Bayesian analysis is not without its problems. Two of the most prominent practical difficulties in applying hierarchical Bayes analysis in practice to analyze source-to-source variability, for example, is its sensitivity to the selection of the first-stage prior and dependence of the rate of convergence on the selection of the first-stage prior. The first-stage prior in hierarchical Bayesian analysis is usually assumed having a parametric form, often conjugate to the likelihood function (aleatory model). To facilitate convergence for hierarchical Bayes models, the first-stage prior is frequently set through empirical Bayes estimate, which can use available historical data to find the parameters of the first-stage prior. The paper discusses a new nonparametric empirical Bayes estimation and compare it to several well-known parametric estimates such as method of moments and maximum likelihood. The new nonparametric technique exploits the prior predictive distribution as integral equation and proceeds to solve it with respect to prior assuming the sampling data distribution and aleatory model are available. The discussion covers topics such as selection of aleatory model for future data, selection of regularization parameter, and density estimation for historical data. For comparison, the paper is using contrived as well as real-world data from utilities. Utilities data represent failure data with Poisson distribution used for aleatory model.
97 MATHEMATICS AND COMPUTING↗
Identifying RNA contacts from SHAPE-MaP by partial correlation analysis
In a recent paper Siegfried et al. published a new sequence-based structural RNA assay that utilizes mutational profiling to detect base pairing (MaP). Output from MaP provides information about both pairing (via reactivities) and contact (via correlations). Reactivities can be coupled to partition function folding models for structural inference, while correlations can reveal pairs of sites that may be in structural proximity. The possibility for inference of 3D contacts via MaP suggests a novel approach to structural prediction for RNA analogous to covariance structural prediction for proteins. We explore this approach and show that partial correlation analysis outperforms na\"ive correlation analysis. Our results should be applicable to a wide range of high-throughput sequencing based RNA structural assays that are under development.
Tambe, Akshay↗
Identifying Cryptographic Functions : from Data Curation to Model Building.
Abstract not provided.
Lee, Jina↗
Arsenic Doping of Polycrystalline CdSeTe Devices for Microsecond Life-times with High Carrier Concentrations
We report seminal advances in fabrication and understanding of group V (As) doped thin-film polycrystalline CdTe-based solar cells. The devices are fabricated using a novel approach, by sublimating layers of CdSeTe and CdSeTe:As. This new method allowed us to achieve minority carrier lifetime of over 1 μs, carrier concentration of more than 5x1015 cc-1 and external radiative efficiency of over 2% in a device configuration. We find an increase in open-circuit voltage when comparing As-doped, Cu-doped and undoped devices. The choice of CdSeTe instead of a CdTe-only absorber has been explained using first-principle density functional theory model. A SCAPS device model is used to analyze the potential causes for lower open-circuit voltage.
14 SOLAR ENERGY↗
Integrating biomechanics in evolutionary studies, with examples from the amphidromous goby model system
The functional capacities of animals are a primary factor determining survival in nature. In this context, understanding the biomechanical performance of animals can provide insight into diverse aspects of their biology, ranging from ecological distributions across habitat gradients to the evolutionary diversification of lineages. To survive and reproduce in the face of environmental pressures, animals must perform a wide range of tasks, some of which entail tradeoffs between competing demands. Moreover, the demands encountered by animals can change through ontogeny as they grow, sexually mature or migrate across environmental gradients. To understand how mechanisms that underlie functional performance contribute to survival and diversification across challenging and variable habitats, we have pursued diverse studies of the comparative biomechanics of amphidromous goby fishes across functional requirements ranging from prey capture and fast-start swimming to adhesion and waterfall climbing. The pan-tropical distribution of these fishes has provided opportunities for repeated testing of evolutionary hypotheses. By synthesizing data from the lab and field, across approaches spanning high-speed kinematics, selection trials, suction pressure recordings, mechanical property testing, muscle fiber-type measurements and physical modeling of bioinspired designs, we have clarified how multiple axes of variation in biomechanical performance associate with the ecological and evolutionary diversity of these fishes. Here our studies of how these fishes meet both common and extreme functional demands add new, complementary perspectives to frameworks developed from other systems, and illustrate how integrating knowledge of the mechanical underpinnings of diverse aspects of performance can give critical insights into ecological and evolutionary questions.
59 BASIC BIOLOGICAL SCIENCES↗
Ocean feature recognition using genetic algorithms with fuzzy fitness functions (GA/F3)
A model for genetic algorithms with semantic nets is derived for which the relationships between concepts is depicted as a semantic net. An organism represents the manner in which objects in a scene are attached to concepts in the net. Predicates between object pairs are continuous valued truth functions in the form of an inverse exponential function (e sub beta lxl). 1:n relationships are combined via the fuzzy OR (Max (...)). Finally, predicates between pairs of concepts are resolved by taking the average of the combined predicate values of the objects attached to the concept at the tail of the arc representing the predicate in the semantic net. The method is illustrated by applying it to the identification of oceanic features in the North Atlantic.
Ankenbrandt, C. A.↗
Cross-polar cap potential drop and the energy coupling function
A numerical model is formulated for correlations between the cross polar cap potential, the solar wind, and the AI index and compared with satellite data. The conductivity model was devised from empirically defined relationships between the Hall and Pedersen conductivities and the measured magnitude of the horizontal magnetic disturbance near College, AK. An instantaneous conductivity distribution was generated from data gathered at, e.g., six IMS meridian chain stations. A strong correlation appeared between an energy coupling function (ECF) and the polar cap potential drop. The ECF was derived from the chain station data, implying that potential drop estimates can be made from ground-based magnetometer data.
Ahn, B.-H.↗
Hierarchy of model Kohn–Sham potentials for orbital-dependent functionals: A practical alternative to the optimized effective potential method
We describe a method for constructing a hierarchy of model potentials approximating the functional derivative of a given orbital-dependent exchange-correlation functional with respect to electron density. Each model is derived by assuming a particular relationship between the self-consistent solutions of Kohn–Sham (KS) and generalized Kohn–Sham (GKS) equations for the same functional. In the KS scheme, the functional is differentiated with respect to density, in the GKS scheme—with respect to orbitals. The lowest-level approximation is the orbital-averaged effective potential (OAEP) built with the GKS orbitals. The second-level approximation, termed the orbital-consistent effective potential (OCEP), is based on the assumption that the KS and GKS orbitals are the same. It has the form of the OAEP plus a correction term. The highest-level approximation is the density-consistent effective potential (DCEP), derived under the assumption that the KS and GKS electron densities are equal. The analytic expression for a DCEP is the OCEP formula augmented with kinetic-energy-density-dependent terms. In the case of exact-exchange functional, the OAEP is the Slater potential, the OCEP is roughly equivalent to the localized Hartree–Fock approximation and related models, and the DCEP is practically indistinguishable from the true optimized effective potential for exact exchange. All three levels of the proposed hierarchy require solutions of the GKS equations as input and have the same affordable computational cost.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
ROM-based Subset Selection Algorithm for Efficient Surrogate Modeling
This summary discusses a new methodology to support the construction of surrogate models for computationally expensive models with many input parameters and output responses. In realistic engineering calculations, such as thermal-hydraulics simulation, the cost of the simulation is typically expensive, especially when detailed models are employed to describe system behavior, thereby justifying the need for surrogate models. The cost of surrogate model construction is typically a function of the number of model parameters and the degree of model nonlinearity. For sufficiently complex models, the cost of constructing the surrogate model could be by itself overwhelmingly expensive, thereby diminishing its value. One key requirement of surrogate model construction is the ability to preselect the best parametric form to describe the model behavior over the expected range of parameter variations. This summary presents a new algorithm to help select the best approximation out of a template of functions using recent advances in reduced order modeling (ROM) techniques. The idea of selecting functions out of a template is typically referred to as subset selection in the statistical and applied mathematics community. Different from existing subset selection algorithms, we rely on ROM to help identify the most influential functions for the surrogate model. Previous work has shown that ROM can help reduce the effective dimensionality of the parameter space, which can help reduce the cost of surrogate model construction. In this summary, we adopt a different philosophy, where the fitting functions are treated as pseudo parameters and ROM is applied on the pseudo parameter space, with the result now being a ranked list of the pseudo parameters along with their contribution to the quality of the surrogate model fit. This approach allows the analyst to discard all terms that have negligible impact on the quantity of interest. A relap5 model is employed to demonstrate the application of the proposed methodology. (authors)
42 ENGINEERING↗
Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry
Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The fragmentation patterns that appear in mass spectrometry are an excellent target for artificial intelligence methods to automate and expedite the analysis of data to identify targets such as functional groups. To develop this approach, we trained models on electron ionization (a reproducible hard fragmentation technique) mass spectra so that not only the final model accuracies but also the reasoning behind model assignments could be evaluated. The convolutional neural network (CNN) models were trained on 2D images of the spectra using transfer learning of Inception V3, and the logistic regression models were trained using array-based data and Scikit Learn implementation in Python. Our training dataset consisted of 21,166 mass spectra from the United States’ National Institute of Standards and Technology (NIST) Webbook. The data was used to train models to identify functional groups, both specific (e.g., amines, esters) and generalized classifications (aromatics, oxygen-containing functional groups, and nitrogen-containing functional groups). We found that the highest final accuracies on identifying new data were observed using logistic regression rather than transfer learning on CNN models. It was also determined that the mass range most beneficial for functional group analysis is 0–100 m/z. We also found success in correctly identifying functional groups of example molecules selected from both the NIST database and experimental data. Beyond functional group analysis, we also have developed a methodology to identify impactful fragments for the accurate detection of the models’ targets. The results demonstrate a potential pathway for analyzing and screening substantial amounts of mass spectral data.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Consultancy on Large-Scale Submerged Aerobic Cultivation Process Design - Final Technical Report: February 1, 2016 -- June 30, 2016
NREL is developing an advanced aerobic bubble column model using Aspen Custom Modeler (ACM). The objective of this work is to integrate the new fermentor model with existing techno-economic models in Aspen Plus and Excel to establish a new methodology for guiding process design. To assist this effort, NREL has contracted Genomatica to critique and make recommendations for improving NREL's bioreactor model and large scale aerobic bioreactor design for biologically producing lipids at commercial scale. Genomatica has highlighted a few areas for improving the functionality and effectiveness of the model. Genomatica recommends using a compartment model approach with an integrated black-box kinetic model of the production microbe. We also suggest including calculations for stirred tank reactors to extend the models functionality and adaptability for future process designs. Genomatica also suggests making several modifications to NREL's large-scale lipid production process design. The recommended process modifications are based on Genomatica's internal techno-economic assessment experience and are focused primarily on minimizing capital and operating costs. These recommendations include selecting/engineering a thermotolerant yeast strain with lipid excretion; using bubble column fermentors; increasing the size of production fermentors; reducing the number of vessels; employing semi-continuous operation; and recycling cell mass.
09 BIOMASS FUELS↗
Self-consistent and detailed opacities from a non-equilibrium average-atom model
Modern density functional theory (DFT) is a powerful tool for accurately predicting self-consistent material properties such as equations of state, transport coefficients and opacities in high energy density plasmas, but it is generally restricted to conditions of local thermodynamic equilibrium (LTE) and produces only averaged electronic states instead of detailed configurations. We propose a simple modification to the bound-state occupation factor of a DFT-based average-atom model that captures essential non-LTE effects in plasmas—including autoionization and dielectronic recombination—thus extending DFT-based models to new regimes. Finally, we then expand the self-consistent electronic orbitals of the non-LTE DFT-AA model to generate multi-configuration electronic structure and detailed opacity spectra. This article is part of the theme issue ‘Dynamic and transient processes in warm dense matter’.
36 MATERIALS SCIENCE↗
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