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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy

Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by the convergence angle of nanoscale focusing optics which creates simultaneous dependency of the far-field scattering data on three independent components of the local strain tensor-corresponding to dilation and two potential rigid body rotations of the unit cell. All three components are in principle resolvable through a spatially mapped sample tilt series; however, traditional data analysis is computationally expensive and prone to artifacts. In this study, we implement NanobeamNN, a convolutional neural network specifically tailored to the analysis of scanning probe X-ray microscopy data. NanobeamNN learns lattice strain and rotation angles from simulated diffraction of a focused X-ray nanobeam by an epitaxial thin film and can directly make reasonable predictions on experimental data without the need for additional fine-tuning. We demonstrate that this approach represents a significant advancement in computational speed over conventional methods, as well as a potential improvement in accuracy over the current standard.

Luo, Aileen [Cornell Univ., Ithaca, NY (United Sta↗

DOE OSTI2016

Understanding to Hierarchical Microstructures of Crab (Chinese hairy) Shell as a Natural Architecture

This work was done to better understand the microstructures, composition and mechanical properties of Chinese hairy crab shell. For fully revealing its hierarchical microstructure, the crab shell was observed with electron microscope under different magnifications from different facets. XRD, EDS, FTIR and TGA techniques have been used to characterize the untreated and chemically-treated crab shells, which provided enough information to determine the species and relative content of components in this biomaterial. Combined the microstructures with constituents analysis, the structural principles of crab shell was detailedly realized from different structural levels beyond former reports. To explore the relationship between structure and function, the mechanical properties of shell have been measured through performing tensile tests. The contributions of organics and minerals in shell to the mechanical properties were also discussed by measuring the tensile strength of de-calcification samples treated with HCl solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

An Integrated Approach to Parameter Learning in Infinite-Dimensional Space

The availability of sophisticated modern physics codes has greatly extended the ability of domain scientists to understand the processes underlying their observations of complicated processes, but it has also introduced the curse of dimensionality via the many user-set parameters available to tune. Many of these parameters are naturally expressed as functional data, such as initial temperature distributions, equations of state, and controls. Thus, when attempting to find parameters that match observed data, being able to navigate parameter-space becomes highly non-trivial, especially considering that accurate simulations can be expensive both in terms of time and money. Existing solutions include batch-parallel simulations, high-dimensional, derivative-free optimization, and expert guessing, all of which make some contribution to solving the problem but do not completely resolve the issue. In this work, we explore the possibility of coupling together all three of the techniques just described by designing user-guided, batch-parallel optimization schemes. Our motivating example is a neutron diffusion partial differential equation where the time-varying multiplication factor serves as the unknown control parameter to be learned. We find that a simple, batch-parallelizable, random-walk scheme is able to make some progress on the problem but does not by itself produce satisfactory results. After reducing the dimensionality of the problem using functional principal component analysis (fPCA), we are able to track the progress of the solver in a visually simple way as well as viewing the associated principle components. This allows a human to make reasonable guesses about which points in the state space the random walker should try next. Thus, by combining the random walker's ability to find descent directions with the human's understanding of the underlying physics, it is possible to use expensive simulations more efficiently and more quickly arrive at the desired parameter set.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Framework for the Economic Analysis of Hybrid Systems Based on Exergy Consumption

Starting from an overview of the dynamic behavior of the electricity market the need of the introduction of energy users that will provide a damping capability to the system is derived as also a qualitative analysis of the impact of uncertainty, both in the demand and supply side, is performed. Then it follows an introduction to the investment analysis methodologies based on the discounting of the cash flow, and then work concludes with the illustration and application of the exergonomic principles to provide a sound methodology for the cost accounting of the plant components to be used in the cash flow analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Machine-Learned Manifold-Based Models for Large Eddy Simulation of Turbulent Combustion

Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration.

47 OTHER INSTRUMENTATION↗

DOE OSTI2012

REMOVAL OF SPECTRO-POLARIMETRIC FRINGES BY TWO-DIMENSIONAL PATTERN RECOGNITION

We present a pattern-recognition-based approach to the problem of the removal of polarized fringes from spectro-polarimetric data. We demonstrate that two-dimensional principal component analysis can be trained on a given spectro-polarimetric map in order to identify and isolate fringe structures from the spectra. This allows us, in principle, to reconstruct the data without the fringe component, providing an effective and clean solution to the problem. The results presented in this paper point in the direction of revising the way that science and calibration data should be planned for a typical spectro-polarimetric observing run.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Pulse shape discrimination in CUPID-Mo using principal component analysis

CUPID-Mo is a cryogenic detector array designed to search for neutrinoless double-beta decay ($0\nu\beta\beta$) of $^{100}$Mo. It uses 20 scintillating $^{100}$Mo-enriched Li$_2$MoO$_4$ bolometers instrumented with Ge light detectors to perform active suppression of $\alpha$ backgrounds, drastically reducing the expected background in the $0\nu\beta\beta$ signal region. As a result, pileup events and small detector instabilities that mimic normal signals become non-negligible potential backgrounds. These types of events can in principle be eliminated based on their signal shapes, which are different from those of regular bolometric pulses. We show that a purely data-driven principal component analysis based approach is able to filter out these anomalous events, without the aid of detector response simulations.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Risk Importance Measures in the Designand Operation of Nuclear Power Plants

This monograph presents and discusses risk importance measures as quantified by the probabilistic risk assessment (PRA) models of nuclear power plants (NPPs) developed according to the current standards and practices. Usually, PRA tools calculate risk importance measures related to a single ?basic event? representing particular failure mode. This is, then, reflected in many current PRA applications. The monograph focuses on the concept of ?component-level? importance measures that take into account different failure modes of the component including common-cause failures (CCFs). In opening sections the roleof risk assessment in safety analysis of an NPP is introduced and discussion given of ?traditional?, mainly deterministic, design principles which have been established to assign a level of importance to a particular system, structure or component. This is followed by an overview of main risk importance measures for risk increase and risk decrease from current PRAs. Basic relations which exist among the measures are shown. Some of the current practical applications of risk importancemeasures from the field of NPP design, operation and regulation are discussed. The core of the monograph provides a discussion on theoreticalbackground and practical aspects of main risk importance measures at the level of ?component? as modeled in a PRA, starting from the simplest case, single basic event, and going toward more complexcases with multiple basic events and involvements in CCF groups. The intent is to express the component-level importance measures via theimportance measures and probabilities of the underlying single basic events, which are the inputs readily available from a PRA model andits results. Formulas are derived and discussed for some typical cases. The formulas and their results are demonstrated through some practicalexamples, done by means of a simplified PRA model developed in and run by RiskSpectrum? tool, which are presented in the appendices. The monograph concludes with discussion of limitations of the use of risk importance measures and a summary of component-level importance cases evaluated.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2014

Mathematical simulation of the amplification of 1790-nm laser radiation in a nuclear-excited He – Ar plasma containing nanoclusters of uranium compounds

The possibility of applying dusty active media in nuclearpumped lasers has been considered. The amplification of 1790-nm radiation in a nuclear-excited dusty He – Ar plasma is studied by mathematical simulation. The influence of nanoclusters on the component composition of the medium and the kinetics of the processes occurring in it is analysed using a specially developed kinetic model, including 72 components and more than 400 reactions. An analysis of the results indicates that amplification can in principle be implemented in an active laser He – Ar medium containing 10-nm nanoclusters of metallic uranium and uranium dioxide. (lasers)

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2016

Phases and interfaces from real space atomically resolved data: Physics-based deep data image analysis

Advances in electron and scanning probe microscopies have led to a wealth of atomically resolved structural and electronic data, often with ~1–10 pm precision. However, knowledge generation from such data requires the development of a physics-based robust framework to link the observed structures to macroscopic chemical and physical descriptors, including single phase regions, order parameter fields, interfaces, and structural and topological defects. Here, we develop an approach based on a synergy of sliding window Fourier transform to capture the local analog of traditional structure factors combined with blind linear unmixing of the resultant 4D data set. This deep data analysis is ideally matched to the underlying physics of the problem and allows reconstruction of the a priori unknown structure factors of individual components and their spatial localization. We demonstrate the principles of this approach using a synthetic data set and further apply it for extracting chemical and physically relevant information from electron and scanning tunneling microscopy data. Furthermore, this method promises to dramatically speed up crystallographic analysis in atomically resolved data, paving the road toward automatic local structure–property determinations in crystalline and quasi-ordered systems, as well as systems with competing structural and electronic order parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Boosting RDataFrame performance with transparent bulk event processing

RDataFrame is ROOT’s high-level interface for Python and C++ data analysis. Since it first became available, RDataFrame adoption has grown steadily and it is now poised to be a major component of analysis software pipelines for LHC Run 3 and beyond. Thanks to its design inspired by declarative programming principles, RDataFrame enables the development of highperformance, highly parallel analyses without requiring expert knowledge of multi-threading and I/O: user logic is expressed in terms of self-contained, small computation kernels tied together by a high-level API. This design completely decouples analysis logic from its actual execution, and opens several interesting avenues for workflow optimization. In particular, in this work we explore the benefits of moving internal data processing from an event-by-event to a bulkby-bulk loop. This refactoring dramatically reduces the framework’s runtime overheads; in collaboration with the I/O layer it improves data access patterns; it exposes information that optimizing compilers might use to auto-vectorize the invocation of user-defined computations; finally, while existing user-facing interfaces remain unaffected, it becomes possible to additionally offer interfaces that explicitly expose bulks of events, useful e.g. for the injection of GPU kernels into the analysis workflow. In order to inform similar future R&D, design challenges will be presented, as well as an investigation of the relevant timememory trade-off backed by novel performance benchmarks.

Guiraud, Enrico↗

DOE OSTI2024

Convergence Analysis for an Online Data-Driven Feedback Control Algorithm

This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system’s state via indirect observations, alongside an efficient stochastic maximum principle-type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Machine learning etudes in astrophysics: selection functions for mock cluster catalogs

Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an observed group of objects is a well-suited problem for machine learning classification. In this paper we use one-class classifiers to learn the properties of an observed catalog of clusters of galaxies from ROSAT and to pick clusters from mock simulations that resemble the observed ROSAT catalog. We show how this method can be used to study the cross-correlations of thermal Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster catalogs. The method reduces the bias due to hand-tuning the selection function and is readily scalable to large catalogs with a high-dimensional space of astrophysical features.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Deciphering a removed text on a 16th c. centrepiece: The benefits of using AGLAEMap and AXSIA to process PIXE maps.

In the field of archaeometry, it is not uncommon to be presented with art objects that contain inscriptions, signatures and other writings that are nearly impossible to read. Scanned microbeam PIXE offers an attractive approach to attack this problem, but even then the distribution of characteristic X-rays of the element(s) used in these writings can remain illegible. We show in this paper that two methods were used to reveal the inscription: first the use of a GUPIXWin, TRAUPIXE and AGLAEMap software suite enables to make quantitative analysis of each pixel, to visualize the results and to select X-ray peaks that could enable to distinguish letters. Then, the Automated eXpert Spectral Image Analysis (AXSIA) program developed at Sandia, which analyzes the x-ray intensity vs. Energy and (X, Y) position “datacubes”, was used to factor the datacube into 1) principle component spectral shapes and 2) the weighting images of these components. The specimen selected for this study was a silver plaque representing a scroll from the so-called “MerkelscheTafelaufsatz,” a centrepiece made by the Nuremberg goldsmith Wenzel Jamnitzer in 1549. X-ray radiography of the plaque shows lines of different silver thicknesses, meaning that a text has been removed. The PIXE analysis used a 3-MeV proton beam focused to 50μm and scanned across the sample on different areas of interest of several cm². This analysis showed major elements of Cu and Ag, and minor elements such as Pb, Au, Hg. X-ray intensity maps were then made by setting windows on the various x-ray peaks but the writing on the centrepiece was not revealed even if the map of Cu after data treatment at AGLAE enabled to distinguish some letters. The AXSIA program enabled to factor two main spectral shapes from the datacube that were quite similar and involved virtually all of the X-rays being generated. Nevertheless, small differences between these factors were observed for the Cu K X-rays, Pb, Bi and Au L X-rays. The plot of the factor with the highest Au signal gave also information on the shape of some letters. The comparison of the results obtained by the two methods shows that they both drastically improve the resolution and contrast of such writings and that each of the method can also bring different information on the composition and thus the techniques used for the writing.

AGLAEMap↗

DOE OSTI2021

A Theoretical Approach for Reliability Within Information Supply Chains With Cycles and Negations

Complex networks of information processing systems, or information supply chains, present challenges for performance analysis. Here, we establish a mathematical setting, in which a process within an information supply chain can be analyzed in terms of the functionality of the system’s components. Principles of this methodology are rigorously defended and induce a model for determining the reliability for the various products in these networks. Our model does not limit us from having cycles in the network, as long as the cycles do not contain negation. It is shown that our approach to reliability resolves the nonuniqueness caused by cycles in a probabilistic Boolean network. An iterative algorithm is given to find the reliability values of the model, using a process that can be fully automated. This automated method of discerning reliability is beneficial for systems managers. As a systems manager considers systems modification, such as the replacement of owned and maintained hardware systems with cloud computing resources, the need for comparative analysis of system reliability is paramount. The model is extended to handle conditional knowledge about the network, allowing one to make predictions of weaknesses in the system. Finally, to illustrate the model’s flexibility over different forms, it is demonstrated on a system of components and subcomponents.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Combining biophysical methods for the analysis of protein complex stoichiometry and affinity in SEDPHAT

Global multi-method analysis for protein interactions (GMMA) can increase the precision and complexity of binding studies for the determination of the stoichiometry, affinity and cooperativity of multi-site interactions. The principles and recent developments of biophysical solution methods implemented for GMMA in the software SEDPHAT are reviewed, their complementarity in GMMA is described and a new GMMA simulation tool set in SEDPHAT is presented. Reversible macromolecular interactions are ubiquitous in signal transduction pathways, often forming dynamic multi-protein complexes with three or more components. Multivalent binding and cooperativity in these complexes are often key motifs of their biological mechanisms. Traditional solution biophysical techniques for characterizing the binding and cooperativity are very limited in the number of states that can be resolved. A global multi-method analysis (GMMA) approach has recently been introduced that can leverage the strengths and the different observables of different techniques to improve the accuracy of the resulting binding parameters and to facilitate the study of multi-component systems and multi-site interactions. Here, GMMA is described in the software SEDPHAT for the analysis of data from isothermal titration calorimetry, surface plasmon resonance or other biosensing, analytical ultracentrifugation, fluorescence anisotropy and various other spectroscopic and thermodynamic techniques. The basic principles of these techniques are reviewed and recent advances in view of their particular strengths in the context of GMMA are described. Furthermore, a new feature in SEDPHAT is introduced for the simulation of multi-method data. In combination with specific statistical tools for GMMA in SEDPHAT, simulations can be a valuable step in the experimental design.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2014

New constraints on cosmic polarization rotation from B-mode polarization in the cosmic microwave background

SPTpol, POLARBEAR, and BICEP2 have recently measured the cosmic microwave background (CMB) B-mode polarization in various sky regions of several tens of square degrees and obtained BB power spectra in the multipole range 20-3000, detecting the components due to gravitational lensing and to inflationary gravitational waves. We analyze jointly the results of these three experiments and propose modifications to their analyses of the spectra to include in the model, in addition to the gravitational lensing and the inflationary gravitational wave components, and also the effects induced by the cosmic polarization rotation (CPR), if it exists within current upper limits. Although in principle our analysis would also lead to new constraints on CPR, in practice these can only be given on its fluctuations (δα{sup 2}), since constraints on its mean angle are inhibited by the derotation which is applied by current CMB polarization experiments, in order to cope with the insufficient calibration of the polarization angle. The combined data fits from all three experiments (with 29% CPR-SPTpol correlation, depending on the theoretical model) gives the constraint (δα{sup 2}){sup 1/2} < 27.3 mrad (1.°56), with r = 0.194 ± 0.033. These results show that the present data are consistent with no CPR detection and the constraint on CPR fluctuation is about 1.°5. This method of constraining the CPR is new, is complementary to previous tests, which use the radio and optical/UV polarization of radio galaxies and the CMB E-mode polarization, and adds a new constraint for the sky areas observed by SPTpol, POLARBEAR, and BICEP2.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models

Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use a principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.

79 ASTRONOMY AND ASTROPHYSICS↗