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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Analytical method for nitroaromatic explosives in radiologically contaminated soil for ISO/IEC 17025 accreditation

Here, unique hazards are presented in the analysis of radiologically contaminated samples. Strenuous safety and security precautions must be in place to protect the analyst, laboratory, and instrumentation used to perform analyses. A validated method has been optimized for the analysis of select nitroaromatic explosives and degradative products using gas chromatography/mass spectrometry via sonication extraction of radiologically contaminated soils, for samples requiring ISO/IEC 17025 laboratory conformance. Target analytes included 2-nitrotoluene, 4-nitrotoluene, 2,6-dinitrotoluene, and 2,4,6-trinitrotoluene, as well as the degradative product 4-amino-2,6-dinitrotoluene. Analytes were extracted from soil in methylene chloride by sonication. Administrative and engineering controls, as well as instrument automation and quality control measures, were utilized to minimize potential human exposure to radiation at all times and at all stages of analysis, from receiving through disposition. Though thermal instability increased uncertainties of these selected compounds, a mean lower quantitative limit of 2.37 µg/mL and mean accuracy of 2.3% relative error and 3.1% relative standard deviation were achieved. Quadratic regression was found to be optimal for calibration of all analytes, with compounds of lower hydrophobicity displaying greater parabolic curve. Blind proficiency testing (PT) of spiked soil samples demonstrated a mean relative error of 9.8%. Matrix spiked analyses of PT samples demonstrated that 99% recovery of target analytes was achieved. To the knowledge of the authors, this represents the first safe, accurate, and reproducible quantitative method for nitroaromatic explosives in soil for specific use on radiologically contaminated samples within the constraints of a nuclear analytical lab.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Analytic Gradients for Equation-of-Motion Coupled Cluster with Single, Double, and Perturbative Triple Excitations

Understanding the process of molecular photoexcitation is crucial in various fields, including drug development, materials science, photovoltaics, and more. The electronic vertical excitation energy is a critical property, for example in determining the singlet-triplet gap of chromophores. However, a full understanding of excited-state processes requires additional explorations of the excited-state potential energy surface and electronic properties, which is greatly aided by the availability of analytic energy gradients. Owing to its robust high accuracy over a wide range of chemical problems, equation-of-motion coupled-cluster with single and double excitations (EOM-CCSD) is a powerful method for predicting excited state properties, and the implementation of analytic gradients of many EOM-CCSD (excitation energies, ionization potentials, electron attachment energies, etc.) along with numerous successful applications high- lights the flexibility of the method. In specific cases where a higher level of accuracy is needed or in more complex electronic structures, the inclusion of triple excitations becomes essential, for example, in the EOM-CCSD* approach of Saeh and Stanton. In this work, we derive and implement for the first time the analytic gradients of EOMEE-CCSD*, which also provides a template for analytic gradients of related ex- cited state methods with perturbative triple excitations. Here, the capabilities of analytic EOMEE-CCSD* gradients are illustrated by several representative examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Electrostatic Screening Modulates Analyte Binding and Emission of Carbon Nanotubes

Many nanomaterials are promising biosensor elements due to capabilities for transduction of biomolecular interactions into an electrical or optical signal. Certain nanomaterials have intrinsic charges and are thus susceptible to electrostatic forces that may enhance or attenuate their response. We are using a combination of experimental and computational approaches, we found and characterized a critical role of the solvent salt conditions in determining the extent of optical changes due to anionic analyte interaction with an intrinsically responsive nanomaterial, single-walled carbon nanotubes. Using a well-characterized model anionic analyte, we found that monovalent salts enabled greater optical changes of a polyanion-bound carbon nanotube in a highly dose-dependent manner but not with a neutrally charged analyte. Molecular dynamics simulations were used to derive a quantitative understanding of this mechanism from a free-energy perspective. We also show that salt can be used to enhance the sensitivity of detection for a polyanionic analyte. These results suggest that electrostatic screening is an important parameter for intrinsically charged nanoparticle sensors and it may be tuned to control the response to analytes.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Analytic structure of the multichannel Jost matrix for potentials with Coulombic tails

A quantum system is considered that can move in N two-body channels with the potentials that may include the Coulomb interaction. For this system, the Jost matrix is constructed in such a way that all its dependencies on the channel momenta and Sommerfeld parameters are factorized in the form of explicit analytic expressions. It is shown that the two remaining unknown matrices are single-valued analytic functions of the energy and therefore can be expanded in the Taylor series near an arbitrary point within the domain of their analyticity. It is derived a system of first-order differential equations whose solutions determine the expansion coefficients of these series. Alternatively, the unknown expansion coefficients can be used as fitting parameters for parametrizing experimental data similarly to the effective-range expansion. Such a parametrization has the advantage of preserving proper analytic structure of the Jost matrix and can be done not only near the threshold energies, but around any collision or even complex energy. As soon as the parameters are obtained, the Jost matrix (and therefore the S-matrix) is known analytically on all sheets of the Riemann surface, and thus enables one to locate possible resonances.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Stabilizing potentials in bound state analytic continuation methods for electronic resonances in polyatomic molecules

The computation of Siegert energies by analytic continuation of bound state energies has recently been applied to shape resonances in polyatomic molecules by several authors. Here, we critically evaluate a recently proposed analytic continuation method based on low order (type III) Padé approximants as well as an analytic continuation method based on high order (type II) Padé approximants. We compare three classes of stabilizing potentials: Coulomb potentials, Gaussian potentials, and attenuated Coulomb potentials. These methods are applied to a model potential where the correct answer is known exactly and to the 2 Π g shape resonance of N 2 - which has been studied extensively by other methods. Both the choice of stabilizing potential and method of analytic continuation prove to be important to the accuracy of the results. We then conclude that an attenuated Coulomb potential is the most effective of the three for bound state analytic continuation methods. With the proper potential, such methods show promise for algorithmic determination of the positions and widths of molecular shape resonances.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

An Analytic Benchmark for Neutron Boltzmann Transport with Downscattering—Part IV: PFNS and $\bar{ν}$ Uncertainty Propagation

An analytic benchmark with continuous-energy cross sections was previously derived to validate criticality calculations. Here, to extend the utility of the analytic benchmark to verify the implementation of $\bar{ν}$ and prompt fission neutron spectrum (PFNS) uncertainty propagation methods, new simplified forms that are dependent on the incident (fission-causing) neutron energy, as well as the outgoing neutron energy for the PFNS, are introduced in this work. The analytical forms for the flux and adjoint flux are derived for the extended benchmark and used to determine the 𝑘-eigenvalue sensitivity to $\bar{ν}$ and PFNS. The 𝑘-eigenvalue uncertainty due to $\bar{ν}$ and PFNS is calculated for the analytic benchmark using simplified$\bar{ν}$ and PFNS representations based on the ENDF-B/VIII.0 239 Pu evaluation. Because of the low sensitivity of the analytic benchmark to the physical PFNS, a nonphysical high-sensitivity PFNS is also presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

AmO 2 Analysis for Analytical Method Testing and Assessment: Analysis Support for AmO 2 Production

Americium oxide samples will be measured for various analytes to support AmO 2 production. The key analytes that are currently requested by the Am production customer at LANL include total Am content, Am isotopics, Pu assay, Pu isotopics, and trace element content including 237 Np content. Multiple analytical methods will be utilized depending on the sensitivity, accuracy and precision needs of the Am matrix. Traceability to the National Institute of Standards and Technology (NIST) will be achieved, where applicable, by running NIST traceable quality control materials. This given that there are no suitable AmO 2 reference materials currently available for requested analytes. The primary objective is to demonstrate the suitability of actinide analytical chemistry methods to support AmO 2 production operations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Analytical solutions of the Arrhenius-Semenov problem for constant volume burn

Analytical solutions to the Semenov thermal ignition problem for constant volume burn governed by Arrhenius reaction kinetics are derived. Specifically, an approximate analytical solution technique for the Arrhenius-Semenov differential equation is derived for reaction orders n ϵ R> 0 and exact solutions are also constructed for reaction orders n ϵ N : n ≤ 3. The approximation technique relies on expansion of the respective nondominant terms in the differential equation at the lower and upper bounds of the reaction progress variable in order to create a pair of integrable series. The two integrated series are then connected to create a single continuous analytical solution. Excellent agreement is observed between the analytical approximation and solutions obtained numerically. The presented approximation constitutes a simple and robust strategy for solving the Arrhenius-Semenov problem analytically.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Building Analytics Tool Deployment at Scale: Benefits, Costs, and Deployment Practices

Buildings are becoming more data-rich. Building analytics tools, including energy information systems (EIS) and fault detection and diagnostic (FDD) tools, have emerged to enable building operators to translate large amounts of time-series data into actionable findings to achieve energy and non-energy benefits. To expedite data analytics adoption and facilitate technology innovation, building owners, technology developers, and researchers need reliable cost–benefit data and evidence-based guidance on deployment practices. This paper fulfills these needs with the energy use and survey data from a wide-ranging research and industry partnership program that covers thousands of buildings installed with analytics tools. The paper indicates that after two years of implementation, organizations using FDD tools and EIS tools achieved 9% and 3% median annual energy savings, respectively. The median base cost and annual recurring cost for FDD are USD 0.65 per square meter (m2) (USD 0.06 per square foot [ft2]) and USD 0.22 per m2 (USD 0.02 per ft2), and are USD 0.11 per m2 (USD 0.01 per ft2) and USD 0.11 per m2 (USD 0.01 per ft2) for EIS. The common metrics and analyses that are used in the tools to support the discovery of energy efficiency measures are summarized in detail. Two best practice examples identified to maximize the benefits of tool implementation are also presented. Opportunities to advance the state of technology include simplified data integration and management, and more efficient processes for acting on analytics outputs. Compared with previous efforts in the literature, the findings presented in this paper demonstrate the effectiveness of building analytics tools with the largest known dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2014

Climate Analytics as a Service

Climate science is a big data domain that is experiencing unprecedented growth. In our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). CAaaS combines high-performance computing and data-proximal analytics with scalable data management, cloud computing virtualization, the notion of adaptive analytics, and a domain-harmonized API to improve the accessibility and usability of large collections of climate data. MERRA Analytic Services (MERRA/AS) provides an example of CAaaS. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of key climate variables. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, CAaaS is providing the agility required to meet our customers' increasing and changing data management and data analysis needs.

big data↗
NASA NTRS2016

Climate Analytics as a Service

Exascale computing, big data, and cloud computing are driving the evolution of large-scale information systems toward a model of data-proximal analysis. In response, we are developing a concept of climate analytics as a service (CAaaS) that represents a convergence of data analytics and archive management. With this approach, high-performance compute-storage implemented as an analytic system is part of a dynamic archive comprising both static and computationally realized objects. It is a system whose capabilities are framed as behaviors over a static data collection, but where queries cause results to be created, not found and retrieved. Those results can be the product of a complex analysis, but, importantly, they also can be tailored responses to the simplest of requests. NASA's MERRA Analytic Service and associated Climate Data Services API provide a real-world example of climate analytics delivered as a service in this way. Our experiences reveal several advantages to this approach, not the least of which is orders-of-magnitude time reduction in the data assembly task common to many scientific workflows.

big data↗
NASA NTRSDate not supplied

Formal Verification of the Interaction Between Semi-Algebraic Sets and Real Analytic Functions

Semi-algebraic sets and real analytic functions are fundamental concepts in Real Algebraic Geometry and Real Analysis, respectively. These concepts interact in the study of Differential Equations, where the real analytic solution to a differential equation is known to enter or exit a semi-algebraic set in a predicable way. Motivated to enhance the capability to reason about differential equations in the Prototype Verification System (PVS), a formalization of multivariate polynomials, semi-algebraic sets, and real analytic functions is developed. The favorable way that a real analytic function enters and exits a semi-algebraic set is proven. It is further shown that if the function is assumed to be smooth, a slightly weaker assumption than real analytic, these favorable interactions with semi-algebraic sets may fail.

Real analytic functions↗
DOE OSTI2026

An analytic result for the 0 → ggHHH amplitude

We present a fully analytic calculation of the leading-order one-loop amplitude for triple Higgs production via gluon fusion, gg → HHH, retaining full dependence on the mass of the heavy quark circulating in the loop. This amplitude provides a direct probe of the triple and quartic Higgs self-couplings, the measurement of which is a central goal of current and future colliders. The amplitude can be presented in compact form thanks to the use of analytic reconstruction techniques, based on finite-field and p-adic evaluations, multivariate partial fraction decompositions, and primary decompositions to identify common numerator factors. Although full analytic results are given in the text and in the supplementary material, the main thrust of this paper is to further test and illustrate these analytic reconstruction techniques in a concrete physical example. Our results provide a compact and efficient representation of the matrix element for this process, enabling evaluations that are more than an order of magnitude faster than existing numerical alternatives. Full analytic control of the leading-order, loop-induced amplitude is an important step towards handling more complex 2-loop or real-radiation corrections to this and related processes.

Campbell, John M. [Fermilab] (ORCID:00000002136193↗
DOE OSTI2026

Toward the analytic bootstrap of energy correlators

In this paper, we present a framework for the analytic bootstrap of three-point energy correlators, a crucial observable in $\mathcal{N}$ = 4 super Yang-Mills theory and quantum chromodynamics (QCD). Our approach combines spherical contour techniques, general physical constraints such as pole cancellations, and power correction data in the singular limits to determine its analytic expression. In contrast to previous bootstrap studies restricted to scattering amplitudes for supersymmetric theories, our framework makes use of the properties of Feynman integrals, marking a significant step toward bootstrapping realistic QCD observables. Using this method, we derive analytic expressions for leading-order three-point energy correlators in the multi-collinear limit with equal and unequal energy weights, where the latter are crucial ingredients for projected N -point energy correlators. We also apply the recently developed technique of analytic regression with lattice reduction as a way to bypass needing explicit expressions for the singular limits. Bridging theoretical advances in scattering amplitudes with the renewed interest in weighted cross-sections, our work opens the door to precision tests of QCD dynamics through analytic event-shape predictions.

jet substructure↗
DOE OSTI2021

Analytical models of the strength and ductility of CNT reinforced metal matrix nano composites under elevated temperatures

Carbon nanotubes (CNTs) can greatly enhance the strength of metal matrix composites while resulting in ductility loss. This strength-ductility tradeoff dilemma always confines the development of material design and real-life applications. Here in this work, a temperature dependent strengthening analytical model is proposed for CNT reinforced metal matrix composites which considers three common strengthening mechanisms: Orowan looping effect, thermal expansion mismatch effect, and load bearing effect. The proposed model can predict composite material strength with different volume fractions of CNTs and under different temperatures. Combining the strengthening model with the stress based modified Mohr-Coulomb (sMMC) ductile fracture model, a ductility analytical model is then derived. This ductility analytical model includes the influences of temperature, multi-axial stress loading conditions, as well as the aforementioned three strengthening mechanisms. A good agreement has been achieved between literature published experimental data and analytical predictions for both composite material strength and ductility loss. The proposed two analytical models can provide a straightforward way to study the strength-ductility relationship for CNT reinforced metal matrix composites over a wide range of temperatures and different stress states, and then provide guidance on new material design and processing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metall: A persistent memory allocator for data-centric analytics

Data analytics applications transform raw input data into analytics-specific data structures before performing analytics. Unfortunately, such data ingestion steps are often more expensive than analytics. In addition, various types of NVRAM devices are already used in many HPC systems today. Such devices will be useful for storing and reusing data structures beyond a single process life cycle. We developed Metall, a persistent memory allocator built on top of the memory-mapped file mechanism. Metall enables applications to transparently allocate custom C++ data structures into various types of persistent memories. Metall incorporates a concise and high-performance memory management algorithm inspired by Supermalloc and the rich C++ interface developed by Boost.Interprocess library. On a dynamic graph construction workload, Metall achieved up to 11.7x and 48.3x performance improvements over Boost.Interprocess and memkind (PMEM kind), respectively. We also demonstrate Metall’s high adaptability by integrating Metall into a graph processing framework, GraphBLAS Template Library. Here this study’s outcomes indicate that Metall will be a strong tool for accelerating future large-scale data analytics by allowing applications to leverage persistent memory efficiently.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

No evidence for Bardeen-Petterson alignment in GRMHD simulations and semi-analytic models of moderately thin, prograde, tilted accretion disks

In this paper, we introduce the first results that use data extracted directly from numerical simulations as inputs to the analytic twisted disk model of Zhuravlev and Ivanov. In both numerical and analytic approaches, fully relativistic models of tilted and twisted disks having a moderate effective viscosity around a slowly rotating Kerr black hole are considered. Qualitatively, the analytic model demonstrates the same dynamics as the simulations, although with some quantitative offset. Namely, the general relativistic magnetohydrodynamic simulations typically give smaller variations of tilt and twist across the disk. When the black hole and the disk rotate in the same direction, the simulated tilted disk and analytic model show no sign of Bardeen-Petterson alignment, even in the innermost parts of the disk where the characteristic time for relaxation to a quasi-stationary configuration is of the same order as the computation time. In the opposite case, when the direction of the disk's rotation is opposite to that of the black hole, a partial alignment is observed, in agreement with previous theoretical estimates. Thus, both fully numerical and analytic schemes demonstrate that the Bardeen-Petterson effect may not be possible for the case of prograde rotation provided that disk's effective viscosity is sufficiently small. This may have implications in modeling of different astrophysical phenomena such as disk spectra and jet orientation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Advanced Health Information Technology Analytic Framework and Application to Hazard Detection

Health Information Technology (HIT) aims to improve healthcare outcomes by organizing and analyzing various health-related data. With data accumulating at a staggering rate, the importance of real-time analytics has been increasing dramatically, shifting the focus of informatics from batch processing to streaming analytics. HIT is also facing unprecedented challenges in adapting to this new requirement and leveraging advanced IT technologies. This paper introduces a HIT data and compute platform that supports multi-granularity real-time analytics from heterogeneous data sources. The paper first identifies functional requirements and proposes a framework that satisfies the requirements using state-of-the-art big data technologies including Apache Kafka, Spark Structured Streaming Engine, and Delta Lake. To demonstrate its capability to support data analytics in multiple time granularities analytics, a statistical process control-based hazard detection algorithm has been implemented on top of the framework to detect unexpected hazards from order cancellation data of the Department of US Veterans Affairs (VA) in near real-time.

Kumar, Mohit↗