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At least 109 records · Page 6

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DOE OSTI2020

Probabilistic learning and updating of a digital twin for composite material systems

This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.

42 ENGINEERING↗

DOE OSTI2017

Cluster decomposition of full configuration interaction wave functions: A tool for chemical interpretation of systems with strong correlation

Approximate full configuration interaction (FCI) calculations have recently become tractable for systems of unforeseen size, thanks to stochastic and adaptive approximations to the exponentially scaling FCI problem. The result of an FCI calculation is a weighted set of electronic configurations, which can also be expressed in terms of excitations from a reference configuration. The excitation amplitudes contain information on the complexity of the electronic wave function, but this information is contaminated by contributions from disconnected excitations, i.e., those excitations that are just products of independent lower-level excitations. The unwanted contributions can be removed via a cluster decomposition procedure, making it possible to examine the importance of connected excitations in complicated multireference molecules which are outside the reach of conventional algorithms. We present an implementation of the cluster decomposition analysis and apply it to both true FCI wave functions, as well as wave functions generated from the adaptive sampling CI algorithm. Furthermore, the cluster decomposition is useful for interpreting calculations in chemical studies, as a diagnostic for the convergence of various excitation manifolds, as well as as a guidepost for polynomially scaling electronic structure models. Applications are presented for (i) the double dissociation of water, (ii) the carbon dimer, (iii) the π space of polyacenes, and (iv) the chromium dimer. While the cluster amplitudes exhibit rapid decay with an increasing rank for the first three systems, even connected octuple excitations still appear important in Cr 2 , suggesting that spin-restricted single-reference coupled-cluster approaches may not be tractable for some problems in transition metal chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1979

Effect of degree of fuel vaporization on emissions for a premixed-prevaporized combustor system

The impact of the degree of fuel vaporization upon emission from a flametube combustor was studied using an inlet air pressure of 3 x 10 to the 5th power pascals, inlet air temperatures of 600K and 700K, a reference velocity of 35 meters per second and equivalence ratios of .6 and .72 using Jet A fuel. Incoming air was preheated to temperatures from 600K to 700K by a nonvitiating preheater. Jet A fuel was injected into this airstream through two different fuel injectors manifolded together and mounted in series upstream of a watercooled preforated plate flameholder. The fuel-air mixture burned in a watercooled combustor section. Samples of the fuel-air mixture upstream of the flameholder were obtained for analysis to determine the local degree of fuel vaporization and the fuel-air ratio. Samples of the combustion products were analyzed to determine gaseous emissions. The effects of vaporization on carbon monoxide and nitric on carbon monoxide and nitric oxide emissions are presented.

Cooper, L. P.↗

NASA NTRS1994

Topological analysis of chaotic orbits: Revisiting Hyperion

There is emerging interest in the possibility of chaotic evolution in astrophysical systems. To mention just one example, recent well-sampled ground-based observations of the Saturian satellite Hyperion strongly suggest that it is exhibiting chaotic behavior. We present a general technique, the method of close returns, for the analysis of data from astronomical objects believed to be exhibiting chaotic motion. The method is based on the extraction of pieces of the evolution that exhibit nearly periodic behavior-episodes during which the object stays near in phase space to some unstable periodic orbit. Such orbits generally act as skeletal features, tracing the topological organization of the manifold on which the chaotic dynamics takes place. This method does not require data sets as lengthy as other nonlinear analysis techniques do and is therefore well suited to many astronomical observing programs. Well sampled data covering between twenty and forty characteristic periods of the system have been found to be sufficient for the application of this technique. Additional strengths of this method are its robustness in the presence of noise and the ability for a user to clearly distinguish between periodic, random, and chaotic behavior by inspection of the resulting two-dimensional image. As an example of its power, we analyze close returns in a numerically generated data set, based on a model for Hyperion extensively studied in the literature, corresponding to nightly observations of the satellite. We show that with a small data set, embedded unstable periodic orbits can be extracted and that these orbits can be responsible for nearly periodic behavior lasting a substantial fraction of the observing run.

Boyd, Patricia T.↗

DOE OSTI2015

A new Stark decelerator based surface scattering instrument for studying energy transfer at the gas-surface interface

We report on the design and characterization of a new apparatus for performing quantum-state resolved surface scattering experiments. The apparatus combines optical state-specific molecule preparation with a compact hexapole and a Stark decelerator to prepare carrier gas-free pulses of quantum-state pure CO molecules with velocities controllable between 33 and 1000 m/s with extremely narrow velocity distributions. The ultrahigh vacuum surface scattering chamber includes homebuilt ion and electron detectors, a closed-cycle helium cooled single crystal sample mount capable of tuning surface temperature between 19 and 1337 K, a Kelvin probe for non-destructive work function measurements, a precision leak valve manifold for targeted adsorbate deposition, an inexpensive quadrupole mass spectrometer modified to perform high resolution temperature programmed desorption experiments and facilities to clean and characterize the surface.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Future Recovered Gas Dryer Development (SRTE FY2019 PRDR SR19009)

A manifold was designed and constructed to evaluate the isotopic exchange of deuterium for hydrogen using Pt catalyzed zeolites, deuterated water, and deuterated ammonia. Using varying catalytic loadings on the 5A substrate and real-time RGA monitoring of the effluent stream, isotopic exchange was successfully demonstrated between H 2 and D 2 O or ND 3 . The catalytic isotopic exchange was further verified when compared against an uncatalyzed sample of zeolite, demonstrating little-to-no activity. Based on the experiments performed here, the removal of tritium from tritiated water and tritiated ammonia is possible for future RGD beds using a Pt catalyzed zeolite. Work is still ongoing with the planned manifold improvements and scheduled testing of other catalyzed zeolite substrates.

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DOE OSTI2023

Multifidelity uncertainty quantification with models based on dissimilar parameters

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the highest-fidelity model, provided that the low-fidelity models are well correlated. However, maintaining a high level of correlation can be challenging, especially when models depend on different input uncertain parameters, which drastically reduces the correlation. Existing MF UQ approaches do not adequately address this issue. In this work, we propose a new sampling strategy that exploits a shared space to improve the correlation among models with dissimilar parameterization. We achieve this by transforming the original coordinates onto an auxiliary manifold using the adaptive basis (AB) method (Tipireddy and Ghanem, 2014). The AB method has two main benefits: (1) it provides an effective tool to identify the low-dimensional manifold on which each model can be represented, and (2) it enables easy transformation of polynomial chaos representations from high- to low-dimensional spaces. This latter feature is used to identify a shared manifold among models without requiring additional evaluations. Here we present two algorithmic flavors of the new estimator to cover different analysis scenarios, including those with legacy and non-legacy high-fidelity (HF) data. We provide numerical results for analytical examples, a direct field acoustic test, and a finite element model of a nuclear fuel assembly. For all examples, we compare the proposed strategy against both single-fidelity and MF estimators based on the original model parameterization.

42 ENGINEERING↗

DOE OSTI2019

Instrumented Lid - System Final Design

Work is ongoing to create an instrumented and sampling system for monitoring aluminum clad spent nuclear fuel (ASNF) in dry storage. The anticipated system will be composed of a custom canister assembly and a modular instrument manifold. The conceptual design was first laid out in the Task Technical and Quality Assurance Plan (SRNL-RP-2019-00225). In this document, we outline the project progress to date, and describe the final system design.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1991

Investigation of loss processes of Tm and Tm,Ho in YAG

The loss of excitation from various manifolds of Tm and Tm,Ho in YAG as a function of temperature and concentration is studied. Two probable loss mechanisms - a Tm up-conversion and a Ho up-conversion - are identified. A 785-nm CW diode laser with 400-nW peak power was focused to a small spot on the sample. The emission from the sample observed at 90 deg was monitored through a monochromator with slits open to 3 mm. Intensity of emission was measured by varying the power of the excitation source using a set of neutral density filters. Power is reported as the percentage of the peak power, and the intensity curves were normalized below 20 percent of transmission. The fact that there is emission above the pump energy indicates an up-conversion from excited manifolds. Nonlinear changes in the intensity of the emission from the Tm 3F4 manifold with the pump power reveals a loss of excitation from this manifold. The linear dependence of the 5I7 manifold emission with pump power at low Tm and high Ho concentrations and the gain of energy in the 5I6 manifold of Ho indicate that the 5I7 manifold loss is due to the coupling of Tm and Ho ions.

Armagan, G.↗

DOE OSTI2025

Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds

Low Earth Orbit (LEO) satellites play a crucial role in enhancing global connectivity, serving a complementary solution to existing terrestrial systems. In wireless networks, scheduling is a vital process that allocates time-frequency resources to users for interference management. However, LEO satellite networks face significant challenges in scheduling their links towards ground users due to the satellites’ mobility and overlapping coverage. This paper addresses the dynamic link scheduling problem in LEO satellite networks by considering spatio-temporal correlations introduced by the satellites’ movements. The first step in the proposed solution involves modeling the network over Riemannian manifolds, thanks to their representation as symmetric positive definite matrices. We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. To accurately predict satellite link states, we present a recurrent neural network (RNN) over Riemannian manifolds, which captures spatio-temporal characteristics over time. Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. Remarkably, unlike other ML-based models that require extensive training data, both models only need 30 training samples to achieve over 99% of the sum rate while maintaining similar computational complexity relative to the benchmark.

42 ENGINEERING↗

DOE OSTI2025

D–MOPH–25: diverse MOF–molecule pairs for Henry’s constants prediction

Computational methods like grand-canonical Monte Carlo simulations and machine learning (ML) have accelerated metal–organic frameworks (MOF) exploration but are typically limited to a narrow range of adsorbates due to data availability and force field constraints. In this study, we introduce a dataset of diverse MOF–molecule pairs for Henry’s constant prediction, D–MOPH–25, which systematically explores a diverse chemical space by combining 113 molecular adsorbates with over 5000 MOF structures through an active learning process. D–MOPH–25 constitutes the most diverse adsorbate dataset used in any ML study of molecular adsorption in MOFs to date. Our workflow builds a benchmark for predicting Henry’s constants at 300 K, leveraging conformal prediction for uncertainty quantification. Assessment through Shannon entropy and uniform manifold approximation and projection confirms the comprehensiveness of D–MOPH–25 while highlighting the importance of robust classification to filter out unphysical data points in regression tasks. Although future enhancements in model architecture and sampling criteria could improve predictive performance, our dataset already spans the target space using only 2.31% of total possibilities. This comprehensive dataset facilitates assessment of model generalizability across adsorbate species and can establish a foundation for high-throughput MOF screening and ML-driven separation processes.

active learning↗

NASA NTRS1986

The Space Shuttle, fluid dynamics, and computer graphics

Several samples are presented of CFD-generated flow ribbon images of fuel flow in the SSME. The images result from simulations of H2 flow through a design iteration of the main injector, the hot gas manifold and the fuel bowl section. The color-coded ribbons provide three-dimensional perspectives which indicate the direction and orientation of the flow and the velocity magnitude in intervals along the flow path.

Belie, R. G.↗

NASA NTRSDate not supplied

Air-Broadened Line Shapes in the 2n3 R Branch of 12ch4 Between 6014 and 6100 cm-1

Complete and accurate information on line shape parameters of 2?3 methane transitions for air broadening as a function of temperature is critical not only for the correct interpretation of the observed atmospheric spectra but also for the development of a reliable theoretical model. For this reason, we obtained a series of high-resolution, high S/N spectra of high-purity 12CH4 and 12CH4 broadened with dry air at temperatures in the 130 to 295 K range using the Bruker IFS 125HR Fourier transform spectrometer at JPL. Two absorption cells were used in the experiment, a White cell with path length of 13 m for room temperature spectra and a 21 m Herriott cella for cold sample spectra. The 15 spectra used in the analysis consisted of 3 low pressure (0.26 to 2.57 Torr) spectra with pure 12CH4 and 12 air-broadened spectra with total sample pressures of 79-805 Torr and volume mixing ratios of methane between 0.23 and 1%. A multispectrum least-squares fitting techniqueb was employed to fit all 15 spectra simultaneously. Preliminary results for select R(J) manifolds will be presented.

V Malathy Devi↗

NASA NTRS2015

Self- and Air-Broadened Line Shapes in the 2v3 P and R Branches of 12CH4

In this paper we report line shape parameters of 12CH4 for several hundred 2V(sub 3) transitions in the spectral regions 5891-5996 cm( exp -1) (P branch) and 6015-6115 cm(exp -1) (R branch). Air- and self-broadening coefficients were measured as a function of temperature; line mixing via off-diagonal relaxation matrix element coefficients was also obtained for 47 transition pairs. In total, nearly 1517 positions and intensities were retrieved, but many transitions were too weak for the line shape study. For this analysis, we used 25 high-resolution (0.0056 and 0.0067 cm(ex[ -1) and high signal-to-noise (S/N) spectra of high-purity 12CH4 and the same high-purity 12CH4 broadened by dry air recorded at different sample temperatures between 130 K and 295 K with the Bruker IFS 125HR Fourier transform spectrometer at JPL. Three different absorption cells were used (1) a White cell set to a path length of 13.09 m for room temperature data, (2) a single-pass 0.2038 m long coolable cell (for self-broadening) and (3) a multipass cell with 20.941 m total path coolable Herriott cell (for air-broadening). In total there were 13 spectra with pure 12CH4 (0.27-599 Torr) and 12 air-broadened spectra with total sample pressures of 80-805 Torr and volume mixing ratios (VMR) of methane between 0.18 and 1.0. An interactive multispectrum nonlinear least-squares technique was employed to fit the individual P10-P1 and R0-R10 manifolds in all the spectra simultaneously. Results obtained from the present analysis are compared to other recent measurements.

Devi, V. Malathy↗

DOE OSTI2025

Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics

ABSTRACT There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high‐dimensionality of the data, the high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be ( γ , σ)‐stable if the probability of the same classification is at least for points sampled in a Gaussian neighborhood of the point with a given standard deviation . We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2008

Fluid control structures in microfluidic devices

Methods and apparatus for implementing microfluidic analysis devices are provided. A monolithic elastomer membrane associated with an integrated pneumatic manifold allows the placement and actuation of a variety of fluid control structures, such as structures for pumping, isolating, mixing, routing, merging, splitting, preparing, and storing volumes of fluid. The fluid control structures can be used to implement a variety of sample introduction, preparation, processing, and storage techniques.

Mathies, Richard A.↗

DOE OSTI2017

Fluid control structures in microfluidic devices

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Fluid control structures in microfluidic devices

Mathies, Richard A.↗