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

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Modeling the use of mobile modular gas samplers in near-field detection

The Wireless Independent Noble Gas Sampler (WINGS) is a mobile, modular gas sampling system designed for use in low infrastructure environments. In the case of a suspected underground nuclear explosion (UNE), WINGS units could be deployed to detect noble gases emanating from the suspected explosion site or identify gases originating from another source entering the local area from offsite. In conclusion, this work uses the atmospheric transport modeling tool inline WRF-HYSPLIT to test the effectiveness of WINGS units around a suspected UNE site as a function of sampler density and distance from the emission point of gases from a UNE.

Air sampling↗
DOE OSTI2017

astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

Given the complexity of modern cosmological parameter inference where we arefaced with non-Gaussian data and noise, correlated systematics and multi-probecorrelated data sets, the Approximate Bayesian Computation (ABC) method is apromising alternative to traditional Markov Chain Monte Carlo approaches in thecase where the Likelihood is intractable or unknown. The ABC method is called"Likelihood free" as it avoids explicit evaluation of the Likelihood by using aforward model simulation of the data which can include systematics. Weintroduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler forparameter estimation. A key challenge in astrophysics is the efficient use oflarge multi-probe datasets to constrain high dimensional, possibly correlatedparameter spaces. With this in mind astroABC allows for massive parallelizationusing MPI, a framework that handles spawning of jobs across multiple nodes. Akey new feature of astroABC is the ability to create MPI groups with differentcommunicators, one for the sampler and several others for the forward modelsimulation, which speeds up sampling time considerably. For smaller jobs thePython multiprocessing option is also available. Other key features include: aSequential Monte Carlo sampler, a method for iteratively adapting tolerancelevels, local covariance estimate using scikit-learn's KDTree, modules forspecifying optimal covariance matrix for a component-wise or multivariatenormal perturbation kernel, output and restart files are backed up everyiteration, user defined metric and simulation methods, a module for specifyingheterogeneous parameter priors including non-standard prior PDFs, a module forspecifying a constant, linear, log or exponential tolerance level,well-documented examples and sample scripts. This code is hosted online athttps://github.com/EliseJ/astroABC

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes

This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on an embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Bioaccumulation Using Surrogate Samplers (Bass): Evaluation Of A Passive Sampler As An Alternative Monitoring Tool For Environmental Contaminants At The Savannah River Site

DOE sites conduct traditional environmental monitoring programs that require collecting, processing, and analyzing water, sediment, and fish samples. However, recently developed passive sampling technologies, such as Diffusive Gradient in Thin films (DGT), may measure the chemical phases that are available and toxic to organisms (the bioavailable fraction), thereby producing more accurate and economical results than traditional methods. Our laboratory study showed that dissolved copper concentrations measured by DGT probes were strongly correlated with the uptake of copper by Lumbriculus variegatus, an aquatic worm, and with concentrations of copper measured by conventional methods. Dissolved copper concentrations in DGT probes increased with time of exposure, paralleling the increase in copper with time that ocurred in Lumbriculus. Additional studies with a combination of seven dissolved metals showed similar results. These findings support the use of DGT as a biomimetic monitoring tool and provide a basis for refinement of these methods for cost-effective environmental monitoring at DOE sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Influence of Sulfide Nanoparticles on Dissolved Mercury and Zinc Quantification by Diffusive Gradient in Thin-Film Passive Samplers

Diffusive gradient in thin-film (DGT) passive samplers are frequently used to monitor the concentrations of metals such as mercury and zinc in sediments and other aquatic environments. The application of these samplers generally presumes that they quantify only the dissolved fraction and not particle-bound metal species that are too large to migrate into the sampler. However, metals associated with very small nanoparticles (smaller than the pore size of DGT samplers) can be abundant in certain environments, yet the implications of these nanoparticles for DGT measurements are unclear. The objective of this study was to determine how the performance of the DGT sampler is affected by the presence of nanoparticulate species of Hg and Zn. DGT samplers were exposed to solutions containing known amounts of dissolved Hg(II) and nanoparticulate HgS (or dissolved Zn(II) and nanoparticulate ZnS). The amounts of Hg and Zn accumulated onto the DGT samplers were quantified over hours to days, and the rates of diffusion of the dissolved metal (i.e., the effective diffusion coefficient D) into the sampler’s diffusion layer were calculated and compared for solutions containing varying concentrations of nanoparticles. The results suggested that the nanoparticles deposited on the surface of the samplers might have acted as sorbents, slowing the migration of the dissolved species into the samplers. The consequence was that the DGT sampler data underestimated the dissolved metal concentration in the solution. In addition, X-ray absorption spectroscopy was employed to determine the speciation of the Hg accumulated on the sampler binding layer, and the results indicated that HgS nanoparticles did not appear to directly contribute to the DGT measurement. Overall, our findings suggest that the deployment of DGT samplers in settings where nanoparticles are relevant (e.g., sediments) may result in DGT data that incorrectly estimated the dissolved metal concentrations. Models for metal uptake into the sampler may need to be reconsidered.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Characteristics of Non-metallic Inclusions in Steel Obtained from Different-Sized Samplers

To determine the effect of sampler size on the detection of inclusions in liquid steel, four different-sized samplers were designed for industrial sampling from a continuous casting tundish. The samples were evaluated via scanning electron microscopy and energy-dispersive spectroscopy. With increasing sample size, the cooling rate of the sample and velocity for particle engulfment decreased. The extent of encapsulation of complex inclusions increased from 0 pct with the smallest sampler to 48.2 pct with the largest sampler. In the samplers that cooled at 41.2 and 26.7 K s{sup −1}, the major inclusions were calcium aluminates; in the larger samplers, which cooled more slowly at 8.2 and 1.0 K s{sup −1}, the complex inclusions predominately comprised Al{sub 2}O{sub 3} + (Ca, Mn)S and Al{sub 2}O{sub 3} + CaS, respectively. The sizes of the sulfides gradually increased with increasing sampler size; for example, the sulfide average diameter increased from 2.5 to 3.5 μm when the sampler inner diameter increased from 50 to 70 mm. The main direction of movement of the inclusions during the sampling process was upward.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Description and Use of SCALE Sampler Parametric Capability for Engineering Analysis and Optimization

The Sampler sequence was introduced into the SCALE nuclear modeling and simulation suite in SCALE 6.2 to perform uncertainty quantification via random sampling of nuclear data, material number densities, and dimensions. Sampler was expanded with the introduction of a parametric capability in SCALE 6.2.2. This paper discusses input for the Sampler parametric sequence and presents two case studies of analyses performed using the sequence. These case studies include preconceptual design of a package for transporting high assay low-enriched uranium (HALEU) oxide and scoping calculations to support subcritical limit development for a future update of the ANSI/ANS-8.1 (ANS-8.1) standard. The parametric capability within Sampler provides many benefits to analysts. For instance, parametric sweeps are frequently used to identify optimum parameter values as part of safety analysis or system design, but such sweeps can require substantial engineering time or may rely on custom-written scripts or scripts such as Write One, Run Many (or WORM) developed outside of any software quality assurance program. With the parametric capabilities in Sampler, however, a large number of inputs can be generated automatically without recourse to scripting by individual analysts. The parametric capability can also be used in lieu of the CSAS5S search sequence to identify optimum parameters more simply with straightforward inputs and outputs. Sampler can also be used to calculate input parameters from engineering specifications. For example, diameters can be converted to radii, or masses can be used to calculate number densities. Overall, the Sampler parametric capability provides a robust feature within SCALE, eliminating the need for user-developed scripting.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Kernel Density Estimators for SAMPLER Analyses [Slides]

This presentation discusses Sampler, specifically in terms of uncertainty quantification and states that for many simulation tasks nominal input parameters have complicated influence on the uncertainty in the final result. Sampler provides a convenient means to perform stochastic uncertainty quantification, brute force sampling parameter space and tallying changes in outputs, and it can be coupled with any SCALE modules. Perturbed responses can be collected and analyzed with standard statistical measures; however, two problems have been identified. The first problem is that for Monte Carlo simulations the individual response uncertainties are ignored, and the second problem is that features of distributions are easily hidden in histograms. They found that the solution is to add Kernel Density Estimator (KDE) analysis to the Sampler output. In conclusion, Sampler is a unique tool for stochastic uncertainty propagation, and it can be coupled with any module in SCALE. Current functionality is limited to histograms, and it may not adequately account for sample uncertainties and/or not adequately display underlying distribution features. Work is in progress to implement KDE in the Sampler module and no additional flagging is needed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Crossing the Streams – Sampler and the TemplateEngine [Slides]

This presentation discusses Sampler, which is a versatile UQ and parametric study tool that can be applied to any SCALE Sequence. Sampler can perturb any quantity in any SCALE input. Recent work at ORNL has developed new types of covariance data that allow Sampler UQ to be applied to nearly all SCALE applications, including reactor depletion, UNF fuel characterization, source term analysis, and decay heat calculation. In SCALE 6.2 releases, CE data in transport cannot be perturbed. Sampler was originally designed for stochastic sampling with any sequence within SCALE and Parametric capability added in SCALE 6.2.2. Sampler can be used for uncertainty quantification, including sample data in static or depletion calculations and sample inputs for uncertainties in compositions and dimensions. The SCALE TemplateEngine allows for expanding templates to full inputs and the combination provides a powerful UQ tool.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Remote Sampler Demonstration Isolok Configuration Test

The accuracy and precision of a new Isolok sampler configuration was evaluated using a recirculation flow loop. The evaluation was performed using two slurry simulants of Hanford high-level tank waste. Through testing, the capability of the Isolok sampler was evaluated. Sample concentrations were compared to reference samples that were simultaneously collected by a two-stage Vezin sampler. The capability of the Isolok sampler to collect samples that accurately reflect the contents in the test loop improved – biases between the Isolok and Vezin samples were greatly reduce for fast settling particles.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Comparison of source-location algorithms for atmospheric samplers

Numerous algorithms have been developed to determine the source characteristics for an atmospheric release, e.g., (Bieringer et al., 2017). This study compares three models that have been applied to the data collected by the International Monitoring System operated by the Comprehensive Nuclear-Test-Ban Treaty Preparatory Commission to estimate source event parameters. Each model uses a different approach to estimate the parameters. A deterministic model uses a possible source region (PSR) approach (Ringbom et al., 2014) that is based on the correlation between predicted and measured sample values. An unnamed model developed at Pacific Northwest National Laboratory (herein called BAYEST) uses a Bayesian formulation (Eslinger et al., 2019; Eslinger et al., 2020; Eslinger and Schrom, 2016). The FREAR model uses a different Bayesian formulation (De Meutter and Hoffman, 2020; De Meutter et al., 2021; Pieter De Meutter, 2021). The performance of the three source-location models is evaluated with 100 synthetic release cases for the single xenon isotope, 133 Xe. The release cases resulted in detections in a fictitious network with 120 noble gas samplers. All three source-location models use the same sampling data. The two Bayesian models yield more accurate location estimates than the deterministic PSR model, with FREAR having slightly better location performance than BAYEST. Samplers with collection periods of 3, 6, 8, 12, and 24-h were used. Results from BAYEST show that location accuracy improves with each reduction in sample collection length. The BAYEST model is slightly better for estimating the start time of the release. The PSR model has about the same spread in start times as the FREAR model, but the PSR results have a better average start time. The Bayesian source-location algorithms give more accurate results than the PSR approach, and provide release magnitude estimates, while the base PSR model does not estimate the release magnitude. This investigation demonstrates that a reasonably dense sampling grid will sometimes yield poor location and time estimates regardless of the model. The poor estimates generally coincide with cases where there is a much larger distance between the release point and the first detecting sampler than the average sampler spacing.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

Application of an in-situ soil sampler for assessing subsurface biogeochemical dynamics in a diesel-contaminated coastal site during soil flushing operations

Subsurface biogeochemistry and contaminant dynamics during the remediation of diesel-contamination by in-situ soil flushing were investigated at a site located in a coastal region. An in-situ sampler containing diesel-contaminated soils separated into two size fractions (<0.063- and <2-mm) was utilized in two monitoring wells: DH1 (located close to the injection and extraction wells for in-situ soil flushing) and DH2 (located beyond sheet piles placed to block the transport of leaked diesel). Total petroleum hydrocarbon (TPH) concentrations and biogeochemical properties were monitored both in soil and groundwater for six months. A shift occurred in the groundwater type from Ca-HCO 3 to Na-CI due to seawater intrusion during intense pumping, while the concentrations of Ni, Cu, Co, V, Cr, and Se increased substantially following surfactant (TWEEN 80) injection. The in-situ sampler with fine particles was more sensitive to variations in conditions during the remedial soil flushing process. In both wells, soil TPH concentrations in the <0.063-mm fraction were much higher than those in the <2-mm fraction. Increases in soil TPH in DH1 were consistent with the expected outcomes following well pumping and surfactant injection used to enhance TPH extraction. However, the number of diesel-degrading microorganisms decreased after surfactant injection. 16S-rRNA gene-based analysis also showed that the community composition and diversity depended on both particle size and diesel contamination. The multidisciplinary approach to the contaminated site assessments showed that soil flushing with surfactant enhanced diesel extraction, but negatively impacted in-situ diesel biodegradation as well as groundwater quality. In conclusion, the results also suggest that the in-situ sampler can be an effective monitoring tool for subsurface biogeochemistry as well as contaminant dynamics.

02 PETROLEUM↗
DOE OSTI2017

Efficacy of passive sampler collection for atmospheric NO 2 isotopes under simulated environmental conditions

Rationale Nitrogen oxides or NO x (NO x = NO + NO 2 ) play an important role in air quality, atmospheric chemistry, and climate. The isotopic compositions of anthropogenic and natural NO 2 sources are wide‐ranging, and they can be used to constrain sources of ambient NO 2 and associated atmospheric deposition of nitrogen compounds. While passive sample collection of NO 2 isotopes has been used in field studies to determine NO x source influences on atmospheric deposition, this approach has not been evaluated for accuracy or precision under different environmental conditions. Methods The efficacy of NO 2 passive sampler collection for NO 2 isotopes was evaluated under varied temperature and relative humidity (RH) conditions in a dynamic flux chamber. The precision and accuracy of the filter NO 2 collection as nitrite (NO 2 – ) for isotopic analysis were determined using a reference NO 2 gas tank and through inter‐calibration with a modified EPA Method 7. The bacterial denitrifer method was used to convert 20 μM of collected NO 2 – or nitrate (NO 3 – ) into N 2 O and was carried out on an Isoprime continuous flow isotope ratio mass spectrometer. Results δ 15 N‐NO 2 values determined from passive NO 2 collection, in conditions of 11–34 °C, 1–78% RH, have an overall accuracy and precision of ±2.1 ‰, and individual run precision of ±0.6 ‰. δ 18 O‐NO 2 values obtained from passive NO 2 sampler collection, under the same conditions, have an overall precision of ± 1.3 ‰. Conclusions Suitable conditions for passive sampler collection of NO 2 isotopes are in environments ranging from 11 to 34 °C and 1 to 78% RH. The passive NO 2 isotope measurement technique provides an accurate method to determine variations in atmospheric δ 15 N‐NO 2 values and a precise method for determining atmospheric δ 18 O‐NO 2 values. The ability to measure NO 2 isotopes over spatial gradients at the same temporal resolution provides a unique perspective on the extent and seasonality of fluctuations in atmospheric NO 2 isotopic compositions. Copyright © 2017 John Wiley & Sons, Ltd.

Coughlin, Justin G.↗
DOE OSTI2025

Efficient Quantum Gibbs Samplers with Kubo–Martin–Schwinger Detailed Balance Condition

Lindblad dynamics and other open-system dynamics provide a promising path towards efficient Gibbs sampling on quantum computers. In these proposals, the Lindbladian is obtained via an algorithmic construction akin to designing an artificial thermostat in classical Monte Carlo or molecular dynamics methods, rather than being treated as an approximation to weakly coupled system-bath unitary dynamics. Recently, Chen, Kastoryano, and Gilyén (arXiv:2311.09207) introduced the first efficiently implementable Lindbladian satisfying the Kubo–Martin–Schwinger (KMS) detailed balance condition, which ensures that the Gibbs state is a fixed point of the dynamics and is applicable to non-commuting Hamiltonians. This Gibbs sampler uses a continuously parameterized set of jump operators, and the energy resolution required for implementing each jump operator depends only logarithmically on the precision and the mixing time. In this work, we build upon the structural characterization of KMS detailed balanced Lindbladians by Fagnola and Umanità, and develop a family of efficient quantum Gibbs samplers using a finite set of jump operators (the number can be as few as one), akin to the classical Markov chain-based sampling algorithm. Compared to the existing works, our quantum Gibbs samplers have a comparable quantum simulation cost but with greater design flexibility and a much simpler implementation and error analysis. Moreover, it encompasses the construction of Chen, Kastoryano, and Gilyén as a special instance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Partially Collapsed Gibbs Samplers for Linear Inverse Problems and Applications to X-ray Imaging

Deblurring is one of the most common linear inverse problems in imaging, and most methods for deblurring an image assume that the convolution kernel, the so-called point spread function (PSF), is known or attempt to reconstruct the PSF simultaneously with the image. For many experimental imaging systems, like high energy X-ray radiography systems, the PSF must be computed from calibration imagery. The associated inverse problem is large-scale, so, in order to compute meaningful uncertainties with the reconstruction, it is common to use hierarchical Bayesian models and Markov chain Monte Carlo methods for sampling from the posterior. Classical Gibbs samplers can suffer from long autocorrelation lengths in the Markov chain when the reconstructed quantity in the inverse problem is finely discretized, resulting in poor computational efficiency of the Markov chain. In this work we present partially collapsed Gibbs samplers for linear inverse problems that greatly decrease correlations in the Markov chain, thus increasing the computational efficiency of the algorithms, and we present the results of the enhanced samplers on the problem of reconstructing the point spread function for an X-ray imaging system at the U.S. Department of Energy's Nevada National Security Site.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Spatiotemporal Changes in Trace Metal Bioavailability in the Sediment Pore water of a Constructed Wetland Using Passive Pore water Samplers

Sediments in aquatic systems often act as a major sink for contaminants. Diffusive gradient in thin films (DGTs) and in situ equilibrium dialysis samplers (peepers) are two major in situ pore water sampling devices that overcome the problems associated with conventional pore water sampling methods. In the present study, DGTs and peepers were used to study the spatial and seasonal effects (cool months, October–February; warm months, May–September) on metal bioavailability in the H‐02 constructed wetland and the sink versus source role of the sediments by calculating the metal resupply capacity. Data showed similar seasonal trends in metal concentrations using passive samplers, peepers, and DGTs. Pooled Cu and Zn concentrations measured using DGTs were lower in warm months (1.67 ± 1.50 and 2.62 ± 0.68 μg L –1 , respectively, p < 0.001) versus in cool months (2.12 ± 0.65 and 5.58 ± 1.33 μg L –1 , respectively, p < 0.001; mean ± 95% confidence interval). Sulfate (SO 4 2− ) concentrations were significantly (p = 0.0139) lower in warm months (averaged at 0.22 ± 0.05 mg L –1 ) compared to in cool months (0.16 ± 0.05 mg L –1 ). The increase in SO 4 2− concentration is an indicator of the lower activity of sulfate‐reducing bacteria, which need SO 4 2− during anaerobic respiration, in which SO 4 2− is reduced to sulfide (S 2– ) that forms insoluble salts with Cu and Zn, which could partially explain the higher bioavailability of these metals in the cool season. Metal resupply capacity of the sediments was mostly <0.2 for Cu and Zn. Taken together, the H0‐2 wetland sediments mostly acted as a sink to both Cu and Zn over the course of the present study. Environ Toxicol Chem 2023;42:2726–2736. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Determination of critical experiment correlations using the sampler sequence within scale 6.2 - 14602

The validation of neutron transport methods used in nuclear criticality safety analyses is required by consensus American National Standards Institute/American Nuclear Society (ANSI/ANS) standards. In the last decade, there has been an increased interest in correlations among critical experiments used in validation that have shared physical attributes and which impact the independence of each measurement. The statistical methods included in many of the frequently cited guidance documents on performing validation calculations incorporate the assumption that all individual measurements are independent, so little guidance is available to practitioners on the topic. Typical guidance includes recommendations to select experiments from multiple facilities and experiment series in an attempt to minimize the impact of correlations or common-cause errors in experiments. Recent efforts have been made both to determine the magnitude of such correlations between experiments and to develop and apply methods for adjusting the bias and bias uncertainty to account for the correlations. This paper describes recent work performed at Oak Ridge National Laboratory using the Sampler sequence from the SCALE code system to develop experimental correlations using a Monte Carlo sampling technique. Sampler will be available for the first time with the release of SCALE 6.2, and a brief introduction to the methods used to calculate experiment correlations within this new sequence is presented in this paper. Techniques to utilize these correlations in the establishment of upper subcritical limits are the subject of a companion paper and will not be discussed here. Example experimental uncertainties and correlation coefficients are presented for a variety of low-enriched uranium water-moderated lattice experiments selected for use in a benchmark exercise by the Working Party on Nuclear Criticality Safety Subgroup on Uncertainty Analysis in Criticality Safety Analyses. The results include studies on the effect of fuel rod pitch on the correlations, and some observations are also made regarding difficulties in determining experimental correlations using the Monte Carlo sampling technique. (authors)

42 ENGINEERING↗
DOE OSTI2020

Time-Resolved Aerosol Filter Sampler Instrument Handbook

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s aerosol counting, composition, extinction, and sizing system (ACCESS) includes a base module (9400), a filter sampler (9401), an advanced mixing condensation particle counter (MCPC, 9403), a miniaturized optical particle counter (MOPC, 9405), and a single-channel tricolor absorption photometer (STAP, 9406). This handbook describes the principles and operations of the filter sampler.

54 ENVIRONMENTAL SCIENCES↗