Search NASA⌕ Search

SEARCH · Search NASA

Results for “sequential sampling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Unraveling resistive versus collisional contributions to relativistic electron beam stopping power in cold-solid and in warm-dense plasmas

We present results on laser-driven relativistic electron beam propagation through aluminum samples, which are either solid and cold or compressed and heated by laser-induced shock. A full numerical description of fast electron generation and transport is found to reproduce the experimental absolute K{sub α} yield and spot size measurements for varying target thicknesses, and to sequentially quantify the collisional and resistive electron stopping powers. The results demonstrate that both stopping mechanisms are enhanced in compressed Al samples and are attributed to the increase in the medium density and resistivity, respectively. For the achieved time- and space-averaged electronic current density, 〈j{sub h}〉∼8×10{sup 10} A/cm{sup 2} in the samples, the collisional and resistive stopping powers in warm and compressed Al are estimated to be 1.5 keV/μm and 0.8 keV/μm, respectively. By contrast, for cold and solid Al, the corresponding estimated values are 1.1 keV/μm and 0.6 keV/μm. Prospective numerical simulations involving higher j{sub h} show that the resistive stopping power can reach the same level as the collisional one. In addition to the effects of compression, the effect of the transient behavior of the resistivity of Al during relativistic electron beam transport becomes progressively more dominant, and for a significantly high current density, j{sub h}∼10{sup 12} A/cm{sup 2}, cancels the difference in the electron resistive stopping power (or the total stopping power in units of areal density) between solid and compressed samples. Analytical calculations extend the analysis up to j{sub h}=10{sup 14} A/cm{sup 2} (representative of the full-scale fast ignition scenario of inertial confinement fusion), where a very rapid transition to the Spitzer resistivity regime saturates the resistive stopping power, averaged over the electron beam duration, to values of ∼1 keV/μm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Lens Modeling of STRIDES Strongly Lensed Quasars Using Neural Posterior Estimation

Strongly lensed quasars can be used to constrain cosmological parameters through time-delay cosmography. Models of the lens masses are a necessary component of this analysis. To enable time-delay cosmography from a sample of $\mathcal{O}(10^3)$ lenses, which will soon become available from surveys like the Rubin Observatory’s Legacy Survey of Space and Time and the Euclid Wide Survey, we require fast and standardizable modeling techniques. To address this need, we apply neural posterior estimation (NPE) for modeling galaxy-scale strongly lensed quasars from the Strong Lensing Insights into the Dark Energy Survey (STRIDES) sample. NPE brings two advantages: speed and the ability to implicitly marginalize over nuisance parameters. We extend this method by employing sequential NPE to increase precision of mass model posteriors. We then fold individual lens models into a hierarchical Bayesian inference to recover the population distribution of lens mass parameters, accounting for out-of-distribution shift. After verifying our method using simulated analogs of the STRIDES lens sample, we apply our method to 14 Hubble Space Telescope single-filter observations. We find the population mean of the power-law elliptical mass distribution slope, γ lens , to be $\mathcal{M}_γ$ lens = 2.13 ± 0.06. Our result represents the first population-level constraint for these systems. This population-level inference from fully automated modeling is an important stepping stone toward cosmological inference with large samples of strongly lensed quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

The Characterization of Dissolved Organic Matter in Reclaimed Water and Its Influence on Copper Toxicity

Water samples were collected from five phases of treatment in a municipal sewage reclaimed water plant and the DOM was characterized. Results indicated that the components and properties of DOM varied notably with sequential treatments, such as the fluorescence intensity, the molecular weight and the total acidity. Meanwhile the accumulation of Cu in Daphnia magna was analyzed following exposure to samples spiked with 50 µg/L copper, which were decreased by the presence of DOM in those water samples. Furthermore, this study found significant associations between fluorescence intensity and Cu accumulation (r{sup 2} = 0.778, p < 0.05), while increased total acidity was found to enhance the unit total organic carbon-Cu accumulation (r{sup 2} = 0.979, p < 0.01). This study provides useful information on the safety and effective management of reclaimed water as a potential water resource.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

A rigorous sequential update strategy for parallel kinetic Monte Carlo simulation

The kinetic Monte Carlo (kMC) method is used in many scientific fields in applications involving rare-event transitions. Due to its discrete stochastic nature, efforts to parallelize kMC approaches often produce unbalanced time evolutions requiring complex implementations to ensure correct statistics. In the context of parallel kMC, the sequential update technique has shown promise by generating high quality distributions with high relative efficiencies for short-range systems. In this work, we provide an extension of the sequential update method in a parallel context that rigorously obeys detailed balance, which guarantees exact equilibrium statistics for all parallelization settings. Furthermore, our approach also preserves nonequilibrium dynamics with minimal error for many parallelization settings, and can be used to achieve highly precise sampling.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Fukushima Daiichi reactor source term attribution using cesium isotope ratios from contaminated environmental samples

Source term attribution of environmental contamination following the Fukushima Daiichi Nuclear Power Plant (FDNPP) disaster is complicated by a large number of possible similar emission source terms (e.g. FDNPP reactor cores 1–3 and spent fuel ponds 1–4). Cesium isotopic analyses can be utilized to discriminate between environmental contamination from different FDNPP source terms and, if samples are sufficiently temporally resolved, potentially provide insights into the extent of reactor core damage at a given time. In this work, rice, soil, mushroom, and soybean samples taken 100–250 km from the FDNPP site were dissolved using microwave digestion. Radiocesium was extracted and purified using two sequential ammonium molybdophosphate-polyacrylonitrile columns, following which 135 Cs/ 137 Cs isotope ratios were measured using thermal ionization mass spectrometry (TIMS). Results were compared with data reported previously from locations to the northwest of FDNPP and 30 km to the south of FDNPP. 135 Cs/ 137 Cs isotope ratios from samples 100–250 km to the southwest of the FDNPP site show a consistent value of 0.376 ± 0.008. 135 Cs/ 137 Cs versus 134 Cs/ 137 Cs correlation plots suggest that radiocesium to the southwest is derived from a mixture of FDNPP reactor cores 1, 2, and 3. Conclusions from the cesium isotopic data are in agreement with those derived independently based upon the event chronology combined with meteorological conditions at the time of the disaster. In conclusion, cesium isotopic analyses provide a powerful tool for source term discrimination of environmental radiocesium contamination at the FDNPP site. For higher precision source term attribution and forensic determination of the FDNPP core conditions based upon cesium, analyses of a larger number of samples from locations to the north and south of the FDNPP site (particularly time-resolved air filter samples) are needed. Published in 2016. This article is a U.S. Government work and is in the public domain in the USA.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Enhanced biofilm formation aids adaptation to extreme warming and environmental instability in the diatom Thalassiosira pseudonana and its associated bacteria

Abstract Recent studies have highlighted the capacity of marine diatoms at the foundation of ocean food webs and biogeochemical cycles to respond to environmental change. These responses are a combination of physiological acclimation and evolutionary responses. The latter occur rapidly within a few hundred generations (translating to a scale of months to years), due to the large standing genetic variation and short generation times of diatom populations. Studies are usually carried out on diatom species in their planktonic state, but this may constitute a stark oversimplification: diatoms readily form biofilms in the water column of shallow coastal areas, where biofilm formation affects carbon export, and in the benthos, where biofilms contribute significantly to sediment stability. Here, I have carried out a 400‐generation selection experiment, where I evolved the diatom Thalassiosira pseudonana and its associated bacteria. Bacteria and diatom were cultured in their planktonic form, and under strong selection for rapid biofilm formation in temperature and nutrient regimes differing in their severity and stability. I find that biofilm formation is enhanced under mild warming and rapid thermal fluctuations but not under extreme warming regardless of nutrient availability. Evolutionary responses are slower in biofilms under benign conditions but speed up under extreme and fluctuating conditions. Taxonomic composition of the associated bacteria is driven by the temperature selection regime in biofilms but not in planktonic samples. I also show that T. pseudonana , when selected for biofilm formation, presents predictable and sequential morphologies that are not usually observed in planktonic T. pseudonana.

Schaum, C. ‐E.↗
DOE OSTI2014

Two-step polarization reversal in biased ferroelectrics

Polarization reversal in polycrystalline ferroelectrics is shown to occur via two distinct and sequential domain reorientation steps. This reorientation sequence, which cannot be readily discriminated in the overall sample polarization, is made apparent using time-resolved high-energy x-ray diffraction. Upon application of electric fields opposite to the initial poling direction, two unique and significantly different time constants are observed. The first (faster time constant) is shown to be derived by the release of a residual stress due to initial electrical biasing and the second (slower time constant) due to the redevelopment of residual stress during further domain wall motion. A modified domain reorientation model is given that accurately describes the domain volume fraction evolution during the reversal process.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Optimization on Manifolds via Graph Gaussian Processes

This paper integrates manifold learning techniques within a Gaussian process upper confidence bound algorithm to optimize an objective function on a manifold. Our approach is motivated by applications where a full representation of the manifold is not available and querying the objective is expensive. We rely on a point cloud of manifold samples to define a graph Gaussian process surrogate model for the objective. Query points are sequentially chosen using the posterior distribution of the surrogate model given all previous queries. We establish regret bounds in terms of the number of queries and the size of the point cloud. Several numerical examples complement the theory and illustrate the performance of our method.

Bayesian optimization↗
DOE OSTI2023

Artificial intelligence for materials research at extremes

Abstract Materials development is slow and expensive, taking decades from inception to fielding. For materials research at extremes, the situation is even more demanding, as the desired property combinations such as strength and oxidation resistance can have complex interactions. Here, we explore the role of AI and autonomous experimentation (AE) in the process of understanding and developing materials for extreme and coupled environments. AI is important in understanding materials under extremes due to the highly demanding and unique cases these environments represent. Materials are pushed to their limits in ways that, for example, equilibrium phase diagrams cannot describe. Often, multiple physical phenomena compete to determine the material response. Further, validation is often difficult or impossible. AI can help bridge these gaps, providing heuristic but valuable links between materials properties and performance under extreme conditions. We explore the potential advantages of AE along with decision strategies. In particular, we consider the problem of deciding between low-fidelity, inexpensive experiments and high-fidelity, expensive experiments. The cost of experiments is described in terms of the speed and throughput of automated experiments, contrasted with the human resources needed to execute manual experiments. We also consider the cost and benefits of modeling and simulation to further materials understanding, along with characterization of materials under extreme environments in the AE loop. Graphical abstract AI sequential decision-making methods for materials research: Active learning, which focuses on exploration by sampling uncertain regions, Bayesian and bandit optimization as well as reinforcement learning (RL), which trades off exploration of uncertain regions with exploitation of optimum function value. Bayesian and bandit optimization focus on finding the optimal value of the function at each step or cumulatively over the entire steps, respectively, whereas RL considers cumulative value of the labeling function, where the latter can change depending on the state of the system (blue, orange, or green).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design, modeling and experimental validation of a micro cantilever beam with an electro-controllable twisting ability

The structure design of micro cantilever beams (MCBs), which has a significant effect on MCBs' controllable actuation range, is usually restricted by the establishment of the inner electric circuit. This paper proposes the design of a micro electro-controllable twisting cantilever beam (METCB). The architecture and the fabrication diagram of the proposed METCB are presented before its features are theoretically and experimentally validated. In detail, the analytical validation includes a sequence of two models: electro-thermal and thermo-mechanical. Through these two models, the thermal distribution along the METCB and the deformation brought by the thermal distribution are investigated, respectively. On the basis of the design and equations, the METCB samples have been manufactured using a planar deposition technique with an e-beam evaporator. Two in-site experiments are then conducted to sequentially figure out the upper voltage limit of the inner electric circuit and the maximum temperature limit of the METCB. Finally, another experiment is carried out to explore the relation between the applied voltage and the relative twisting angle of the METCB sample. Here, the theoretical prediction and the observed experiment result both prove the feasibility of the proposed METCB and its electro-controllable twisting ability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Rare Earth Element (REE) and Critical Mineral Fractions of Central Appalachian Coal-Related Strata Determined by 7-Step Sequential Extraction

Rare earth elements (REEs) and critical minerals (CMs) are used in many modern industries, including the automotive sector, generation and storage, clean energy, and defense. The demand for REEs is increasing, and the REE supply chain is unpredictable. The US has driven to assess non-conventional sources of REE (such as coal underclay) to identify domestic resources to stabilize this uncertainty in supply. Knowledge of the minerology, distribution, and modes of occurrence of REEs is integral to the assessment of non-conventional sources. Additionally, extraction techniques can be optimized and targeted when REE distribution in different solid fractions from source material is understood. In this study, four bituminous coal-related samples associated with the Lower and Middle Kittanning coal seams in the Appalachian Basin (US) underwent a seven-step sequential extraction procedure, primarily targeting the water-soluble, exchangeable, acid soluble, mildly reducible, moderately reducible, strongly reducible, and oxidizable fractions. The REE and other elements of interest from each extraction step were analyzed, and the percentages of element extracted from raw solids were calculated. REEs extracted from the total seven steps were reported as the extractable fraction, whereas the fractions in the residual solids were reported as the non-extractable fraction. Less than 6% of REE were extracted from three samples. Twenty-one percent of REE was extracted from the fourth sample, mainly from the steps targeting oxidizable and exchangeable phases. Co-extraction of critical metals (Co, Ni, Cu, and Zn) occurred during the oxidizable, exchangeable, acid soluble, and water-soluble steps for the four samples. In the extracted fractions, the four samples all exhibited a middle and heavy REE enrichment relative to light REE. The mobility of major cation (e.g., Ca, Fe, and P) and REE is associated with exchangeable, oxidizable, and acid soluble fractions. Non-extractable REE is likely held in refractory apatitic phases, and/or primary REE-phosphates (e.g., monazite and xenotime).

58 GEOSCIENCES↗
DOE OSTI2021

Apparatus for high-throughput sequential tensile testing and methods thereof

The present invention relates, in part, to an apparatus configured to test a plurality of test samples within a sample cartridge. Such an apparatus can facilitate high-throughput tensile testing of such test samples. Also described herein are methods for using such an apparatus and for testing such test samples.

Boyce, Brad↗
DOE OSTI2018

A monolayer microfluidic device supporting mouse spermatogenesis with improved visibility

In our previous study, we produced a microfluidic device (MFD) which successfully maintained spermatogenesis for over 6 months in mouse testis tissues loaded in the device. In the present study, we developed a new MFD, a monolayer device (ML-D) with a barrier structure consisting of pillars and slits, which is simpler in design and easier to make. This ML-D was also effective for inducing mouse spermatogenesis and maintained it for a longer period than the conventional culture method. In addition, we devised a way of introducing sample tissue into the device during its production, just before bonding the upper layer of polydimethylsiloxane (PDMS) and bottom glass slide. The tissue can obtain nutrients horizontally from the medium running beside it and oxygen vertically from above through PDMS. In addition, the glass slide set at the bottom improved the visibility of the sample tissue with an inverted microscope. When we took photos of cultured tissue of the Acr-Gfp transgenic mouse testis in ML-D sequentially every day, morphological changes of the acrosome during spermiogenesis were successfully recorded. The ML-D is simple in design and useful for culturing testis tissue for inducing and maintaining spermatogenesis with clearer visibility. Due to the new method of sample loading, tissues other than testis should also be applicable.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Spin-lattice coupling, Jahn-Teller effect and the influence of the measurement rate in La{sub 0.7}Ca{sub 0.3−x}Sr{sub x}MnO{sub 3} manganites

This work presents a study of how the magnetic and temperature variables, as well as the measurement rate, affect magnetic and structural phase transitions for La{sub 0.7}Ca{sub 0.3−x}Sr{sub x}MnO{sub 3} (x=0.15, 0.08, 0.07 and 0.06) manganites. A single phase rhombohedral crystal structure for Sr{sub 0.15} compound and a single phase orthorhombic crystal structure for Sr{sub 0.08}, Sr{sub 0.07}, and Sr{sub 0.06} compounds were identified by X ray diffraction. The Curie temperature adjustment to room temperature was reached for Sr{sub 0.07} sample, in which a Jahn-Teller transition was observed. This Jahn-Teller transition showed a susceptible response to 0.5, 1, 2, 3, 5 and 7 K/min measurement rates, and was revealed without measurement sequence at 234, 257, 196, 233, 245 and 238 K respectively in thermomagnetic curves (10 mT). Also, Curie temperature increases sequentially around 2 K for the same measurement rates. To avoid magnetic field effect, a differential scanning calorimetry for Sr{sub 0.07} sample was obtained in the same temperature region, for 2, 3, 5, 7 K/min showing the Jahn-Teller phase transition at 225 K and a vibrational like behavior in the heat flux below 225 K, for all the temperature rates. The latter, is related to the static to dynamic Jahn-Teller transition. Furthermore, Curie temperature appears around 284 K.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A Multi-Armed Bayesian Ordinal Outcome Utility-Based Sequential Trial with a Pairwise Null Clustering Prior

A multi-armed trial based on ordinal outcomes is proposed that leverages a flexible non-proportional odds cumulative logit model and numerical utility scores for each outcome to determine treatment optimality. This trial design uses a Bayesian clustering prior on the treatment effects that encourages the pairwise null hypothesis of no differences between treatments. A group sequential design is proposed to determine which treatments are clinically different with an adaptive decision boundary that becomes more aggressive as the sample size or clinical significance grows, or the number of active treatments decreases. A simulation study is conducted for 3 and 5 treatment arms, which shows that the design has superior operating characteristics (family wise error rate, generalized power, average sample size) compared to utility designs that do not allow clustering, a frequentist proportional odds model, or a permutation test based on empirical mean utilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Methodology for Extracting High-Molecular-Weight DNA from Field Collections of Macrofungi

Many macrofungi are impractical or impossible to culture. Consequently, DNA for long-read sequencing required for the assembly of high-quality genomes must be isolated from samples taken from the environment. Collection is often in remote locations, limiting the options for stabilising samples to methods that do not require refrigeration. Fungi contain species-specific arrays of metabolites that may complicate purification techniques and call for judgement to be made to apply appropriate modifications to the DNA extraction protocol in specific cases. The protocols and commentary we describe are informed by the preparation of DNA from a range of Australasian ectomycorrhizal and saprotrophic macrofungi. We collect samples into isopropanol at ambient temperature and employ a strategy of chromatin isolation followed by the sequential removal of unwanted molecular components to purify DNA.

Burgoyne, Leigh A↗