Search NASA⌕ Search

SEARCH · Search NASA

Results for “representative 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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Acceleration Factors for Combined‐Accelerated Stress Testing of Photovoltaic Modules

Combined‐accelerated stress testing (C‐AST) is developed to establish the durability of photovoltaic (PV) products, including for degradation modes that are not a priori known or examined in standardized tests. C‐AST aims to comprehensively represent the sample, stress factors, and their combinations using levels at the statistical tails of the natural environment. Acceleration factors for relevant climate sequences within the C‐AST cycle with respect to the Florida USA climate are estimated for selected degradation mechanisms. It is found that for degradation of the outer backsheet polymer layer, the acceleration factor of the tropical climate sequence (the longest of the climate sequences) is f ( T , G ) = 17.3 with ultraviolet photodegradation; for polyethylene terephthalate hydrolysis (backsheets), f ( T , RH ) = 426; for electrochemical corrosion (PV cell), f ( I ) = 14.1; and for PbSn solder fatigue f (Δ T , r ( T )) = 17.3. Here, T is the module temperature, G is the broadband spectrum irradiance on the plane of array of the module, RH is the relative humidity on the module surface, I is the leakage current through the module packaging, and r ( T ), the number of temperature reversals. The methods discussed herein are generally applicable for evaluating acceleration factors in other accelerated test methods.

14 SOLAR ENERGY↗
DOE OSTI2023

Comprehensive Analysis of Model Parameter Uncertainty Influence on Evaluation of HVAC Operation to Mitigate Indoor Virus: A Case Study for an Office Building in a Cold and Dry Climate

Simulation-based studies of HVAC operation to mitigate indoor virus have been conducted to understand tradeoffs between indoor air quality (IAQ) and energy consumption. However, the influence of model parameter uncertainty in these studies has not been systematically quantified, which is critical when providing guidance to building operators. To address this gap, we identify 20 model parameters for a typical medium office building system in a cold and dry climate that can influence IAQ and energy consumption for indoor virus scenarios. Next, the distributions of the parameter values are estimated from literature and a set of simulation samples for three representative days is generated that simultaneously sample all of the parameters from their distributions. Two HVAC virus mitigation strategies are studied: increased filtration using MERV 13 filters and increased ventilation, via supply of 100% outdoor air into buildings. The model parameter uncertainty leads to significant variability in the results, particularly for the IAQ because of the highly uncertain virus generation rate. Use of 100% outdoor air can be beneficial for some uncertain scenarios on the hot day, but shows less IAQ improvement and/or significant energy increases on the other days. The virus removal efficiency and pressure drop of the HVAC filter, fan efficiency, and internal heat gain are the most important parameters to determining the tradeoffs of the two strategies. Our results demonstrate how this model parameter uncertainty analysis methodology can provide practical guidance to building operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Wettability variation and its impact on CO 2 storage capacity at the Wyoming CarbonSAFE storage hub: An experimental approach

Meeting global and national net zero carbon emission targets will require geologic carbon disposal. The U.S. Department of Energy (DOE) has accordingly funded significant research in this area, including the Wyoming CarbonSAFE project at Dry Fork Station (DFS) in Campbell County, Wyoming. This work studied wettability on micro- and macro-scales, CO 2 storage potential, and the correlation between the two to support the Wyoming CarbonSAFE project’s subsurface assessment. During the study, a target formation’s wettability was found to affect how much CO 2 can be stored in a given formation. Here, in this study, representative rock samples were selected from the target storage formations— Lakota, Hulett, and Minnelusa—based on the heterogeneity of the lithology, permeability, and porosity of the respective formations. The rock samples are all fine-grained sandstone with variable cementation and bedding structure, including different scales of laminated bedding. The porosity and permeability vary within the range of 9.0–14.3% and 0.1–28.9 mD, respectively. These rock samples were prepared for the micro-scale wettability (contact angle measurement), macro-scale wettability (wettability index derived from unsteady-state flow characterization for the core plugs), and CO 2 storage evaluation. The macro-scale experiments suggested that wettability appeared to dominate the CO 2 storage potential performance during the drainage process, where less water-wet behavior promoted higher CO 2 storage potential. The micro-scale wettability tests showed that the rock samples at the studied reservoir conditions behaved water-wet and became more water-wet as pressure increased. This kind of wettability change discourages further CO 2 storage potential yet benefits the CO 2 residual trapping as the CO 2 injection proceeds for the studied area. The results allow the recommendation of the best reservoir candidate for storage based on wettability that affects CO 2 storage. The work presented in this study provides valuable insights into wettability’s effect on the CO 2 storage capacity and wettability’s importance when identifying the optimal CO 2 storage formation to meet the project’s goals.

58 GEOSCIENCES↗
DOE OSTI2021

Novel data analysis method for obtaining better performance from a complex 3D-printed collimator

Additively manufactured scattered beam collimators are increasingly being employed to boost the sample to cell peak signal ratio in high pressure neutron diffraction studies because of manufacturing versatility and performance improvements. We study how the measured diffraction pattern is affected by the presence of a collimator downstream of the sample, and develop a novel protocol that provides more effective background rejection. This protocol takes into account critical performance-determinants that were identified in this study, namely: (i) effectively identifying the collimator pattern on the detector; (ii) understanding the dependence of this pattern on sample and cell composition; and (iii) accurately identifying and differentiating the different regions of the pattern on the detector based on the dependency of the cell or sample and finally (iv) resolving the intensities at regions of the detector where neutrons scattered from the sample are preferentially represented, in order to boost the sample to cell peak signal ratio. Application of this novel analysis protocol is shown to increase the collimator performance over the traditional method.

3D printing↗
DOE OSTI2022

A universal all-solid synthesis for high throughput production of halide perovskite

Abstract Halide perovskites show ubiquitous presences in growing fields at both fundamental and applied levels. Discovery, investigation, and application of innovative perovskites are heavily dependent on the synthetic methodology in terms of time-/yield-/effort-/energy- efficiency. Conventional wet chemistry method provides the easiness for growing thin film samples, but represents as an inefficient way for bulk crystal synthesis. To overcome these, here we report a universal solid state-based route for synthesizing high-quality perovskites, by means of simultaneously applying both electric and mechanical stress fields during the synthesis, i.e., the electrical and mechanical field-assisted sintering technique. We employ various perovskite compositions and arbitrary geometric designs for demonstration in this report, and establish such synthetic route with uniqueness of ultrahigh yield, fast processing and solvent-free nature, along with bulk products of exceptional quality approaching to single crystals. We exemplify the applications of the as-synthesized perovskites in photodetection and thermoelectric as well as other potentials to open extra chapters for future technical development.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery

Abstract Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as powerful inverse design tools, often functioning in discrete action spaces. This constrains their application in materials design problems, which involve continuous search spaces. Here, we introduce an RL-based framework CASTING (Continuous Action Space Tree Search for inverse design), that employs a decision tree-based Monte Carlo Tree Search (MCTS) algorithm with continuous space adaptation through modified policies and sampling. Using representative examples like Silver (Ag) for metals, Carbon (C) for covalent systems, and multicomponent systems such as graphane, boron nitride, and complex correlated oxides, we showcase its accuracy, convergence speed, and scalability in materials discovery and design. Furthermore, with the inverse design of super-hard Carbon phases, we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Systematic characterization of selenium speciation in coal fly ash

Millions of tons of coal fly ashes (CFAs) are produced annually during coal combustion in the U.S., which are commonly beneficially used in the concrete industry or disposed of in ash ponds. CFAs contain trace amounts of a range of toxic heavy metals including selenium (Se). Because the toxicity of Se is dependent on its speciation, investigating Se speciation in CFAs as affected by coal source and combustion conditions can help understand the related environmental and human health impacts during disposal or beneficial reuse. In this study, a set of representative CFA samples were characterized for Se speciation using synchrotron X-ray absorption spectroscopy (XAS) and micro-X-ray fluorescence spectromicroscopy (μ-XRF/XAS). Se-containing particles were highly heterogeneous, and individual particles might contain multiple oxidation states including Se(0), Se(IV), and Se(VI). Principal component analysis was performed for sample characteristics including Al 2 O 3 , SiO 2 , CaO, FeO, loss on ignition, average particle size, Se concentration, and Se oxidation state. Selective catalytic reduction (SCR), which is used to limit nitrogen oxide (NO x ) emissions during coal combustion, was found to be associated with the presence of reduced Se oxidation states, with up to 90% Se(0) observed in samples with SCR. Alongside SCR, FeO content may also influence Se speciation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Machine Learning of Performance Space Mapping for the DPD Simulation of Drug Delivery to Endothelial Cells

Despite huge effort over the years, the design of functionalised nanocarriers (NCs) for targeted drug delivery to endothelial cells is still to be completely unveiled. Dissipative particle dynamics (DPD) simulation is used to study the adhesion of NCs to endothelial cells under the influence of series of parameters such as shape, size, and ligand density of the NCs. However, preparing a performance space mapping that illustrates the penetration depths of NCs as a function of variations in their properties requires simulations of all possible NCs with the above-mentioned properties, which is not feasible. This challenge was addressed by leveraging a Gaussian process regression (GPR)-informed active learning strategy and an extensive exploration of numerous samples, each representing different properties of NCs. The performance space mapping reveals that NCs with rod and disc shapes exhibit superior penetration capabilities compared to those with a spherical shape. Furthermore, it demonstrates that smaller-sized rod-shaped NCs and larger-sized disc-shaped NCs tend to achieve better penetration. When considering smaller NCs, the influence of ligand density appears to be limited. On the contrary, for larger NCs, an increase in ligand density correlates with greater penetration depth, underscoring its substantial role in shaping their penetration capabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Selection of a Pair of Experiments to Optimally Reduce Uncertainty in Targeted Nuclear Data

We propose a novel process to select a pair of differential and integral experiments that best reduce uncertainties in targeted 239 ⁢Pu nuclear data while compressing the current nuclear data pipeline from 20 to 3 years. 239⁢ Pu nuclear data are poorly understood for neutrons in the intermediate energy range due to sparsity and uncertainty in historical experiments. New experiments targeting this range will enable better understanding of these nuclear data, but choosing the ideal experiments to conduct is challenging. Beginning with a prior distribution represented by samples of nuclear data generated from theory, generalized least squares adjustments are made to incorporate data from historical experiments. To quantify potential uncertainty reduction obtainable from a pair of candidate experiments, we compute the D-optimality criterion of the posterior covariance of intermediate energy range nuclear data compared to the equivalent covariance after additional adjustment to the pair of candidate experiments. Repeating the process for each of many candidate pairs facilitates the final selection. Results support 63⁢ Cu total cross section measurements for differential experiments and alumina and alumina/graphite configurations for integral experiments. This analysis enables choosing differential and integral experiments to be executed concurrently while shortening decision times relative to the current nuclear data pipeline.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Extensive analysis of reconstruction algorithms for DESI 2024 baryon acoustic oscillations

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ∼2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the “standard” reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove non-linearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Clues on the history of early-type galaxies from SDSS spectra and GALEX photometry

ABSTRACT Stellar population studies of early-type galaxies (ETGs) based on their optical stellar continuum suggest that these are quiescent systems. However, emission lines and ultraviolet photometry reveal a diverse population. We use a new version of the starlight spectral synthesis code and state-of-the-art stellar population models to simultaneously fit Sloan Digital Sky Survey spectra and Galaxy Evolution Explorer photometry for a sample of 3453 galaxies at z < 0.1 with near ultraviolet (NUV) − r > 5 that are classified as elliptical by Galaxy Zoo. We reproduce far ultraviolet (FUV) magnitudes for 80 per cent of UV upturn galaxies selected using criteria from the literature, suggesting that additional stellar population ingredients such as binaries and extreme horizontal branch stars may have a limited contribution to the UV upturn. The addition of ultraviolet data leads to a broadening of the distributions of mean stellar ages, metallicities, and attenuation. Stellar populations younger than $1\,$ Gyr are required to reproduce the ultraviolet emission in 17 per cent of our sample. These systems represent 43 per cent of the sample at 5 < NUV − r < 5.5 and span the same stellar mass range as other ETGs in our sample. ETGs with young stellar components have larger H α equivalent widths (WH α) and larger dust attenuation. Emission line ratios and WH α indicate that the ionizing source in these systems is a mixture of young and old stellar populations. Their young stellar populations are metal-poor, especially for high-mass galaxies, indicating recent star formation associated with rejuvenation events triggered by external processes, such as minor mergers.

Werle, A.↗
DOE OSTI2026

Measurement of the 73 As ⁢(𝑛,𝛾) cross section at thermal neutron energies

A measurement of the thermal neutron capture cross section of 73 As (t 1/2 = 80.3 d) was performed utilizing the University of Missouri Research Reactor. Two sealed quartz tubes containing 73 As were prepared and characterized before being irradiated for ≈1 week. The resulting production of 74 As was quantified with γ-ray spectroscopy. Monitor foils of Fe, Zr, and Mo were irradiated alongside the 73 As samples to assess the thermal and resonance region neutron fluxes. In conclusion, the thermal 73 As ⁢(𝑛,𝛾) cross section was determined to be 56.1 ± 8.9 b based on the average of the two 73 As samples and represents the first neutron-induced reaction cross section measured on 73 As.

and nuclear chemistry↗
DOE OSTI2024

Oxygen vacancies in niobium pentoxide as a source of two-level system losses in superconducting niobium

We identify a major source of quantum decoherence in three-dimensional superconducting radio-frequency (SRF) resonators and two-dimensional transmon qubits composed of oxidized niobium: oxygen vacancies in the niobium pentoxide, which drive two-level system (TLS) losses. By probing the effect of sequential in situ vacuum-baking treatments on the rf performance of bulk Nb SRF resonators and on the oxide structure of a representative Nb sample using TOF SIMS, we find a nonmonotonic evolution of cavity quality factor Q 0 , which correlates with the interplay of Nb 2 O 5 vacancy generation and oxide-thickness reduction. We localize this effect to the oxide itself and present the insignificant role of diffused interstitial oxygen in the underlying Nb by regrowing the oxide via wet oxidation, which reveals a mitigation of aggravated TLS losses. We hypothesize that such vacancies in the pentoxide serve as magnetic impurities and are a source of TLS-driven rf loss.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

SAS PDF: pair distribution function analysis of nanoparticle assemblies from small-angle scattering data

SASPDF, a method for characterizing the structure of nanoparticle assemblies (NPAs), is presented. The method is an extension of the atomic pair distribution function (PDF) analysis to the small-angle scattering (SAS) regime. The PDFgetS3 software package for computing the PDF from SAS data is also presented. An application of the SASPDF method to characterize structures of representative NPA samples with different levels of structural order is then demonstrated. The SASPDF method quantitatively yields information such as structure, disorder and crystallite sizes of ordered NPA samples. The method was also used to successfully model the data from a disordered NPA sample. The SASPDF method offers the possibility of more quantitative characterizations of NPA structures for a wide class of samples.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Comprehensive framework for assessing and optimizing existing research networks

Conservation, monitoring, and research networks, or collections of ecological research sites unified under a common mission of data collection or a research mission, are essential infrastructure for understanding large landscapes. However, most networks developed opportunistically over decades rather than through systematic design, creating potential limitations in the ability to address conservation challenges across entire regions. We developed a framework to evaluate how well an existing research network represents the environmental conditions its members study and devised an approach to rank sites of priority for strategic expansion. Our approach measures performance through environmental representativeness, geographic coverage, and adequacy for scientific inference and thus optimizes limited monitoring resources to maximize scientific impact. We demonstrated this approach with the U.S. Department of Agriculture (USDA) Forest Service Experimental Forests and Ranges Network (EFRN), a 79‐site network across the United States that grew opportunistically over a century. At the national scale, the network effectively captured high‐biomass forests important for carbon cycle research; 82% of forest biomass was in well‐represented areas. Some areas in Texas, Florida, the Rocky Mountains, and the West Coast had no relevant EFRN sites, which limits the ability to make regional inferences. A fundamental challenge for the EFRN was that sites improving regional extent coverage sometimes provided minimal national benefits, which can create conflicts between local and global priorities. Adding the highest‐ranked candidate site provided a relevant site for 17% of currently poorly represented 1‐km pixel cells nationally, but regional and national site rankings varied considerably due to nested spatial inference. This framework provides quantitative tools for strategic infrastructure decision‐making, ensures that limited monitoring resources maximize conservation impact, and can be applied broadly to address the widespread challenge of optimizing conservation and monitoring networks worldwide.

additional site↗
DOE OSTI2022

QUEST: systematically approximating Quantum circuits for higher output fidelity

We present QUEST, a procedure to systematically generate approximations for quantum circuits to reduce their CNOT gate count. Our approach employs circuit partitioning for scalability with procedures to 1) reduce circuit length using approximate synthesis, 2) improve fidelity by running circuits that represent key samples in the approximation space, and 3) reason about approximation upper bound. Our evaluation results indicate that our approach of "dissimilar"approximations provides close fidelity to the original circuit. Overall, the results indicate that QUEST can reduce CNOT gate count by 30-80% on ideal systems and decrease the impact of noise on existing and near-future quantum systems.

Patel, Tirthak↗
DOE OSTI2023

Standard experimental paradigm designs and data exclusion practices in cognitive psychology can inadvertently introduce systematic “shadow” biases in participant samples

Abstract Standard cognitive psychology research practices can introduce inadvertent sampling biases that reduce the reliability and generalizability of the findings. Researchers commonly acknowledge and understand that any given study sample is not perfectly generalizable, especially when implementing typical experimental constraints (e.g., limiting recruitment to specific age ranges or to individuals with normal color vision). However, less obvious systematic sampling constraints, referred to here as “shadow” biases, can be unintentionally introduced and can easily go unnoticed. For example, many standard cognitive psychology study designs involve lengthy and tedious experiments with simple, repetitive stimuli. Such testing environments may 1) be aversive to some would-be participants (e.g., those high in certain neurodivergent symptoms) who may self-select not to enroll in such studies, or 2) contribute to participant attrition, both of which reduce the sample’s representativeness. Likewise, standard performance-based data exclusion efforts (e.g., minimum accuracy or response time) or attention checks can systematically remove data from participants from subsets of the population (e.g., those low in conscientiousness). This commentary focuses on the theoretical and practical issues behind these non-obvious and often unacknowledged “shadow” biases, offers a simple illustration with real data as a proof of concept of how applying attention checks can systematically skew latent/hidden variables in the included population, and then discusses the broader implications with suggestions for how to manage and reduce, or at a minimum acknowledge, the problem.

Siritzky, Emma M.↗
DOE OSTI2022

A tailored fast thioacidolysis method incorporating multi-reaction monitoring mode of GC-MS for higher sensitivity on lignin monomer quantification

Abstract Thioacidolysis is widely used for lignin structural characterization by cleaving β-aryl ethers to release syringyl (S), guaiacyl (G), and p -hydroxyphenyl (H) monomers followed by GC analysis. However, the traditional thioacidolysis method requires tedious extraction steps with chlorinated solvent underlying harmful to health, limiting its efficiency and application. Herein, an improved thioacidolysis method with high sensitivity for the quantitation of lignin-derived monomers was developed. The improved protocol used a quick, streamlined procedure to recover the monomeric products using ethyl acetate as extracting solvent and MS detector in multi-reaction monitoring mode to enhance its ability to detect extremely low concentration (0.1 ppb with signal-to-noise higher than 2) of monomeric products. Additionally, a fast GC program was established to speed up the GC quantitation. Several representative lignocellulose samples, including gymnosperm, angiosperm, and poaceae, were used to test this tailored method. The results demonstrated that the ratios of lignin monomer compositions determined by this method were consistent with that of traditional procedure despite the slightly higher monomer yields measured. More importantly, this method uses non-chlorinated solvent for microscale extraction and requires no evaporation step for workup, which is a green and efficient way for the quantification of lignin monomer compositions.

Forestry↗