Soiling, cleaning, and abrasion: The results of the 5-year photovoltaic glass coating field study
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Bomber, Joanna↗
SEARCH · Engineering Papers
Results for “Study”
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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Experimental X-ray Charge-Density Studies–A Suitable Probe for Superconductivity? A Case Study on MgB 2
Case studies of 1T-TiSe 2 and YBa 2 Cu 3 O 7-δ have demonstrated that X-ray diffraction (XRD) studies can be used to trace even subtle structural phase transitions which are inherently connected with the onset of superconductivity in these benchmark systems. However, the utility of XRD in the investigation of superconductors like MgB 2 lacking an additional symmetry-breaking structural phase transition is not immediately evident. Nevertheless, high-resolution powder XRD experiments on MgB 2 in combination with maximum entropy method analyses hinted at differences between the electron density distributions at room temperature and 15 K, that is, below the T c of approx. 39 K. The high-resolution single-crystal XRD experiments in combination with multipolar refinements presented here can reproduce these results but show that the observed temperature-dependent density changes are almost entirely due to a decrease of atomic displacement parameters as a natural consequence of a reduced thermal vibration amplitude with decreasing temperature. Our investigations also shed new light on the presence or absence of magnesium vacancies in MgB 2 samples–a defect type claimed to control the superconducting properties of the compound. Here, we propose that previous reports on the tendency of MgB 2 to form non-stoichiometric Mg 1–x B 2 phases (1 – x ~ 0.95) during high-temperature (HT) synthesis might result from the interpretation of XRD data of insufficient resolution and/or usage of inflexible refinement models. Indeed, advanced refinements based on an Extended Hansen–Coppens multipolar model and high-resolution X-ray data, which consider explicitly the contraction of core and valence shells of the magnesium cations, do not provide any significant evidence for the formation of non-stoichiometric Mg 1–x B 2 phases during HT synthesis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Computational Study of RNA Tetraloop Thermodynamics, Including Misfolded States
An important characteristic of RNA folding is the adoption of alternative configurations of similar stability, often referred to as misfolded configurations. These configurations are considered to compete with correctly folded configurations, although their rigorous thermodynamic and structural characterization remains elusive. Tetraloop motifs found in large ribozymes are ideal systems for an atomistically detailed computational quantification of folding free energy landscapes and the structural characterization of their constituent free energy basins, including nonnative states. In this work, we studied a group of closely related 10-mer tetraloops using a combined parallel tempering and metadynamics technique that allows a reliable sampling of the free energy landscapes, requiring only knowledge that the stem folds into a canonical A-RNA configuration. Here we isolated and analyzed unfolded, folded, and misfolded populations that correspond to different free energy basins. We identified a distinct misfolded state that has a stability very close to that of the correctly folded state. This misfolded state contains a predominant population that shares the same structural features across all tetraloops studied here and lacks the noncanonical A-G base pair in its loop portion. Further analysis performed with biased trajectories showed that although this competitive misfolded state is not an essential intermediate, it is visited in most of the transitions from unfolded to correctly folded states. Moreover, the tetraloops can transition from this misfolded state to the correctly folded state without requiring extensive unfolding.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Comparative Study of Physics‐Informed and Data‐Driven Neural Networks for Compound Flood Simulation at River‐Ocean Interfaces: A Case Study of Hurricane Irene
Simulating compound flooding (CF) at the river-ocean interface within large-scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics-informed and data-driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data-driven neural network models (i.e., CNNs, U-Net, Long Short-Term Memory (LSTM), Gated Recurrent Unit), and physics-informed neural network (PINN) models, including vanilla PINN and a finite-difference-based PINN (FD-PINN). Specifically, FD-PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data-driven model training, a new data-generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one-dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD-PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data-driven models, the best overall performance is achieved by a CNN-LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN-FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence-aware designs for robust generalization. This study reveals the trade-offs between physics-informed and data-driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.
Earth Systems Modeling↗
CoNiCuCrS alloy nanoparticles: synthesis and atomically resolved T/STEM studies
Multimetallic alloy nanoparticles as a model for bulk high entropy alloy studies.
Rufino da Silva, Carlos E.↗
The use of synchrotron X-ray fluorescent imaging to study distribution and content of elements in chemically fixed single cells: a case study using mouse pancreatic beta-cells
Synchrotron X-ray fluorescence microscopy (SXRF) presents a valuable opportunity to study the metallome of single cells because it simultaneously provides high-resolution subcellular distribution and quantitative cellular content of multiple elements. Different sample preparation techniques have been used to preserve cells for observations with SXRF, with a goal to maintain fidelity of the cellular metallome. In this case study, mouse pancreatic beta-cells have been preserved with optimized chemical fixation. We show that cell-to-cell variability is normal in the metallome of beta-cells due to heterogeneity and should be considered when interpreting SXRF data. Additionally, we determined the impact of several immunofluorescence (IF) protocols on metal distribution and quantification in chemically fixed beta-cells and found that the metallome of beta-cells was not well preserved for quantitative analysis. However, zinc and iron qualitative analysis could be performed after IF with certain limitations. To help minimize metal loss using samples that require IF, we describe a novel IF protocol that can be used with chemically fixed cells after the completion of SXRF.
59 BASIC BIOLOGICAL SCIENCES↗
Separability of the Dirac equation in the STU black hole spacetime: Pairwise-equal charge case study
We study the separability of the Dirac equation in the background of four dimensional charged rotating asymptotically flat black hole solution of supergravity and the low energy string theory known as STU black hole. In particular, we analyze in detail the separability conditions in the pairwise equal charge STU black hole space-time. While in the latter case the minimally coupled Dirac equation is not separable, the introduction of a specific torsion term ensures the separability. The source of the torsion is the Kalb-Ramond field, which is an integral part of string theory, but further aspects of its properties and coupling to fermionic fields remain to be studied. To derive the torsion, two different approaches are used in conformally related frames, showing that the torsion is not unique. The correspondingly modified Dirac equations in the Einstein and string frames are shown to be separable. Furthermore, the massless radial and angular wave equations are examined; they show close similarity with corresponding equations for the standard Kerr background. A generalization of the Teukolsky equation for the pairwise equal case is conjectured. We also briefly analyze a technically sophisticated radial equation in the massive case.
Dirac equation↗
A Fast and Accurate Transient Stability Assessment Method Based on Deep Learning: WECC Case Study
Transient stability is one of the critical aspects of power system stability assessment. The increasing integration of inverter-based resources and the retirement of conventional synchronous generators result in the decreasing system inertia and growing complexity of system operating conditions. Using a few selected typical operating conditions cannot guarantee system transient stability in all operating conditions, and the time-domain simulation of all operating conditions requires tremendous time and is often infeasible. This paper proposes a more efficient transient stability assessment method based on deep learning. The binary search method is used to determine the critical clearing time (CCT) in creating training databased by time-domain simulation. This method is fast and accurate with 1 ms resolution. The buses whose CCTs are lower than 200 ms are considered critical buses. Buses close to each other are grouped based on their mutual admittance matrix to reduce the search space of the critical buses. This paper also proposes the generator feature normalization based on the physical model. Case study on the reduced 240-bus WECC system model demonstrates that the proposed method can predict CCT accurately and efficiently.
critical clearing time↗
Stochastic Look-Ahead Commitment: A Case Study in MISO
This paper introduces the Stochastic Look Ahead Commitment (SLAC) software prototyped and tested for the Midcontinent Independent System Operator (MISO) look ahead commitment process. SLAC can incorporate hundreds of wind, load, and net scheduled interchange (NSI) uncertainty scenarios. It uses a progressive hedging method to solve a novel two-stage stochastic unit commitment. The first stage commitment decisions, made only for those generators whose decision to commit or not in each time period cannot be deferred, can cover the uncertainties within the next three hours. The second stage includes both the dispatch for each of the scenarios and the commitment decisions that can be deferred. Study results on 15 MISO production days show that SLAC may bring economic and reliability benefits under uncertainty.
MATHEMATICS AND COMPUTING↗
Comparison of Real-Time Pressure Rail Selection Algorithms for the Hybrid Hydraulic Electric Architecture: Case Study on a Track Loader
Abstract The hybrid hydraulic electric architecture (HHEA) seeks to combine the high power/torque/force density of hydraulics with the efficiency of electric machines. A set of common pressure rails is used to provide a majority of the power and this power is modulated by small electric machines to provide precise control for the operator. The HHEA has been studied in previous work using off-line dynamic programming optimization to determine energy efficient pressure rail selections, but this approach requires drive cycle information apriori. A Lagrange multiplier method has also been investigated where a set of gains (Lagrange multipliers) are optimized off-line with the idea the these gains, once determined, could be used for real-time operation. In this work, three new real-time pressure rail selection algorithms that do not require future drive cycle information are investigated; greedy, torque minimizing, and thresholding. The greedy control is found to only use 1% more energy than the globally optimal dynamic programming solution; but a model of energy loss is required.
24 POWER TRANSMISSION AND DISTRIBUTION↗
STUDIES ON PIPELINE POLYETHYLENES IN HYDROGEN GAS ENVIRONMENTS USING IN-SITU AND EX-SITU CHARACTERIZATION METHODS
Polymeric materials are commonplace in the natural gas infrastructure as distribution pipes, coatings, seals, and gaskets. Under the auspices of the U.S. Department of Energy HyBlend program, one of the means to reduce greenhouse gas emissions is with replacing natural gas, either partially or completely, with hydrogen. This approach makes it imperative that we conduct near-term and long-term materials compatibility research in these relevant environments. Insights into the effects of hydrogen and hydrogen gas blends on polymer integrity can be gained through both ex-situ and in-situ analytical methods. Our work represented here highlights a study of the behavior of pipeline polyethylene (PE) materials, including HDPE (Dow 2490 and GDB50) and MDPE (Ineos and legacy Dupont Aldyl A), when exposed to hydrogen by means of in-situ X-ray scattering and ex-situ Raman spectroscopy techniques. Samples were tested in ex-situ hydrogen and argon gas environments and in-situ hydrogen environments to identify differences due to permeation and solubility of these gases. These methods complemented each other because Raman spectroscopy could capture permanent effects after materials were removed from gaseous environments, and in-situ X-ray scattering analysis collected real-time data to elucidate the impact of the gas environment on polymer microstructure. Data collected revealed that the aforementioned polymers did not show significant changes in crystallinity and microstructure under the exposure conditions tested. Our findings from these studies will help establish real-time effects caused by hydrogen gas transport through pipeline polyethylenes by way of its influence on polymer structure and chemistry, which is directly related to pipeline mechanical strength and longevity of service.
Hydrogen materials compatibility, Natural gas pipe↗
Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize
Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.
AI/ML↗
Inelastic Scattering Study of Liquid Gallium
Collective excitations in liquid gallium from room temperature to 950 K were studied using inelastic neutron scattering measurements.
36 MATERIALS SCIENCE↗
Status of permanent magnet radiation resiliency studies at CEBAF
An ongoing investigation for the future of Jefferson Lab’s Continuous Electron Beam Accelerator Facility (CEBAF) lies in upgrading its maximum nominal energy using Fixed-Field Alternating-gradient (FFA) technology for its recirculating arcs, using permanent magnets for the FFA arcs. A common concern among the community is the degradation of these permanent magnets during operation due to the radiation environment in which they will be present. This work, funded by a Laboratory Directed R&D grant, aims to measure the permanent magnet degradation in the CEBAF tunnel enclosure, and extrapolate to the energies expected from the upgrade. We present the latest results of this study, as well as plans moving forward.
Accelerator Physics↗
Improved Platform for the Benchtop Study of Complex Radiological Environments that Affect Fallout Formation: DHS Feasbility Study (Final Report)
Fallout analysis is a crucial field of study as it provides insight into the structural interactions that occur between radiological components and their surroundings following a nuclear event. Due to the complexity of actual fallout samples, setups like the plasma flow reactor (PFR) at LLNL have been developed to synthesize fallout in a controlled environment. While the PFR allows for control of parameters such as initial temperature, cooling rate, and concentration of radiological species, controlling the concentration of water has remained a challenge as samples are introduced into the setup via aqueous solution. To that end, our goal with this project was to develop methodologies to (1) monitor and (2) control the presence of water in the PFR. We found that a commercially available FTIR and MCT detector was sufficient to monitor the presence of both condensed and vapor-phase water, while a desolvating nebulizer could be used to control the amount of water injected into the system. In total, the results of this feasibility study have expanded the ability of the flow reactor to investigate key sensitivities that influence fallout formation.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Scoping Study: Scintillator Array for Fission Correlation Studies
This scoping study uses a simulation based on the PROSPECT (1) detector to assess the conceptual design of a modular system that could efficiently and simultaneously measure the Prompt Fast Neutron Spectrum (PFNS), the prompt fission neutron multiplicity, the prompt fission gamma-ray multiplicity and total energy, as well as temporal and angular correlations between the detected particles. The proposed experiment would require a mono-energetic pulsed neutron beam incident on an actinide target in the center of the detector. Beam induced backgrounds due to elastic and inelastic neutron scattering on the target are considered, while other backgrounds are ignored for this study.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Low-Temperature Plasma Assisted Kinetics Study of Ethanol
Non-equilibrium plasma assisted kinetics of ethanol at 0.5 atm over a temperature range of 523-1203 K were studied at stoichiometric conditions by using a plasma flow reactor (PFR). The effects of plasma chemistry on fuel and its influence on neutral chemistry is decoupled with both experimental and modelling efforts. Enhanced reactivity was observed experimentally in both plasma-assisted oxidation and pyrolysis cases with fuel consumption beginning at temperatures as low as 523 K. Absolute fuel consumption was observed at temperatures nearly 200 K earlier than pure thermal reactions highlighting the effectiveness of non-equilibrium plasma to promote reactivity. Furthermore, modelling results illustrated the effect of electron impact reactions towards accelerating fuel consumption and promoting low temperature chemistry by generating excited state N2 and reactive O and OH radicals.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Multi-Site Observational Study to Assess Biomarkers for Susceptibility or Resilience to Chronic Pain: The Acute to Chronic Pain Signatures (A2CPS) Study Protocol
Chronic pain has become a global health problem contributing to years lived with disability and reduced quality of life. Advances in the clinical management of chronic pain have been limited due to incomplete understanding of the multiple risk factors and molecular mechanisms that contribute to the development of chronic pain. The Acute to Chronic Pain Signatures (A2CPS) Program aims to characterize the predictive nature of biomarkers (brain imaging, high-throughput molecular screening techniques, or “omics,” quantitative sensory testing, patient-reported outcome assessments and functional assessments) to identify individuals who will develop chronic pain following surgical intervention. The A2CPS is a multisite observational study investigating biomarkers and collective biosignatures (a combination of several individual biomarkers) that predict susceptibility or resilience to the development of chronic pain following knee arthroplasty and thoracic surgery. This manuscript provides an overview of data collection methods and procedures designed to standardize data collection across multiple clinical sites and institutions. Pain-related biomarkers are evaluated before surgery and up to 3 months after surgery for use as predictors of patient reported outcomes 6 months after surgery. The dataset from this prospective observational study will be available for researchers internal and external to the A2CPS Consortium to advance understanding of the transition from acute to chronic postsurgical pain.
60 APPLIED LIFE SCIENCES↗