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At least 451 records · Page 25

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DOE OSTI2023

Bright Fluorophores in the Second Near-Infrared Window: HgSe/CdSe Quantum Dots

Fluorophores emitting in the NIR-IIb wavelength range (1.5-1.7 μm) show great potential for bioimaging due to their large tissue penetration. However, current fluorophores suffer from poor emission with quantum yields similar to 2% in aqueous solvents. In this work, we report the synthesis of HgSe/CdSe core/shell quantum dots (QDs) emitting at 1.7 μm through the interband transition. Growth of a thick shell led to a drastic increase in the photoluminescence quantum yield, with a value of 63% in nonpolar solvents. The quantum yields of our QDs and other reported QDs are explained well by a model of Forster resonance energy transfer to ligands and solvent molecules. The model predicts a quantum yield >12% when these HgSe/CdSe QDs are solubilized in water. In conclusion, our work demonstrates the importance of a thick type-I shell to obtain bright emission in the NIR-IIb region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Predictive Model for Workload in Remote Operators During sUAS Contingency Scenarios

The increase in automated capabilities of small Uncrewed Aerial Systems (sUAS) has enabled the human operators to manage larger numbers of vehicles simultaneously. As this happens, the operational paradigm shifts to an m:N configuration where multiple operators (m) are managing multiple vehicles (N) together. However, many questions about how operators will interact with each other and share interaction across the vehicle pool are yet unanswered. Therefore, stakeholders from government and industry have partnered to develop ground control station concepts for such operations. The work presented in this paper aims to identify factors that contribute to operator workload. A supervised machine learning-based method built using Support Vector Machines and K-fold cross-validation was used to create workload prediction models for various NASA TLX subscales by leveraging features related to interactions and their relative timings during m:N operations. Results show that the models yielded fairly high predictive accuracies ranging from ~60-75%.

workload prediction↗

NASA NTRS1988

Particle-laden weakly swirling free jets: Measurements and predictions

Bulzan, Daniel L.↗

NASA NTRS1988

Particle-laden weakly swirling free jets - Measurements and predictions

Bulzan, Daniel L.↗

NASA NTRS1988

Particle-laden weakly swirling free jets: Measurements and predictions

A theoretical and experimental investigation of particle-laden, weakly swirling, turbulent free jets was conducted. Glass particles, having a Sauter mean diameter of 39 microns, with a standard deviation of 15 microns, were used. A single loading ratio (the mass flow rate of particles per unit mass flow rate of air) of 0.2 was used in the experiments. Measurements are reported for three swirl numbers, ranging from 0 to 0.33. The measurements included mean and fluctuating velocities of both phases, and particle mass flux distributions. Measurements were also completed for single-phase non-swirling and swirling jets, as baselines. Measurements were compared with predictions from three types of multiphase flow analysis, as follows: (1) locally homogeneous flow (LHF) where slip between the phases was neglected; (2) deterministic separated flow (DSF), where slip was considered but effects of turbulence/particle interactions were neglected; and (3) stochastic separated flow (SSF), where effects of both interphase slip and turbulence/particle interactions were considered using random sampling for turbulence properties in conjunction with random-walk computations for particle motion. Single-phase weakly swirling jets were considered first. Predictions using a standard k-epsilon turbulence model, as well as two versions modified to account for effects of streamline curvature, were compared with measurements. Predictions using a streamline curvature modification based on the flux Richardson number gave better agreement with measurements for the single-phase swirling jets than the standard k-epsilon model. For the particle-laden jets, the LHF and DSF models did not provide very satisfactory predictions. The LHF model generally overestimated the rate of decay of particle mean axial and angular velocities with streamwise distance, and predicted particle mass fluxes also showed poor agreement with measurements, due to the assumption of no-slip between phases. The DSF model also performed quite poorly for predictions of particle mass flux because turbulent dispersion of the particles was neglected. The SSF model, which accounts for both particle inertia and turbulent dispersion of the particles, yielded reasonably good predictions throughout the flow field for the particle-laden jets.

Bulzan, Daniel L.↗

NASA NTRS2017

Tidal Prediction

Tides are the most predictable of oceanographic phenomena, due both to the simplicity and predictability of the astronomical forcing and to the near linearity of the ocean's dynamical response. In the classical and simplest scenario, tidal prediction is based on harmonic analysis of past measurements at a fixed location. Limits to predictability arise because isolated astronomical spectral lines are broadened into "cusps" of incoherent energy, for example through interactions with non-tidal flows. Tidal prediction at locations without past measurements has historically been a major challenge, but, owing to near-global observations of modern satellite altimeters, the empirical harmonic approach now yields reasonably accurate predictions throughout most of the open ocean. Advances in numerical modeling and data assimilation allow these predictions to be refined (especially in shallow seas where observations remain insufficient to directly constrain tidal wave structure) and extended to include tidal currents. We review recent progress in the development of global and regional-scale tidal prediction capabilities, summarize accuracy of available charts, and briefly consider outstanding issues. Satellite altimetry has also helped unravel the global tidal energy budget and has clarified the role of internal tides as a sink of tidal energy. We summarize these results, and then turn to the challenging problem of predicting internal tides. To the extent that low modes remain coherent with the surface tide, elevations can be directly mapped. We review current efforts in this direction, which are already producing charts with predictive capability. However, internal tides (especially higher modes) interact much more strongly with lower-frequency ocean flows, so a significant fraction of this tidal signal is intermittent and incoherent. We close with a brief review of ongoing efforts to model global tides in combination with wind-forced ocean motions.

Harmonic Constants↗

DOE OSTI2022

Contributions to Dielectric Constant Enhancement in Thin Film Metal Insulator Metal Capacitors

Thin film metal-insulator-metal capacitors were fabricated with varying dielectric and electrode thicknesses and areas. Measurement of the dielectric properties of the capacitors consistently yielded higher than predicted capacitance values, which prompted the exploration of three factors that could enhance the capacitance. Modeling of the fringing capacitance and field enhancement due to the capacitor geometry, both yield capacitance gains, albeit insufficient to fully explain the observed behavior. The possible role of changes in polarizability of the dielectric film constituents, due to the amorphous nature of the films was also evaluated, but the results of the investigation of this contribution were inconclusive. Herein, the role of accumulation/depletion layers on capacitance enhancement was also analyzed. For capacitors fabricated with thinner dielectrics, the presence of these layers can effectively reduce the dielectric thickness, resulting in contributions to capacitance that are significant. The calculations completed suggest these contributions may be more important than fringing field contributions. Additional studies of these mechanisms are in progress.

36 MATERIALS SCIENCE↗

NASA NTRS2005

Effects of Incident Electron Fluence and Energy on the Election Yield Curves and Emission Spectra of Dielectrics

We present an experimental study of evolution of electron emission yields and spectra as a result of internal charge build up due to electron dose. Reliable total, backscattered and secondary yield curves and electron emission spectra for un-charged insulators using a low fluence, pulsed electron beam (= or < 5 microsec at = or < 3 nA/sq mm or = or < 10(exp 5) e/sq mm per pulse) with low energy electron and UV flooding to neutralize the charging between pulses. Quantifiable changes in yield curves are observed due to < 100 fC/sq mm fluences for several excellent dielectric thin film materials. We find good agreement with a phenomenological argument based on insulator charging predicted by the yield curve; this includes an approximately linear decrease in the magnitude of the yield as incident energies approach the crossover energies and an exponential decrease in yield as accumulated internal charge reduces the landing energy to asymptotically approach a steady state surface charge and unity yield. We also find that the exponential decay of yield curves with fluence exhibit an energy dependent decay constant, alpha(E), over a broad range of incident energies below, between and above the crossover energies. Finally, we present some preliminary physics-based models for this energy dependence and attempt to relate our charging measurements to knowledge of how charge is deposited within the insulator, the mechanisms for charge trapping and transport, and how the profile of trapped charge affects the transport and emission of charges from insulators.

Sim, Alec↗

DOE OSTI2023

Bifacial PV Module Energy Modeling Validation Study

The cost delta between monofacial and bifacial photovoltaic (PV) modules was decreasing in 2018 when this study was initially proposed, making bifacial modules an attractive offering. However, there were limited field studies available that could be considered utility-scale representative, and PV module manufacturers were stating large ranges for the expected energy yield improvements of bifacial technology. This generated uncertainties regarding bifacial performance gains and the actual LCOE (levelized cost of energy). There was also relatively low confidence in the industry’s energy modeling tools’ ability to accurately predict bifacial energy yield, meaning that early adopters of bifacial modules were not able to fully account for the increased energy yield in their energy and financial models. The intent of this project was to provide the solar industry with greater certainty on the energy yield gains of bifacial modules and provide higher confidence in the ability for different energy modeling software to model bifacial PV site performance. Achieving this would allow for bifacial technology to become bankable (i.e. accepted by Independent Engineers, site financiers and other PV site stakeholders), offering a step change in PV site performance that had not been seen since the widespread adoption of the single-axis tracker. Over the course of this study other economic factors including a significant tariff exemption for bifacial modules resulted in accelerated adoption of bifacial technology throughout the US utility scale solar segment. The number of early adopters increased rapidly, and with many investors accepting this module choice the question of bifacial bankability was seemingly answered faster than expected. However, PVEL’s study has still achieved significant accomplishments in demonstrating the accuracy of bifacial modeling across three different software platforms. The results of this study have shown that the mean biased error (MBE) between the field data and the predicted values from all three software platforms were aligned with a maximum MBE of 1.3% and a minimum MBE of -1.8%.

14 SOLAR ENERGY↗

NASA NTRS2012

Electric Propulsion Induced Secondary Mass Spectroscopy

A document highlights a means to complement remote spectroscopy while also providing in situ surface samples without a landed system. Historically, most compositional analysis of small body surfaces has been done remotely by analyzing reflection or nuclear spectra. However, neither provides direct measurement that can unambiguously constrain the global surface composition and most importantly, the nature of trace composition and second-phase impurities. Recently, missions such as Deep Space 1 and Dawn have utilized electric propulsion (EP) accelerated, high-energy collimated beam of Xe+ ions to propel deep space missions to their target bodies. The energies of the Xe+ are sufficient to cause sputtering interactions, which eject material from the top microns of a targeted surface. Using a mass spectrometer, the sputtered material can be determined. The sputtering properties of EP exhaust can be used to determine detailed surface composition of atmosphereless bodies by electric propulsion induced secondary mass spectroscopy (EPI-SMS). EPI-SMS operation has three high-level requirements: EP system, mass spectrometer, and altitude of about 10 km. Approximately 1 keV Xe+ has been studied and proven to generate high sputtering yields in metallic substrates. Using these yields, first-order calculations predict that EPI-SMS will yield high signal-to-noise at altitudes greater than 10 km with both electrostatic and Hall thrusters.

Amini, Rashied↗

NASA NTRS1979

The erosion of planetary and satellite atmospheres by energetic atomic particles

Analytic expressions are presented which may be used to compute the sputter-erosion yield from any unimolecular gravitationally bound gas by any atomic charged particle of any energy. A calculation of solar wind proton and alpha particle induced erosion of the CO2 atmosphere of Mars predicts molecular sputtering yields. An expression for the emission yield of energetic molecular fragments produced in primary knock-on events is given in closed form; such fragment emission is of secondary importance for mass loss compared to the molecular yield itself. Erosion by radiation belt protons of a hypothetical thin O2 atmosphere associated with the Jovian satellite Ganymede is considered, and molecular sputtering yields for proton energies are determined.

Haff, P. K.↗

NASA NTRS1987

A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. Part 2: Theoretical development of a dynamic model and application to rain fade durations and tolerable control delays for fade countermeasures

A dynamic rain attenuation prediction model is developed for use in obtaining the temporal characteristics, on time scales of minutes or hours, of satellite communication link availability. Analagous to the associated static rain attenuation model, which yields yearly attenuation predictions, this dynamic model is applicable at any location in the world that is characterized by the static rain attenuation statistics peculiar to the geometry of the satellite link and the rain statistics of the location. Such statistics are calculated by employing the formalism of Part I of this report. In fact, the dynamic model presented here is an extension of the static model and reduces to the static model in the appropriate limit. By assuming that rain attenuation is dynamically described by a first-order stochastic differential equation in time and that this random attenuation process is a Markov process, an expression for the associated transition probability is obtained by solving the related forward Kolmogorov equation. This transition probability is then used to obtain such temporal rain attenuation statistics as attenuation durations and allowable attenuation margins versus control system delay.

Manning, Robert M.↗

DOE OSTI2021

Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, the present study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modelling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within three percent of measured values.

Compressors↗

NASA NTRS1974

Assembly and analysis of fragmentation data for liquid propellant vessels

Fragmentation data was assembled and analyzed for exploding liquid propellant vessels. These data were to be retrieved from reports of tests and accidents, including measurements or estimates of blast yield, etc. A significant amount of data was retrieved from a series of tests conducted for measurement of blast and fireball effects of liquid propellant explosions (Project PYRO), a few well-documented accident reports, and a series of tests to determine auto-ignition properties of mixing liquid propellants. The data were reduced and fitted to various statistical functions. Comparisons were made with methods of prediction for blast yield, initial fragment velocities, and fragment range. Reasonably good correlation was achieved. Methods presented in the report allow prediction of fragment patterns, given type and quantity of propellant, type of accident, and time of propellant mixing.

Baker, W. E.↗

DOE OSTI2020

Quantified uncertainties in fission yields from machine learning

As machine learning methods gain traction in the nuclear physics community, especially those methods that aim to propagate uncertainties to unmeasured quantities, it is important to understand how the uncertainty in the training data coming either from theory or experiment propagates to the uncertainty in the predicted values. Gaussian Processes and Bayesian Neural Networks are being more and more widely used, in particular to extrapolate beyond measured data. However, studies are typically not performed on the impact of the experimental errors on these extrapolated values. In this work, we focus on understanding how uncertainties propagate from input to prediction when using machine learning methods. We use a Mixture Density Network (MDN) to incorporate experimental error into the training of the network and construct uncertainties for the associated predicted quantities. Systematically, we study the effect of the size of the experimental error, both on the reproduced training data and extrapolated predictions for fission yields of actinides.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Mechanical separations of corn stover anatomical fractions in an integrated feedstock preprocessing system: An experimental and data-driven modeling study

High variabilities of material attributes in lignocellulosic biomass present risks for biofuel and biochemical productions and must be mitigated via preprocessing. Since almost no mechanical device is originally designed for processing biomass, how to operate existing apparatuses with efficient performance has not been investigated extensively. This work presents a study on an integrated screening and air classification to separate cobs and stalks from husks and leaves in corn stover. Prototype machine learning models were developed to assess the feasibility of predicting the process outcome based on the measurable parameters. The models trained upon limited experimental data rendered decent predictive accuracy of yield and purity. The experimental data and modeling results collectively suggest decreasing throughput leads to a higher purity. To the contrary, if throughput increases, a lower purity is likely. A possible trade-off between yield and purity of the separated streams indicates the need for optimal combinations of feedstock size, moisture, and throughput to achieve optimized separations. The results of this study also suggest the need to further improve model predictability by developing more accurate formulations for physics governing the integrated unit operations. To accomplish this, additional experimental data needs to be generated for model training.

09 - BIOMASS FUELS↗

DOE OSTI2022

Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, this study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modeling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Here, the results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within 3% of measured values.

42 ENGINEERING↗

DOE OSTI2023

Local adaptation of switchgrass drives trait relations to yield and differential responses to climate and soil environments

Abstract Switchgrass, a potential biofuel crop, is a genetically diverse species with phenotypic plasticity enabling it to grow in a range of environments. Two primary divergent ecotypes, uplands and lowlands, exhibit trait combinations representative of acquisitive and conservative growth allocation strategies, respectively. Whether these ecotypes respond differently to various types of environmental drivers remains unclear but is crucial to understanding how switchgrass varieties will respond to climate change. We grew two upland, two lowland, and two intermediate/hybrid cultivars of switchgrass at three sites along a latitudinal gradient in the central United States. Over a 4‐year period, we measured plant functional traits and biomass yields and evaluated genotype‐by‐environment (G × E) interaction effects by analyzing switchgrass responses to soil and climate variables. We found substantial evidence of G × E interactions on biomass yield, primarily due to deviations in the response of the southern lowland cultivar Alamo, which produced more biomass in hotter and drier environments relative to other cultivars. While lowland cultivars had the highest potential for yield, their yields were more variable year‐to‐year compared to other cultivars, suggesting greater sensitivity to environmental perturbations. Models comparing soil and climate principal components as explanatory variables revealed soil properties, especially nutrients, to be most effective at predicting switchgrass biomass yield. Also, positive correlations between biomass yield and conservative plant traits, such as high stem mass and tiller height, became stronger at lower latitudes where the climate is hotter and drier, regardless of ecotype. Lowland cultivars, however, showed a greater predisposition to exhibit these conservative traits. These results suggest switchgrass trait allocation trade‐offs that prioritize aboveground biomass production are more tightly associated in hot, dry environments and that lowland cultivars may exhibit a more specialized strategy relative to other cultivars. Altogether, this research provides essential knowledge for improving the viability of switchgrass as a biofuel crop.

09 BIOMASS FUELS↗