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

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Near‐Surface Hydrology and Soil Properties Drive Heterogeneity in Permafrost Distribution, Vegetation Dynamics, and Carbon Cycling in a Sub‐Arctic Watershed

Abstract Discontinuous permafrost environments exhibit strong spatial heterogeneity at scales too small to be driven by weather forcing or captured by Earth System Models. Here we analyze effects of observed spatial heterogeneity in soil and vegetation properties, hydrology, and thermal dynamics on ecosystem carbon dynamics in a watershed on the Seward Peninsula in Alaska. We apply a Morris global sensitivity analysis to a process‐rich, successfully tested terrestrial ecosystem model (TEM), ecosys , varying soil properties, boundary conditions, and weather forcing. We show that landscape heterogeneity strongly impacts soil temperatures and vegetation composition. Snow depth, O‐horizon thickness, and near‐surface water content, which vary at scales of O(m), control the soil thermal regime more than an air temperature gradient corresponding to a 140 km north–south distance. High shrub productivity is simulated only in talik (perennially unfrozen) soils with high nitrogen availability. Through these effects on plant and permafrost dynamics, landscape heterogeneity impacts ecosystem productivity. Simulations with near‐surface taliks have higher microbial respiration (by 78.0 gC m −2 yr −1 ) and higher net primary productivity (by 104.9 gC m −2 yr −1 ) compared to runs with near‐surface permafrost, and simulations with high shrub productivity have outlying values of net carbon uptake. We explored the prediction uncertainty associated with ignoring observed landscape heterogeneity, and found that watershed net carbon uptake is 60% larger when heterogeneity is accounted for. Our results highlight the complexity inherent in discontinuous permafrost environments and demonstrate that missing representation of subgrid heterogeneity in TEMs could bias predictions of high‐latitude carbon budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ferroelectricity in hafnia controlled via surface electrochemical state

Ferroelectricity in binary oxides including hafnia and zirconia has riveted the attention of the scientific community due to the highly unconventional physical mechanisms and the potential for the integration of these materials into semiconductor workflows. Over the last decade, it has been argued that behaviours such as wake-up phenomena and an extreme sensitivity to electrode and processing conditions suggest that ferroelectricity in these materials is strongly influenced by other factors, including electrochemical boundary conditions and strain. Here we argue that the properties of these materials emerge due to the interplay between the bulk competition between ferroelectric and structural instabilities, similar to that in classical antiferroelectrics, coupled with non-local screening mediated by the finite density of states at surfaces and internal interfaces. Via the decoupling of electrochemical and electrostatic controls, realized via environmental and ultra-high vacuum piezoresponse force microscopy, we show that these materials demonstrate a rich spectrum of ferroic behaviours including partial-pressure-induced and temperature-induced transitions between ferroelectric and antiferroelectric behaviours. In conclusion, these behaviours are consistent with an antiferroionic model and suggest strategies for hafnia-based device optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Lost alpha Faraday cup foil noise characterization during Joint European Torus plasma post-processing analysis

Capacitive plasma pickup is a well-known and difficult problem for plasma-facing edge diagnostics. This problem must be addressed to ensure an accurate and robust interpretation of the real signal measurements vs noise. The Faraday cup fast ion loss detector array of the Joint European Torus (JET) is particularly prone to this issue and can be used as a testbed to prototype solutions. The issue of separation and distinction between warranted fast ion signal and electromagnetic plasma noise has traditionally been solved with hardware modifications, but a more versatile post-processing approach is of great interest. This work presents post-processing techniques to characterize the signal noise. While hardware changes and advancements may be limited, the combination with post-processing procedures allows for more rapid and robust analysis of measurements. The characterization of plasma pickup noise is examined for alpha losses in a discharge from JET’s tritium campaign. In addition to highlighting the post-processing methodology, the spatial sensitivity of the detector array is also examined, which presents significant advantages for the physical interpretation of fast ion losses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Ionization effects in single-shot carrier-envelope phase detection with gas-gap devices

The carrier-envelope phase (CEP) is a key parameter for attosecond waveform control of ultrashort laser pulses. For laser systems with high repetition rates, however, single-shot CEP detection is still challenging. Building on recent findings on phase detection with electric current generation in gases, we show that with an optimized detection method, a long-term stable single-shot phase detection is feasible. We investigate the achievable performance depending on various parameters such as pulse duration, gas pressure, or incident intensity. The latter exhibits a regime where the saturation of the ionization process boosts the phase sensitivity of the signal, which is elucidated by numerical calculations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Niobium coaxial cavities with internal quality factors exceeding 1.4 × 10 9 for circuit quantum electrodynamics

Group-V materials such as niobium and tantalum have become popular choices for extending the performance of circuit quantum electrodynamics (cQED) platforms, allowing for quantum processors and memories with reduced error rates and more modes. The complex surface chemistry of niobium, however, makes identifying the main modes of decoherence difficult at millikelvin temperatures and single-photon powers. We use niobium coaxial quarter-wave cavities to study the impact of etch chemistry, prolonged atmospheric exposure, and the significance of cavity conditions prior to and during cooldown—in particular, niobium hydride evolution—on single-photon coherence. We demonstrate cavities with quality factors Q int ≳ 1.4 × 10 9 in the single-photon regime, a 15-fold improvement over aluminum cavities of the same geometry. We rigorously quantify the sensitivity of our fabrication process to various loss mechanisms and demonstrate a two- to fourfold reduction in the two-level system loss tangent and a three- to fivefold improvement in the residual resistance over traditional buffered chemical polishing etching techniques. Finally, we demonstrate transmon integration and coherent cavity control while maintaining a cavity coherence of 11.3 ms. The accessibility of our method, which can be easily replicated in academic laboratory settings, together with the demonstration of its performance, mark an advancement in three-dimensional cQED.

Oriani, Andrew E. [Chicago U., EFI; Chicago U.] (O↗
DOE OSTI2024

Robust finite-temperature many-body scarring on a quantum computer

Mechanisms for suppressing thermalization in disorder-free many-body systems, such as Hilbert space fragmentation and quantum many-body scars, have recently attracted much interest in foundations of quantum statistical physics and potential quantum information processing applications. However, their sensitivity to realistic effects such as finite temperature remains largely unexplored. Here, we have utilized IBM's Kolkata quantum processor to demonstrate an unexpected robustness of quantum many-body scars at finite temperatures when the system is prepared in a thermal Gibbs ensemble. We identify such robustness in the PXP model, which describes quantum many-body scars in experimental systems of Rydberg atom arrays and ultracold atoms in tilted Bose-Hubbard optical lattices. By contrast, other theoretical models which host exact quantum many-body scars are found to lack such robustness and their scarring properties quickly decay with temperature. Our study sheds light on the important differences between scarred models in terms of their algebraic structures, which impacts their resilience to finite temperature. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Using kernel-based statistical distance to study the dynamics of charged particle beams in particle-based simulation codes

Measures of discrepancy between probability distributions (statistical distance) are widely used in the fields of artificial intelligence and machine learning. We describe how certain measures of statistical distance can be implemented as numerical diagnostics for simulations involving charged-particle beams. Related measures of statistical dependence are also described. The resulting diagnostics provide sensitive measures of dynamical processes important for beams in nonlinear or high-intensity systems, which are otherwise difficult to characterize. Here, the focus is on kernel-based methods such as maximum mean discrepancy, which have a well-developed mathematical foundation and reasonable computational complexity. Several benchmark problems and examples involving intense beams are discussed. While the focus is on charged-particle beams, these methods may also be applied to other many-body systems such as plasmas or gravitational systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

New Direct Limit on Neutrinoless Double Beta Decay Half-Life of 128 $\mathrm{Te}$ with CUORE

The Cryogenic Underground Observatory for Rare Events (CUORE) at Laboratori Nazionali del Gran Sasso of INFN in Italy is an experiment searching for neutrinoless double beta (0νββ) decay. Its main goal is to investigate this decay in 130 Te, but its ton-scale mass and low background make CUORE sensitive to other rare processes as well. Here, in this Letter, we present our first results on the search for 0νββ decay of 128 Te, the Te isotope with the second highest natural isotopic abundance. We find no evidence for this decay, and using a Bayesian analysis we set a lower limit on the 128 Te 0νββ decay half-life of T 1/2 > 3.6 x 10 24 yr (90% CI). This represents the most stringent limit on the half-life of this isotope, improving by over a factor of 30 the previous direct search results, and exceeding those from geochemical experiments for the first time.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Organic matter in carbonaceous chondrite lithologies of Almahata Sitta: Incorporation of previously unsampled carbonaceous chondrite lithologies into ureilitic regolith

The Almahata Sitta (AhS) meteorite is a unique polymict ureilite. Recently, carbonaceous chondritic lithologies were identified in AhS. Organic matter (OM) is ubiquitously found in primitive carbonaceous chondrites. The molecular and isotopic characteristics of this OM reflect its origin and parent body processes, and are particularly sensitive to heating. The C1 lithologies AhS 671 and AhS 91A were investigated, focusing mainly on the OM. We found that the OM in these lithologies is unique and contains primitive isotopic signatures, but experienced slight heating possibly by short-term heating event(s). These characteristics support the idea that one or more carbonaceous chondritic bodies were incorporated into the ureilitic parent body. Here, the uniqueness of the OM in the AhS samples implies that there were large variations in primitive carbonaceous chondritic materials in the solar system other than known primitive carbonaceous chondrite groups such as CI, CM, and CR chondrites.

58 GEOSCIENCES↗
DOE OSTI2021

Scaling of atomic layer etching of SiO 2 in fluorocarbon plasmas: Transient etching and surface roughness

Fabricating sub-10 nm microelectronics places plasma processing precision at atomic dimensions. Atomic layer etching (ALE) is a cyclic plasma process used in semiconductor fabrication that has the potential to remove a single layer of atoms during each cycle. In self-limiting ideal ALE, a single monolayer of a material is consistently removed in each cycle, typically expressed as EPC (etch per cycle). In plasma ALE of dielectrics, such as SiO 2 and Si 3 N 4 , using fluorocarbon gas mixtures, etching proceeds through deposition of a thin polymer layer and the process is not strictly self-terminating. As a result, EPC is highly process dependent and particularly sensitive to the thickness of the polymer layer. In this paper, results are discussed from a computational investigation of the ALE of SiO 2 on flat surfaces and in short trenches using capacitively coupled plasmas consisting of a deposition step (fluorocarbon plasma) and an etch step (argon plasma). We found that ALE performance is a delicate balance between deposition of polymer during the first half cycle and etching (with polymer removal) during the second half cycle. In the absence of complete removal of the overlying polymer in each cycle, ALE may be transient as the polymer thickness grows with each cycle with a reduction in EPC until the thickness is too large to enable further etching. Small and statistical amounts of polymer left from a previous cycle can produce statistical variation in polymer thickness on the next cycle, which in turn can lead to a spatially dependent EPC and ALE roughness. Based on synergy between T i (sputtering time) and T p (passivation time), dielectric ALE can be described as having three modes: deposition, roughening surface (transitioning to etch-stop), and smooth surface with steady-state EPC.

Materials Science↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
DOE OSTI2023

Climate Impacts of Convective Cloud Microphysics in NCAR CAM5

Here we improved the treatments of convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3) by 1) implementing new terminal velocity parameterizations for convective ice and snow particles, 2) adding graupel microphysics, 3) considering convective snow detrainment, and 4) enhancing rain initiation and generation rate in warm clouds. Furthermore, we evaluated the impacts of improved microphysics on simulated global climate, focusing on simulated cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation. Compared to CAM5.3 with the default convective microphysics, the too-strong cloud shortwave radiative forcing due primarily to excessive convective cloud liquid is largely alleviated over the tropics and midlatitudes after rain initiation and generation rate is enhanced, in better agreement with the CERES-EBAF estimates. Geographic distributions of graupel occurrence are reasonably simulated over continents; whereas the graupel occurrence remains highly uncertain over the oceanic storm-track regions. When evaluated against the CloudSat–CALIPSO estimates, the overestimation of convective ice mass is alleviated with the improved convective ice microphysics, among which adding graupel microphysics and the accompanying increase in hydrometeor fall speed play the most important role. The probability distribution function (PDF) of rainfall intensity is sensitive to warm rain processes in convective clouds, and enhancement in warm rain production shifts the PDF toward heavier precipitation, which agrees better with the TRMM observations. Common biases of overestimating the light rain frequency and underestimating the heavy rain frequency in GCMs are mitigated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ultrasonic waveguide reflectometry for quench detection (CRADA Final Report)

Early detection of thermal runaway (quench) is critical for safely operating high-temperature superconductor (HTS) magnets. Voltage-based techniques provide late detection of quenching in HTS, which can lead to magnet burnout and system damage. Various non-voltage quench-detection approaches are therefore investigated nowadays that are based on magnetic, acoustic, and optical sensing. LBNL has been actively developing the quench detection principle based on diffuse ultrasonic wave propagation since 2017 and has demonstrated the approach's viability with several peer-reviewed publications and a patent application. Over the last decade, Etegent, LBNL partner for this proposal and effort, has been developing a sensing technology that utilizes the propagation of stress waves through the solid metallic and nonmetallic waveguides (WG). The method is based on the dependence of materials' elastic properties and the speed of sound on temperature. Compared with optical analog, acoustic WG operates at much lower frequencies, eliminating the need for expensive, complex, highly sensitive receivers and data processing. Etegent proved this technology in many harsh environments, mainly concentrated high-temperature measurements. The initial contact between LBNL and Etegent started in 2020, aiming at combining our knowledge and effort toward developing a viable ultrasound-based quench detection technique for use with HTS magnets. A preliminary study conducted jointly in 2022 found no principle restrictions for expanding the WG technology toward cryogenic temperature operation. The present CRADA work aimed at designing, building, and testing ultrasonic waveguide temperature sensors at relevant cryogenic conditions, and demonstrating this technology's applicability to detecting heating and quenching in High Temperature Superconductor (HTS)-based cables and magnets. The technical objectives of the project included: (1) Design and fabricate ultrasonic WG sensors suitable for cryogenic operation, (2) Test ultrasonic WGs under cryogenic conditions, (3) Determining a suitable way of insulating WGs to prevent ultrasonic leakage while maintaining good thermal contact with the surrounding medium, (4) WG integration and cryogenic test with HTS conductors and HTS magnets, and (5) Design and basic prototyping of a standalone WG-based quench detection system. This project adds value to the present LBNL magnet diagnostics portfolio and enables a breakthrough in addressing the HTS magnet quench detection challenge.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Rare Neutrino Interaction and $\pi^0$ Production Cross Sections with MicroBooNE

MicroBooNE is a Liquid Argon Time Projection Chamber, able to image neutrino interactions with excellent spatial and timing resolution, enabling the identification of complex final states resulting from neutrino-nucleus interactions. This poster will provide an overview of measurements for rare final states, such as and η production. These processes both provide unique sensitivities to the interplay between nucleon-level cross-section physics and nuclear-level physics, as well as account for sources of background in proton decay experiments. Furthermore, this poster showcases MicroBooNE’s measurements of total and first-order differential cross-sections on argon for muon neutrino interactions producing neutral pions in the final-state. These interactions will dominate the event rates observed at forthcoming high-precision neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

42 ENGINEERING↗
DOE OSTI2025

Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains

Gravity waves are a common occurrence in the atmosphere, with a variety of generation mechanisms. Their impact on wind farms has only recently gained attention, with most studies focused on wind farm-induced gravity waves. In this study, the interaction between a wind farm and gravity waves generated by an atmospheric bore event is assessed using multiscale large-eddy simulations. The atmospheric bore is created by a thunderstorm downdraft from a nocturnal mesoscale convective system (MCS). The associated gravity waves impact the wind resource and power production at a nearby wind farm during the American Wake Experiment (AWAKEN) in the US southern Great Plains. A two-domain nested setup (Δx=300 and 20 m) is used in the Weather Research and Forecasting (WRF) model, forced with data from the High-Resolution Rapid Refresh model, to capture both the formation of the bore and its interaction with individual wind turbines. The MCS is resolved on the large outer domain, where the structure of the bore and the associated gravity waves are found to be especially sensitive to parameterized microphysics processes. On the finer inner domain, gravity wave interactions with individual wind turbines are resolved; wake dynamics are captured using a generalized actuator disk parameterization in WRF. The gravity waves are found to have a strong effect on the atmosphere above the wind farm; however, the effect of the waves is more nuanced closer to the surface where there is additional turbulence, both ambient and wake-generated. Notably, the gravity waves modulate the mesoscale environment by weakening and dissipating the preexisting low-level jet, which reduces hub-height wind speed and hence the simulated power output, which is confirmed by the observed supervisory control and data acquisition (SCADA) power data. Additionally, the gravity waves induce local wind direction variations correlated with fluctuations in pressure, which lead to fluctuations in the simulated power output as various turbines within the farm are subjected to waking from nearby turbines.

17 WIND ENERGY↗
NASA NTRS1974

Preliminary performance estimates of an oblique, all-wing, remotely piloted vehicle for air-to-air combat

A computerized aircraft synthesis program has been used to assess the effects of various vehicle and mission parameters on the performance of an oblique, all-wing, remotely piloted vehicle (RPV) for the highly maneuverable, air-to-air combat role. The study mission consists of an outbound cruise, an acceleration phase, a series of subsonic and supersonic turns, and a return cruise. The results are presented in terms of both the required vehicle weight to accomplish this mission and the combat effectiveness as measured by turning and acceleration capability. This report describes the synthesis program, the mission, the vehicle, and results from sensitivity studies. An optimization process has been used to establish the nominal RPV configuration of the oblique, all-wing concept for the specified mission. In comparison to a previously studied conventional wing-body canard design for the same mission, this oblique, all-wing nominal vehicle is lighter in weight and has higher performance.

Nelms, W. P., Jr.↗
NASA NTRS1975

Low gravity synthesis of polymers with controlled molecular configuration

Heterogeneous chemical systems have been studied for the synthesis of isotactic polypropylene in order to establish baseline parameters for the reaction process and to develop sensitive and accurate methods of analysis. These parameters and analytical methods may be used to make a comparison between the polypropylene obtained at one g with that of zero g (gravity). Baseline reaction parameters have been established for the slurry (liquid monomer in heptane/solid catalyst) polymerization of propylene to yield high purity, 98% isotactic polypropylene. Kinetic data for the slurry reaction showed that a sufficient quantity of polymer for complete characterization can be produced in a reaction time of 5 min; this time is compatible with that available on a sounding rocket for a zero-g simulation experiment. The preformed (activated) catalyst was found to be more reproducible in its activity than the in situ formed catalyst.

Heimbuch, A. H.↗