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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Description and Evaluation of a New Deep Convective Cloud Model Considering In-cloud Inhomogeneity

Convections still need to be parameterized in general circulation models (GCMs) in the coming decades. Performances of GCMs are significantly influenced by the convection schemes used. In contrast to most conventional cloud models that ignore in-cloud inhomogeneities, a new convective cloud model explicitly considering in-cloud inhomogeneities is developed. The new model adopts a single bulk plume approach, but divides the plume into a series of interacting sub-plumes in order to mimic the transition from the plume core to its edges. We implemented the new cloud model in NCAR Community Atmosphere Model version 5.0 (CAM5) and evaluated its performance. Single-column tests show a stronger mass flux profile and low-level detrainment with the new model. In global simulations, the long-lasting wet bias in the tropical free troposphere of CAM5 is alleviated. Spatial pattern and intensity distribution of precipitation is improved, but the global hydrological cycle is slightly over-enhanced. The diurnal cycle of tropical precipitation is also better captured though the precipitation peak still occurs earlier than the observations and the amplitude of the cycle tends to weaken. MJO simulation is slightly improved with the new scheme. In addition, the new cloud model generated more tropical cyclones, in better agreement with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

Abstract Deep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Real-space chirality from crystalline topological defects in the Kitaev spin liquid

We show that certain crystalline topological defects in the gapless Kitaev honeycomb spin liquid model generate a chirality and Majorana fermion orbital magnetization that depends in a universal manner on their emergent flux. Focusing on 5–7 dislocations as building blocks, consisting of pentagon and heptagon disclinations, we identify the Kitaev bond label configurations that preserve solvability. By computing two formulations of local markers M(r) we find that the 5 and 7 lattice defects generate a real-space contribution to Chern number and an associated Majorana fermion orbital magnetization proportional to M(r). The sign of the M(r) contribution from each 5/7 defect, i.e. its q M = ± 1 chirality, is determined by the defect Frank angle sign F and emergent gauge field flux W = ± i through the expression q M = − iFW. Remarkably, though lattice curvature and torsion can interplay with the surrounding gapless background to modify the profile of M(r), its sign q M is determined locally, implying that crystalline defects in the Kitaev spin liquid can generate a robust and observable chirality.

Magnetic properties and materials↗
DOE OSTI2024

Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity

Abstract Wetland methane (CH 4 ) emissions over the Boreal–Arctic region are vulnerable to climate change and linked to climate feedbacks, yet understanding of their long-term dynamics remains uncertain. Here, we upscaled and analysed two decades (2002–2021) of Boreal–Arctic wetland CH 4 emissions, representing an unprecedented compilation of eddy covariance and chamber observations. We found a robust increasing trend of CH 4 emissions (+8.9%) with strong inter-annual variability. The majority of emission increases occurred in early summer (June and July) and were mainly driven by warming (52.3%) and ecosystem productivity (40.7%). Moreover, a 2 °C temperature anomaly in 2016 led to the highest recorded annual CH 4 emissions (22.3 Tg CH 4 yr −1 ) over this region, driven primarily by high emissions over Western Siberian lowlands. However, current-generation models from the Global Carbon Project failed to capture the emission magnitude and trend, and may bias the estimates in future wetland CH 4 emission driven by amplified Boreal–Arctic warming and greening.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Catalysis-Hub.org, an open electronic structure database for surface reactions

Abstract We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Limited-angle computed tomography with deep image and physics priors

Computed tomography is a well-established x-ray imaging technique to reconstruct the three-dimensional structure of objects. It has been used extensively in a variety of fields, from diagnostic imaging to materials and biological sciences. One major challenge in some applications, such as in electron or x-ray tomography systems, is that the projections cannot be gathered over all the angles due to the sample holder setup or shape of the sample. This results in an ill-posed problem called the limited angle reconstruction problem. Typical image reconstruction in this setup leads to distortion and artifacts, thereby hindering a quantitative evaluation of the results. To address this challenge, we use a generative model to effectively constrain the solution of a physics-based approach. Our approach is self-training that can iteratively learn the nonlinear mapping from partial projections to the scanned object. Because our approach combines the data likelihood and image prior terms into a single deep network, it is computationally tractable and improves performance through an end-to-end training. We also complement our approach with total-variation regularization to handle high-frequency noise in reconstructions and implement a solver based on alternating direction method of multipliers. We present numerical results for various degrees of missing angle range and noise levels, which demonstrate the effectiveness of the proposed approach.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Abstract Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure

Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy 2 Ti 2 O 7 , using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Generation kinetics of color centers in irradiated poly(4-methyl-1-pentene)

The transient absorbance of poly(4-methyl-1-pentene) (PMP) irradiated with gamma rays at elevated temperatures has been investigated. The absorbance in the ultraviolet and visible range increases with gamma ray dose. A bathochromic shift in transmission spectra emerges significantly upon irradiation. A first-order generation model is proposed to analyze the kinetics of color centers during annealing. The activation energy of the color center increases with increasing gamma ray dose. The equilibrium behavior of color centers in PMP is similar to that of vacancies in metals, and the formation energy of color centers in PMP decreases with increasing gamma ray dose. However, annealable color centers are not observed in this study.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Neutrino resonance production at very low momentum transfer

I look back at comparisons of CC neutrino resonance interaction data at very low Q{sup 2}, from seven experiments, compared to the predictions of current neutrino event generators. When compared to data, the generator models, primarily based on the model of Rein and Sehgal, overpredict the event rate at very low Q{sup 2} for samples that are enhanced in resonance {pi}{sup +} interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Measurement and reduction of low-level radon background in the KATRIN experiment

The KArlsruhe TRItium Neutrino (KATRIN) experiment is a next generation, model independent, large scale experiment to determine the mass of the electron anti-neutrino by investigating the kinematics of tritium beta decay with a sensitivity of 200 meV/c{sup 2}. The measurement setup consists of a high luminosity windowless gaseous molecular tritium source (WGTS), a differential and cryogenic pumped electron transport and tritium retention section, a tandem spectrometer section (pre-spectrometer and main spectrometer) for energy analysis, followed by a detector system for counting transmitted beta decay electrons. Measurements performed at the KATRIN pre-spectrometer test setup showed that the decay of radon (Rn) atoms in the volume of the KATRIN spectrometers is a major background source. Rn atoms from low-level radon emanation of materials inside the vacuum region of the KATRIN spectrometers are able to penetrate deep into the magnetic flux tube so that the alpha decay of Rn contributes to the background. Of particular importance are electrons emitted in processes accompanying the Rn alpha decay, such as shake-off, internal conversion of excited levels in the Rn daughter atoms and Auger electrons. Lowenergy electrons (< 100 eV) directly contribute to the background in the signal region. High-energy electrons can be stored magnetically inside the volume of the spectrometer and are able to create thousands of secondary electrons via subsequent ionization processes with residual gas molecules. In order to reduce the Rn induced background different active and passive counter measures were developed and tested. This proceeding will give an overview on Rn sources within the KATRIN spectrometer, describes how Rn decays inside the spectrometer produce background events at the detector and presents different counter measures to reduce the Rn induced background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Stochastic generation of hourly rainstorm events in Johor

Engineers and researchers in water-related studies are often faced with the problem of having insufficient and long rainfall record. Practical and effective methods must be developed to generate unavailable data from limited available data. Therefore, this paper presents a Monte-Carlo based stochastic hourly rainfall generation model to complement the unavailable data. The Monte Carlo simulation used in this study is based on the best fit of storm characteristics. Hence, by using the Maximum Likelihood Estimation (MLE) and Anderson Darling goodness-of-fit test, lognormal appeared to be the best rainfall distribution. Therefore, the Monte Carlo simulation based on lognormal distribution was used in the study. The proposed model was verified by comparing the statistical moments of rainstorm characteristics from the combination of the observed rainstorm events under 10 years and simulated rainstorm events under 30 years of rainfall records with those under the entire 40 years of observed rainfall data based on the hourly rainfall data at the station J1 in Johor over the period of 1972–2011. The absolute percentage error of the duration-depth, duration-inter-event time and depth-inter-event time will be used as the accuracy test. The results showed the first four product-moments of the observed rainstorm characteristics were close with the simulated rainstorm characteristics. The proposed model can be used as a basis to derive rainfall intensity-duration frequency in Johor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Effect of excess superthermal hot electrons on finite amplitude ion-acoustic solitons and supersolitons in a magnetized auroral plasma

The effect of excess superthermal electrons is investigated on finite amplitude nonlinear ion-acoustic waves in a magnetized auroral plasma. The plasma model consists of a cold ion fluid, Boltzmann distribution of cool electrons, and kappa distributed hot electron species. The model predicts the evolution of negative potential solitons and supersolitons at subsonic Mach numbers region, whereas, in the case of Cairn's nonthermal distribution model for the hot electron species studied earlier, they can exist both in the subsonic and supersonic Mach number regimes. For the dayside auroral parameters, the model generates the super-acoustic electric field amplitude, speed, width, and pulse duration of about 18 mV/m, 25.4 km/s, 663 m, and 26 ms, respectively, which is in the range of the Viking spacecraft measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Nonlinear low frequency electrostatic structures in a magnetized two-component auroral plasma

Finite amplitude nonlinear ion-acoustic solitons, double layers, and supersolitons in a magnetized two-component plasma composed of adiabatic warm ions fluid and energetic nonthermal electrons are studied by employing the Sagdeev pseudopotential technique and assuming the charge neutrality condition at equilibrium. The model generates supersoliton structures at supersonic Mach numbers regime in addition to solitons and double layers, whereas in the unmagnetized two-component plasma case only, soliton and double layer solutions can be obtained. Further investigation revealed that wave obliqueness plays a critical role for the evolution of supersoliton structures in magnetized two-component plasmas. In addition, the effect of ion temperature and nonthermal energetic electron tends to decrease the speed of oscillation of the nonlinear electrostatic structures. The present theoretical results are compared with Viking satellite observations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

PolyODENet: Deriving mass-action rate equations from incomplete transient kinetics data

Kinetics of a reaction network that follows mass-action rate laws can be described with a system of ordinary differential equations (ODEs) with polynomial right-hand side. However, it is challenging to derive such kinetic differential equations from transient kinetic data without knowing the reaction network, especially when the data are incomplete due to experimental limitations. We introduce a program, PolyODENet, toward this goal. Based on the machine-learning method Neural ODE, PolyODENet defines a generative model and predicts concentrations at arbitrary time. As such, it is possible to include unmeasurable intermediate species in the kinetic equations. Importantly, we have implemented various measures to apply physical constraints and chemical knowledge in the training to regularize the solution space. Here, using simple catalytic reaction models, we demonstrate that PolyODENet can predict reaction profiles of unknown species and doing so even reveal hidden parts of reaction mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A new synthetic correlation electron cyclotron emission diagnostic for validating nonlinear gyrokinetic simulations of electron temperature turbulence

To validate nonlinear gyrokinetic simulations of electron temperature turbulence, the experimental correlation electron cyclotron emission (CECE) measurements are to be compared using a synthetic CECE diagnostic, which generates modeled CECE measurement quantities by implementing realistic measurement parameters (e.g., spatial and wavenumber resolutions, radial location, etc.) to nonlinear gyrokinetic simulations. In this work, we calculate the radial and vertical spatial and wavenumber transfer functions, which are defined by the electron cyclotron emission emissivity radial profile and vertical probing antenna pattern, respectively. These transfer functions are applied to nonlinear gyrokinetic simulations of electron temperature turbulence using the continuum gyrokinetic code. A simultaneous comparison of the experimental electron temperature turbulence power spectrum and root-mean-square (RMS) level, as well as the radial correlation length with the new synthetic CECE diagnostic at a core location ρ ∼ 0.75 in an L-mode DIII-D tokamak plasma, is presented. Here, the preliminary result shows that the synthetic CECE output underestimates the RMS level by ∼42% and overestimates the radial correlation length by ∼40%.

Cyclotron radiation↗
DOE OSTI2024

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

60 APPLIED LIFE SCIENCES↗