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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators

Highlights: • A rapid, cost-effective, and comparative methodology was proposed to evaluate the HVs of burning MSW online. • Models ranked in descending order of performance are ANFIS, RF, SVM, MLP. • A well trained model is capable of characterizing the variation trend of HVs accurately. - Abstract: The heating values, particularly lower heating values of burning municipal solid waste are critically important parameters in operating circulating fluidized bed incineration systems. However, the heating values change widely and frequently, while there is no reliable real-time instrument to measure heating values in the process of incinerating municipal solid waste. A rapid, cost-effective, and comparative methodology was proposed to evaluate the heating values of burning MSW online based on prior knowledge, expert experience, and data-mining techniques. First, selecting the input variables of the model by analyzing the operational mechanism of circulating fluidized bed incinerators, and the corresponding heating value was classified into one of nine fuzzy expressions according to expert advice. Development of prediction models by employing four different nonlinear models was undertaken, including a multilayer perceptron neural network, a support vector machine, an adaptive neuro-fuzzy inference system, and a random forest; a series of optimization schemes were implemented simultaneously in order to improve the performance of each model. Finally, a comprehensive comparison study was carried out to evaluate the performance of the models. Results indicate that the adaptive neuro-fuzzy inference system model outperforms the other three models, with the random forest model performing second-best, and the multilayer perceptron model performing at the worst level. A model with sufficient accuracy would contribute adequately to the control of circulating fluidized bed incinerator operation and provide reliable heating value signals for an automatic combustion control system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. VI. Radio Constraints on a Relativistic Jet and Predictions for Late-time Emission from the Kilonova Ejecta

We present Very Large Array (VLA) and Atacama Large Millimeter/submillimeter Array (ALMA) radio observations of GW170817, the first Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo gravitational wave (GW) event from a binary neutron star merger and the first GW event with an electromagnetic (EM) counterpart. Our data include the first observations following the discovery of the optical transient at both the centimeter (13.7 hr post-merger) and millimeter (2.41 days post-merger) bands. We detect faint emission at 6 GHz at 19.47 and 39.23 days after the merger, but not in an earlier observation at 2.46 days. We do not detect cm/mm emission at the position of the optical counterpart at frequencies of 10–97.5 GHz at times ranging from 0.6 to 30 days post-merger, ruling out an on-axis short gamma-ray burst (SGRB) for energies ≳10{sup 48} erg. For fiducial SGRB parameters, our limits require an observer viewer angle of ≳20°. The radio and X-ray data can be jointly explained as the afterglow emission from an SGRB with a jet energy of ∼10{sup 49}--10{sup 50} erg that exploded in a uniform density environment with n∼10{sup −4}--10{sup −2} cm{sup −3}, viewed at an angle of ∼20°–40° from the jet axis. Using the results of our light curve and spectral modeling, in conjunction with the inference of the circumbinary density, we predict the emergence of late-time radio emission from the deceleration of the kilonova (KN) ejecta on a timescale of ∼5–10 years that will remain detectable for decades with next-generation radio facilities, making GW170817 a compelling target for long-term radio monitoring.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Equation of state at neutron-star densities and beyond from perturbative QCD

We explore the consequences of imposing robust thermodynamic constraints arising from perturbative quantum chromodynamics (QCD) when inferring the dense-matter equation-of-state (EOS). We find that the termination density, up to which the EOS modeling is performed in an inference setup, strongly affects the constraining power of the QCD input. This sensitivity in the constraining power arises from EOSs that have a specific form, with drastic softening immediately above the termination density followed by a strong stiffening. Here we also perform explicit modeling of the EOS down from perturbative-QCD densities to construct a new QCD likelihood function that incorporates additional perturbative-QCD calculations of the sound speed and is insensitive to the termination density, which we make publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems

We formulate, and present a numerical method for solving, an inverse problem for inferring parameters of a deterministic model from stochastic observational data on quantities of interest. The solution, given as a probability measure, is derived using a Bayesian updating approach for measurable maps that finds a posterior probability measure, that when propagated through the deterministic model produces a push-forward measure that exactly matches the observed probability measure on the data. Our approach for finding such posterior measures, which we call consistent Bayesian inference or push-forward based inference, is simple and only requires the computation of the push-forward probability measure induced by the combination of a prior probability measure and the deterministic model. We establish existence and uniqueness of observation-consistent posteriors and present both stability and error analyses. We also discuss the relationships between consistent Bayesian inference, classical/statistical Bayesian inference, and a recently developed measure-theoretic approach for inference. Finally, analytical and numerical results are presented to highlight certain properties of the consistent Bayesian approach and the differences between this approach and the two aforementioned alternatives for inference.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

MetaText: Compositional Generalization in Deep Language Models

Compositional generalization, the ability to infer and reason using novel combinations of previously encountered entities and structures, is a trait of great utility across a variety of deep learning tasks; it implies a certain ability to reason using consistent and therefore interpretable rules, to understand the constituent parts of its input at multiple levels of granularity, to be robust to spurious differences between semantically equivalent inputs, and more. We seek to understand the degree to which existing natural language models achieve or fall short of compositional generalization across a variety of tasks, whether there are distinct types of compositional generalization in practice, and to identify avenues of intervention through which we can improve models’ compositional generalization ability. After exploring these questions using an array of models, tasks, and potential interventions we see that large, pretrained language models have encountered sufficient training data to account for a variety of fine-grained compositional behavior but still struggle to reason at the level of phrases or larger language structures. Non-intrusive, data-based interventions in the form of augmenting individual sequences with compressed versions of themselves or deriving new examples from induced grammars prove insufficient to encourage greater levels of compositional reasoning, indicating that future work might benefit most from focusing on changes to a model’s inductive bias at the architectural or loss level, or by integrating compositionality-boosting data interventions into the large-scale pretraining process itself.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Seismic Waveform Inversion Capability on Resource-Constrained Edge Devices

Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used to reconstruct subsurface velocity images, however it is time consuming, has high computational cost and depend heavily on human interaction. Recently, deep learning has accelerated it’s use in several data-driven techniques, however most deep learning techniques suffer from overfitting and stability issues. In this work, we propose an edge computing-based data-driven inversion technique based on supervised deep convolutional neural network to accurately reconstruct the subsurface velocities. Deep learning based data-driven technique depends mostly on bulk data training. In this work, we train our deep convolutional neural network (DCN) (UNet and InversionNet) on the raw seismic data and their corresponding velocity models during the training phase to learn the non-linear mapping between the seismic data and velocity models. The trained network is then used to estimate the velocity models from new input seismic data during the prediction phase. The prediction phase is performed on a resource-constrained edge device such as Raspberry Pi. Raspberry Pi provides real-time and on-device computational power to execute the inference process. In addition, we demonstrate robustness of our models to perform inversion in the presence on noise by performing both noise-aware and no-noise training and feeding the resulting trained models with noise at different signal-to-noise (SNR) ratio values. We make great efforts to achieve very feasible inference times on the Raspberry Pi for both models. Specifically, the inference times per prediction for UNet and InversionNet models on Raspberry Pi were 22 and 4 s respectively whilst inference times for both models on the GPU were 2 and 18 s which are very comparable. Finally, we have designed a user-friendly interactive graphical user interface (GUI) to automate the model execution and inversion process on the Raspberry Pi.

Manu, Daniel (ORCID:0000000154982677)↗
DOE OSTI2014

A Sparse Grid Method for Bayesian Uncertainty Quantification with Application to Large Eddy Simulation Turbulence Models

There is wide agreement that the accuracy of turbulence models suffer from their sensitivity with respect to physical input data, the uncertainties of user-elected parameters, as well as the model inadequacy. However, the application of Bayesian inference to systematically quantify the uncertainties in parameters, by means of exploring posterior probability density functions (PPDFs), has been hindered by the prohibitively daunting computational cost associated with the large number of model executions, in addition to daunting computation time per one turbulence simulation. In this effort, we perform in this paper an adaptive hierarchical sparse grid surrogate modeling approach to Bayesian inference of large eddy simulation (LES). First, an adaptive hierarchical sparse grid surrogate for the output of forward models is constructed using a relatively small number of model executions. Using such surrogate, the likelihood function can be rapidly evaluated at any point in the parameter space without simulating the computationally expensive LES model. This method is essentially similar to those developed in Zhang et al. (Water Resour Res 49:6871–6892, 2013) for geophysical and groundwater models, but is adjusted and applied here for a much more challenging problem of uncertainty quantification of turbulence models. Through a numerical demonstration of the Smagorinsky model of two-dimensional flow around a cylinder at sub-critical Reynolds number, our approach is proven to significantly reduce the number of costly LES executions without losing much accuracy in the posterior probability estimation. Here, the model parameters are calibrated against synthetic data related to the mean flow velocity and Reynolds stresses at different locations in the flow wake. The influence of the user-elected LES parameters on the quality of output data will be discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Physics-tailored machine learning reveals unexpected physics in dusty plasmas

Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result, the effective forces between particles can be nonconservative and nonreciprocal. Machine learning (ML) models are a promising route to learn these complex forces, yet their structure should match the underlying physical constraints to provide useful insight. Here, we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in a laboratory dusty plasma. Trained on 3D particle trajectories, the model accounts for inherent symmetries, nonidentical particles, and learns the effective nonreciprocal forces between particles with exquisite accuracy (R 2 > 0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model’s accuracy enables precise measurements of particle charge and screening length, identifying large deviations from common theoretical assumptions. Our ability to identify unknown physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.

Science & Technology - Other Topics↗
DOE OSTI2019

Ringed seal demography in a changing climate

Abstract Climate change is affecting species’ distributions and abundances worldwide. Baseline population estimates, against which future observations may be compared, are necessary if we are to detect ecological change. Arctic sea ice ecosystems are changing rapidly and we lack baseline population estimates for many ice‐associated species. Provided we can detect them, changes in Arctic marine ecosystems may be signaled by changes in indicator species such as ringed seals ( Pusa hispida ). Ringed seal monitoring has provided estimates of survival and fertility rates, but these have not been used for population‐level inference. Using matrix population models, we synthesized existing demographic parameters to obtain estimates of historical ringed seal population growth and structure in Amundsen Gulf and Prince Albert Sound, Canada. We then formalized existing hypotheses about the effects of emerging environmental stressors (i.e., earlier spring ice breakup and reduced snow depth) on ringed seal pup survival. Coupling the demographic model to ice and snow forecasts available from the Coupled Model Intercomparison Project resulted in projections of ringed seal population size and structure up to the year 2100. These projections showed median declines in population size ranging from 50% to 99%. Corresponding to these projected declines were substantial changes in population structure, with increasing proportions of ringed seal pups and adults and declining proportions of juveniles. We explored if currently collected, harvest‐based data could be used to detect the projected changes in population stage structure. Our model suggests that at a present sample size of 100 seals per year, the projected changes in stage structure would only be reliably detected by mid‐century, even for the most extreme climate models. This modeling process revealed inconsistencies in existing estimates of ringed seal demographic rates. Mathematical population models such as these can contribute both to understanding past population trends as well as predicting future ones, both of which are necessary if we are to detect and interpret future observations.

Reimer, Jody R.↗
DOE OSTI2022

Electroweak constraints from the COHERENT experiment

We compute bounds on coefficients of effective operators in the Standard Model that can be inferred from observations of neutrino scattering by the COHERENT experiment. While many operators are bound extremely well by past experiments the full future data set from COHERENT will provide modest improvements for some operators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Strength, deformation, and equation of state of tungsten carbide to 66 GPa

Here, strength, texture, and equation of state of hexagonal tungsten monocarbide (WC) have been determined under quasi-hydrostatic and non-hydrostatic compression to 66 GPa using angle-dispersive X-ray diffraction in the diamond anvil cell. Quasi-hydrostatic compression in a Ne pressure medium demonstrates that nanocrystalline WC is slightly less incompressible than bulk-scale WC, with respective bulk moduli of K 0 = 377 ± 7 and 397 ± 7 GPa and pressure derivatives K 0 ’ = 3.8 ± 0.3 and 3.7 ±0.3. This decrease in incompressibility with grain size is similar to behavior observed in other ceramics. Under nonhydrostatic compression, WC supports a mean differential stress of ~12-15 GPa at plastic yielding, which occurs at ~30 GPa. Strength in WC is anisotropic, with the (001) plane supporting 29-42% higher stress than stresses calculated from mean strain. Simulations using an Elasto-ViscoPlastic Self-Consistent model indicate that strength inferred from lattice strain theory may be overestimated due to effects of plastic deformation. Plastic deformation generates a texture maximum near $\langle\bar{2}110\rangle$ in the compression orientation, initially through prismatic slip on the {$10\bar{1}0$}$\langle\bar{1}2\bar{1}0\rangle$ and {$10\bar{1}0$}$\langle0001\rangle$ slip systems, followed by activation of pyramidal slip on {$10\bar{1}1$}$\langle\bar{2}113\rangle$ at ~40-50 GPa.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Molecular dynamics simulations of cesium adsorption on illite nanoparticles

Here we present that the charged surfaces of micaceous minerals, especially illite, regulate the mobility of the major radioisotopes of Cs ( 134 Cs, 135 Cs, 137 Cs) in the geosphere. Despite the long history of Cs adsorption studies, the nature of the illite surface sites remains incompletely understood. To address this problem, we present atomistic simulations of Cs competition with Na for three candidate illite adsorption sites – edge, basal plane, and interlayer. Our simulation results are broadly consistent with affinities and selectivities that have been inferred from surface complexation models. Cation exchange on the basal planes is thermodynamically ideal, but exchange on edge surfaces and within interlayers shows complex, thermodynamically non-ideal behavior. The basal planes are weakly Cs-selective, while edges and interlayers have much higher affinity for Cs. The dynamics of Na-Cs exchange are rapid for both cations on the basal planes, but considerably slower for Cs localized on edge surfaces. Lastly, in addition to new insights into Cs adsorption and exchange with Na on illite, we report the development of a methodology capable of simulating fully-flexible clay mineral nanoparticles with stable edge surfaces using a well-tested interatomic potential model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Unraveling Spatio-Temporal Chemistry Evolution in Laser Ablation Plumes and Its Relation to Initial Plasma Conditions

The chemistry evolution in a laser ablation plume depends strongly on its initial physical conditions. In this article, we investigate the impact of plasma generation conditions on the interrelated phenomena of expansion dynamics, plasma chemistry, and physical conditions. Plasmas are produced from a uranium metal target in air using nanosecond, femtosecond, and femtosecond filament-assisted laser ablation. Time-resolved two-dimensional spectral imaging was performed for evaluating the spatio-temporal evolu-tion of atoms, diatoms, polyatomic molecules, and nanoparticles in situ. Emission spectral features reveal that molecular formation occurs at early times in both femtosecond and filament ablation plumes, although with different temporal decay. In contrast, mo-lecular formation is found to occur at much later times in nanosecond plasma evolution. Spectral modeling is used to infer tem-poral behavior of plasma excitation temperature, and results highlighted that nanosecond plume temperatures decay rapidly while plumes generated by filament ablation show slowest decay. In this work, we find U atoms and UO molecules co-exist in ultrafast laser produced plasmas even at early times after plasma onset owing to favorable temperatures for molecular formation. Regardless of irradiation conditions, plume emission features showed presence of higher oxides (i.e. UxOy), although with different temporal histories. Our study provides insight into the impact of plasma generation conditions on chemistry evolution in plasmas produced from traditional focused femtosecond, nanosecond, and filament-assisted laser ablation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Arctic Cloud‐Base Ice Precipitation Properties Retrieved Using Bayesian Inference

Abstract Cloud‐climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long‐lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud‐base properties a more appealing target for inference and evaluation of model simulations. Here I describe an inverse model for the estimation of cloud base ice precipitation properties over Utqiagvik, North Slope of Alaska, using the synthesis of ground‐based radar and lidar measurements. By leveraging a Markov Chain Monte Carlo algorithm as the core of the inverse model, a wide range of particle size distributions are sampled, and different combinations of ice habit models are examined, both of which are typically fixed in other retrieval methods. Results show intriguing links between different cloud base thermodynamic and ice precipitation properties. Apparent ice number concentration enhancements at temperatures of −5 and −15°C suggest possible secondary ice production (SIP). The analysis alludes to an overestimation of SIP occurrence and intensity, especially in studies relying only on radar or lidar measurements. Finally, reflectivity‐dependent ice precipitation rate and ice water content parameterizations are presented.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Constraining Bedrock Groundwater Residence Times in a Mountain System With Environmental Tracer Observations and Bayesian Uncertainty Quantification

Groundwater residence time distributions provide fundamental insights on the hydrological processes within watersheds. Yet, observations that can constrain groundwater residence times over broad timescales remain scarce in mountain catchment studies. We use environmental tracers (CFC-12, SF 6 , 3 H, and 4 He) to investigate groundwater residence times along a hillslope in the East River Watershed, Colorado, USA. We develop a Bayesian inference framework that applies a Markov-chain Monte Carlo (MCMC) approach to estimate noble gas recharge temperature, elevation, and excess-air parameters and the resulting environmental tracer concentrations. MCMC is then used to propagate the environmental tracer uncertainties to estimates of groundwater mean residence times inferred with lumped parameter models. All samples contain 3 H, CFC-12, and SF 6 in addition to terrigenic 4 He, suggesting a mixture of water characterized by modern and premodern residence times. 4He exponential mean residence times range from hundreds of years at the upslope well to thousands of years at the toe-slope well assuming average crustal production rates. We find that binary mixing residence time distributions with separate young and old mixing fractions are needed to predict the 4 He, CFC-12, SF 6 , and 3 H observations, supporting the importance of flow path mixing in this bedrock system. Our findings that the fractured bedrock hosts groundwater with a mixture of residence times ranging from decades to millennia suggest variable recharge dynamics and flow path mixing along the hillslope and highlight the importance of characterizing groundwater systems with observations that are sensitive to transport over a broad range of residence times.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Early aqueous activity on the ordinary and carbonaceous chondrite parent bodies recorded by fayalite

Here, chronology of aqueous activity on chondrite parent bodies constrains their accretion times and thermal histories. Radiometric 53 Mn– 53 Cr dating has been successfully applied to aqueously formed carbonates in CM carbonaceous chondrites. Owing to the absence of carbonates in ordinary (H, L and LL), and CV and CO carbonaceous chondrites, and the lack of proper standards, there are no reliable ages of aqueous activity on their parent bodies. Here we report the first 53 Mn– 53 Cr ages of aqueously formed fayalite in the L3 chondrite Elephant Moraine 90161 as 2.4 +1.8 -1.3 Myr after calcium–aluminium-rich inclusions (CAIs), the oldest Solar System solids. In addition, measurements using our synthesized fayalite standard show that fayalite in the CV3 chondrite Asuka 881317 and CO3-like chondrite MacAlpine Hills 88107 formed and 4.2 +0.8 -0.7 Myr after CAIs, respectively. Thermal modelling, combined with the inferred conditions (temperature and water/rock ratio) and 53 Mn– 53 Cr ages of aqueous alteration, suggests accretion of the L, CV and CO parent bodies ~1.8–2.5 Myr after CAIs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Hardware-accelerated inference for real-time gravitational-wave astronomy

The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Overall, our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗