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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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NASA NTRS1971

Analytical and experimental investigations of human spine flexure.

The authors report on experiments to measure the resistance of fresh human spines to flexion in the upper lumbar and lower thoracic regions and evaluate results by using a combination of strength of materials theory and effects of shear and comparing with data reported by other authors. The test results indicate that the thoraco-lumbar spine behaves approximately as a linear elastic beam, without relaxation effects. The authors formulate a simple continuum dynamic model of the spine simulating aircraft ejection and solve the resulting boundary value problem to illustrate the importance of the flexural mode. A constant cross-section, the selected model is a sinusoidally curved elastic beam with an end mass subjected to a Heaviside axial acceleration at the other end. The paper presents transient response results for the spinal model axial and bending displacements and axial force.-

Moffatt, C. A.↗

NASA NTRS1989

Remotely sensed soil moisture input to a hydrologic model

The possibility of using detailed spatial soil moisture maps as input to a runoff model was investigated. The water balance of a small drainage basin was simulated using a simple storage model. Aircraft microwave measurements of soil moisture were used to construct two-dimensional maps of the spatial distribution of the soil moisture. Data from overflights on different dates provided the temporal changes resulting from soil drainage and evapotranspiration. The study site and data collection are described, and the soil measurement data are given. The model selection is discussed, and the simulation results are summarized. It is concluded that a time series of soil moisture is a valuable new type of data for verifying model performance and for updating and correcting simulated streamflow.

Engman, E. T.↗

DOE OSTI2020

Power system resilience through defender-attacker-defender models with uncertainty: an overview

Protecting and fortifying a power system to make it resilient is an important and hard problem to solve. The interaction between defenders and attackers, the availability of information and the complexity of power system require carefully selecting models and presenting the underlying assumptions. Trilevel defender-attacker-defender models have been developed to investigate the resilience of power systems. In this paper, we review trilevel models in power system application, with the aim of demonstrating the accessibility and applicability of these complex optimization formulations and discussing the underlying assumptions from these models. In particular, we highlight modeling choices, algorithmic details, and how operational and information uncertainty affects resilience versus the traditional complete-knowledge-based optimal solutions. We also describe other similar relevant models which lack such uncertainty and discuss insights on how to address operational, attacker, and defender related uncertainties in future research efforts.

multi-level optimization, defender-attacker-defend↗

DOE OSTI2021

Bayesian inference for plasmonic nanometrology

Here, we introduce a Bayesian method for the characterization of plasmonic nanoparticles, which is applicable to both near- and far-field problems. Designed to combine data generated from any photon-plasmon interaction experiment with physically motivated theoretical models, our approach leverages state-of-the-art Markov chain Monte Carlo sampling techniques and returns parameter estimates on nanometric scales. Simulated spectral data sets, describing resonant scattering of photons from ellipsoidal and toroidal nanoparticles, are explored as concrete examples of our approach, with the resulting Bayesian estimates showing excellent agreement with the ground truth, even under conditions of high statistical noise. By incorporating Bayes factors into the method as well, we reveal how model selection can determine which one of competing geometric shapes better explains the observed data. Our comprehensive nanometrology procedure can be tailored to a variety of light-particle interaction models, and its reliance on Bayesian inference furnishes automatic uncertainty quantification. In addition to applicability to a host of plasmonic configurations such as nanoparticle dimers, trimers, and array studies, it is proposed that the presented analysis can be extended to the quantum regime, where nonclassical photon statistics may provide additional insight for inference of scatterer properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Short-Term Probabilistic Solar Forecasting via Reinforcement Learning over ECMWF

In this paper, we present an innovative reinforcement learning approach for short-term solar forecasting, leveraging data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The methodology begins with the application of the System Advisor Model (SAM) to transform various ECMWF numerical weather prediction members into predictive photovoltaic power generation. To enhance the precision of deterministic forecasting, we introduce a dynamic model selection algorithm based on Q-learning. This algorithm dynamically identifies and utilizes the most accurate ensemble member for forecasting purposes. Furthermore, we employ a support vector regression surrogate model with a Gaussian distribution to generate probabilistic forecasts, providing a holistic view of solar energy generation uncertainty. To expedite the training process and make it more practical for real-world applications, we integrate a rolling update workflow. This innovative workflow reduces the training period from months to a mere 19 days, making our method highly efficient. Numerical results of the case study show that in comparison to benchmark models, the proposed method improves the deterministic and probabilistic solar forecasting accuracy by up to 40.84% and 48.42%, respectively.

ensemble forecasting↗

DOE OSTI2024

Data-driven search for promising intercalating ions and layered materials for metal-ion batteries

The rise in demand for lithium-ion batteries has led to a large-scale search for electrode materials and intercalating ion species to meet the demands of next-generation energy technologies. Recent efforts largely focus on searching for cathodes that can accommodate large amounts of intercalating ions, but similar work on anodes is relatively limited. This study utilizes machine learning methods to find alternative two-dimensional (2D) materials and intercalating ions beyond Li for metal-ion batteries with high-power efficiencies. The approach first uses density functional theory (DFT) calculations to estimate the theoretical capacities and voltages of various metal ions on 2D materials. The DFT-generated data also provide insights into the local structural accommodation upon ion intercalation on various 2D materials. Significant changes to the lattice can result in irreversible changes to the bonding environments in the anode material, resulting in poor cycling stability. Next, this study develops a binding energy and structural accommodation-based classification model to screen anode materials for next-generation batteries. The classification model selects intercalating ions and 2D material pairs suitable for batteries based on the calculated voltage and volumetric changes in the 2D material upon intercalation. Finally, this study builds a regression model to accurately predict the binding energies of the various intercalating ions on 2D materials. The approach highlights the importance of different elemental and structural features for classification and regression tasks. In conclusion, the insights gained from this study on the role of involved features, such as electronegativities of the constituent ions and the presence of unfilled electronic levels, will help to streamline further studies towards the search for future layered battery materials.

36 MATERIALS SCIENCE↗

NASA NTRS2017

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗

DOE OSTI2021

Wolf-Rayet Galaxies in SDSS-IV MaNGA. II. Metallicity Dependence of the High-mass Slope of the Stellar Initial Mass Function

As hosts of living high-mass stars, Wolf-Rayet (WR) regions or WR galaxies are ideal objects for constraining the high-mass end of the stellar initial mass function (IMF). We construct a large sample of 910 WR galaxies/regions that cover a wide range of stellar metallicity (from Z ~ 0.001 to 0.03) by combining three catalogs of WR galaxies/regions previously selected from the SDSS and SDSS-IV/MaNGA surveys. We measure the equivalent widths of the WR blue bump at ~4650 Å for each spectrum. They are compared with predictions from stellar evolutionary models Starburst99 and BPASS, with different IMF assumptions (high-mass slope α of the IMF ranging from 1.0 to 3.3). Both singular evolution and binary evolution are considered. We also use a Bayesian inference code to perform full spectral fitting to WR spectra with stellar population spectra from BPASS as fitting templates. We then make a model selection among different α assumptions based on Bayesian evidence. These analyses have consistently led to a positive correlation of the IMF high-mass slope α with stellar metallicity Z, i.e., with a steeper IMF (more bottom-heavy) at higher metallicities. Specifically, an IMF with α = 1.00 is preferred at the lowest metallicity (Z ~ 0.001), and an Salpeter or even steeper IMF is preferred at the highest metallicity (Z ~ 0.03). These conclusions hold even when binary population models are adopted.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2016

The NANOGrav Nine-Year Data Set: Limits on the Isotropic Stochastic Gravitational Wave Background

We compute upper limits on the nanohertz-frequency isotropic stochastic gravitational wave background (GWB) using the 9 year data set from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration. Well-tested Bayesian techniques are used to set upper limits on the dimensionless strain amplitude (at a frequency of 1 yr(exp -1) for a GWB from supermassive black hole binaries of A(sub gw) less than 1.5 x 10(exp -15). We also parameterize the GWB spectrum with a broken power-law model by placing priors on the strain amplitude derived from simulations of Sesana and McWilliams et al. Using Bayesian model selection we find that the data favor a broken power law to a pure power law with odds ratios of 2.2 and 22 to one for the Sesana and McWilliams prior models, respectively. Using the broken power-law analysis we construct posterior distributions on environmental factors that drive the binary to the GW-driven regime including the stellar mass density for stellar-scattering, mass accretion rate for circumbinary disk interaction, and orbital eccentricity for eccentric binaries, marking the first time that the shape of the GWB spectrum has been used to make astrophysical inferences. Returning to a power-law model, we place stringent limits on the energy density of relic GWs, OMEGA(sub gw) (f) h squared less than 4.2 x 10(exp -10). Our limit on the cosmic string GWB, OMEGA(sub gw) (f) h squared less than 2.2 x 10(exp -10), translates to a conservative limit on the cosmic string tension with G mu less than 3.3 x 10(exp -8), a factor of four better than the joint Planck and high-l‚ cosmic microwave background data from other experiments.

Arzoumanian, Z.↗

NASA NTRS1986

Constitutive modeling for isotropic materials (HOST)

The results of the third year of work on a program which is part of the NASA Hot Section Technology program (HOST) are presented. The goals of this program are: (1) the development of unified constitutive models for rate dependent isotropic materials; and (2) the demonstration of the use of unified models in structural analyses of hot section components of gas turbine engines. The unified models selected for development and evaluation are those of Bodner-Partom and of Walker. A test procedure was developed for assisting the generation of a data base for the Bodner-Partom model using a relatively small number of specimens. This test procedure involved performing a tensile test at a temperature of interest that involves a succession of strain-rate changes. The results for B1900+Hf indicate that material constants related to hardening and thermal recovery can be obtained on the basis of such a procedure. Strain aging, thermal recovery, and unexpected material variations, however, preluded an accurate determination of the strain-rate sensitivity parameter is this exercise. The effects of casting grain size on the constitutive behavior of B1900+Hf were studied and no particular grain size effect was observed. A systematic procedure was also developed for determining the material constants in the Bodner-Partom model. Both the new test procedure and the method for determining material constants were applied to the alternate material, Mar-M247 . Test data including tensile, creep, cyclic and nonproportional biaxial (tension/torsion) loading were collected. Good correlations were obtained between the Bodner-Partom model and experiments. A literature survey was conducted to assess the effects of thermal history on the constitutive behavior of metals. Thermal history effects are expected to be present at temperature regimes where strain aging and change of microstructure are important. Possible modifications to the Bodner-Partom model to account for these effects are outlined. The use of a unified constitutive model for hot section component analyses was demonstrated by applying the Walker model and the MARC finite-element code to a B1900+Hf airfoil problem.

Chan, Kwai S.↗

DOE OSTI2023

MaNGA DynPop – I. Quality-assessed stellar dynamical modelling from integral-field spectroscopy of 10K nearby galaxies: a catalogue of masses, mass-to-light ratios, density profiles, and dark matter

ABSTRACT This is the first paper in our series on the combined analysis of the Dynamics and stellar Population (DynPop) for the MaNGA survey in the final SDSS Data Release 17 (DR17). Here, we present a catalogue of dynamically determined quantities for over 10 000 nearby galaxies based on integral-field stellar kinematics from the MaNGA survey. The dynamical properties are extracted using the axisymmetric Jeans Anisotropic Modelling (JAM) method, which was previously shown to be the most accurate for this kind of study. We assess systematic uncertainties using eight dynamical models with different assumptions. We use two orientations of the velocity ellipsoid: either cylindrically aligned JAMcyl or spherically aligned JAMsph. We also make four assumptions for the models’ dark versus luminous matter distributions: (1) mass-follows-light, (2) free NFW dark halo, (3) cosmologically constrained NFW halo, (4) generalized NFW dark halo, i.e. with free inner slope. In this catalogue, we provide the quantities related to the mass distributions (e.g. the density slopes and enclosed mass within a sphere of a given radius for total mass, stellar mass, and dark matter mass components). We also provide the complete models which can be used to compute the full luminous and mass distribution of each galaxy. Additionally, we visually assess the qualities of the models to help with model selections. We estimate the observed scatter in the measured quantities which decreases as expected with improvements in quality. For the best data quality, we find a remarkable consistency of measured quantities between different models, highlighting the robustness of the results.

Astronomy & Astrophysics↗

DOE OSTI2020

Uncertainty Quantification in Scientific ML

The intricate interactions between data sampling, model selection and the inherent randomness in complex systems strongly emphasize the need for a rigorous characterization of ML algorithms. In conventional statistics, uncertainty quantification (UQ) provides this characterization by measuring how accurately a model reflects the physical reality and by studying the impact of different error sources on the prediction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Understanding the cold plasma synthesis of ammonia with model metal catalysts through plasma diagnostics (Final Report)

The industrial synthesis of ammonia, which amounts to over 200 million tons annually, is the most energy-intensive chemical process. Therefore, there is a critical need and increased interest in exploring less energetic routes to produce ammonia. Not only does ammonia have a direct impact on the food market, but also it has the potential as a fuel and hydrogen carrier. Recently, plasma catalysis has emerged as a promising alternative for synthesizing ammonia at mild (pressure, temperature and power) conditions. The key to this catalytic process is the synergy between the plasma and the catalyst, where the non-equilibrium plasma allows the generation of excited species, which recombine at the catalyst surface to form ammonia. However, our current understanding of this process is in its infancy. In this respect, model metals are ideal candidates for gaining a basic understanding of this reaction. Moreover, a major roadblock to rationally designing novel effective catalysts for plasma-assisted ammonia production is the need for fundamental aspects of this process. Through a comprehensive plan that integrates model metals as catalysts and world-class diagnostics, the proposed work aims to provide fundamental knowledge about the nature of reactive processes occurring during plasma-assisted catalysis and to demonstrate the selective production of ammonia, catalyzed by employing selected metals under non-thermal plasma conditions. Toward this goal, the central thrust of this proposal was to demonstrate that the synergy between plasma and rationally selected model metals will boost ammonia yields during plasma-assisted ammonia synthesis by delaying hydrogen recombination. Specifically, we aimed to (1) understand the formation and role of gas-phase active species such as NH, N 2 , N 2 + and Ha during the plasma-enhanced synthesis of ammonia through OES and FTIRAS using different reaction configurations: a) only plasma (non-packed DBD reactor) and b) packed DBD reactor with metal nanoparticles. This proposal was awarded/recommended with a run time on the PCRF facility FY20.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

KELT-11 b: Abundances of Water and Constraints on Carbon-bearing Molecules from the Hubble Transmission Spectrum

In the past decade, the analysis of exoplanet atmospheric spectra has revealed the presence of water vapor in almost all the planets observed, with the exception of a fraction of overcast planets. Indeed, water vapor presents a large absorption signature in the wavelength coverage of the Hubble Space Telescope’s (HST) Wide Field Camera 3 (WFC3), which is the main space-based observatory for atmospheric studies of exoplanets, making its detection very robust. However, while carbon-bearing species such as methane, carbon monoxide, and carbon dioxide are also predicted from current chemical models, their direct detection and abundance characterization has remained a challenge. Here we analyze the transmission spectrum of the puffy, clear hot-Jupiter KELT-11 b from the HST WFC3 camera. We find that the spectrum is consistent with the presence of water vapor and an additional absorption at longer wavelengths than 1.5 μm, which could well be explained by a mix of carbon bearing molecules. CO{sub 2}, when included is systematically detected. One of the main difficulties to constrain the abundance of those molecules is their weak signatures across the HST WFC3 wavelength coverage, particularly when compared to those of water. Through a comprehensive retrieval analysis, we attempt to explain the main degeneracies present in this data set and explore some of the recurrent challenges that are occurring in retrieval studies (e.g., the impact of model selection, the use of free versus self-consistent chemistry, and the combination of instrument observations). Our results make this planet an exceptional example of a chemical laboratory to test current physical and chemical models of the atmospheres of hot Jupiters.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1971

Control theory analysis of a three-axis VTOL flight director

A control theory analysis of a VTOL flight director and the results of a fixed-based simulator evaluation of the flight-director commands are discussed. The VTOL configuration selected for this study is a helicopter-type VTOL which controls the direction of the thrust vector by means of vehicle-attitude changes and, furthermore, employs high-gain attitude stabilization. This configuration is the same as one which was simulated in actual instrument flight tests with a variable stability helicopter. Stability analyses are made for each of the flight-director commands, assuming a single input-output, multi-loop system model for each control axis. The analyses proceed from the inner-loops to the outer-loops, using an analytical pilot model selected on the basis of the innermost-loop dynamics. The time response of the analytical model of the system is primarily used to adjust system gains, while root locus plots are used to identify dominant modes and mode interactions.

Niessen, F. R.↗

DOE OSTI2020

Determining the Length Scale of Transport Impedances in Li-Ion Electrodes: Li(Ni 0.33 Mn 0.33 Co 0.33 )O 2

Battery cathodes are complex multiscale, multifunctional materials. The length scale at which the dominant impedance arises may be difficult to determine even with the most advanced experimental characterization efforts, and thus modeling can play an important role in analysis. Discharge and voltage relaxation curves, interrogated with theory, are used to distinguish between transport impedance that arise on the scale of the active crystal and on the scale of agglomerates (secondary particles) comprised of nanoscale crystals. Model-selection algorithms are applied to determine that the agglomerate scale is dominant in the Li Ni 0.33 Mn 0.33 Co 0.33 O 2 electrode studied here. Furthermore, conditions where the agglomerate and crystal-scale models yield distinct simulation results are demonstrated, providing approaches that can be applied to other systems.

25 ENERGY STORAGE↗

NASA NTRS1989

Three-dimensional thermochemical nonequilibrium flow modeling for hypersonic flows

A three-dimensional thermochemical nonequilibrium model has been developed and applied to the study of entry flows surrounding space vehicles. The model accounts for both chemical and vibrational nonequilibrium phenomena behind the bow shock. The thermodynamic state of a real gas is modeled with a translational-rotational temperature and a electron-vibrational temperature. Their internal energies are averaged to determine the temperature used in the reaction rates calculation. In order to establish the validity of the selected models, both one- and two-temperature models with seven and/or eleven species were investigated. Several numerical experiments that include a sphere, the RAMC vehicle and 3D AFE forebody flows were performed. Preliminary results were compared with RAMC-II experimental data. Good agreement was obtained after a two-temperature model with eleven species and thirty reactions was incorporated into the study.

Tam, L. T.↗

DOE OSTI2022

Materials and Fuels Complex Operations Management Improvement Strategy for FY2022

The Materials and Fuels Complex (MFC) has experienced substantial growth in terms of staff, research, and production in recent years. MFC operational performance has effectively kept pace with this growth. However, to capture the continuous improvement actions needed to improve effectiveness and increase the efficiency of our management systems, a broad operations management strategy is necessary. The MFC Operations Management Improvement (OMI) Strategy is complementary to the MFC Five-Year Mission and Investment Strategies and the MFC Management Plan. The OMI strategy is structured to address the management systems outlined in the Nuclear Facility Management Standard Operations Model. Selected management systems are evaluated independently in chapters that describe the prior 5 years of performance improvement, a description of improvement actions for the MFC staff that directly perform within or contribute to the management system, process improvements, and any needed equipment improvements. Chapter selection is based on a management system’s need for improvement. In some cases, management systems may be combined in a single chapter. Additional chapters are added to address subject areas not formally described in the standard operations model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗