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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A variational encoder–decoder approach to precise spectroscopic age estimation for large Galactic surveys

Constraints on the formation and evolution of the Milky Way Galaxy require multidimensional measurements of kinematics, abundances, and ages for a large population of stars. Ages for luminous giants, which can be seen to large distances, are an essential component of studies of the Milky Way, but they are traditionally very difficult to estimate precisely for a large data set and often require careful analysis on a star-by-star basis in asteroseismology. Because spectra are easier to obtain for large samples, being able to determine precise ages from spectra allows for large age samples to be constructed, but spectroscopic ages are often imprecise and contaminated by abundance correlations. Here we present an application of a variational encoder–decoder on cross-domain astronomical data to solve these issues. The model is trained on pairs of observations from APOGEE and Kepler of the same star in order to reduce the dimensionality of the APOGEE spectra in a latent space while removing abundance information. The low dimensional latent representation of these spectra can then be trained to predict age with just ∼1000 precise seismic ages. We demonstrate that this model produces more precise spectroscopic ages (∼ 22 per cent overall, ∼ 11 per cent for red-clump stars) than previous data-driven spectroscopic ages while being less contaminated by abundance information (in particular, our ages do not depend on [α/M]). We create a public age catalogue for the APOGEE DR17 data set and use it to map the age distribution and the age-[Fe/H]-[α/M] distribution across the radial range of the Galactic disc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid–liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid–liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

Normal mode energetics and error analysis of GLA GCM simulations with different horizontal resolutions during a winter month

Comparative energetics is presented for a series of four general circulation model simulations for January 1979 conducted by the Goddard Laboratory for Atmospheres. The simulations include cases of coarse and fine horizontal model resolutions with two slightly different initial conditions. Using a three-dimensional normal mode expansion, it is found that the gravity-mode energy levels are significantly reduced in the higher wavenumbers and in the higher-order internal vertical modes by the increase of horizontal model resolution.

Tanaka, H. L.↗
DOE OSTI2019

Viscosity of Iron Oxide Aluminosilicate Melts

A structure-based viscosity model has been developed for the CaO-MgO-FeO-SiO{sub 2}-Al{sub 2}O{sub 3} system and its subsystems. A critical analysis of the experimental data for iron oxide aluminosilicate systems has been conducted and used in the optimization of a model, which provides a link between the viscosity and the structures of the melts. The structural characteristics of aluminosilicate melt can be described by four types of oxygen: bridging oxygen O{sub i}{sup 0}, non-bridging oxygen O{sub j}{sup −}, free oxygen O{sup 2−}, and excess bridging oxygen O{sup *}. The present model is capable of predicting the viscosities in the CaO-MgO-FeO-SiO{sub 2}-Al{sub 2}O{sub 3} system and its subsystems over wide ranges of composition and temperature above the liquidus within experimental uncertainties, and the average of relative errors for this model was found to be 19.94 pct. This model includes FeO-SiO{sub 2}, CaO-SiO{sub 2}-FeO, Al{sub 2}O{sub 3}-SiO{sub 2}-FeO, MgO-SiO{sub 2}-FeO, CaO-SiO{sub 2}-MgO-FeO, CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-FeO, and CaO-MgO-SiO{sub 2}-Al{sub 2}O{sub 3}-FeO. It was found that the effects of different metal cations on the viscosity can be determined, and the ability to reduce viscosity in the silicate melts follows the order: FeO > CaO > MgO. In the present model, the predicted viscosity decreased when CaO was initially initial added to the Al{sub 2}O{sub 3}-MgO-FeO-SiO{sub 2} system at a fixed SiO{sub 2} content because Ca{sup 2+} has the highest priority for charge compensation and FeO is the strongest modifier for this slag.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Adaptive Reweighted Variance Estimation for Monte Carlo Eigenvalue Simulations

Monte Carlo (MC) simulation is used to solve the eigenvalue form of the Boltzmann transport equation to estimate various parameters such as fuel pin flux distributions that are crucial for the safe and efficient operation of nuclear systems (e.g., a nuclear reactor). Monte Carlo eigenvalue simulation uses a sample mean over many stationary cycles (iterations) to estimate various parameters important to nuclear systems. A variance estimate of the sample mean is often used for calculating the confidence intervals. However, MC eigenvalue simulation variance estimators that ignore the intercycle correlation underestimate the true variance of the estimated quantity. This paper presents novel data-adaptive approaches based on a simple autoregressive (AR) model and sigmoid functions to improve MC variance estimation. The standard MC sample-based variance estimator (or naïve estimator) and the spectral density–based MC variance estimator are enhanced by adding data-adaptive components that reduce their bias and improve performance. By investigating the frequency pattern of the AR(1) (order 1) model, two adaptive spectral estimators and one adaptive naïve estimator are proposed. The proposed estimators manifest superior performance when applied to three test problems compared to the standard spectral density–based estimator previously introduced by the authors. In conclusion, these new estimators are straightforward, as they use online algorithms and do not require storage of tallies from all active cycles.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1993

Failure modes of reduced-order orbit determination filters and their remedies

Ways in which failure can occur in reduced-order, orbit determination filter, error covariance calculations are discussed. In the context of this article, reduced-order filters denote nonoptimal filters which include fixed levels of uncertainty in some parameters of the measurement models or in the spacecraft dynamical model which are not explicitly estimated in the filter equations. Failure is defined as an increase in the orbit determination covariance with the addition of data or as an unreasonable growth in the covariance with time, i.e., nonasymptotic behavior of the covariance. Some simple, known cases of failure are discussed along with their traditional remedies. In addition, more modern remedies are discussed which are currently under development at the Jet Propulsion Laboratory. The article first describes the known problems of reduced-order filters when they are employed for orbit determination, and their traditional remedies. Then, having defined these, the relevancy and desirability of the more modern remedies are made apparent.

Scheeres, D. J.↗
DOE OSTI2024

Analysis of Waste Material Feedstocks Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Predicting properties such as heating value, ash fusion temperature, and mineral ash composition from Laser-Induced Breakdown Spectroscopy (LIBS) data can make gasifiers more flexible to different feedstocks. Understanding these feedstock properties in-situ improves feedstock conversion modelling methods that allow for consistent operation, higher carbon conversion, and reduced fouling and erosion rates. The purpose of this study is to demonstrate methods for model creation that take LIBS data as predictor features and estimate higher order material properties as a function of feedstock material properties. Six samples were chosen to represent a mixture of abundant and carbon rich waste materials. LIBS measurements were performed on these samples for elemental wavelengths and intensity values. Laboratory analytical results were obtained for each sample’s heating value, proximate and ultimate analysis, mineral ash composition, ash fusion temperatures, and viscosity temperatures. Thermal conductivity was measured using a HotDisk TPS 2500S. LIBS measurements were processed and used as predictor features for machine learning (ML) models to predict the sample’s material properties. Predictor feature selection algorithms, particularly minimum redundancy maximum relevance (mRMR), reduced the dimensionality of ML models. Many modelling methods such as Gaussian process regression (GPR), regression tree, neural networks (NN), and support vector machines (SVM) were demonstrated to be effective at predicting higher order properties; however, mRMR with GPR stood out as a clear winning combination.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2016

Data-driven operator inference for nonintrusive projection-based model reduction

We presenta a nonintrusive projection-based model reduction method for full models based on time-dependent partial differential equations. Projection-based model reduction constructs the operators of a reduced model by projecting the equations of the full model onto a reduced space. Traditionally, this projection is intrusive, which means that the full-model operators are required either explicitly in an assembled form or implicitly through a routine that returns the action of the operators on a given vector; however, in many situations the full model is given as a black box that computes trajectories of the full-model states and outputs for given initial conditions and inputs, but does not provide the full-model operators. Our nonintrusive operator inference approach infers approximations of the reduced operators from the initial conditions, inputs, trajectories of the states, and outputs of the full model, without requiring the full-model operators. Our operator inference is applicable to full models that are linear in the state or have a low-order polynomial nonlinear term. The inferred operators are the solution of a least-squares problem and converge, with sufficient state trajectory data, in the Frobenius norm to the reduced operators that would be obtained via an intrusive projection of the full-model operators. Our numerical results reflect operator inference on a linear climate model and on a tubular reactor model with a polynomial nonlinear term of third order.

42 ENGINEERING↗
DOE OSTI2018

Efficient modeling of optically-complex, non-coplanar exterior shading: Validation of matrix algebraic methods

It has long been established that shading windows with overhangs, fins, and other types of non-coplanar systems (NCS) is one of the most effective ways of controlling solar heat gains in buildings because they intercept solar radiation prior to entry into the building. Designers however often specify non-opaque materials (e.g., louvers, fritted glass, expanded metal mesh) for these systems in order to admit daylight, reduce lighting energy use, and improve indoor environmental quality. Most simulation tools rely on geometric calculations and radiosity methods to model the solar heat gain impacts of NCS and cannot model optically-complex materials or geometries. For daylighting analysis, optically-complex NCS can be modeled using matrix algebraic methods, although time-efficient parametric analysis has not yet been implemented. Determining the best design and/or material for static or operable NCS that minimize cooling, heating, and lighting energy use and peak demand requires an iterative process. This study describes and validates a matrix algebraic method that enables parametric energy analysis of NCS. Such capabilities would be useful not only for design but also for development of prescriptive energy-efficiency standards, rating and labeling systems for commercial products, development of design guidelines, and development of more optimal NCS technologies. A facade or “F” matrix, which maps the transfer of flux from the NCS to the surface of the window, is introduced and its use is explained. A field study was conducted in a full-scale outdoor testbed to measure the daylight performance of an operable drop-arm awning. Simulated data were compared to measured data in order to validate the models. Results demonstrated model accuracy: simulated workplane illuminance was within 11–13%, surface luminance was within 16–18%, and the daylight glare probability was within 6–9% of measured results. Methods used to achieve accurate results are discussed. Results of the validation of daylighting performance are applicable to solar heat gain performance. Since exterior shading can also significantly reduce peak demand, these models enable stakeholders to more accurately assess HVAC and lighting impacts in support of grid management and resiliency goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Seasonality of isoprenoid emissions from a primary rainforest in central Amazonia

Tropical rainforests are an important source of isoprenoid and other volatile organic compound (VOC) emissions to the atmosphere. The seasonal variation of these compounds is however still poorly understood. In this study, vertical profiles of mixing ratios of isoprene, total monoterpenes and total sesquiterpenes, were measured within and above the canopy, in a primary rainforest in central Amazonia, using a proton transfer reaction – mass spectrometer (PTR-MS). Fluxes of these compounds from the canopy into the atmosphere were estimated from PTR-MS measurements by using an inverse Lagrangian transport model. Measurements were carried out continuously from September 2010 to January 2011, encompassing the dry and wet seasons. Mixing ratios were higher during the dry (isoprene – 2.68 ± 0.9 ppbv, total monoterpenes – 0.67 ± 0.3 ppbv; total sesquiterpenes – 0.09 ± 0.07 ppbv) than the wet season (isoprene – 1.66 ± 0.9 ppbv, total monoterpenes – 0.47 ± 0.2 ppbv; total sesquiterpenes – 0.03 ± 0.02 ppbv) for all compounds. Ambient air temperature and photosynthetically active radiation (PAR) behaved similarly. Daytime isoprene and total monoterpene mixing ratios were highest within the canopy, rather than near the ground or above the canopy. By comparison, daytime total sesquiterpene mixing ratios were highest near the ground. Daytime fluxes varied significantly between seasons for all compounds. The maximums for isoprene (2.53 ± 0.5 µmol m -2 h -1 ) and total monoterpenes (1.77 ± 0.05 µmol m -2 h -1 ) were observed in the late dry season, whereas the maximum for total sesquiterpenes was found during the dry-to-wet transition season (0.77 ± 0.1 µmol m -2 h -1 ). These flux estimates suggest that the canopy is the main source of isoprenoids emitted into the atmosphere for all seasons. However, uncertainties in turbulence parameterization near the ground could affect estimates of fluxes that come from the ground. Leaf phenology seemed to be an important driver of seasonal variation of isoprenoid emissions. Although remote sensing observations of changes in leaf area index were used to estimate leaf phenology, MEGAN 2.1 did not fully capture the behavior of seasonal emissions observed in this study. This could be a result of very local effects on the observed emissions, but also suggest that other parameters need to be better determined in biogenic volatile organic compound (BVOC) models. Our results support established findings that seasonality of isoprenoids are driven by seasonal changes in light, temperature and leaf phenology. However, they suggest that leaf phenology and its role on isoprenoid production and emission from tropical plant species needs to be better understood in order to develop mechanistic explanations for seasonal variation in emissions. This also may reduce the uncertainties of model estimates associated with the responses to environmental factors. Therefore, this study strongly encourages long-term measurements of isoprenoid emissions, environmental factors and leaf phenology from leaf to ecosystem scale, with the purpose of improving BVOC model approaches that can characterize seasonality of isoprenoid emissions from tropical rainforests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Cubic-scaling algorithm and self-consistent field for the random-phase approximation with second-order screened exchange

The random-phase approximation with second-order screened exchange (RPA+SOSEX) is a model of electron correlation energy with two caveats: its accuracy depends on an arbitrary choice of mean field, and it scales as O(n{sup 5}) operations and O(n{sup 3}) memory for n electrons. We derive a new algorithm that reduces its scaling to O(n{sup 3}) operations and O(n{sup 2}) memory using controlled approximations and a new self-consistent field that approximates Brueckner coupled-cluster doubles theory with RPA+SOSEX, referred to as Brueckner RPA theory. The algorithm comparably reduces the scaling of second-order Møller-Plesset perturbation theory with smaller cost prefactors than RPA+SOSEX. Within a semiempirical model, we study H{sub 2} dissociation to test accuracy and H{sub n} rings to verify scaling.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

Electromagnetic deep-probing (100-1000 KMS) of the Earth's interior from artificial satellites: Constraints on the regional emplacement of crustal resources

The two stages of analysis of MAGSAT magnetic data which are designed to evaluate electromagnetic induction effects are described. The first stage consists of comparison of data from contiguous orbit passes over large scale geologic boundaries, such as ocean-land interfaces, at several levels of magnetic disturbance. The purpose of these comparisons is to separate induction effects from effects of lithospheric magnetization. The procdure for reducing the data includes: (1) identifying and subtracting quiet time effects; (2) modelling and subtracting first order ring current effects; and (3) projecting an orbit track onto a map as a nearly straight line so it can serve as an axis on which to plot the corresponding orbit pass data in the context of geography. The second stage consists of comparison of MAGSAT data with standard hourly observatory data. The purpose is to constrain the time evolution of ionospheric and magnetospheric current systems. Qualitative features of the ground based dataset are discussed. Methods for reducing the ground based data are described.

Hermance, J. F.↗
NASA NTRS1975

Aeroacoustic performance of scale model sonic inlets

Various means can be used to vary engine inlet throat areas in order to reduce noise generated at takeoff and approach. Low-speed wind-tunnel tests were performed to evaluate the aeroacoustic performance of two takeoff geometries (cylindrical centerbody and bulb-shaped centerbody) and four approach geometries (bulb-shaped centerbody, annular ring, radial vanes, and step diffuser). The effects of inlet internal lip shape and diffuser length on sonic inlet performance were also investigated. It was found that the single-passage geometries, in particular a cylindrical centerbody takeoff geometry and a bulb-shaped centerbody approach geometry, provide the highest level of aeroacoustic performance. Increasing the inlet lip contraction ratio extends the maximum incidence angle for attached lip flow, while increasing the inlet diffuser length increases total pressure recovery for a given amount of noise suppression.

Abbott, J. M.↗
DOE OSTI2014

On the nonlinear dissipative dynamics of weakly overdamped oscillators

We consider the motion of weakly overdamped linear oscillators. Weak overdamping of an oscillator is defined as a slight excess of the damping decrement over its natural frequency. Exact solutions are obtained for a certain relation between the decrement and the natural frequency and qualitatively different regimes of motion are analyzed. The threshold conditions corresponding to changes of regimes are established; one-component models with an arbitrary degree of nonlinearity are analyzed, and quadratic and cubic nonlinearities are considered in detail. If the nonlinearity in a multicomponent model is determined by a homogeneous function, transformations of the Kummer-Liouville type can be reduced to an autonomous system of second-order differential equations in the case when the relation between the decrement and the natural frequency has been established. Some integrable multicomponent models with quadratic and cubic nonlinearities are analyzed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1987

Scattering models for some vegetation samples

The Helmholtz integral equation is presently derived for a scatterer of arbitrary shape, and reduced in order to obtain the far zone-scattered field in terms of the field within the scatterer. Attention is given to the effect of different approaches to field estimation within the scatterer on the backscattering cross section, as illustrated numerically by the cases of a circular disk, a needle, and a finite-length cylinder. A comparison is made of the results obtained by modeling a leaf by means of a circular disk within the Shifrin approximation, and a tree branch by means of a finite-length cylinder, with measurements from a single leaf and a single branch.

Karam, M. A.↗
DOE OSTI2022

Implementation of a Model Predictive Control Strategy to Regulate Temperature Inside Plug-Flow Solar Reactor With Countercurrent Flow

Abstract Solar-driven thermochemical energy storage systems are proven to be promising energy carriers (solar fuels) to utilize solar energy by using reactive solid-state pellets. However, the production of solar fuel requires a quasi-steady-state process temperature, which represents the main challenge due to the transient nature of solar power. In this work, an adaptive model predictive controller (MPC) is presented to regulate the temperature inside a tubular solar reactor to produce solid-state solar fuel for long-term thermal storage systems. The solar reactor system consists of a vertical tube heated circumferentially over a segment of its length by concentrated solar power, and the reactive pellets (MgMn2O4) are fed from the top end and flow downwards through the heated tube. A countercurrent flowing gas supplied from the lower end interacts with flowing pellets to reduce it thermochemically at a temperature range of 1000—1500 °C. A low-order physical model was developed to simulate the dynamics of the solar reactor including the reaction kinetics, and the proposed model was validated numerically by using a 7-kW electric furnace. The numerical model then was utilized to design the MPC controller, where the control system consists of an MPC code linked to an adaptive system identification code that updates system parameters online to ensure system robustness against external disturbances (sudden change in the flow inside the reactor), model mismatches, and uncertainty. The MPC controller parameters are tuned to enhance the system performance with minimum steady-state error and overshoot. The controller is tested to track different temperature ranges between 500 °C and 1400 °C with different particles/gas mass flowrates and ramping temperature profiles. Results show that the MPC controller successfully regulated the reactor temperature within ± 1 °C of its setpoint and maintained robust performance with minimum input effort when subjected to sudden changes in the amount of flowing media and the presence of chemical reaction.

Engineering↗
DOE OSTI2017

A multiphase three-dimensional multi-relaxation time (MRT) lattice Boltzmann model with surface tension adjustment

The interdependence of surface tension and density ratio is a weakness of pseudo-potential based lattice Boltzmann models (LB). In this paper, we propose a 3D multi-relaxation time (MRT) model for multiphase flows at large density ratios. The proposed model is capable of adjusting the surface tension independently of the density ratio. We also present the 3D macroscopic equations recovered by the proposed forcing scheme. A high order of isotropy for the interaction force is used to reduce the amplitude of spurious currents. The proposed 3D-MRT model is validated by verifying Laplace's law and by analyzing its thermodynamic consistency and the oscillation period of a deformed droplet. The model is then applied to the simulation of the impact of a droplet on a dry surface. Impact dynamics are determined and the maximum spread factor calculated for different Reynolds and Weber numbers. The numerical results are in agreement with data published in the literature. The influence of surface wettability on the spread factor is also investigated. Finally, our 3D-MRT model is applied to the simulation of the impact of a droplet on a wet surface. The propagation of transverse waves is observed on the liquid surface.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

TESTING MODELS OF INTRINSIC BRIGHTNESS VARIATIONS IN TYPE Ia SUPERNOVAE AND THEIR IMPACT ON MEASURING COSMOLOGICAL PARAMETERS

For spectroscopically confirmed Type Ia supernovae we evaluate models of intrinsic brightness variations with detailed data/Monte Carlo comparisons of the dispersion in the following quantities: Hubble-diagram scatter, color difference (B - V - c) between the true B - V color and the fitted color (c) from the SALT-II light curve model, and photometric redshift residual. The data sample includes 251 ugriz light curves from the three-season Sloan Digital Sky Survey-II and 191 griz light curves from the Supernova Legacy Survey 3 year data release. We find that the simplest model of a wavelength-independent (coherent) scatter is not adequate, and that to describe the data the intrinsic-scatter model must have wavelength-dependent variations resulting in a {approx}0.02 mag scatter in B - V - c. Relatively weak constraints are obtained on the nature of intrinsic scatter because a variety of different models can reasonably describe this photometric data sample. We use Monte Carlo simulations to examine the standard approach of adding a coherent-scatter term in quadrature to the distance-modulus uncertainty in order to bring the reduced {chi}{sup 2} to unity when fitting a Hubble diagram. If the light curve fits include model uncertainties with the correct wavelength dependence of the scatter, we find that this approach is valid and that the bias on the dark energy equation-of-state parameter w is much smaller ({approx}0.001) than current systematic uncertainties. However, incorrect model uncertainties can lead to a significant bias on the distance moduli, with up to {approx}0.05 mag redshift-dependent variation. This bias is roughly reduced in half after applying a Malmquist bias correction. For the recent SNLS3 cosmology results, we estimate that this effect introduces an additional systematic uncertainty on w of {approx}0.02, well below the total uncertainty. This uncertainty depends on the choice of viable scatter models and the choice of supernova (SN) samples, and thus this small w-uncertainty is not guaranteed in future cosmology results. For example, the w-uncertainty for SDSS+SNLS (dropping the nearby SNe) increases to {approx}0.04.

79 ASTRONOMY AND ASTROPHYSICS↗