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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Deformed Fredkin model for the ν = 5 / 2 Moore-Read state on thin cylinders

We propose a frustration-free model for the Moore-Read quantum Hall state on sufficiently thin cylinders with circumferences ≲ 7 magnetic lengths. While the Moore-Read Hamiltonian involves complicated long-range interactions between triplets of electrons in a Landau level, our effective model is a simpler one-dimensional chain of qubits with deformed Fredkin gates. We show that the ground state of the Fredkin model has high overlap with the Moore-Read wave function and accurately reproduces the latter's entanglement properties. Moreover, we demonstrate that the model captures the dynamical response of the Moore-Read state to a geometric quench, induced by suddenly changing the anisotropy of the system. We elucidate the underlying mechanism of the quench dynamics and show that it coincides with the linearized bimetric field theory. The minimal model introduced here can be directly implemented as a first step towards quantum simulation of the Moore-Read state, as we demonstrate by deriving an efficient circuit approximation to the ground state and implementing it on an IBM quantum processor. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2022

Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach

During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents(GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (|BN|) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting |BN|. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for |BN|. The neural network models show significant increases over the linear regression models for |BN| thresholds. We also calculate the Heidke skill scores for d|BN|/dt by deriving d|BN|/dt from |BN| predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict BN instead of |BN|, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times.

Matthew Blandin↗
DOE OSTI2022

Catalytic Upgrading of Ethanol to C8+ Distillate Range Ethers via Guerbet Coupling and Etherification

Guerbet coupling followed by etherification is a two step process for conversion of ethanol into distillate range molecules. This involves first alcohol oligomerization, followed by acid-catalyzed dehydration. As we will show in this presentation, ethanol can be converted to higher linear and α-branched alcohols by C-C coupling reactions (ethanol oligomerization) as well as to high molecular weight esters by C-O coupling reactions with Cu/MgxAlOy (CuHT) catalysts. Alcohols may be converted to ethers via bimolecular dehydration in a subsequent step. We have identified more than 160 number of species in the products including alcohols, esters, aldehydes, ketonces, and olefins. Alcohols range from C4 to C10. Both alcohols and esters follow a Schulz-Flory chain growth model. We show the relationship between the catalyst properties (BET surface area, acid and base site count and Cu loading and synthesis method) and the performance in the reactions. We also show that physical mixtures of CuHT and HT can have similar product selectivity of low loading CuHT catalysts. The selectivity towards diesel fuel precursor compounds (hereafter ‘DFPC’) increased with conversion until reaching a plateau at high ethanol conversion (~70%). Alcohol selectivity follows a Schultz-Flory distribution at all studied conversions, and that adsorbed ethanol-derived species may undergo surface oligomerization into 1-butanol and higher alcohols before desorbing in a chain-growth mechanism. Zeolite catalysts convert the C4+ alcohols into C8+ ethers in both batch and continuous flow reactors. Selectivities of up to 80% to C8+ ethers at around 70% conversion using a single pass continuous flow system are able to be achieved. The final product feedstocks obtained from etherification have been used in technoeconomic (TEA) and lifecycle analysis (LCA), which indicate a reduction in greenhouse gas (GHG) emissions of 50% relative to conventional diesel. These final feedstocks are undergoing engine testing to elucidate the physiochemical properties and will be compared to diesel #2 ASTM standards as a reference and will serve as a basis to improve the final bio-diesel blendstocks from etherification.

Huber, George↗
DOE OSTI2023

Catalytic upgrading of ethanol to C8+ distillate range ethers via Guerbet coupling and etherification

In this presentation we describe the catalytic approaches for conversion of ethanol into diesel fuel ethers. This involves first alcohol oligomerization, followed by acid-catalyzed dehydration. As we will show in this presentation, ethanol can be converted to higher linear and α-branched alcohols by C-C coupling reactions (ethanol oligomerization) as well as to high molecular weight esters by C-O coupling reactions with Cu/MgxAlOy (CuHT) catalysts. Alcohols may be converted to ethers via bimolecular dehydration in a subsequent step. We have identified more than 160 number of species in the products including alcohols, esters, aldehydes, ketones, and olefins. Alcohols range from C4 to C10. Both alcohols and esters follow a Schulz-Flory chain growth model. We show the relationship between the catalyst properties (BET surface area, acid and base site count and Cu loading and synthesis method) and the performance in the reactions. We also show that physical mixtures of CuHT and HT can have similar product selectivity of low loading CuHT catalysts. The selectivity towards diesel fuel precursor compounds (hereafter ‘DFPC’) increased with conversion until reaching a plateau at high ethanol conversion (~70%). Alcohol selectivity follows a Schultz-Flory distribution at all studied conversions, and that adsorbed ethanol-derived species may undergo surface oligomerization into 1-butanol and higher alcohols before desorbing in a chain-growth mechanism. Zeolite catalysts convert the C4+ alcohols into C8+ ethers in both batch and continuous flow reactors. Selectivities of up to 80% to C8+ ethers at around 70% conversion using a single pass continuous flow system are achieved. The final product feedstocks obtained from etherification have been used in technoeconomic (TEA) and lifecycle analysis (LCA), which indicate a reduction in greenhouse gas (GHG) emissions of 50% relative to conventional diesel and diesel fuel prices that are lower than biodiesel. These final feedstocks are undergoing engine testing to elucidate the physiochemical properties and compare well to diesel #2 ASTM standards.

Huber, George↗
NASA NTRS1989

Projection methods for the numerical solution of Markov chain models

Projection methods for computing stationary probability distributions for Markov chain models are presented. A general projection method is a method which seeks an approximation from a subspace of small dimension to the original problem. Thus, the original matrix problem of size N is approximated by one of dimension m, typically much smaller than N. A particularly successful class of methods based on this principle is that of Krylov subspace methods which utilize subspaces of the form span(v,av,...,A(exp m-1)v). These methods are effective in solving linear systems and eigenvalue problems (Lanczos, Arnoldi,...) as well as nonlinear equations. They can be combined with more traditional iterative methods such as successive overrelaxation, symmetric successive overrelaxation, or with incomplete factorization methods to enhance convergence.

Saad, Youcef↗
DOE OSTI2024

Adaptive Variational Quantum Computing Approaches for Green’s Functions and Nonlinear Susceptibilities

Here, we present and benchmark quantum computing approaches for calculating real-time single-particle Green’s functions and nonlinear susceptibilities of Hamiltonian systems. The approaches leverage adaptive variational quantum algorithms for state preparation and propagation. Using automatically generated compact circuits, the dynamical evolution is performed over sufficiently long times to achieve adequate frequency resolution of the response functions. We showcase accurate Green’s function calculations using a statevector simulator on classical hardware for Fermi-Hubbard chains of 4 and 6 sites, with maximal ansatz circuit depths of 65 and 424 layers, respectively, and for the molecule LiH with a maximal ansatz circuit depth of 81 layers. Additionally, we consider an antiferromagnetic quantum spin-1 model that incorporates the Dzyaloshinskii-Moriya interaction to illustrate calculations of the third-order nonlinear susceptibilities, which can be measured in two-dimensional coherent spectroscopy experiments. These results demonstrate that real-time approaches using adaptive parametrized circuits to evaluate linear and nonlinear response functions can be feasible with near-term quantum processors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

BOTTLE 2 - Analysis

The use of analysis is foundational to BOTTLE. In particular, we conduct techno-economic analysis (TEA), life cycle assessment (LCA), and supply chain modeling for each new BOTTLE innovation, spanning new deconstruction and upcycling technologies to redesigned polymers. These critical analysis tools are used in parallel with laboratory research to ensure that BOTTLE technologies can ultimately meet our key metrics. The BOTTLE team used analysis early in the current 3-year project cycle to identify research priorities from a scale, energy, and economics perspective (Nicholson et al., Joule 2021), and recently we presented a comprehensive framework for comparing new circularity-focused approaches to linear, incumbent practices (Nicholson et al., Ann. Rev. Chem. Biomolec. Eng. 2022). This presentation will also review case studies in analysis for closed-loop PET recycling across a range of recycling methods, baseline studies that we have conducted for plastics pyrolysis and gasification as another comparator for BOTTLE technologies, and a comprehensive study of existing closed-loop recycling technologies from a full suite of environmental impacts and feedstock quality metrics. We have undertaken studies of circular polymers being developed in the Redesign task, and we will present an exemplary case for a bio-based acrylic polymer that can replace polymethyl methacrylate. Overall, analysis is critical to enable the BOTTLE Consortium to focus on impactful, realistic technologies.

BIOMASS FUELS↗
DOE OSTI2021

Effect of Microstructure on Chain Flexibility and Glass Transition Temperature of Polybenzofulvene

Polybenzofulvene (PBF) is a polydiene with a very high glass transition temperature (T g ), which makes it a potential candidate for use as the hard block for high-temperature thermoplastic elastomer applications. The T g s of polydienes are known to be related to chain flexibility. However, no studies have been reported that correlate the chain flexibility to the microstructure of PBF. Herein, we present a study of solution properties of linear PBFs with narrow molecular weight dispersity and having 1,2-addition ranging from 23% to 99%. The materials were prepared by living anionic polymerization under different conditions. Specifically, the chain flexibility as defined by the Flory's characteristic ratio, , and dependence of chain flexibility on microstructure by combined measurements of intrinsic viscosity and molecular weight using triple-detector size exclusion chromatography (SEC) is studied. The persistence lengths (l p ) and chain diameters (d B ) were also estimated using the touched-bead wormlike chain model. To the best of our knowledge, the characteristic ratio of PBF is found to be the highest of all polydienes that have been reported so far.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Entanglement entropy of (2 +1)-dimensional SU(2) lattice gauge theory on plaquette chains

We study the entanglement entropy of Hamiltonian SU(2) lattice gauge theory in 2 +1 dimensions on linear plaquette chains and show that the entanglement entropies of both ground and excited states follow Page curves. The transition of the subsystem size dependence of the entanglement entropy from the area law for the ground state to the volume law for highly excited states is found to be described by a universal crossover function. Quantum many-body scars in the middle of the spectrum, which are present in the electric flux truncated Hilbert space, where the gauge theory can be mapped onto an Ising model, disappear when higher electric field representations are included in the Hilbert space basis. This suggests the continuum (2+1)-dimensional SU(2) gauge theory does not have such scarred states.

Astronomy & Astrophysics↗
DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗
NASA NTRS2001

SRS Computer Animation and Drive Train System

The spinning rocket simulator (SRS) is an ongoing project at Oral Roberts University. The goal of the SRS is to gather crucial data concerning a spinning rocket under thrust for the purpose of analysis and correction of the coning motion experienced by this type of spacecraft maneuver. The computer animation simulates a virtual, scale model of the component of the SRS that represents the spacecraft itself. This component is known as the (VSM), or virtual spacecraft model. During actual physical simulation, this component of the SRS will experience a coning. The goal of the animation is to cone the VSM within that range to accurately represent the motion of the actual simulator. The drive system of the SRS is the apparatus that turns the actual simulator. It consists of a drive motor, motor mount and chain to power the simulator into motion. The motor mount is adjustable and rigid for high torque application. A digital stepper motor controller actuates the main drive motor for linear acceleration. The chain transfers power from the motor to the simulator via sprockets on both ends.

Arthun, Daniel↗
NASA NTRS2021

Fitting strategies of accretion column models and application to the broadband spectrum of Cen X-3

Due to the complexity of modeling the radiative transfer inside the accretion columns of neutron star binaries, their X-ray spectra are still commonly described with phenomenological models, e.g., a cut off power law. While the behavior of these models is well understood and they allow for a comparison of different sources and studying source behavior, the extent to which the underlying physics can be derived from the model parameters is very limited. During recent years, several physically motivated spectral models have been developed to overcome these limitations. Their application, however, is generally computationally much more expensive and they require a high number of parameters which are difficult to constrain. In particular, Becker & Wolff(2007) presented an analytical solution to the radiative transfer equation inside the accretion column assuming a velocity profile that is linear in the optical depth. An implementation of this model that is both fast and accurate enough to be fitted to observed spectra is available in XSPEC. The main difficulty of this implementation is that some solutions violate energy conservation and therefore have to be rejected by the user. We propose a novel fitting strategy that ensures energy conservation during theχ2-minimization which simplifies the application of the model considerably. We demonstrate this approach as well as a study of possible parameter degeneracies with a comprehensive Markov-Chain Monte Carlo analysis of the complete parameter space for a combined NuSTAR and Swift/XRT dataset of Cen X-3.The derived accretion-flow structure features a small column radius of∼63 m and a spectrum dominated by bulk-Comptonization of bremsstrahlung seed photons, in agreement with previous studies.

X-rays: binaries↗
NASA NTRS2021

Fitting Strategies of Accretion Column Models and Application to the Broadband Spectrum of Cen X-3

Due to the complexity of modeling the radiative transfer inside the accretion columns of neutron star binaries, their X-ray spectra are still commonly described with phenomenological models, e.g., a cut off power law. While the behavior of these models is well understood and they allow for a comparison of different sources and studying source behavior, the extent to which the underlying physics can be derived from the model parameters is very limited. During recent years, several physically motivated spectral models have been developed to overcome these limitations. Their application, however, is generally computationally much more expensive and they require a high number of parameters which are difficult to constrain. In particular, Becker & Wolff(2007) presented an analytical solution to the radiative transfer equation inside the accretion column assuming a velocity profile that is linear in the optical depth. An implementation of this model that is both fast and accurate enough to be fitted to observed spectra is available in XSPEC. The main difficulty of this implementation is that some solutions violate energy conservation and therefore have to be rejected by the user. We propose a novel fitting strategy that ensures energy conservation during theχ2-minimization which simplifies the application of the model considerably. We demonstrate this approach as well as a study of possible parameter degeneracies with a comprehensive Markov-Chain Monte Carlo analysis of the complete parameter space for a combined NuSTAR and Swift/XRT data-set of Cen X-3.The derived accretion-flow structure features a small column radius of∼63 m and a spectrum dominated by bulk-Comptonization of bremsstrahlung seed photons, in agreement with previous studies

Philipp Thalhammer↗
NASA NTRS1976

A model for the pyrolysis of unfilled and filled polymers and comparisons with NBS smoke-density chamber data

This paper considers a model for the pyrolysis of polymers for use in mass loss and smoke density predictions in a fire situation. It is based on the fundamental postulate that the overall rate-limiting reactions are in the relatively low temperature condensed phase; the rate limiting step is the polymer degradation to a vaporizable state. The state of the polymer (chain length) at the surface is specified by the vapor pressure equilibrium criterion. For the case of polymers with inert fillers, like alumina trihydrate, the further assumption is made that the linear regression rate of the material is identical to the unfilled material's at the same surface temperature. The fraction of polymer mass loss converted to smoke is inferred from the literature. The smoke density in the NBS-smoke density chamber is predicted for a polyester and the same polyester with two different loads of alumina trihydrate filler. Diffusional effects in the smoke spreading are considered in an elementary manner. The comparisons with experimental data are encouraging. The overall fire characteristics are predicted using only the fundamental physicochemical property values of ingredients.

Kumar, R. N.↗
DOE OSTI2020

Contribution of the Hydroxyl Group on Interfacial Heat Conduction of Monohydric Alcohols: A Molecular Dynamics Study

Monohydric alcohols have been used as promising phase change materials (PCMs) for low-temperature latent heat storage. However, the heat storage/retrieval rates are limited due to the low thermal conductivity of such alcohols. In this work, nonequilibrium molecular dynamics (NEMD) simulations were performed to study the microscopic heat conduction in example monohydric alcohols, i.e., 1-dodecanol (C12H26O), 1-tetradecanol (C14H30O), and 1-hexadecanol (C16H34O). A simplified ideal crystal model was proposed to exploit the potential for improving the thermal conductivity of monohydric alcohols. The effect of ideal crystalline structures, especially the contribution of the hydroxyl group, on the microscopic heat conduction process was analyzed. The thermal conductivity of the ideal crystals of the various monohydric alcohols was predicted to be more than twice as compared to that of their respective solids. The major thermal resistance in the ideal crystals was found around the molecular interfaces, as a result of the excellent heat conduction performance along the linear molecular chains. The calculated vibrational density of states (VDOS) and interfacial heat transfer were then investigated. When the interfaces are surrounded by hydroxyl groups as walls, strong hydrogen bond (HB) interactions were observed. The interfacial heat transfer coefficient of the ideal crystalline structures of 1-tetradecanol was found to reach up to ~735.6 MW/m2 W. It was elucidated that the high interfacial heat transfer rate is clearly related to the stronger intermolecular interactions.

Engineering↗
NASA NTRS1992

Dynamic modeling and control of multibody mechanical systems which are structurally flexible

This paper concerns the dynamic modeling and control of multibody, open-chained, structurally flexible, mechanical systems where the bodies are connected by revolute joints. The equations of motion are formulated based on a matrix form of Lagrange's equations for inertial quasi-coordinates. Each body is treated as a substructure of the system. For the purposes of simulation and control, the equations of motion are separated into two sets of equations using a perturbation approach: one to describe large rigid-body motions of the articulated system and the other to describe small linear motions of the bodies about the large motions. A biologically natural control strategy is used for vibration suppression and tracking the prescribed motion.

Quinn, R. D.↗
NASA NTRS1994

Operations for Learning with Graphical Models

This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian net- works, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. These operations adapt existing techniques from statistics and automatic differentiation to graphs. Two standard algorithm schemes for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Some algorithms are developed in this graphical framework including a generalized version of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing some popular algorithms that fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

Buntine, Wray L.↗
DOE OSTI2024

Heuristic solutions to the single depot electric vehicle scheduling problem with next day operability constraints

This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming, we generate bus blocks that accommodate electric vehicles (EVs), ensuring successful completion of each block while considering recharging requirements between blocks and during off-hours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP, and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints. Further, a case study conducted reveals that nearly 100% electrification for Chicago, IL and Austin, TX transit buses is viable yet requires 1.6 EVs at 150-mile range per diesel vehicle.

33 ADVANCED PROPULSION SYSTEMS↗