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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 OSTI2020

Allocation and Operation of A Hydropneumatic Energy Storage with Building Microgrid

Behind-meter application for a recently invented hydropneumatic Ground-Level Integrated Diverse Energy Storage (GLIDES) is focused in this research. Considering possible uncertainties from solar radiation and electricity load in a building microgrid, two-stage stochastic programming is adopted for 15-min operation and a mixed-integer nonlinear stochastic model is built with integrated energy systems of GLIDES, solar panel and power generating unit. In order to make the model computationally tractable, we use sample average approximate method to decrease associated complexity with sample size and improve solving efficiency. The experimental results in deterministic and stochastic operation indicate that best candidate solution from SAA are superior to the solution from expected value problem in terms of optimality gap and variance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Nonclassical Particle Transport in the 1-D Diffusive Limit

A nonclassical linear Boltzmann equation has been recently proposed to address nonexponential attenuation of the particle flux in certain inhomogeneous random media applications. In particular, this effect arises in Pebble Bed reactor cores, in which the locations of the pebbles are spatially correlated. In this paper, we investigate nonclassical particle transport taking place in a 1-D random periodic diffusive system. We provide computational results that validate the theoretical predictions, demonstrating for the first time that the solution of the nonclassical particle transport equation is well-approximated by the solution of the nonclassical diffusion equation. For simplicity, we consider the following assumptions: (i) transport occurs in rod geometry, in which particles can only move in the directions μ = ±1; (ii) transport is monoenergetic; and (iii) scattering is isotropic. This paper provides numerical results that demonstrate the validity of the nonclassical diffusion approximation to the nonclassical transport equation in certain 1-D diffusive systems. To our knowledge, this is the first time computational results that validate the predictions of the asymptotic theory for the nonclassical model are presented. This result provides a more solid foundation in which to improve this theory for relevant nuclear applications. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Conversion acoustic resonances in orthorhombic crystals

A classification of acoustic-beam reflection resonances in orthorhombic crystals under conditions where a proximity to conversion is implemented in the vicinity of total internal reflection is proposed. In this case, the energy from the incident pump beam falls almost entirely into a narrow intense reflected beam propagating at a small angle with respect to the surface. The crystal boundary is parallel to one of the elastic symmetry planes, and the excited beam propagates near one of axes 2 in this plane. Depending on the relations between the elastic moduli and the chosen propagation geometry, 18 types of resonances may occur, but no more than three in each crystal. The developed theory combines an approximate analytical description and accurate computer analysis. The relations between the elastic moduli providing minimum energy loss over the parasite reflected wave are determined. Some crystals with resonant excitation very close to conversion are revealed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Embedded Model Error Representation for Bayesian Model Calibration

Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, also known as structural error or model inadequacy,is often the largest contributor to the overall predictive uncertainty. This work builds on a recently developed framework of embedded, internal model correction, in order to represent and quantify structural errors, together with model parameters, within a Bayesian inference context. We focus specifically on a Polynomial Chaos representation with additive modification of existing model parameters, enabling a non-intrusive procedure for efficient approximate likelihood construction, model error estimation, and disambiguation of model and data errors' contributions to predictive uncertainty. As a result, the framework is demonstrated on several synthetic examples, as well as on a chemical ignition problem

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Band gap renormalization, carrier mobilities, and the electron-phonon self-energy in crystalline naphthalene

Organic molecular crystals are expected to feature appreciable electron-phonon interactions that influence their electronic properties at zero and finite temperature. Here, we report first-principles calculations and an analysis of the electron-phonon self-energy in naphthalene crystals. We compute the zero-point renormalization and temperature dependence of the fundamental band gap, and the resulting scattering lifetimes of electronic states near the valence- and conduction-band edges employing density functional theory. Further, our calculated phonon renormalization of the GW-corrected quasiparticle band structure predicts a fundamental band gap of 5 eV for naphthalene at room temperature, in good agreement with experiments. From our calculated phonon-induced electron lifetimes, we obtain the temperature-dependent mobilities of electrons and holes in good agreement with experimental measurements at room temperature. Finally, we show that an approximate energy self-consistent computational scheme for the electron-phonon self-energy leads to the prediction of strong satellite bands in the electronic band structure. We find that a single calculation of the self-energy can reproduce the self-consistent results of the band gap renormalization and electrical mobilities for naphthalene, provided that the on-the-mass-shell approximation is used, i.e., if the self-energy is evaluated at the bare eigenvalues.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

On the prediction of pressure effects for the combination kinetics of two alkyl radicals with the geometric mean rule

Here, alkyl combination reactions, along with their reverse alkane unimolecular decomposition reactions, are widely present in pyrolysis and combustion systems. These reactions occur mainly at temperatures at which their rate coefficients depend on pressure, and only a few studies have explored this dependence for large size alkyls. The accurate calculation of these kinetic data is limited by the size of the alkyl fragments involved, since the theoretical methods used are computationally expensive. Therefore, little reliable data is available for the modeling of real fuels alkane decomposition kinetics. In this paper, we propose an alternative approach that allows an accurate estimation of pressure-dependent kinetic rates for the combination of long-chain alkyl radicals, based on tabulated reaction models. The rates of combination of linear alkyls radicals forming ethane to pentane were calculated using the CASPT2-F12/cc-pVDZ-F12 level within VRC-TST theory combined with master-equation simulations. The computed high-pressure limit rate coefficients showed that n-propyl could be a minimum-size representative structure for larger n-alkyl chain, but this approach fails to capture the pressure dependence of the reaction that remains size-dependent for larger alkyls. The Geometric Mean Rule (GMR), a simple rule that links the self and cross-reactions of three alkyl radicals, is used to address this issue. Our theoretical calculations show for the first time that the GMR applies not only at the high-pressure limit, but also for every pressure explored. The GMR is shown to be accurate, with a maximum 22 % deviation in the falloff region for the estimation of rates for small alkyl combinations. Using this method, we propose a tabulated reaction rate rule for the combination of alkyl radicals up to C5+C5. The use of computed rate coefficients with the GMR approximation opens ways to develop pressure-dependent reaction rate rules for large n-alkyl combinations, based on accurate and computationally expensive theoretical calculations on smaller alkyl fragments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Highly frustrated spin-lattice models of magnetism and their quantum phase transitions: A microscopic treatment via the coupled cluster method

We outline how the coupled cluster method of microscopic quantum many-body theory can be utilized in practice to give highly accurate results for the ground-state properties of a wide variety of highly frustrated and strongly correlated spin-lattice models of interest in quantum magnetism, including their quantum phase transitions. The method itself is described, and it is shown how it may be implemented in practice to high orders in a systematically improvable hierarchy of (so-called LSUBm) approximations, by the use of computer-algebraic techniques. The method works from the outset in the thermodynamic limit of an infinite lattice at all levels of approximation, and it is shown both how the 'raw' LSUBm results are themselves generally excellent in the sense that they converge rapidly, and how they may accurately be extrapolated to the exact limit, m → ∞, of the truncation index m, which denotes the only approximation made. All of this is illustrated via a specific application to a two-dimensional, frustrated, spin-half J{sub 1}{sup XXZ}−J{sub 2}{sup XXZ} model on a honeycomb lattice with nearest-neighbor and next-nearest-neighbor interactions with exchange couplings J{sub 1} > 0 and J{sub 2} ≡ κJ{sub 1} > 0, respectively, where both interactions are of the same anisotropic XXZ type. We show how the method can be used to determine the entire zero-temperature ground-state phase diagram of the model in the range 0 ≤ κ ≤ 1 of the frustration parameter and 0 ≤ Δ ≤ 1 of the spin-space anisotropy parameter. In particular, we identify a candidate quantum spin-liquid region in the phase space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

SU-F-T-144: Analytical Closed Form Approximation for Carbon Ion Bragg Curves in Water

Purpose: Semi-empirical modeling is a powerful computational method in radiation dosimetry. A set of approximations exist for proton ion depth dose distribution (DDD) in water. However, the modeling is more complicated for carbon ions due to fragmentation. This study addresses this by providing and evaluating a new methodology for DDD modeling of carbon ions in water. Methods: The FLUKA, Monte Carlo (MC) general-purpose transport code was used for simulation of carbon DDDs for energies of 100–400 MeV in water as reference data model benchmarking. Based on Thomas Bortfeld’s closed form equation approximating proton Bragg Curves as a basis, we derived the critical constants for a beam of Carbon ions by applying models of radiation transport by Lee et. al. and Geiger to our simulated Carbon curves. We hypothesized that including a new exponential (κ) residual distance parameter to Bortfeld’s fluence reduction relation would improve DDD modeling for carbon ions. We are introducing an additional term to be added to Bortfeld’s equation to describe fragmentation tail. This term accounts for the pre-peak dose from nuclear fragments (NF). In the post peak region, the NF transport will be treated as new beams utilizing the Glauber model for interaction cross sections and the Abrasion- Ablation fragmentation model. Results: The carbon beam specific constants in the developed model were determined to be : p= 1.75, β=0.008 cm-1, γ=0.6, α=0.0007 cm MeV, σmono=0.08, and the new exponential parameter κ=0.55. This produced a close match for the plateau part of the curve (max deviation 6.37%). Conclusion: The derived semi-empirical model provides an accurate approximation of the MC simulated clinical carbon DDDs. This is the first direct semi-empirical simulation for the dosimetry of therapeutic carbon ions. The accurate modeling of the NF tail in the carbon DDD will provide key insight into distal edge dose deposition formation.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2013

First principles modeling of microscopic scintillation mechanisms using a Many-Body Green’s function approach (Final Report)

In 2010, we started the first lifecycle of a project on first principles modeling of microscopic unit processes that occur during scintillation in order to bring about a capability of formulating physics-based reaction-diffusion models that can predict scintillator light yield and explain their non-proportional response to the energy of incident ionizing radiation, with the hope that this will accelerate materials discovery that can mitigate this problem. During the ensuing years we focused on investigating from first principles the elementary quantum mechanical processes leading to polaron formation and exciton self-trapping in scintillators and identify the microscopic pathways leading to their transport. The effort to derive the master equation for carrier and exciton transport in realistic scintillators materials from first principles constitutes a challenge that requires an extensive background in various areas of computational physics. Previous theoretical investigations involved significant approximations and were limited in their scope. Most notably, the standard workhorse of the computational material scientist, density-functional theory, fails in describing the localized nature of self-trapped carriers as well as the electronic structure of rare-earth dopants. Advances in both algorithms and computational resources however, have rendered a comprehensive and accurate approach feasible. This has been amply demonstrated by the results produced by us during the first lifecycle of this project, which impressed upon the reviewers of the Independent Project Review: “The Review Team agreed, and commented that ’amazing and excellent progress had been made to date.’ The researchers tried and did improve the technology to advance the state of the art and show that it was adequate for the project.” In the following, we detail the progress achieved during each year of the project. We conclude by a final section where all our main achievements are summarized.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Ground-State and Thermodynamical Properties of Uranium Mononitride from Anharmonic First-Principles Theory

We report on an advanced density-functional theory (DFT) approach for investigating the ground-state and thermodynamical properties of uranium mononitride (UN). The electronic structure for UN at zero temperature is obtained from DFT that utilizes the generalized gradient approximation (GGA) for the electron exchange and correlation functional and includes spin-orbit interaction and an extension with orbital polarization. Thermodynamical properties are computed within the quasi-harmonic approximation in the Debye–Grüneisen model while anharmonicity is captured in the self-consistent ab initio lattice dynamics (SCAILD) scheme. Anharmonic phonons have heretofore never been modeled from first-principles for UN but they turn out to be important. The computed free energy compares well with that of a CALPHAD (CALculation of PHAse Diagrams) assessment of available experimental data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Effect of Packing Nonuniformity at the Fiber Bundle–Case Interface on Performance of Hollow Fiber Membrane Gas Separation Modules

High-fidelity simulations of momentum and mass transfer within a hollow fiber gas separation membrane module are here reported. The simulations capture the potential detrimental effects of poor fiber packing at the bundle–case interface on fluid distribution and performance. Results are presented for both circular and planar fiber bundles. The length over which bundle–case gaps affects flow is determined. The length increases dramatically with increasing fiber packing fraction. As the packing fraction approaches 0.6, the impact extends over the entire bundle diameter for small modules (<1000 fibers). The results clearly demonstrate the detrimental effect of poor packing along the case and can be used to develop module manufacturing guidelines. To reduce computational costs, an equivalent planar bundle module approximation is developed. The approximate simulations agree well with results from full 3-D simulations and can reduce computational costs without sacrificing fidelity.

42 ENGINEERING↗
DOE OSTI2024

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. Here, to address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Embedded Model Error Representation for Bayesian Model Calibration

Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, also known as structural error or model inadequacy, is often the largest contributor to the overall predictive uncertainty. This work builds on a recently developed framework of embedded, internal model correction, in order to represent and quantify structural errors, together with model parameters,within a Bayesian inference context. We focus specifically on a Polynomial Chaos representation with additive modification of existing model parameters, enabling a non-intrusive procedure for efficient approximate likelihood construction, model error estimation, and disambiguation of model and data errors’ contributions to predictive uncertainty. The framework is demonstrated on several synthetic examples, as well as on a chemical ignition problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

AN EFFICIENT APPROXIMATION TO THE LIKELIHOOD FOR GRAVITATIONAL WAVE STOCHASTIC BACKGROUND DETECTION USING PULSAR TIMING DATA

Direct detection of gravitational waves by pulsar timing arrays will become feasible over the next few years. In the low frequency regime (10{sup -7} Hz-10{sup -9} Hz), we expect that a superposition of gravitational waves from many sources will manifest itself as an isotropic stochastic gravitational wave background. Currently, a number of techniques exist to detect such a signal; however, many detection methods are computationally challenging. Here we introduce an approximation to the full likelihood function for a pulsar timing array that results in computational savings proportional to the square of the number of pulsars in the array. Through a series of simulations we show that the approximate likelihood function reproduces results obtained from the full likelihood function. We further show, both analytically and through simulations, that, on average, this approximate likelihood function gives unbiased parameter estimates for astrophysically realistic stochastic background amplitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Dimension Reduction and Redundancy Removal through Successive Schmidt Decompositions

Quantum computers are believed to have the ability to process huge data sizes, which can be seen in machine learning applications. In these applications, the data, in general, are classical. Therefore, to process them on a quantum computer, there is a need for efficient methods that can be used to map classical data on quantum states in a concise manner. On the other hand, to verify the results of quantum computers and study quantum algorithms, we need to be able to approximate quantum operations into forms that are easier to simulate on classical computers with some errors. Motivated by these needs, in this paper, we study the approximation of matrices and vectors by using their tensor products obtained through successive Schmidt decompositions. We show that data with distributions such as uniform, Poisson, exponential, or similar to these distributions can be approximated by using only a few terms, which can be easily mapped onto quantum circuits. The examples include random data with different distributions, the Gram matrices of iris flower, handwritten digits, 20newsgroup, and labeled faces in the wild. Similarly, some quantum operations, such as quantum Fourier transform and variational quantum circuits with a small depth, may also be approximated with a few terms that are easier to simulate on classical computers. Furthermore, we show how the method can be used to simplify quantum Hamiltonians: In particular, we show the application to randomly generated transverse field Ising model Hamiltonians. The reduced Hamiltonians can be mapped into quantum circuits easily and, therefore, can be simulated more efficiently.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A randomized sketching trust-region secant method for low-memory dynamic optimization

The numerical solution of dynamic optimization problems is often limited by the memory required to store the state trajectory, which is used to evaluate the objective function and its derivatives. Recently, [R. Muthukumar et al., SIAM Journal on Optimization 31(2), pp. 1242–1275 (2021)] introduced a trust-region method for dynamic optimization that employs randomized sketching to compress the state trajectory, resulting in inexact derivative computations. By adaptively learning the sketch rank, the trust-region algorithm achieves rigorous convergence guarantees. Here, we extend this approach to use secant Hessian approximations. Due to the randomness introduced by the sketch, the traditional secant update formulae can produce poor Hessian approximations. In particular, the difference of two gradients, computed from two different sketches, may be inconsistent. To overcome this, we employ a sketched approximation of the Hessian application, in lieu of computing the gradient difference. We numerically demonstrate the improved stability of this approach on an example from PDE-constrained optimization.

dynamic optimization↗
DOE OSTI2015

Structural and elastic anisotropy of crystals at high pressures and temperatures from quantum mechanical methods: The case of Mg{sub 2}SiO{sub 4} forsterite

We report accurate ab initio theoretical predictions of the elastic, seismic, and structural anisotropy of the orthorhombic Mg{sub 2}SiO{sub 4} forsterite crystal at high pressures (up to 20 GPa) and temperatures (up to its melting point, 2163 K), which constitute earth’s upper mantle conditions. Single-crystal elastic stiffness constants are evaluated up to 20 GPa and their first- and second-order pressure derivatives reported. Christoffel’s equation is solved at several pressures: directional seismic wave velocities and related properties (azimuthal and polarization seismic anisotropies) discussed. Thermal structural and average elastic properties, as computed within the quasi-harmonic approximation of the lattice potential, are predicted at high pressures and temperatures: directional thermal expansion coefficients, first- and second-order pressure derivatives of the isothermal bulk modulus, and P-V-T equation-of-state. The effect on computed properties of five different functionals, belonging to three different classes of approximations, of the density functional theory is explicitly investigated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling

Abstract Effective plasma transport modeling of magnetically confined fusion devices relies on having an accurate understanding of the ion composition and radiative power losses of the plasma. Generally, these quantities can be obtained from solutions of a collisional-radiative (CR) model at each time step within a plasma transport simulation. However, even compact, approximate CR models can be computationally onerous to evaluate, and in-situ evaluation of these models within a larger plasma transport code can lead to a rigid bottleneck. As a way to bypass this bottleneck, we propose deploying artificial neural network (ANN) surrogates to allow rapid evaluation of the necessary plasma quantities. However, one issue with training an accurate ANN surrogate is the reliance on a sufficiently large and representative training and validation data set, which can be time-consuming to generate. In this work we explore a data-driven active learning and training routine to allow autonomous adaptive sampling of the problem parameter space to ensure a sufficiently large and meaningful set of training data is assembled for the network training. As a result, we can demonstrate approximately order-of-magnitude savings in required training data samples to produce an accurate surrogate.

97 MATHEMATICS AND COMPUTING↗