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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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DOE OSTI2017

Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario

This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Galactic cosmic-ray modulation near the heliopause

We investigate the modulation of galactic cosmic rays in the inner and outer heliosheaths using three-dimensional numerical simulations. The model is based on the Parker transport equation integrated using a stochastic phase-space trajectory method. Integration is performed on a plasma background obtained from a global three-dimensional magnetohydrodynamic simulations. Our results predict a negligible amount of modulation in the outer heliosheath because of weak scattering of cosmic ray ions owing to very low levels of magnetic fluctuation power at wavenumbers relevant to the transport of cosmic rays with MeV to GeV energies. This means that the heliopause may be treated as a Dirichlet-type boundary for the purpose of energetic particle modeling. We present models with and without drift velocity to facilitate comparison with papers published earlier. We also attempt to reproduce the sudden step-like increases of cosmic-ray intensity observed by Voyager 1 before its encounter with the heliopause. Our results indicate that very slow cross-field diffusion in the outer heliosheath could produce a large gradient of cosmic rays inside the heliospheric boundary. The resulting large gradient in cosmic-ray intensity near the heliopause qualitatively agrees with recent Voyager 1 observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Time dependent solution for acceleration of tau-leaping

The tau-leaping method is often effective for speeding up discrete stochastic simulation of chemically reacting systems. However, when fast reactions are involved, the speed-up for this method can be quite limited. One way to address this is to apply a stochastic quasi-steady state assumption. However we must be careful when using this assumption. If the fast subsystem cannot reach a steady distribution fast enough, the quasi-steady-state assumption will propagate error into the simulation. To avoid these errors, we propose to use the time dependent solution rather than the quasi-steady-state. Generally speaking, the time dependent solution is not easy to derive for an arbitrary network. However, for some common motifs we do have time dependent solutions. We derive the time dependent solutions for these motifs, and then show how they can be used with tau-leaping to achieve substantial speed-ups, including for a realistic model of blood coagulation. Although the method is complicated, we have automated it.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Pseudoreversible Normalizing Flow for Stochastic Dynamical Systems with Various Initial Distributions

Here, we present a pseudoreversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with various initial distributions. The primary objective is to construct an accurate and efficient sampler that can be used as a surrogate model for computationally expensive numerical integration of SDEs, such as those employed in particle simulation. After training, the normalizing flow model can directly generate samples of the SDE’s final state without simulating trajectories. The existing normalizing flow model for SDEs depends on the initial distribution, meaning the model needs to be retrained when the initial distribution changes. The main novelty of our normalizing flow model is that it can learn the conditional distribution of the state, i.e., the distribution of the final state conditional on any initial state, such that the model only needs to be trained once and the trained model can be used to handle various initial distributions. This feature can provide a significant computational saving in studies of how the final state varies with the initial distribution. Additionally, we propose to use a pseudoreversible network architecture to define the normalizing flow model, which has sufficient expressive power and training efficiency for a variety of SDEs in science and engineering, e.g., in particle physics. We provide a rigorous convergence analysis of the pseudoreversible normalizing flow model to the target probability density function in the Kullback–Leibler divergence metric. Numerical experiments are provided to demonstrate the effectiveness of the proposed normalizing flow model.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

The dust in the hydrogen-poor ejecta of Abell 30

We present new optical and near-infrared images of the hydrogen-poor planetary nebula Abell 30 and produce detailed models that account for the major observed morphological and IR properties. By imaging the nebula in the K band, we confirm the presence of hot dust in an expanding equatorial ring of H-poor gas. No emission was detected from the H-poor polar knots, suggesting a dust deficiency htere relative to the equatorial ring. The near-IR emission is attributed to small carbonaceous dust grains which are stochastically heated by stellar ultraviolet photons. Using an adaptive version of a matrix method devised by Guhathakurta & Draine (1989) to model stochastic heating, we find that the near-IR spectrum observed by Dinerstein & Lester (1984) requires the presence of dust grains down to approximately 0.0007 microns in radius. This minimum grain radius is in excellent agreement with our calculations of the grain destruction by energetic stellar UV photons: we find that carbon clusters with less than approximately 140 atoms (0.0007 microns in radius) are destroyed by stellar UV photons in approximately 1000 yr, the kinematic age of H-poor ejecta. Modeling of the far-IR dust emission implies that the bulk of the dust mass in A30 must reside at distances several times greater than the distance of the equatorial ring from the central star. This spatial dust distribution is attributed to the interaction of the stellar wind with the inhomogeneous H-poor ejecta. Most of the H-poor gas and dust has been apparently carried outward by the stellar wind, leaving behing dense, H-poor knots with prominent wind-blown tails in the equatorial ring and in the polar knots. This picture is supported by the presence of a stellar wind-blown bubble within the H-rich envelope in our optical images.

Borkowski, Kazimierz J.↗
DOE OSTI2019

Approximate Bayesian Model Inversion for PDEs with Heterogeneous and State-Dependent Coefficients

We present two approximate Bayesian inference methods for parameter estimation in partial differential equation (PDE) models with space-dependent and state-dependent parameters. We demonstrate that these methods provide accurate and cost-effective alternatives to Markov Chain Monte Carlo simulation. We assume a parameterized Gaussian prior on the unknown functions, and approximate the posterior density by a parameterized multivariate Gaussian density. The parameters of the prior and posterior are estimated from sparse observations of the PDE model's states and the unknown functions themselves by maximizing the evidence lower bound (ELBO), a lower bound on the log marginal likelihood of the observations. The first method, Laplace-EM, employs the expectation maximization algorithm to maximize the ELBO, with a Laplace approximation of the posterior on the E-step, and minimization of a Kullback-Leibler divergence on the M-step. The second method, DSVI-EB, employs the doubly stochastic variational inference (DSVI) algorithm, in which the ELBO is maximized via gradient-based stochastic optimization, with nosiy gradients computed via simple Monte Carlo sampling and Gaussian backpropagation. We apply these methods to identifying diffusion coefficients in linear and nonlinear diffusion equations, and we find that both methods provide accurate estimates of posterior densities and the hyperparameters of Gaussian priors. While the Laplace-EM method is more accurate, it requires computing Hessians of the physics model. The DSVI-EB method is found to be less accurate but only requires gradients of the physics model.

approximate Bayesian inference, model inversion, v↗
DOE OSTI2015

Semi-stochastic full configuration interaction quantum Monte Carlo: Developments and application

We expand upon the recent semi-stochastic adaptation to full configuration interaction quantum Monte Carlo (FCIQMC). We present an alternate method for generating the deterministic space without a priori knowledge of the wave function and present stochastic efficiencies for a variety of both molecular and lattice systems. The algorithmic details of an efficient semi-stochastic implementation are presented, with particular consideration given to the effect that the adaptation has on parallel performance in FCIQMC. We further demonstrate the benefit for calculation of reduced density matrices in FCIQMC through replica sampling, where the semi-stochastic adaptation seems to have even larger efficiency gains. We then combine these ideas to produce explicitly correlated corrected FCIQMC energies for the beryllium dimer, for which stochastic errors on the order of wavenumber accuracy are achievable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Quantum optimization algorithms: Energetic implications

Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. Here in this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least 2x to 4x more energy efficient than their classical counterparts; why feedback-based quantum optimization is energy-inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of ≥2 0 x. Finally, we use the NchooseK high-level programming model to run optimization problems on both gate-based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate-based counterparts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Semiclassical Trajectory Studies of Reactive and Nonreactive Scattering of OH( 2 Σ + ) by H 2 Based on an Improved Full-Dimensional Ab Initio Diabatic Potential Energy Matrix

Here we present a new full-dimensional diabatic potential energy matrix (DPEM) for electronically nonadiabatic collisions of OH(A 2 Σ + ) with H 2 , and we calculate the probabilities of electronically adiabatic inelastic collisions, nonreactive quenching, and reactive quenching to form H 2 O+H. The DPEM was fitted using a many-body expansion with permutationally invariant polynomials in bond-order functions to represent the many-body part. The dynamics calculations were carried out with the fewest-switches with time uncertainty and stochastic decoherence (FSTU/SD) semiclassical trajectory method. We present results both for head-on collisions (impact parameter b equal to zero) and for a full range of impact parameters. The results are compared to experiment and to earlier FSTU/SD and quantum dynamics calculations with a previously published DPEM. The various theoretical results all agree that nonreactive quenching dominates reactive quenching, but there are quantitative differences between the two DPEMs and between the b=0 results and the all-b results, especially for the probability of reactive quenching.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Simulation of the Quality Criterion of a Mixture in a Drum-Belt Apparatus

A method is proposed for calculating the inhomogeneity coefficient in the workspace of a drum-belt mixer of bulk components with brush elements in a three-stage batch mixing method with non-uniform proportioning. The method is based on a stochastic model of the motion of rarefied particle flows following interaction with flexible elements fixed along a helical line on the surface of the mixer drums. The principal coefficients affecting the quality of a bulk mixture are analyzed in the form of combined structural and regime complexes of parameters of the mixer and the characteristics of the physical and mechanical state of the mixed media are analyzed. The results obtained can be used to develop an engineering technique for calculating the parameters of a new drum-belt mixer of friable components.

42 ENGINEERING↗
DOE OSTI2019

Impact of climate change on European winter and summer flood losses

Climate change is expected to alter European floods and associated economic losses in various ways. Here we investigate the impact of precipitation change on European average winter and summer financial losses due to flooding under a 1.5 °C warming scenario (reflecting a projected climate in the year 2115 according to RCP2.6)and for a counterfactual current-climate scenario where the climate has evolved without anthropogenic influence (reflecting a climate corresponding to pre-industrial conditions). Climate scenarios were generated with the Community Atmospheric Model (CAM)version 5. For each scenario, we derive a set of weights that when applied to the current climate's precipitation results in a climatology that approximates that of the scenario. We apply the weights to annual losses from a well-calibrated (to the current climate)flood loss model that spans 50,000 years and re-compute the average annual loss to assess the impact of precipitation changes induced by anthropogenic climate change. The method relies on a large stochastic set of physically based flood model simulations and allows quick assessment of potential loss changes due to change in precipitation based on two statistics, namely total precipitation, and total precipitation of very wet days (defined here as the total precipitation of days above the 95th percentile of daily precipitation). We compute the statistics with the raw CAM precipitation and bias-corrected precipitation. Our results show that for both raw and bias-corrected statistics i)average flood loss in Europe generally tend to increase in winter and decrease in summer for the future scenario, and consistent with that change we also show that ii)average flood losses have increased (decreased)for winter (summer)from pre-industrial conditions to the current day. Finally, the magnitude of the change varies among scenarios and statistics chosen.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Transient Multi-Level method for Monte Carlo reactor statics calculations

The Transient Multi-Level (TML) method is applied to a time-dependent Monte Carlo transport solver to offload some of the computational burden of the expensive Monte Carlo solve to lower-order Coarse Mesh Finite Difference (CMFD) and Exact Point Kinetics Equations (EPKE) solvers via factorization of the neutron flux at the transport and CMFD levels using the Predictor Corrector Quasi-Static Method (PCQM). The Monte Carlo transient is solved by a modified fission source iteration scheme that introduces a single transient source bank. The method is implemented in the production-level Monte Carlo code, Shift, and verified with prescribed reactivity ramps from the two-dimensional version of the C5G7-TD reactor benchmark. The results show that, as compared to other quasi-static methods, the TML reduces the stochastic noise inherent to the transient Monte Carlo solver by factors of ~2 to 6 for various norm comparisons of the reactor power amplitude. Finally, the TML additionally reduces the number of Monte Carlo evaluations needed to simulate the transient, leading to roughly an order of magnitude improvement in CPU time relative to the standard PCQM for the problems tested.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Nuclear data uncertainty propagation and modeling uncertainty impact evaluation in neutronics core simulation

Uncertainty analysis is a critical requirement in reactor simulation as it is used to quantify the reliability of best-estimate calculation. A comprehensive uncertainty analysis should characterize all sources of uncertainties in a computationally-feasible and scientifically-defendable manner. Here we employ a well-established reduced order modeling (ROM) based uncertainty quantification methodology to propagate uncertainties throughout neutronic calculations. ROM relies on recent advances in randomized data mining techniques applied to large data streams. In our proposed implementation, the nuclear data uncertainties are first propagated from multi-group level through lattice physics calculation to generate few-group parameter uncertainties, described using a vector of mean values and a covariance matrix. Employing an ROM-based compression of the covariance matrix, the few-group uncertainties are then propagated through downstream core simulation in a computationally efficient manner. This straightforward approach, albeit efficient as compared to brute force forward and/or adjoint-based methods, often employs a number of assumptions that have been unquestioned in the literature of neutronic uncertainty analysis. This manuscript argues that these assumptions could introduce another source of uncertainty referred to as modeling uncertainties, whose magnitude needs to be quantified in tandem with nuclear data uncertainties. Thus, our primary goal is to explore the interactions between these two uncertainty sources in order to assess whether modeling uncertainties have an impact on parameter uncertainties. To explore this endeavor, the impact of a number of modeling assumptions on core attributes uncertainties is quantified. The study employs a CANDU reactor model, with Serpent and NEWT as lattice physics solvers and NESTLE-C as core simulator. The modeling assumptions investigated include those related with the uncertainty propagation method employed, e.g., deterministic vs. stochastic, the few-group energy structure employed to represent the cross-sections, the resonance treatment in lattice physics calculation, the reference values for the cross-section, and the number of samples employed to render ROM compression. Results indicate that some of the modeling assumptions could have a non-negligible impact on the core responses propagated uncertainties, highlighting the need for a more comprehensive approach to combine parameter and modeling uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Brownian aggregation rate of colloid particles with several active sites

We theoretically analyze the aggregation kinetics of colloid particles with several active sites. Such particles (so-called “patchy particles”) are well known as chemically anisotropic reactants, but the corresponding rate constant of their aggregation has not yet been established in a convenient analytical form. Using kinematic approximation for the diffusion problem, we derived an analytical formula for the diffusion-controlled reaction rate constant between two colloid particles (or clusters) with several small active sites under the following assumptions: the relative translational motion is Brownian diffusion, and the isotropic stochastic reorientation of each particle is Markovian and arbitrarily correlated. This formula was shown to produce accurate results in comparison with more sophisticated approaches. Also, to account for the case of a low number of active sites per particle we used Monte Carlo stochastic algorithm based on Gillespie method. Simulations showed that such discrete model is required when this number is less than 10. Finally, we applied the developed approach to the simulation of immunoagglutination, assuming that the formed clusters have fractal structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Theoretical analysis of selectivity mechanisms in molecular transport through channels and nanopores

Selectivity is one of the most fundamental concepts in natural sciences, and it is also critically important in various technological, industrial, and medical applications. Although there are many experimental methods that allow to separate molecules, frequently they are expensive and not efficient. Recently, a new method of separation of chemical mixtures based on utilization of channels and nanopores has been proposed and successfully tested in several systems. However, mechanisms of selectivity in the molecular transport during the translocation are still not well understood. Here, we develop a simple theoretical approach to explain the origin of selectivity in molecular fluxes through channels. Our method utilizes discrete-state stochastic models that take into account all relevant chemical transitions and can be solved analytically. More specifically, we analyze channels with one and two binding sites employed for separating mixtures of two types of molecules. The effects of the symmetry and the strength of the molecular-pore interactions are examined. It is found that for one-site binding channels, the differences in the strength of interactions for two species drive the separation. At the same time, in more realistic two-site systems, the symmetry of interaction potential becomes also important. The most efficient separation is predicted when the specific binding site is located near the entrance to the nanopore. In addition, the selectivity is higher for large entrance rates into the channel. It is also found that the molecular transport is more selective for repulsive interactions than for attractive interactions. The physical-chemical origin of the observed phenomena is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Finite-volume pionless effective field theory for few-nucleon systems with differentiable programming

Finite-volume pionless effective field theory provides an efficient framework for the extrapolation of nuclear spectra and matrix elements calculated at finite volume in lattice QCD to infinite volume, and to nuclei with larger atomic number. In this work, it is demonstrated how this framework may be implemented via a set of correlated Gaussian wave functions optimized using differentiable programming and via solution of a generalized eigenvalue problem. This approach is shown to be significantly more efficient than a stochastic implementation of the variational method based on the same form of correlated Gaussian wave functions, yielding comparably accurate representations of the ground-state wave functions with an order of magnitude fewer terms. The efficiency of representation allows such calculations to be extended to larger systems than in previous work. Further, the method is demonstrated through calculations of the binding energies of nuclei with atomic number A ϵ {2,3,4} in finite volume, matched to lattice QCD calculations at quark masses corresponding to m π = 806 MeV, and infinite-volume effective field theory calculations of A ϵ {2,3,4,5,6} systems based on this matching.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

An automated approach to the alignment of compound refractive lenses

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are used to focus the beam or image the sample in many beamlines at X-ray facilities. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam, often requiring regular readjustments. Some groups circumvent the full realignment procedure by using engineering controls ( e.g. mounting optics) that sacrifice some of the beam's focusing precision, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. This approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder–Mead (SNM) simplex method. This method is outlined and its efficacy is demonstrated with numerical simulation that is tested in real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗