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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Efficient calibration for imperfect computer models

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L 2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Here, numerical examples show that the proposed method outperforms the existing ones.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Stochastic Inversion of Gaussian Random Media Using Transverse Coherence Functions for Reflected Waves: Theory and Method

The transverse coherence functions (TCFs) of phase and amplitude fluctuations of a seismic wave are powerful to estimate the spatial distribution, length scales, and strength of random heterogeneities. However, TCFs have been formulated for transmitted waves only, not for reflected waves. In this paper, we derive reflection TCFs for Gaussian random media. Furthermore, we propose to invert for Gaussian random media using the reflection TCFs based on the grid search. We validate the new reflection TCF formulas using 2D finite-difference numerical experiments. The numerical example also illustrates the feasibility and efficiency of the inversion. The stochastic inversion using reflected waves can be used in both exploration and global seismology.

58 GEOSCIENCES↗
DOE OSTI2018

An Improved Discrete Least-Squares/Reduced-Basis Method for Parameterized Elliptic PDEs

It is shown that the computational efficiency of the discrete least-squares (DLS) approximation of solutions of stochastic elliptic PDEs is improved by incorporating a reduced-basis method into the DLS framework. In particular, we consider stochastic elliptic PDEs with an affine dependence on the random variable. The goal is to recover the entire solution map from the parameter space to the finite element space. To this end, first, a reduced-basis solution using a weak greedy algorithm is constructed, then a DLS approximation is determined by evaluating the reduced-basis approximation instead of the full finite element approximation. The main advantage of the new approach is that one only need apply the DLS operator to the coefficients of the reduced-basis expansion, resulting in huge savings in both the storage of the DLS coefficients and the online cost of evaluating the DLS approximation. In addition, the recently developed quasi-optimal polynomial space is also adopted in the new approach, resulting in superior convergence rates for a wider class of problems than previous analyzed. Numerical experiments are provided that illustrate the theoretical results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

AlphaBeta: computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants

Stochastic changes in DNA methylation (i.e., spontaneous epimutations) contribute to methylome diversity in plants. Here, we describe AlphaBeta, a computational method for estimating the precise rate of such stochastic events using pedigree-based DNA methylation data as input. We demonstrate how AlphaBeta can be employed to study transgenerationally heritable epimutations in clonal or sexually derived mutation accumulation lines, as well as somatic epimutations in long-lived perennials. Application of our method to published and new data reveals that spontaneous epimutations accumulate neutrally at the genome-wide scale, originate mainly during somatic development and that they can be used as a molecular clock for age-dating trees.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Parallel multiscale simulations of a brain aneurysm

Cardiovascular pathologies, such as a brain aneurysm, are affected by the global blood circulation as well as by the local microrheology. Hence, developing computational models for such cases requires the coupling of disparate spatial and temporal scales often governed by diverse mathematical descriptions, e.g., by partial differential equations (continuum) and ordinary differential equations for discrete particles (atomistic). However, interfacing atomistic-based with continuum-based domain discretizations is a challenging problem that requires both mathematical and computational advances. We present here a hybrid methodology that enabled us to perform the first multiscale simulations of platelet depositions on the wall of a brain aneurysm. The large scale flow features in the intracranial network are accurately resolved by using the high-order spectral element Navier–Stokes solver NεκTαr. The blood rheology inside the aneurysm is modeled using a coarse-grained stochastic molecular dynamics approach (the dissipative particle dynamics method) implemented in the parallel code LAMMPS. The continuum and atomistic domains overlap with interface conditions provided by effective forces computed adaptively to ensure continuity of states across the interface boundary. A two-way interaction is allowed with the time-evolving boundary of the (deposited) platelet clusters tracked by an immersed boundary method. The corresponding heterogeneous solvers (NεκTαr and LAMMPS) are linked together by a computational multilevel message passing interface that facilitates modularity and high parallel efficiency. Results of multiscale simulations of clot formation inside the aneurysm in a patient-specific arterial tree are presented. We also discuss the computational challenges involved and present scalability results of our coupled solver on up to 300 K computer processors. Validation of such coupled atomistic-continuum models is a main open issue that has to be addressed in future work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Two-Stage Decomposition Approach for AC Optimal Power Flow

The alternating current optimal power flow (AC-OPF) problem is critical to power system operations and planning, but it is generally hard to solve due to its nonconvex and large-scale nature. Furthermore, this paper proposes a scalable decomposition approach in which the power network is decomposed into a master network and a number of subnetworks, where each network has its own AC-OPF subproblem. This formulates a two-stage optimization problem and requires only a small amount of communication between the master and subnetworks. The key contribution is a smoothing technique that renders the response of a subnetwork differentiable with respect to the input from the master problem, utilizing properties of the barrier problem formulation that naturally arises when subproblems are solved by a primal-dual interior-point algorithm. Consequently, existing efficient nonlinear programming solvers can be used for both the master problem and the subproblems. The advantage of this framework is that speedup can be obtained by processing the subnetworks in parallel, and it has convergence guarantees under reasonable assumptions. The formulation is readily extended to instances with stochastic subnetwork loads. Numerical results show favorable performance and illustrate the scalability of the algorithm which is able to solve instances with more than 11 million buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Stochastic Approaches for Calculating and Aggregating Detection Probabilities for Nuclear Material Diversion

The goal of this project is to develop statistical methods and tools that can be used to rigorously calculate achieved DPs on individual material strata and then aggregate these to the facility and state levels. In particular, this project is evaluating the utility of stochastic ("Monte Carlo") methods as a potentially more powerful and flexible option to the analytical solutions traditionally employed by IAEA statisticians.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2015

Studying the effect of meteorological factors on the SO2 and PM10 pollution levels with refined versions of the SARIMA model

Numerous time series methods are used in environmental sciences allowing the detailed investigation of air pollution processes. The goal of this study is to present the empirical analysis of various aspects of stochastic modeling and in particular the ARIMA/SARIMA methods. The subject of investigation is air pollution in the town of Kardzhali, Bulgaria with 2 problematic pollutants – sulfur dioxide (SO2) and particulate matter (PM10). Various SARIMA Transfer Function models are built taking into account meteorological factors, data transformations and the use of different horizons selected to predict future levels of concentrations of the pollutants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a novel stability analysis method and provide sufficient conditions of DMM's stochastic stability. The proposed stability analysis is based on the contraction of probabilistic maps modeled by deep neural networks. We make connections between the spectral properties of neural network's weights and different types of used activation function on the stability and overall dynamic behavior of DMMs with Gaussian distributions. Based on the theory, we propose a few practical methods for designing constrained DMMs with guaranteed stability. We empirically substantiate our theoretical results via intuitive numerical experiments using the proposed stability constraints.

Drgona, Jan↗
DOE OSTI2021

Stochastic evaluation of fourth-order many-body perturbation energies

A scalable, stochastic algorithm evaluating the fourth-order many-body perturbation (MP4) correction to energy is proposed. Three hundred Goldstone diagrams representing the MP4 correction are computer generated and then converted into algebraic formulas expressed in terms of Green’s functions in real space and imaginary time. They are evaluated by the direct (i.e., non-Markov, non-Metropolis) Monte Carlo (MC) integration accelerated by the redundant-walker and control-variate algorithms. The resulting MC-MP4 method is efficiently parallelized and is shown to display O(n 5.3 ) size-dependence of cost, which is nearly two ranks lower than the O(n 7 ) dependence of the deterministic MP4 algorithm. Furthermore, it evaluates the MP4/aug-cc-pVDZ energy for benzene, naphthalene, phenanthrene, and corannulene with the statistical uncertainty of 10 mE h (1.1% of the total basis-set correlation energy), 38 mE h (2.6%), 110 mE h (5.5%), and 280 mE h (9.0%), respectively, after about 10 9 MC steps.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1990

Estimation of subpixel vegetation cover using red-infrared scattergrams

The bulk properties of discontinuous vegetation canopies are estimated at subpixel scales by applying the method of moments to a linear stochastic geometric model of canopy-soil reflectance and one set of multispectral observations without ground truth. The procedure involves the formulation of conditional moments for subsets of pixels that possess similar properties and can be identified through their common orientation in red-infrared scattergrams. The analysis is facilitated by assuming geometric similarity among the canopy elements and by formulating a sampling scale ratio in terms of the bulk geometric scales of the canopy and the pixel. Three versions of the method are demonstrated using two simulated scenes and an actual forested watershed for which aerial radiometric data and corresponding ground truth were obtained.

Jasinski, Michael F.↗
DOE OSTI2026

Stochastic equilibrium Raman spectroscopy (STERS)

In this manuscript, we propose a new method for cavity- and surface-enhanced Raman spectroscopy (SERS) with improved temporal resolution in the measurement of stochastic Raman spectral fluctuations. Our approach combines Fourier spectroscopy and photon correlation to decouple the integration time from the temporal resolution. Using statistical optics Monte Carlo simulations, we establish the relationship between time resolution and Raman signal strength, revealing that typical Raman spectral fluctuations, commensurate with molecular conformational dynamics, can theoretically be resolved on micro- to millisecond timescales. The method can further extract average single-molecule dynamics from small sub-ensembles, thereby potentially mitigating challenges in achieving strictly single-molecule isolation on SERS substrates.

Cobb-Bruno, Colburn [University of California, Ber↗
DOE OSTI2023

Insights into Methodologies and Stochastic Optimization of Thermal Energy Storage-Coupled Advanced Reactor Systems: A Comparison of Methods for Accessing Long-Term Sub-System Sizing Adequacy

This paper investigates the potential of coupling Thermal Energy Storage (TES) with Advanced Reactors (ARs) to address uncertainties posed by climate change in deep decarbonized power systems. The TES Use-case Team at Idaho National Laboratory (INL) has examined the potential of storing thermal energy from ARs during low demand periods and optimizing discharge during peak-priced hours, in both steady-state and transient conditions. Building on this groundwork, this study bridges the gaps in optimal sizing of the sub-system of TES-coupled AR systems using Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON), INL?s framework for grid optimization. By applying this framework, we present statistically-robust optimal charge, discharge including balance of plant (BOP), and storage sizing for the High-Temperature Gas-Cooled Reactor (HTGR) with 203 MWth output. To this end, we generated synthetic price samples for 30 years using 2018 ? 2021 real-time market data from ERCOT, PJM and MISO. Our results reveals that the TES-coupled HTGR system is highly effective in maximizing revenue from electricity sales. We observed a substantial increase of 40 % in ERCOT and a noteworthy 15 % increase in PJM and MISO when compared to the conventional BOP without TES. This improvement is achieved through regionally-tailored sub-system sizing, which ranges from 398 to 416 MWth for the discharge system and 610 to 1029 MWth for the TES. We find that the average electricity price directly impacts the overall economics, while price volatility influences storage size. Additional sensitivity analyses were performed to access the impact of key assumptions on system economics and sizing, differentiating the optimization window (i.e., 24 ? 219 hours of chronological observations) and by imposing storage continuity condition in tracking TES cycles. We observed that at the 120-hour of the optimization window, a reasonable balance between computation time and accuracy was achieved. Our analysis also highlights the significance of conducting multi-day cycle analysis (> 120-hour) for TES to capture interaction between electricity prices and storage dynamics, providing a comprehensive understanding of TES behavior that AR developers should integrate into their plant designs.

25 ENERGY STORAGE↗
DOE OSTI2014

Adaptive two-regime method: Application to front propagation

The Adaptive Two-Regime Method (ATRM) is developed for hybrid (multiscale) stochastic simulation of reaction-diffusion problems. It efficiently couples detailed Brownian dynamics simulations with coarser lattice-based models. The ATRM is a generalization of the previously developed Two-Regime Method [Flegg et al., J. R. Soc., Interface 9, 859 (2012)] to multiscale problems which require a dynamic selection of regions where detailed Brownian dynamics simulation is used. Typical applications include a front propagation or spatio-temporal oscillations. In this paper, the ATRM is used for an in-depth study of front propagation in a stochastic reaction-diffusion system which has its mean-field model given in terms of the Fisher equation [R. Fisher, Ann. Eugen. 7, 355 (1937)]. It exhibits a travelling reaction front which is sensitive to stochastic fluctuations at the leading edge of the wavefront. Previous studies into stochastic effects on the Fisher wave propagation speed have focused on lattice-based models, but there has been limited progress using off-lattice (Brownian dynamics) models, which suffer due to their high computational cost, particularly at the high molecular numbers that are necessary to approach the Fisher mean-field model. By modelling only the wavefront itself with the off-lattice model, it is shown that the ATRM leads to the same Fisher wave results as purely off-lattice models, but at a fraction of the computational cost. The error analysis of the ATRM is also presented for a morphogen gradient model.

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

Simple eigenvalue-self-consistent $\overline{Δ}GW_0$

We show that a rigid scissors-like GW self-consistency approach, labeled here $\overline{Δ}GW_0$, can be trivially implemented at zero additional cost for large scale one-shot $G_0W_0$ calculations. The method significantly improves one-shot $G_0W_0$ and for large systems is very accurate. $\overline{Δ}GW_0$ is similar in spirit to ev$GW_0$ where the self-consistency is only applied on the eigenvalues entering Green's function, while both W and the eigenvectors of Green's function are held fixed. $\overline{Δ}GW_0$ further assumes that the shift of the eigenvalues is rigid scissors-like so that all occupied states are shifted by the same amount and analogously for all the unoccupied states. We show that this results in a trivial modification of the time-dependent $G_0W_0$ self-energy, enabling an a posteriori self-consistency cycle. The method is applicable for our recent stochastic-$GW$ approach, thereby enabling self-consistent calculations for giant systems with thousands of electrons. The accuracy of $\overline{Δ}GW_0$ increases with the system size. For molecules, it is up to 0.4-0.5 eV away from coupled-cluster single double triple (CCSD(T)), but for tetracene and hexacene, it matches the ionization energies from both CCSD(T) and ev$GW_0$ to better than 0.05 eV. For solids, as exemplified here by periodic supercells of semiconductors and insulators with 6192 valence electrons, the method matches ev$GW_0$ quite well and both methods are in good agreement with the experiment.

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

CO 2 water-alternating-gas injection for enhanced oil recovery: Optimal well controls and half-cycle lengths

We report that CO 2 water-alternating-gas (WAG) injection is an enhanced oil recovery method designed to improve sweep efficiency during CO 2 injection with the injected water to control the mobility of CO 2 and to stabilize the gas front. Optimization of CO 2 -WAG injection is widely regarded as a viable technique for controlling the CO 2 and oil miscible process. Poor recovery from CO 2 -WAG injection can be caused by inappropriately designed WAG parameters. In previous study (Chen and Reynolds, 2016), we proposed an algorithm to optimize the well controls which maximize the life-cycle net-present-value (NPV). However, the effect of injection half-cycle lengths for each injector on oil recovery or NPV has not been well investigated. In this paper, an optimization framework based on augmented Lagrangian method and the newly developed stochastic-simplex-approximate-gradient (StoSAG) algorithm is proposed to explore the possibility of simultaneous optimization of the WAG half-cycle lengths together with the well controls. Finally, the proposed framework is demonstrated with three reservoir examples.

42 ENGINEERING↗
DOE OSTI2024

Numerical modeling of a proof-of-principle experiment on optical stochastic cooling at an electron storage ring

Cooling of beams circulating in storage rings is critical for many applications including particle colliders and synchrotron light sources. A method enabling unprecedented beam-cooling rates, optical stochastic cooling (OSC), was recently demonstrated in the Integrable Optics Test Accelerator (IOTA) electron storage ring at Fermilab [J. Jarvis , ]. This paper describes the numerical implementation of the OSC process in the particle-tracking program and discusses the validation of the developed model with available experimental data. The model is also employed to highlight some features associated with different modes of operation of OSC. The developed simulation tool should be valuable in guiding future configurations of optical stochastic cooling and, more broadly, modeling self-field-based beam manipulations. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence.

Efficiently performing predictive studies of irradiated particle-laden turbulent flows has the potential of providing significant contributions towards better understanding and optimizing, for example, concentrated solar power systems. As there are many uncertainties inherent in such flows, conducting uncertainty quantification analyses is fundamental to improve the predictive capabilities of the numerical simulations. For largescale, multi-physics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as many methods require a large number of high-fidelity, forward model solves. This requirement results in the need for a possibly infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method relies on exploiting the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. The application of this bi-fidelity approximation results in accurate estimates of the QoI statistics while requiring a small number of high-fidelity model evaluations. It also enables efficient computation of sensitivity analyses which highlight that epistemic uncertainty plays an important role in the solution of irradiated, particle-laden turbulent flow.

42 ENGINEERING↗