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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of the uncertainty quantification toolkit's python interface and surrogate construction tutorial

The uncertainty quantification toolkit (UQTk) is a collection of c++ libraries that assess the confidence of numerical models. Surrogate approximations, often polynomial chaos expansions (PCEs), lessen the computational cost of these assessments. I developed a Python interface in UQTk for regression and Bayesian compressive sensing to add to the existing Galerkin projection method. These methods receive an object containing the polynomial basis information and NumPy arrays of sample points, call c++ methods, and return the PCE coefficients in a NumPy array. To demonstrate these methods, I wrote a tutorial in which I use them to construct surrogates for Genz functions and calculate the resulting error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Near-infrared observations of sio maser-emitting asymptotic giant branch (AGB) stars

Near-infrared (NIR) monitoring observations of asymptotic giant branch stars exciting bright SiO masers have been made with the 1 m telescope of Kagoshima University. In order to investigate the properties of these stars and their envelopes, we combined our NIR photometric data with mid- and far-infrared flux data obtained by the IRAS satellite, SiO maser flux data provided by the Nobeyama Radio Observatory, visual magnitude data provided by the AAVSO, and the reported data on the expansion velocities of the circumstellar envelopes. The absolute magnitudes at the K-band and the distances are estimated using the period–luminosity relation of Mira variables determined by Feast et al. Then, mass-loss rates and isotropic luminosities of an SiO maser are estimated. The mass-loss rates range from approximately 10{sup −8} M{sub ⊙} yr{sup −1} to over 10{sup −5} M{sub ⊙} yr{sup −1}. We found that the NIR pulsation amplitudes are correlated with the pulsation periods and the observed wavelengths. We also found correlations of the isotropic luminosities of SiO masers with the mass-loss rates and absolute magnitudes at the K-band. These results will help us to understand the pumping mechanism of SiO masers. We measured, for the first time, the periods and/or NIR magnitudes of TX Cam, BW Cam, IRAS 06297+4045, IRAS 18387–0423, and RT Cep.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Quantifying How Density Gradients and Front Curvature Affect Carbon Detonation Strength during SNe Ia

Accurately reproducing the physics behind the detonations of SNe Ia and the resultant nucleosynthetic yields is important for interpreting observations of spectra and remnants. The scales of the processes involved span orders of magnitudes, making the problem computationally impossible to ever fully resolve in full-star simulations in the present and near future. In the lower density regions of the star, the curvature of the detonation front will slow the detonation, affecting the production of intermediate mass elements. We find that shock strengthening due to the density gradient present in the outer layers of the progenitor is essential for understanding the nucleosynthesis there, with burning extending well below the density at which a steady-state detonation is extinct. We show that a complete reaction network is not sufficient to obtain physical detonations at high densities and modest resolution due to numerical mixing at the unresolved reaction front. At low densities, below 6 × 10 5 g cm -3 , it is possible to achieve high enough resolution to separate the shock and the reaction region, and the abundance structure predicted by fully resolved quasi-steady-state calculations is obtained. For our best current benchmark yields, we utilize a method in which the unresolved portion of Lagrangian histories are reconstructed based on fully resolved quasi-steady-state detonation calculations. These computations demonstrate that under-resolved simulations agree approximately, ~10% in post-shock values of temperature, pressure, density, and abundances, with expected detonation structures sufficiently far from the under-resolved region, but that there is still room for some improvement in the treatment of subgrid reactions in the hydrodynamics to before better than 1% can be achieved at all densities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape

Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO 2 , CH 4 ) exchanges with the atmosphere range from the molecular scale (pore-scale O 2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" that reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 10 3 ) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.

Pau, G. S. H.↗
DOE OSTI2013

Metallic Fuel Casting Development and Parameter Optimization Simulations

One of the advantages of metallic fuel is the abilility to cast the fuel slugs to near net shape with little additional processing. However, the high aspect ratio of the fuel is not ideal for casting. EBR-II fuel was cast using counter gravity injection casting (CGIC) but, concerns have been raised concerning the feasibility of this process for americium bearing alloys. The Fuel Cycle Research and Development program has begun developing gravity casting techniques suitable for fuel production. Compared to CGIC gravity casting does not require a large heel that then is recycled, does not require application of a vacuum during melting, and is conducive to re-usable molds. Development has included fabrication of two separate benchscale, approximately 300 grams, systems. To shorten development time computer simulations have been used to ensure mold and crucible designs are feasible and to identify which fluid properties most affect casting behavior and therefore require more characterization.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Twist-3 spin observables for single-hadron production in DIS

Recently, three twist-3 spin asymmetries for single-inclusive hadron production in deep-inelastic lepton-nucleon scattering have been computed using collinear factorization and the leading order approximation. Here we summarize the main findings of these studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

DIATOM (Data Initialization and Modification) Library Version 7.0

DIATOM is a library that provides numerical simulation software with a computational geometry front end that can be used to build up complex problem geometries from collections of simpler shapes. The library provides a parser which allows for application-independent geometry descriptions to be embedded in simulation software input decks. Descriptions take the form of collections of primitive shapes and/or CAD input files and material properties that can be used to describe complex spatial and temporal distributions of numerical quantities (often called “database variables” or “fields”) to help define starting conditions for numerical simulations. The capability is designed to be general purpose, robust and computationally efficient. By using a combination of computational geometry and recursive divide-and-conquer approximation techniques, a wide range of primitive shapes are supported to arbitrary degrees of fidelity, controllable through user input and limited only by machine resources. Through the use of call-back functions, numerical simulation software can request the value of a field at any time or location in the problem domain. Typically, this is used only for defining initial conditions, but the capability is not limited to just that use. The most recent version of DIATOM provides the ability to import the solution field from one numerical solution as input for another.

Crawford, David A.↗
DOE OSTI2021

Coordinated Optimization and Control of Residential Solid State Power Substations in Electrical Distribution Network

This paper proposes a computationally efficient mixed integer linear programming (MILP) model for the coordinated optimization of solid-state power substations (SSPSs) in a feeder considering the full unbalanced three-phase structure of the distribution grid and its characteristics. The proposed model determines the optimal real and reactive power of each SSPS at the point of common coupling (PCC) that minimizes the operating cost and maximizes the system performance, e.g., voltage regulation and phase balancing. To improve the computational efficiency, an inscribed octagon is introduced to approximate the quadratic capacity constraints of components. Numerical simulation results show the effectiveness of the proposed model and significant improvements in voltage profiles and power imbalance between phases.

Liu, Guodong↗
DOE OSTI2021

Simulations of Quantum Circuits with Approximate Noise using qsim and Cirq

We introduce multinode quantum trajectory simulations with qsim, an open source high performance simulator of quantum circuits. qsim can be used as a backend of Cirq, a Python software library for writing quantum circuits. We present a novel delayed inner product algorithm for quantum trajectories which can result in an order of magnitude speedup for low noise simulation. We also provide tools to use this framework in Google Cloud Platform, with high performance virtual machines in a single mode or multinode setting. Multinode configurations are well suited to simulate noisy quantum circuits with quantum trajectories. Finally, we introduce an approximate noise model for Google's experimental quantum computing platform and compare the results of noisy simulations with experiments for several quantum algorithms on Google's Quantum Computing Service.

Isakov, Sergei V.↗
DOE OSTI2019

Particle Size Analysis and Distribution

Scanning Electron Microscope (SEM) is a instrument used for examining samples at magnifications from 32 X to 500,000 X (for our instrument). The SEM is attached with an electron gun and is directed onto the sample. The sample then produces an image where it is read on the monitor. The electron gun is positioned at the top because it needs to shoot at the sample that is being tested. The electrons are then shot in a downward motion traveling through several lenses, such as the condenser lens for a more precise reading. Once the samples have been read by the instrument, it will show a picture that has a certain number of particles. Particle size distribution have many uses that are essential to life habits (health) or even conventional use for example construction. Here are just a few examples of how particle size analysis and distribution are being used even today. This Standard Reference Material (SRM) is primarily intended for use in the calibration of equipment used to measure particle size distributions (PSD) in the 9 μm to 30 μm range. A unit of SRM 1984 consists of a single bottle containing approximately 14 g of tungsten carbide/cobalt powder. Certified values: The PSD values at five mass percentiles were measured by scanning electron microscopy (SEM) and laser light scattering (LLS). The NIST certified PSD values by SEM are listed. These certified values were determined by the measurement of over 10 000 individual particles from three bottles. Comparing the certified NIST SRM 1984 tungsten carbide/cobalt powder with our results are almost identical. This is great because more accurate work can be done and proves that our laboratory is able to reproduce NIST results. In conclusion, the scanning electron microscope is used to capture the sampled element and display it to be counted. Without the use of particle distribution, it would be a difficult time to get necessities such as medicine, asthma pumps, constructing materials, or as simple as ink. Overall, our study is an experimental comparison of the techniques we have in house, which are LLS and SEM (combined with the NIH ImageJ software) using the NIST SRM-1984 as a common, well characterized sample so that we have an idea of how the results from the techniques compare.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

Linear approximation commonly used in solving alternating-current optimal power flow (AC-OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violation. In this paper, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4,518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.

approximation algorithms↗
DOE OSTI2013

Verifying the error bound of numerical computation implemented in computer systems

A verification tool receives a finite precision definition for an approximation of an infinite precision numerical function implemented in a processor in the form of a polynomial of bounded functions. The verification tool receives a domain for verifying outputs of segments associated with the infinite precision numerical function. The verification tool splits the domain into at least two segments, wherein each segment is non-overlapping with any other segment and converts, for each segment, a polynomial of bounded functions for the segment to a simplified formula comprising a polynomial, an inequality, and a constant for a selected segment. The verification tool calculates upper bounds of the polynomial for the at least two segments, beginning with the selected segment and reports the segments that violate a bounding condition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Two-Yukawa fluid at a hard wall: Field theory treatment

We apply a field-theoretical approach to study the structure and thermodynamics of a two-Yukawa fluid confined by a hard wall. We derive mean field equations allowing for numerical evaluation of the density profile which is compared to analytical estimations. Beyond the mean field approximation, analytical expressions for the free energy, the pressure, and the correlation function are derived. Subsequently, contributions to the density profile and the adsorption coefficient due to Gaussian fluctuations are found. Both the mean field and the fluctuation terms of the density profile are shown to satisfy the contact theorem. We further use the contact theorem to improve the Gaussian approximation for the density profile based on a better approximation for the bulk pressure. The results obtained are compared to computer simulation data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Approximate Green’s Function Coupled Cluster A Method Employing Effective Dimension Reduction

The Green’s function coupled cluster (GFCC) method, originally proposed in the early 1990s, is a powerful many-body tool for computing and analyzing the electronic structures of molecular structures and periodic systems, especially when electrons are strongly correlated. However, in order for the GFCC method to become an approach that can be routinely used in the electronic structure calculations, more robust linear algebraic techniques and approximations need to be employed to reduce its extremely high computational overhead. In our recent studies, we showed that the GFCC equation can be numerically solved by seeking solutions of multiple linear systems in the frequency regime of interest, which can be easily distributed in a massively parallel environment, and broaden the avenue for more state-of-the-art numerical linear algebraic tools associated with multiple linear systems to be kicked in to this field. In the present pursuit of this practice, we show a successful application of model order reduction (MOR) techniques in the GFCC framework. Briefly speaking, for a frequency regime of interest that requires a high resolution of spectral function, instead of solving GFCC linear equation in the original dimension for every single frequency point, an easily-solvable linear system model with reduced dimension can be built upon projecting the original GFCC linear system into a subspace, from which the interpolation and extrapolation can give a reasonable approximation to the results of the original GFCC linear equations. Here, we show that the subspace can be properly constructed in an iterative manner from the auxiliary vectors of the GFCC linear equations at some select frequencies inside the frequency regime. During the iterations, the quality of the subspace, as well as the linear system model, can be systematically improved. The approach is tested in this work in terms of the efficiency and accuracy of computing spectral functions for some typical molecular systems including carbon monoxide, 1,3-butadiene, benzene, and adenine molecules. As a byproduct, the high quality linear system model is also found to be able to provide high quality initial guess for the existing linear solver to improve the convergence performance.

Peng, Bo↗
DOE OSTI2024

Surrogate Model Guided Optimization of Expensive Black-Box Multi-Objective Problems: A Posteriori Methods

Many engineering applications require the simultaneous optimization of multiple conflicting objective functions. Often, these objective functions are evaluated using highly accurate computer simulations that are computationally too expensive to be evaluated hundreds or thousands of times during optimization. Thus, the goal is to find good approximations of the Pareto front using as few of these expensive simulations as possible. Here, we describe an optimization approach based on surrogate models and diverse sampling strategies to accelerate the search for the Pareto solutions. We use a separate surrogate model for approximating each objective function and then we use the surrogate models to inform where additional expensive simulations should be run. The surrogate models are updated in an active learning framework whenever new information from the expensive simulations becomes available. The sampling strategies aim at balancing local improvements of the approximate Pareto front and global exploration to identify the extrema and fill in large gaps of the approximate Pareto front. We demonstrate on a large set of benchmark problems the effectiveness of the method for finding good approximations of the Pareto front.

MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Joint Chance Constraints Reduction Through Learning in Active Distribution Networks

Due to an increase in distributed generation and controllable loads, distribution networks are frequently operating under high levels of uncertainty. Joint chance constraints, which seek to satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints for optimization and control of these networks. Due to the complexity of evaluating these constraints directly, sampling approaches or approximations can be used to transform the joint chance constraint into deterministic constraints. However, sampling techniques may be extremely computationally expensive and not suitable for physical networks operating on fast timescales, and conservative approximations may needlessly result in a much higher cost of system operation. The proposed framework aims to provide a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and uses this additional information to accurately and efficiently approximate the joint chance constraint directly.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2023

pnnl/projection_ntk

This is a repository for work conducted in our research paper we would like to make open source. The main features are that we forked another open source library and added functionality that enabled us to compute neural-kernel objects using random vector projection software for both computer vision and large language text models. The new features are specifically code that enables the calculation of objects we define and refer to as the projection pseudo neural tangent kernel and the projection trace neural tangent kernel; both of which are approximations of the empirical neural tangent kernel. The software that computes the projections themselves are not part of the invention, but are necessarily tied to our software and licensed via MIT license, and BSD 2 license.

Georgiev, Kristian↗