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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A computationally efficient ductile damage model accounting for nucleation and micro-inertia at high triaxialities

The computational formulation of a micro-mechanical material model for the dynamic failure of ductile metals is presented in this paper. The statistical nature of porosity initiation is accounted for by introducing an arbitrary probability density function which describes the pores nucleation pressures. Each micropore within the representative volume element is modeled as a thick spherical shell made of plastically incompressible material. The treatment of porosity by a distribution of thick-walled spheres also allows for the inclusion of micro-inertia effects under conditions of shock and dynamic loading. The second order ordinary differential equation governing the microscopic porosity evolution is solved with a robust implicit procedure. A new Chebyshev collocation method is employed to approximate the porosity distribution and remapping is used to optimize memory usage. The adaptive approximation of the porosity distribution leads to a reduction of computational time and memory usage of up to two orders of magnitude. Moreover, the proposed model affords consistent performance: changing the nucleation pressure probability density function and/or the applied strain rate does not reduce accuracy or computational efficiency of the material model. The numerical performance of the model and algorithms presented is tested against three problems for high density tantalum: single void, one-dimensional uniaxial strain, and two-dimensional plate impact. Here, the results using the integration and algorithmic advances suggest a significant improvement in computational efficiency and accuracy over previous treatments for dynamic loading conditions.

42 ENGINEERING↗
DOE OSTI2021

Resolving Anodic Current and Temperature Distributions in a Polymer Electrolyte Membrane Water Electrolysis Cell Using a Pseudo-Two-Phase Computational Fluid Dynamics Model

Expanding upon our prior experimental work, we constructed a three-dimensional model of a polymer electrolyte membrane water electrolyzer using computational fluid dynamics. We applied the assumption of pseudo-two-phase flow, the flow of two phases with equal velocity. Experimental data were used to obtain parameters and to determine the conditions under which this model was valid. Anodic distributions of current density, temperature, liquid saturation, and relative humidity were obtained at various flow rates. The overall current density and temperature difference from inlet to outlet at the anode agreed strongly with experimental measurements under most circumstances. This verification allowed us to further examine the apparent gas coverage calculated from experimental and model temperature data. Results suggested a low liquid saturation and low relative humidity at the anode due to the consumption of liquid water and water vapor. However, we questioned the accuracy of the pseudo-two-phase assumption at low water feed rates. We concluded that the model was applicable to systems with liquid water feed rates greater than 0.6 ml min -1 cm -2 . Therefore, it is a fair screening method that can advise which operating conditions lead to excessive temperatures or drying at the anode, thereby promoting the longevity of the membrane and catalyst.

25 ENERGY STORAGE↗
DOE OSTI2014

Likelihood-informed dimension reduction for nonlinear inverse problems

The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator. From a Bayesian perspective, changes from the prior to the posterior may, in many problems, be confined to a relatively low-dimensional subspace of the parameter space. We present a dimension reduction approach that defines and identifies such a subspace, called the 'likelihood-informed subspace' (LIS), by characterizing the relative influences of the prior and the likelihood over the support of the posterior distribution. This identification enables new and more efficient computational methods for Bayesian inference with nonlinear forward models and Gaussian priors. In particular, we approximate the posterior distribution as the product of a lower-dimensional posterior defined on the LIS and the prior distribution marginalized onto the complementary subspace. Markov chain Monte Carlo sampling can then proceed in lower dimensions, with significant gains in computational efficiency. We also introduce a Rao–Blackwellization strategy that de-randomizes Monte Carlo estimates of posterior expectations for additional variance reduction. We demonstrate the efficiency of our methods using two numerical examples: inference of permeability in a groundwater system governed by an elliptic PDE, and an atmospheric remote sensing problem based on Global Ozone Monitoring System (GOMOS) observations.

42 ENGINEERING↗
DOE OSTI2021

Closures and Simulation for Thermal Radiation Transport in Stochastic Media with Nonlinear Temperature Dependence

Because of the practical challenge of rendering very complex realistic spatial structures for numerical work, it is common practice to resort to characterizing such media as stochastic mixtures of materials, ideally parametrized with low order statistics such as the mean, variance, and correlation functions of the now random material properties. This enables realizations of the medium to be repeatedly generated and radiation transport computations to, in principle, be performed for a large ensemble of these realizations to obtain a statistically well-characterized radiation field. Statistical post-processing yields desired quantities such as conditional and unconditional mean radiation flux and probability distributions of transmitted radiation. However, such computations prove expensive for all but the simplest stochastic geometries and are most suited for benchmarking approximate models. The most common approximations lead to homogenized media so that transport computations are required only on a single medium realization but by construct provide only limited statistical information on the radiation field. Almost all approximate approaches to this problem attempt to develop equations for low order moments of the radiation intensity (mean, second moment, correlation function) but inevitably encounter a closure problem: the equation for any statistical moment will contain terms depending on unknown higher-order moments. Thus, the challenge shifts to one of developing closure relations that relate the unknown moments to the lower order moments. Under very special conditions, an exact closure can be derived but in general closures are heuristically stated constitutive relations. Also, closure approaches depend on whether the mixing statistics are spatially and/or temporally continuous as in fluctuating turbulent fields, or discontinuous as in randomly mixed solid chunks of material. Thus, unconditional averaging is generally applied in the former case but conditional averaging is more appropriate when the mixing is discontinuous. In this work, the emphasis is on binary statistical mixtures of immiscible fluids as as such the quantities of interest are averages (flux, temperature) conditioned on the material type.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Parallel peak pruning for scalable SMP contour tree computation

As data sets grow to exascale, automated data analysis and visualisation are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. Here in this paper, we report the first shared SMP algorithm for fully parallel contour tree computation, withfor-mal guarantees of O(lgnlgt) parallel steps and O(n lgn) work, and implementations with up to 10x parallel speed up in OpenMP and up to 50x speed up in NVIDIA Thrust.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Scalable Contour Tree Computation by Data Parallel Peak Pruning

As data sets grow to exascale, automated data analysis and visualization are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. Furthermore, we report the first shared SMP algorithm for fully parallel contour tree computation, with formal guarantees of O(lg V lg t) parallel steps and O(V lg V) work for data with V samples and t contour tree supernodes, and implementations with more than 30× parallel speed up on both CPU using TBB and GPU using Thrust and up 70× speed up compared to the serial sweep and merge algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Towards FAIR Workflows for Federated Experimental Sciences

A de-centralized, peer-to-peer AI metadata framework is demonstrated which can enable end-to-end metadata & lineage tracking for distributed Machine Learning pipelines spanning edge, High Performance Computing, and cloud environments. With a specific example of end-to-end microscopy algorithm and datasets, the proposed method shows how to enable reproducibility, audit trail, provenance of metadata artifacts. The emerging needs of automation in experimental sciences, ML-centric workflows, and FAIR metadata management across federated compute environments is addressed.

machine learning↗
DOE OSTI2018

The non-equilibrium attractor for kinetic theory in relaxation time approximation

I demonstrate that the concept of a non-equilibrium attractor can be extended beyond the lowest-order moments typically considered in hydrodynamic treatments. Using a previously obtained exact solution to the relaxation-time approximation Boltzmann equation for a transversally homogeneous and boost-invariant system subject to Bjorken flow, I derive an equation obeyed by all moments of the one-particle distribution function. Using numerical solutions, I show that, similar to the pressure anisotropy, all moments of the distribution function exhibit attractor-like behavior wherein all initial conditions converge to a universal solution after a short time with the exception of moments which are sensitive to modes with zero longitudinal momentum and high transverse momentum. In addition, I compute the exact solution for the distribution function itself on very fine lattices in momentum space and demonstrate that (a) an attractor for the full distribution function exists and (b) solutions with generic initial conditions relax to this solution, first at low momentum and later at high momentum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

A physics-based model for industrial steam-methane reformer optimization with non-uniform temperature field

In an industrial hydrogen production facility, steam-methane reforming reactions take place inside hundreds of catalyst-filled tubes placed in a large scale, high temperature furnace. Process efficiency depends strongly on the wall temperature distribution of the ensemble of reformer tubes; a narrower distribution has a process intensification effect, by providing similar processing experience to every feedstock molecule. Such process intensification efforts require a furnace model that can predict the temperature distribution as a function of operating conditions. Currently available furnace modeling solutions are either computationally intensive, making them unsuitable for (online) optimization calculations, or empirical, having limited accuracy when wide changes in operating conditions are required. Here in this work, a physics-based furnace model is presented that overcomes these limitations. Empirical perturbations in a Hottel zone radiation model are proposed to capture the spatially non-symmetrical temperature distribution. The low computational time makes the model suitable for operational intensification based on reduction of temperature distribution non-uniformity.

42 ENGINEERING↗
DOE OSTI2011

Probability density function transformation using seeded localized averaging

Seeded Localized Averaging (SLA) is a spectrum acquisition method that averages pulse-heights in dynamic windows. SLA sharpens peaks in the acquired spectra. This work investigates the transformation of the original probability density function (PDF) in the process of applying SLA procedure. We derive an analytical expression for the resulting probability density function after an application of SLA. In addition, we prove the following properties: 1) for symmetric distributions, SLA preserves both the mean and symmetry. 2) for uni-modal symmetric distributions, SLA reduces variance, sharpening the distributions peak. Our results are the first to prove these properties, reinforcing past experimental observations. Specifically, our results imply that in the typical case of a spectral peak with Gaussian PDF the full width at half maximum (FWHM) of the transformed peak becomes narrower even with averaging of only two pulse-heights. While the Gaussian shape is no longer preserved, our results include an analytical expression for the resulting distribution. Examples of the transformation of other PDFs are presented. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

SU-F-T-380: Comparing the Effect of Respiration On Dose Distribution Between Conventional Tangent Pair and IMRT Techniques for Adjuvant Radiotherapy in Early Stage Breast Cancer

Purpose: In this project, we compared the conventional tangent pair technique to IMRT technique by analyzing the dose distribution. We also investigated the effect of respiration on planning target volume (PTV) dose coverage in both techniques. Methods: In order to implement IMRT technique a template based planning protocol, dose constrains and treatment process was developed. Two open fields with optimized field weights were combined with two beamlet optimization fields in IMRT plans. We compared the dose distribution between standard tangential pair and IMRT. The improvement in dose distribution was measured by parameters such as conformity index, homogeneity index and coverage index. Another end point was the IMRT technique will reduce the planning time for staff. The effect of patient’s respiration on dose distribution was also estimated. The four dimensional computed tomography (4DCT) for different phase of breathing cycle was used to evaluate the effect of respiration on IMRT planned dose distribution. Results: We have accumulated 10 patients that acquired 4DCT and planned by both techniques. Based on the preliminary analysis, the dose distribution in IMRT technique was better than conventional tangent pair technique. Furthermore, the effect of respiration in IMRT plan was not significant as evident from the 95% isodose line coverage of PTV drawn on all phases of 4DCT. Conclusion: Based on the 4DCT images, the breathing effect on dose distribution was smaller than what we expected. We suspect that there are two reasons. First, the PTV movement due to respiration was not significant. It might be because we used a tilted breast board to setup patients. Second, the open fields with optimized field weights in IMRT technique might reduce the breathing effect on dose distribution. A further investigation is necessary.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Inverse patchy colloids with two and three patches. Analytical and numerical study

We propose an analytical solution of the multi-density Ornstein-Zernike equation supplemented by the associative Percus-Yevick closure relations specifically designed to describe the equilibrium properties of the novel class of patchy colloidal particles represented by the inverse patchy colloids with arbitrary number of patches. Using Baxter’s factorization method, we reduce solution of the problem to the solution of one nonlinear algebraic equation for the fraction of the particles with one non-bonded patch. We present closed-form expressions for the structure (structure factor) and thermodynamic (internal energy) properties of the system in terms of this fraction (and parameters of the model). We perform computer simulation studies and compare theoretical and computer simulation predictions for the pair distribution function, internal energy, and number of single and double bonds formed in the system, for two versions of the model, each with two and three patches. We consider the models with formation of the double bonds blocked by the patch-patch repulsion and the models without patch-patch repulsion. In general very good agreement between theoretical and computer simulation results is observed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Rapid QSTS Simulations for High-Resolution Comprehensive Assessment of Distributed PV

The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modeling and impact analysis. Unlike conventional scenario-based studies, quasi-static time-series (QSTS) simulations can realistically model time-dependent voltage controllers and the diversity of potential impacts that can occur at different times of year. However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1-second resolution is often required, which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled. This computational burden is a clear limitation to the adoption of QSTS simulations in interconnection studies and for determining optimal control solutions for utility operations. The solutions we developed include accurate and computationally efficient QSTS methods that could be implemented in existing open-source and commercial software used by utilities and the development of methods to create high-resolution proxy data sets. This project demonstrated multiple pathways for speeding up the QSTS computation using new and innovative methods for advanced time-series analysis, faster power flow solvers, parallel processing of power flow solutions and circuit reduction. The target performance level for this project was achieved with year-long high-resolution time series solutions run in less than 5 minutes within an acceptable error.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Reduction of Carbon Footprint and Energy Efficiency Improvement in Aluminum Production by Use of Novel Wireless Instrumentation Integrated with Mathematical Modeling

The work addressed the greenhouse gas emission and electrical energy consumption of the aluminum industry. The objective was to provide a means for reducing both through the application of wireless instrumentation, coupled to mathematical modeling. Worldwide the aluminum industry consumes more electrical energy than all activities in many major countries (e.g. the UK) and emits more greenhouse gasses (e.g. than France). Most of these excesses are in the 'primary production' of aluminum; that is the conversion of aluminum oxide to metal in large electrolytic cells operating at hundreds of thousands of amps. An industry-specific GHG emission has been the focus of the work. The electrolytic cells periodically, but at irregular intervals, experience an upset condition known as an 'anode effect'. During such anode effects the cells emit fluorinated hydrocarbons (PFCs, which have a high global warming potential) at a rate far greater than in normal operation. Therefore curbing anode effects will reduce GHG emissions. Prior work had indicated that the distribution of electrical current within the cell experiences significant shifts in the minutes before an anode effect. The thrust of the present work was to develop technology that could detect and report this early warning of an anode effect so that the control computer could minimize GHG emissions. A system was developed to achieve this goal and, in collaboration with Alcoa, was tested on two cells at an Alcoa plant in Malaga, Washington. The project has also pointed to the possibility of additional improvements that could result from the work. Notable among these is an improvement in efficiency that could result in an increase in cell output at little extra operating cost. Prospects for commercialization have emerged in the form of purchase orders for further installations. The work has demonstrated that a system for monitoring the current of individual anodes in an aluminum cell is practical. Furthermore the system has been installed twice on a smelter in the US without exposing workers to hazards usually associated with running signal wires in aluminum plants. The results display the early warning of an anode effect that potentially can be used to minimize such anode effects with their excessive GHG emissions. They also point to a possible, but substantial, economic benefit that could result in improved current efficiency by anode adjustment based on individual anode current measurements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Computational fluid dynamics study of full-scale aerobic bioreactors: Evaluation of gas-liquid mass transfer, oxygen uptake, and dynamic oxygen distribution

Hydrodynamics, oxygen transfer, and oxygen uptake in bubble-column and airlift bioreactors were studied using multiphase Euler-Euler computational fluid dynamics (CFD) simulations. Interphase mass transfer of oxygen was modeled with a liquid mass-transfer coefficient and sustained driving force provided by a phenomenological model for microbial oxygen uptake rate (OUR). Laboratory-scale reactor simulations of a bubble-column (0.15 m diameter and 0.75 m initial liquid height) showed reasonable agreement with gas holdup and mass transfer experiments reported in the literature. Commercial-scale bubble-column and airlift reactor simulations (5 m diameter and 25 m initial liquid height) were simulated using this validated model. Similar trends for the variation of overall gas holdup and oxygen concentrations with superficial velocity were observed for both of the commercial-scale reactors. However, the simulation results indicate differences in hydrodynamics, such as better recirculation in the airlift reactors that enables favorable distribution of oxygen in the reactor.

09 BIOMASS FUELS↗
DOE OSTI2016

TU-H-206-07: Assessment of Geometric Distortion in EPI with a SPAMM Tagged Acquisition

Purpose: Echo planar imaging (EPI) is known to exhibit gross geometric distortion caused by multiple factors, including B0 inhomgeneity and transient eddy currents. However, diffusion weighted (DW) EPI has become indispensable for diagnosis and therapy assessment. We propose a methodology for quantifying distortion in EPI sequences that does not require the use of dedicated spatial accuracy phantoms, enabling flexibility in phantom design for QA of distortion effects in EPI protocols. Methods: The proposed methodology utilizes a saturation technique known as Spatial Modulation of Magnetization (SPAMM) that tags the imaging subject with saturated grid lines. Originally intended for tracking cardiac motion, these grids are applied to assess differences between diffusion weighting directions and b-values, or against a more geometrically robust sequence such as fast spin echo (FSE). The saturation preparation sequence consists of binomially weighted (e.g. 1-3-3-1) pulses interleaved with gradient blips along the frequency encode direction, followed by the same sequence with gradient blips in the phase encode direction. Three phantoms were assessed with these sequences: a spherical head-sized phantom, a large shimming phantom, and a modified PET ACR phantom that included compartments of water, air, oil, and Teflon. Each phantom was acquired with three sequences using parameters from a clinically appropriate protocol (22 cm head or 46 cm abdomen): a conventional DW-EPI sequence (3 DW directions), and both the DW-EPI and FSE sequences with tagging. Differences in grid locations were visualized with minimum intensity projection between images, and measured using intersecting locations on the grids. Results: Grid lines were clearly visualized on tagged images and enabled quantification of distortions. Maximum eddy current induced errors of 10.8 to 14.8 mm were observed in areas away from isocenter with DW gradients applied in various directions. Conclusion: SPAMM tagging provides a promising mechanism for assessing spatial distribution of distortion in EPI sequences.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Numerical approach of the injection molding process of fiber-reinforced composite with considering fiber orientation

One of the most important challenges in the injection molding process of the short-glass fiber/thermoplastic composite parts is being able to predict the fiber orientation, since it controls the mechanical and the physical properties of the final parts. Folgar and Tucker included into the Jeffery equation a diffusive type of term, which introduces a phenomenological coefficient for modeling the randomizing effect of the mechanical interactions between the fibers, to predict the fiber orientation in concentrated suspensions. Their experiments indicated that this coefficient depends on the fiber volume fraction and aspect ratio. However, a definition of the fiber interaction coefficient, which is very necessary in the fiber orientation simulations, hasn't still been proven yet. Consequently, this study proposed a developed fiber interaction model that has been introduced a fiber dynamics simulation in order to obtain a global fiber interaction coefficient. This supposed that the coefficient is a sum function of the fiber concentration, aspect ratio, and angular velocity. The proposed model was incorporated into a computer aided engineering simulation package C-Mold. Short-glass fiber/polyamide-6 composites were produced in the injection molding with the fiber weight concentration of 30 wt.%, 50 wt.%, and 70 wt.%. The physical properties of these composites were examined, and their fiber orientation distributions were measured by micro-computed-tomography equipment μ-CT. The simulation results showed a good agreement with experiment results.

36 MATERIALS SCIENCE↗