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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Communication: Modeling of concentration dependent water diffusivity in ionic solutions: Role of intermolecular charge transfer

The translational diffusivity of water in solutions of alkali halide salts depends on the identity of ions, exhibiting dramatically different behavior even in solutions of similar salts of NaCl and KCl. The water diffusion coefficient decreases as the salt concentration increases in NaCl. Yet, in KCl solution, it slightly increases and remains above bulk value as salt concentration increases. Previous classical molecular dynamics simulations have failed to describe this important behavior even when polarizable models were used. Here, we show that inclusion of dynamical charge transfer among water molecules produces results in a quantitative agreement with experiments. Our results indicate that the concentration-dependent diffusivity reflects the importance of many-body effects among the water molecules in aqueous ionic solutions. Comparison with quantum mechanical calculations shows that a heterogeneous and extended distribution of charges on water molecules around the ions due to ion-water and also water-water charge transfer plays a very important role in controlling water diffusivity. Explicit inclusion of the charge transfer allows us to model accurately the difference in the concentration-dependent water diffusivity between Na{sup +} and K{sup +} ions in simulations, and it is likely to impact modeling of a wide range of systems for medical and technological applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Influence of skin surface roughness degree on energy characteristics of light scattered by a biological tissue

We present the results of modelling of photometric characteristics of light in soft tissues illuminated by a parallel beam along the normal to the surface, obtained with allowance for the skin roughness parameters and the angular structure of radiation approaching the surface from within the tissue. The depth structure of the fluence rate and the spectra of the diffuse reflection of light by the tissue in the interval of wavelengths 300 – 1000 nm are considered. We discuss the influence of the tilt angle variance of rough surface microelements and light refraction on the studied characteristics. It is shown that these factors lead to the reduction of the radiation flux only in the near-surface tissue layer and practically do not affect the depth of light penetration into the tissue. On the other hand, the degree of the surface roughness and the conditions of its illumination from within the tissue essentially affect the coefficient of diffuse reflection of light and lead to its considerable growth compared to the cases of a smooth interface and completely diffuse illumination, often considered to simplify the theoretical problem solution. The role of the roughness of skin surface is assessed in application to the solution of different direct and inverse problems of biomedical optics. (paper)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A SELF-CONSISTENT EXPLANATION OF TeV EMISSIONS FROM HESS J1640-465 AND HESS J1641-463

The bright TeV source HESS J1640-465 is positionally coincident with the young supernova remnant (SNR) G338.3-0.0, and the nearby HESS J1641-463 with TeV gamma-ray emission seems to be closely associated with it. Based on the nonlinear diffusion shock acceleration model, we explore the emission from these two TeV sources, the particle diffusion is assumed to be different inside and outside the absorbing boundary of the particles accelerated in the SNR shock. The results indicate that (1) the GeV–TeV emission from the region of the HESS J1640-465 is produced as a result of the particle acceleration inside the SNR G338.3-0.0 and (2) the runaway cosmic-ray particles outside the SNR are interacting with the nearby dense molecular cloud (MC) at the region of the HESS J1641-463, corresponding π{sup 0} decay gamma-ray in proton–proton collision contribute to the TeV emission from the HESS J1641-463. Also, we investigate the possible X-ray emission in MC from the synchrotron procedure by secondary e{sup ±} produced through escaped protons interaction with the MC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages ( Dozor and DIALS ), demonstrating that this is an appropriate framework to support future extensions and improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

graph algorithms, high performance comptuing↗
DOE OSTI2020

Multilevel Techniques for Compression and Reduction of Scientific Data---The Unstructured Case

Previous work on multilevel techniques for compression and reduction of scientific data is extended to the case of data given on unstructured meshes in two and three dimensions. The centerpiece of the work is a decomposition algorithm which is shown to be optimal, in terms of both storage and operational complexity, applicable to unstructured grids in both two and three dimensions, and which implicitly gives a Riesz basis that can be exploited to reduce the data while maintaining rigorous bounds on the loss incurred. The flexibility of the approach is illustrated by applications to potential flow around an airfoil and the effect of compression on quantities of interest relevant to airfoil design; compression of computational simulation of a nonlinear reaction-diffusion system with special attention given to the problem of time series reduction; and, data from a simulation of magnetically confined plasma in a fusion reactor reduced so as to preserve the electric field computed from the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for (1) learning predictive models from data and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addressed the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models---the so-called outer loop. This report summarizes the work done under DE-SC0021077 on (1) nonlocal models for solidification problems, (2) a multifidelity method for a nonlocal diffusion model, and (3) multifidelity Monte Carlo methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Neural Network Enhanced RKPM for Electrochemical-Mechanical Coupled Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant charge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. For Li-ion batteries, for example, lithium moving between electrodes during charging and discharging process causes expansion and contraction of grains, and the strongly anisotropic and nonlinearly [Li]-dependent grain material properties can cause grains to expand into and contract away from each other, leading to chemo-mechanical cracking. In the first part of this work, a RKPM based computational framework for solving the coupled solid-phase lithium conservation with Fickian diffusion and the lithium concentration dependent anisotropic mechanical problem subjected to a highly nonlinear Butler-Volmer boundary condition is introduced. The choice of RKPM completeness conditions for lithium concentration and mechanical deformation fields, and the variational consistency condition for the domain integration of the coupled problem is first determined. In the second part of this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material, by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that is necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure.

damage modeling↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (Distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

Sattar, Naw Safrin↗
DOE OSTI2024

Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution

A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution. (Model and code can be found at imageomics.github.io/phylo-diffusion)

Khurana, Mridul↗
DOE OSTI2026

Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography

Diffusion models have emerged as powerful priors for solving inverse problems in computed tomography (CT). In certain applications, such as neutron CT, it can be expensive to collect large amounts of measurements even for a single scan leading to sparse data sets from which it is challenging to obtain high quality reconstructions even with diffusion models. One strategy to mitigate this challenge is to leverage a complementary, easily available imaging modality; however, such approaches typically require retraining the diffusion model with large datasets. In this work, we propose incorporating an additional modality without retraining the diffusion prior, enabling accelerated imaging of costly modalities. We further examine the impact of imperfect side modalities on cross-modal guidance. Our method is evaluated on sparse-view neutron computed tomography, where reconstruction quality is substantially improved by incorporating X-ray computed tomography of the same samples.

Efimov, Timofey [ORNL] (ORCID:000900090098471X)↗
DOE OSTI2023

Simulation study of the effects of polymer network dynamics and mesh confinement on the diffusion and structural relaxation of penetrants

The diffusion of small molecular penetrants through polymeric materials represents an important fundamental problem, relevant to the design of materials for applications such as coatings and membranes. Polymer networks hold promise in these applications because dramatic differences in molecular diffusion can result from subtle changes in the network structure. In this paper, we use molecular simulation to understand the role that cross-linked network polymers have in governing the molecular motion of penetrants. By considering the local, activated alpha relaxation time of the penetrant and its long-time diffusive dynamics, we can determine the relative importance of activated glassy dynamics on penetrants at the segmental scale vs entropic mesh confinement on penetrant diffusion. We vary several parameters, such as the cross-linking density, temperature, and penetrant size, to show that cross-links primarily affect molecular diffusion through the modification of the matrix glass transition, with local penetrant hopping at least partially coupled to the segmental relaxation of the polymer network. This coupling is very sensitive to the local activated segmental dynamics of the surrounding matrix, and we also show that penetrant transport is affected by dynamic heterogeneity at low temperatures. To contrast, only at high temperatures and for large penetrants or when the dynamic heterogeneity effect is weak, does the effect of mesh confinement become significant, even though penetrant diffusion more broadly empirically follows similar trends as established models of mesh confinement-based transport.

Chemistry↗
DOE OSTI2014

The iterative thermal emission method: A more implicit modification of IMC

For over 40 years, the Implicit Monte Carlo (IMC) method has been used to solve challenging problems in thermal radiative transfer. These problems typically contain regions that are optically thick and diffusive, as a consequence of the high degree of “pseudo-scattering” introduced to model the absorption and reemission of photons from a tightly-coupled, radiating material. IMC has several well-known features that could be improved: a) it can be prohibitively computationally expensive, b) it introduces statistical noise into the material and radiation temperatures, which may be problematic in multiphysics simulations, and c) under certain conditions, solutions can be nonphysical, in that they violate a maximum principle, where IMC-calculated temperatures can be greater than the maximum temperature used to drive the problem. We have developed a variant of IMC called iterative thermal emission IMC, which is designed to have a reduced parameter space in which the maximum principle is violated. ITE IMC is a more implicit version of IMC in that it uses the information obtained from a series of IMC photon histories to improve the estimate for the end of time step material temperature during a time step. A better estimate of the end of time step material temperature allows for a more implicit estimate of other temperature-dependent quantities: opacity, heat capacity, Fleck factor (probability that a photon absorbed during a time step is not reemitted) and the Planckian emission source. We have verified the ITE IMC method against 0-D and 1-D analytic solutions and problems from the literature. These results are compared with traditional IMC. We perform an infinite medium stability analysis of ITE IMC and show that it is slightly more numerically stable than traditional IMC. We find that significantly larger time steps can be used with ITE IMC without violating the maximum principle, especially in problems with non-linear material properties. The ITE IMC method does however yield solutions with larger variance because each sub-step uses a different Fleck factor (even at equilibrium)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Monte Carlo N-Particle Tracking of Ultrafine Particle Flow in Bent Micro-Tubes

The problem of large pressure-differential driven laminar convective-diffusive ultrafine aerosol flow through bent micro-tubes is of interest in several contemporary research areas including; release of contents from pressurized containment vessels, aerosol sampling equipment, advanced scientific instruments, gas-phase micro-heat exchangers, and microfluidic devices. In each of these areas, the predominant problem is the determination of the fraction of particles entering the micro-tube that is deposited within the tube and the fraction that is transmitted through. Due to the extensive parameter restrictions of this class of problems, a Lagrangian particle tracking method making use of the coupling of the analytical stream line solutions of Dean and the simplified Langevin equation is quite a useful tool in problem characterization. This method is a direct analog to the Monte Carlo N-Particle method of particle transport extensively used in nuclear physics and engineering. In this work, 10 nm diameter particles with a density of 1 g/cm3 are tracked within micro-tubes with toroidal bends with pressure differentials ranging between 0.2175 and 0.87 atmospheres. The tubes have radii of 25 microns and 50 microns and the radius of curvature is between 1 m and 0.3183 cm. The carrier gas is helium, and temperatures of 298 K and 558 K are considered. Numerical convergence is considered as a function of time step size and of the number of particles per simulation. Particle transmission rates and deposition patterns within the bent micro-tubes are calculated.

Casella, Andrew M.↗
DOE OSTI2016

A FLEXIBLE NONLINEAR DIFFUSION ACCELERATION METHOD FOR THE FIRST ORDER EIGENVALUE SN EQUATIONS DISCRETIZED WITH DISCONTINUOUS FEM

This work describes a Nonlinear Diffusion Acceleration (NDA) method for the first order SN equations that discretizes both the transport equation and the associated diffusion system using discontinuous finite elements; the diffusion system is solved on a potentially coarser mesh and with a coarser energy group structure than the SN equations. The developed method differs from current methods by allowing the SN and diffusion system to be discretized with arbitrary FEM bases and by using an additive update of the SN system. The benefit of this capability is extension of the stability region to optically thicker mesh cells and stability without the necessity to underrelax the iterative algorithm. The presented method introduces the notion of consistency of the SN and diffusion solutions and derives suitable definitions of the cross sections, drift vectors, and element face closures. The NDA equations are solved using a Picard iteration scheme. Numerical results are presented for the C5G7 benchmark problem highlighting the effectiveness of the methods when deployed with the diffusion system discretized on the same mesh as the SN system. Additional results are presented for cases where the diffusion system features a coarser mesh, coarser energy group structure, or both, demonstrating its stability and effectiveness in these cases. It is observed that the effectiveness of the NDA method reduces when reducing the resolution of the associated diffusion system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Applying Legendre wavelet method with Tikhonov regularization for one-dimensional time-fractional diffusion equations

In this paper, a new method based on two-dimensional Legendre wavelet basis is proposed to solve time-fractional diffusion equations. This technique is used to convert the problem into a system of linear algebraic equations via expanding the required approximation based on the Legendre wavelet basis. The effectiveness of the proposed method is examined by comparing the numerical results with other methods. When the desired system is large, it is ill-conditioned; in this case, Tikhonov regularization method, with discrepancy principle for finding the regularization parameter, is applied to stabilize the solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Finite Volume Method Based Neutronic Solvers for Steady and Transient Analysis of Molten Salt Reactors

In recent years, the coupled neutronics and thermal hydraulics analysis becomes more and more important, because it helps to identify the most relevant safety issues without conservative assumptions. In addition, such coupled simulations are essential for the generation IV advanced nuclear power systems such as molten salt reactor (MSR), because the presence of strong coupling between different phenomena, like the transport of delayed neutron precursors by the fluid flow?. Due to the different discretization methods used in reactor physics analysis and thermal-hydraulics analysis, it is hard to couple different kinds of codes. This work presents an attempt to solve the multigroup neutron diffusion equation with the finite volume method which is quite widely used for the numerical simulation of a variety of applications involving fluid flow and heat and mass transfer. The unstructured finite volume method (FVM) based on open source C++ library OpenFOAM was chosen as the development platform?. The objective of this paper is to concisely present the main features of the developed codes in terms of the theoretical background and numerical approach and as can be seen in the following sections, the calculation results obtained by the NDSFoam which solves the steady-state neutron diffusion equation and NDTFoam which solves the transient-state neutron diffusion equation are discussed and compared to the benchmark problems. In this paper, two new neutronic solvers using finite volume method have been developed to perform the neutron physics analysis. The solvers are based on OpenFOAM, and have been tested with several steady state and transient-state benchmark problems. By verifications of the codes, it is proved that the development of NDSFoam and NDTFoam are correct. In our future work, both of these two solvers will be coupled with the precompiled CFD solvers in OpenFOAM to analyze the molten salt fast reactors. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗