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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Reduced-order modeling for nonlocal diffusion problems

In several settings, diffusive behavior is observed to not follow the rate of spread predicted by parabolic partial differential equations (PDEs) such as the heat equation. Such behaviors, often referred to as anomalous diffusion, can be modeled using nonlocal equations for which points at a finite distance apart can interact. An example of such models is provided by fractional derivative equations. Because of the nonlocal interactions, discretized nonlocal systems have less sparsity, often significantly less, compared with corresponding discretized PDE systems. As such, the need for reduced-order surrogates that can be used to cheaply determine approximate solutions is much more acute for nonlocal models compared with that for PDEs. In this paper, we consider the construction, application, and testing of proper orthogonal decomposition (POD) reduced models for an integral equation model for nonlocal diffusion. For certain modeling parameters, the model we consider allows for discontinuous solutions and includes fractional Laplacian kernels as a special case. Preliminary computational results illustrate the potential of using POD to obtain accurate approximations of solutions of nonlocal diffusion equations at much lower costs compared with, for example, standard finite element methods. Copyright © 2016 John Wiley & Sons, Ltd.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

A time-dependent diffusion convection model for the long term modulation of cosmic rays

A model is developed which incorporates to first order the direct effects of the time dependent diffusive propagation of interstellar cosmic rays in a slowly changing interplanetary medium. The model provides a physical explanation for observed rigidity-dependent phase lags in modulated spectra (cosmic ray hysteresis). The average distance to the modulating boundary during the last solar cycle is estimated.

Gallagher, J. J.↗
DOE OSTI2010

Hydrogen refinement during solid phase epitaxy of buried amorphous silicon layers

The effect of hydrogen on the kinetics of solid phase epitaxy (SPE) have been studied in buried amorphous Si layers. The crystallization rate of the front amorphous/crystalline (a/c) interface is monitored with time resolved reflectivity. Secondary ion mass spectrometry (SIMS) is used to examine H implanted profiles at selected stages of the anneals. The H retardation of the SPE rate is determined up to a H concentration of 2.3x10{sup 20} cm{sup -3} where the SPE rate decreases by 80%. Numerical simulations are performed to model the H diffusion, the moving a/c interfaces and the refinement of the H profile at these interfaces. Despite the high H concentration involved, a simple Fickian diffusion model results in good agreement with the SIMS data. The segregation coefficient is estimated to be 0.07 at 575 deg. C. A significant fraction of the H escapes from the a-Si layer during SPE especially once the two a/c interfaces meet which is signified by the lack of H-related voids after a subsequent high temperature anneal.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Generative AI models for learning flow maps of stochastic dynamical systems in bounded domains

Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approach that combines a conditional diffusion model with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena. Our ML model consists of two major components: a neural network that learns exit probabilities using binary cross-entropy loss with rigorous convergence guarantees, and a training-free diffusion model that generates state transitions for non-exiting particles using closed-form score functions. The two components are integrated through a probabilistic sampling algorithm that determines particle exit at each time step and generates appropriate state transitions. Here, the performance of the proposed approach is demonstrated via three test cases: a one-dimensional simplified problem for theoretical verification, a two-dimensional advection-diffusion problem in a bounded domain, and a three-dimensional problem of interest to magnetically confined fusion plasmas.

Bounded domains↗
NASA NTRS1977

A computationally fast one-dimensional diffusion-photochemistry model of SST wakes

A computational technique applicable to analysis of supersonic transport (SST) wake photochemistry and diffusion is presented. Sensitivity studies of SST effluent effects upon ozone depletion are facilitated by the computational rapidity of the method. The article compares results from other studies and predictions of some variables related to global NOx input. Results indicate that the NO/NO2 ratio in an SST wake at photochemical equilibrium is a sensitive function of photolysis rates.

Matloff, G. L.↗
DOE OSTI2011

Fractional dynamics in the light scattering intensity fluctuation in dusty plasma

Light scattering intensity fluctuation in dusty plasma system is studied. The scattered electric field of the laser light is treated as a stationary stochastic process. A nonstandard form of fractional Langevin equation is solved using Green's function approach to obtain the so-called fractional Ornstein-Uhlenbeck process. The empirical correlation function of light intensity fluctuation is fitted with four model correlation functions which are representative of different mechanisms for monodisperse particle transport, namely, the kinetic (ballistic) model, the hydrodynamical (diffusive) model, the hybrid kinetic-hydrodynamic model, and the fractional kinetic model for polydisperse particles. The shifted fractional derivative index is found to be related to power-law exponent of polydisperse dust mass distribution. It is shown that the correlation model based on fractional Ornstein-Uhlenbeck process may provide a novel insight into the complex transport behaviors in dusty plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Measurement of the dynamic charge susceptibility near the charge density wave transition in ErTe 3

A charge density wave (CDW) is a phase of matter characterized by a periodic modulation of valence electron density coupled with lattice distortion. Its formation is closely tied to the dynamical charge susceptibility, χ(q, ω) , which reflects the collective electron dynamics of the material. Despite decades of study, χ(q, ω) near a CDW transition has never been measured at nonzero momentum, q, with meV energy resolution. Here, we investigate the canonical CDW transition in ErTe 3 using momentum-resolved electron energy loss spectroscopy, a technique uniquely sensitive to valence band charge excitations. Unlike phonons, which soften via the Kohn anomaly, we find the electronic excitations exhibit purely relaxational dynamics well described by a diffusive model, with the diffusivity peaking just below the critical temperature, T C1 . Additionally, we report for the first time a divergence in the real part of χ(q, ω) in the static limit (ω → 0), a long-predicted hallmark of CDWs. Unexpectedly, this divergence occurs as T → 0, with only a weak thermodynamic signature at T = T C1 . Our study necessitates a reexamination of the traditional description of CDW formation in quantum materials.

M-EELS↗
DOE OSTI2020

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Toward a microscopic picture of hadronization and multi-parton processes

This project advanced the understanding of how quarks and gluons produced in high-energy collisions transform into the hadrons observed in particle detectors, a fundamental process known as quantum chromodynamics (QCD) hadronization. By combining theoretical calculations, quantum simulation methods, and modern AI techniques, the research developed new tools to study multi-parton dynamics and nonperturbative effects that are essential for interpreting data from current and future nuclear physics experiments. Key outcomes include new theoretical frameworks for jet and hadron measurements, pioneering quantum simulation algorithms for real-time dynamics in field theories, and the development of advanced machine-learning models, such as diffusion models and explainable classifiers, to simulate and analyze collider events. These results are directly relevant to experiments at Jefferson Lab, Brookhaven National Laboratory, and the future Electron-Ion Collider, and they also have a broader impact in areas such as quantum information science and data-driven modeling of complex systems. The project supported the training of graduate students and postdoctoral fellows and contributed to the broader scientific community through publications, workshops, and collaborative activities. Overall, this work provides new insights into the microscopic mechanisms of hadron formation and establishes a foundation for future studies at the intersection of nuclear physics, artificial intelligence, and quantum computing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2005

Energy Spectra of H and He from the ATIC-2 Experiment

The Advanced Thin Ionization Calorimeter (ATIC) experiment measures the energy spectra of individual elements, from H to Fe, in the energy region from about 100 GeV to tens of TeV. The ATIC instrument was flown twice in long-duration balloon flights around the South Pole in 2000-2001 (ATIC-1) and 2002-2003 (ATIC-2). ATIC-2 gathered about 18 days of data at an altitude of 36 km. In this paper we discuss the separation of protons from helium and present preliminary energy spectrum of each component, including deconvolution of the measured energy deposit spectra. The results are compared to previous data and to different cosmic ray propagation models, including a diffusion model with weak re-acceleration.

Wefel, J. P.↗
DOE OSTI2015

Multiscale simulation of xenon diffusion and grain boundary segregation in UO₂

In light water reactor fuel, gaseous fission products segregate to grain boundaries, resulting in the nucleation and growth of large intergranular fission gas bubbles. The segregation rate is controlled by diffusion of fission gas atoms through the grains and interaction with the boundaries. Based on the mechanisms established from earlier density functional theory (DFT) and empirical potential calculations, diffusion models for xenon (Xe), uranium (U) vacancies and U interstitials in UO₂ have been derived for both intrinsic (no irradiation) and irradiation conditions. Segregation of Xe to grain boundaries is described by combining the bulk diffusion model with a model for the interaction between Xe atoms and three different grain boundaries in UO₂ (Σ5 tilt, Σ5 twist and a high angle random boundary), as derived from atomistic calculations. The present model does not attempt to capture nucleation or growth of fission gas bubbles at the grain boundaries. The point defect and Xe diffusion and segregation models are implemented in the MARMOT phase field code, which is used to calculate effective Xe and U diffusivities as well as to simulate Xe redistribution for a few simple microstructures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Interplay between inhibited transport and reaction in nanoporous materials

This work presents a detailed formulation of reaction and diffusion dynamics of molecules in confined pores such as mesoporous silica and zeolites. A general reaction-diffusion model and discrete Monte Carlo simulations are presented. Both transient and steady state behavior is covered. Failure of previous mean-field models for these systems is explained and discussed. A coarse-grained, generalized hydrodynamic model is developed that accurately captures the interplay between reaction and restricted transport in these systems. This method incorporates the non-uniform chemical diffusion behavior present in finite pores with multi-component diffusion. Two methods of calculating these diffusion values are developed: a random walk based approach and a driven diffusion model based on an extension of Fick's law. The effects of reaction, diffusion, pore length, and catalytic site distribution are investigated. In addition to strictly single file motion, quasi-single file diffusion is incorporated into the model to match a range of experimental systems. The connection between these experimental systems and model parameters is made through Langevin dynamics modeling of particles in confined pores.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Probing exciton diffusion dynamics in photosynthetic supercomplexes via exciton–exciton annihilation

Photosynthesis converts solar energy into chemical energy through coordinated energy transfer between light-harvesting complexes and reaction centers (RCs). Understanding exciton motion, particularly the exciton diffusion length, is essential for optimizing energy efficiency in photosystems. In this work, we combine intensity-cycling transient absorption spectroscopy with kinetic Monte Carlo (kMC) simulation to investigate exciton motion in the C2S2 photosystem II supercomplex of spinach. Using exciton–exciton annihilation, revealed in the fifth-order response, we experimentally estimate an exciton diffusion length of 10.9 nm based on a 3D normal diffusion model, suggesting the ability of excitons to traverse the supercomplex. However, kMC simulations reveal that exciton motion is sub-diffusive because of spatial constraints and the strong RC traps. An anomalous diffusion model analysis of the experimental data yields a diffusion length of 9.7 nm, while the simulated diffusion length is 7.4 nm. The variable exciton residence time across subunits, partly influenced by their connectivity to the trap, indicates inhomogeneous annihilation probability and suggests how plants balance efficient light harvesting with photoprotection. We also explore the influence of specific assumptions in the annihilation simulation, which are challenging to access in more complex environments, such as the thylakoid membrane. Our study provides a framework for studying exciton dynamics using exciton–exciton annihilation, which can be extended to understand the light-harvesting efficiencies of larger, more complex photosynthetic assemblies.

Zhang, Kunyan (ORCID:000000026830409X)↗
DOE OSTI2023

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging inverse problem. We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLCE can form high-quality images from severely under-sampled data by integrating data-consistency updates with the sampling updates of a diffusion model, which is conditioned on the transformed limited-angle data. We show through extensive experimentation on several challenging real LACT datasets that, the same pre-trained DOLCE model achieves the SOTA performance on drastically different types of images. Additionally, we show that, unlike standard LACT reconstruction methods, DOLCE naturally enables the quantification of the reconstruction uncertainty by generating multiple samples consistent with the measured data.

Kim, Hyojin↗
DOE OSTI2025

The accuracy of multi-group models for nonlocal electron transport in magnetized plasmas

In the extreme conditions of inertial confinement fusion experiments, heat flow plays a vital role, but local diffusive models frequently break down and overestimate the heat flow. The situation becomes more complicated again in the significant magnetic fields generated during laser–plasma interactions or in magnetized fusion schemes. Accurate non-local and magnetized heat flow computations can be carried out using Vlasov–Fokker–Planck (VFP) simulations, but these are computationally expensive. There is, therefore, significant interest in using faster multi-group models to accurately calculate the non-local heat flow in magnetized plasmas. We benchmark two such multi-group models for calculating the heat flow, M1 and hybrid-AWBS-BGK, against diffusive models and full VFP simulations, before applying the models to realistic example test cases, both magnetized and unmagnetized. We find that the multi-group models generally perform very well for moderate non-localities up to kλmfp∼0.01, but the computational cost increases dramatically. hybrid-AWBS-BGK performs more effectively than M1 at high non-localities, up to kλmfp∼1, due to its adaptive solver and robust P1 closure, but tends to fail in very strong magnetic fields. Both codes are much faster than VFP simulations but are still slow in steep temperature gradients.

Arran, C. (ORCID:0000000286448118)↗
DOE OSTI2018

Modeling of Diffusivity for 2D Vacancy Nanopits and Comparison with 2D Adatom Nanoislands on Metal(100) Surfaces Including Analysis for Ag(100)

Diffusion coefficients, D N , for 2D vacancy nanopits are compared with those for 2D homoepitaxial adatom nanoislands on metal(100) surfaces, focusing on the variation of DN with size, N. Here, N is measured in missing atoms for pits and adatoms for islands. Analysis of D N is based on kinetic Monte Carlo simulations of a tailored stochastic lattice-gas model, where pit and island diffusion are mediated by periphery diffusion, i.e., by edge atom hopping. Precise determination of D N versus N for typical parameters reveals a cyclical variation with an overall decrease in magnitude for increasing moderate O(10 2 ) ≤ N ≤ O(10 3 ). Monotonic decay, D N ~ N -β , is found for N ≥ O(10 2 ) with effective exponents, β = β eff , for both pits and islands, both well below the macroscopic value of βmacro = 3/2. D N values for vacancy pits are significantly lower (higher) than for adatom islands for moderate N in the case of low (high) kink rounding barrier. However, D N values for pits and islands slowly merge, and β eff → 3/2 for sufficiently large N. The latter feature is expected from continuum Langevin formulations appropriate for large sizes. Finally, we compare predictions from our model incorporating appropriate energetic parameters for Ag(100) with different sets of experimental data for diffusivity at 300 K, including assessment of β eff , for experimentally observed sizes N from ~100 to ~1000.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Simulations of Xe and U diffusion in UO 2

Diffusion of xenon (Xe) and uranium (U) in UO 2 is controlled by vacancy mechanisms and under irradiation the formation of mobile vacancy clusters is important. Based on the vacancy and cluster diffusion mechanisms established from density functional theory (DFT) calculations, we derive continuum thermodynamic and diffusion models for Xe and U in UO 2 . In order to capture the effects of irradiation, vacancies (Va) are explicitly coupled to the Xe and U dynamics. Segregation of defects to grain boundaries in UO 2 is described by combining the bulk diffusion model with models of the interaction between Xe atoms and vacancies with grain boundaries, which were derived from atomistic calculations. The diffusion and segregation models were implemented in the MOOSE-Bison-Marmot (MBM) finite element (FEM) framework and the Xe/U redistribution was simulated for a few simple microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Parareal in time 3D numerical solver for the LWR Benchmark neutron diffusion transient model

In this paper we present a time-parallel algorithm for the 3D neutrons calculation of a transient model in a nuclear reactor core. The neutrons calculation consists in numerically solving the time dependent diffusion approximation equation, which is a simplified transport equation. The numerical resolution is done with finite elements method based on a tetrahedral meshing of the computational domain, representing the reactor core, and time discretization is achieved using a θ-scheme. The transient model presents moving control rods during the time of the reaction. Therefore, cross-sections (piecewise constants) are taken into account by interpolations with respect to the velocity of the control rods. The parallelism across the time is achieved by an adequate use of the parareal in time algorithm to the handled problem. This parallel method is a predictor corrector scheme that iteratively combines the use of two kinds of numerical propagators, one coarse and one fine. Our method is made efficient by means of a coarse solver defined with large time step and fixed position control rods model, while the fine propagator is assumed to be a high order numerical approximation of the full model. The parallel implementation of our method provides a good scalability of the algorithm. Numerical results show the efficiency of the parareal method on large light water reactor transient model corresponding to the Langenbuch–Maurer–Werner benchmark.

97 MATHEMATICS AND COMPUTING↗