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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Automated Symbolic Upscaling: 1. Model Generation for Extended Applicability Regimes

Abstract In porous media theory, upscaling techniques are fundamental to deriving rigorous Darcy‐scale models for flow and reactive transport in subsurface systems. Due to limitations in classical techniques, a number of ad hoc approaches have been proposed to address physical regimes in which reactive time scales are similar to, or faster than, diffusive time scales. In Part 1 of this two part series, we present a strategy for expanding the applicability of classical homogenization theory by generalizing the assumed closure form. We detail the implementation of this strategy on two reactive mass transport problems with moderately reactive physics. The strategy produces nontrivial homogenized models with emergent terms and effective parameters that couple reactive, diffusive, and advective transport. The differences in equation forms between the macroscopic and pore‐scale descriptions advise caution to further studies where the forms of macroscopic equations are assumed, as opposed to rigorously derived. Numerical validation is provided for each problem to show that the error estimates of homogenization theory are satisfied, and to justify the implemented strategy. In Part 2, the presented strategy is automated using symbolic computing to expedite its implementation.

Pietrzyk, Kyle↗
DOE OSTI2016

Diffuse coronae in cosmological simulations of Milky Way-sized galaxies

We investigate the properties of halo gas using three cosmological “zoom-in” simulations of realistic Milky Way-galaxy analogs with varying sub-grid physics. In all three cases, the mass of hot (T > 10{sup 6} K) halo gas is ∼1% of the host's virial mass. The X-ray luminosity of two of the runs is consistent with observations of the Milky Way, while the third simulation is X-ray bright and resembles more closely a very massive, star-forming spiral. Hot halos extend to 140 kpc from the galactic center and are surrounded by a bubble of warm-hot (T=10{sup 5}−10{sup 6} K) gas that extends to the virial radius. Simulated halos agree well outside 20–30 kpc with the β-model of Miller and Bregman based on O vii absorption and O viii emission measurements. Warm-hot and hot gas contribute up to 80% of the total gas reservoir, and contain nearly the same amount of baryons as the stellar component. The mass of warm-hot and hot components falls into the range estimated for L{sup ∗} galaxies. With key observational constraints on the density of the Milky Way corona being satisfied, we show that concealing of the ubiquitous warm-hot baryons, along with the ejection of just 20%–30% of the diffuse gas out of the potential wells by supernova-driven outflows, can solve the “missing baryon problem.” The recovered baryon fraction within 3 virial radii is 90% of the universal value. With a characteristic density of ∼10{sup −4} cm{sup −3} at 50–80 kpc, diffuse coronae meet the requirement for fast and complete ram-pressure stripping of the gas reservoirs in dwarf galaxy satellites.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Risk-Averse Control of Fractional Diffusion with Uncertain Exponent

In this paper, we introduce and analyze a new class of optimal control problems constrained by elliptic equations with uncertain fractional exponents. We utilize risk measures to formulate the resulting optimization problem. We develop a functional analytic framework, study the existence of solution and rigorously derive the first-order optimality conditions. Additionally, we employ a sample-based approximation for the uncertain exponent and the finite element method to discretize in space. Further, we prove the rate of convergence for the optimal risk neutral controls when using quadrature approximation for the uncertain exponent and conclude with illustrative examples.

finite element method↗
DOE OSTI2017

Calculation of Higher Eigenpairs of the Transport Equation Using IRAM Based on Domain Decomposition

The higher eigenpairs of the neutron transport (or diffusion) equation are useful in core flux distribution reconstruction, transient analysis, stability analysis, and loading pattern optimization. There are various methods for the calculation of higher eigenpairs, such as the power iteration method with filtering technique, the Generalized Modified Power Method (GMPM), the power iteration method with innovative boundary conditions for symmetric cores, and Krylov subspace methods. In regard to Krylov subspace methods, the Implicitly Restarted Arnoldi Method (IRAM) and ORTHOMIN were implemented to calculate higher eigenpairs for both transport and diffusion equations. In Warsa's work, however, there was a significant drawback that problems with energy upscatter could not be treated efficiently. In Modak's work, the mode to which his method was likely to converge depended on the shape of initial guess flux, and only one mode could be computed each time, making it impractical for real use. Furthermore, the computational burden for higher eigenpairs is generally much larger than for only the first eigenpair, especially for neutron transport equations. Therefore, the implementation of parallel computation for higher eigenpairs is essential to improve the efficiency, and no such work has been reported in literature so far. In this summary, we propose an innovative method to calculate higher eigenpairs of the neutron transport equation based on high performance computation. In this method, 2D Matrix Method of Characteristic (MMOC) is used to discretize the neutron transport equation; IRAM is used to evaluate higher eigenpairs; spatial domain decomposition is used to realize parallel computation. The highlight of this method is using the MGMD PGMRES (Multi-Group and Multi-Domain coupled Parallel Generalized Minimal Residual Method) algorithm to simultaneously accelerate the upscatter iterations and the inner interface angular fluxes iterations, avoiding the drawback in Warsa's method and the efficiency problem of conventional Jacobi iteration. To the best of our knowledge, our work represents the first application of IRAM based on domain decomposition for higher eigenpairs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Galvanostatic Intermittent Titration Technique Reinvented: Part II. Experiments

Following a critical review of the galvanostatic intermittent titration technique in Part I, here we experimentally demonstrate how to extract chemical diffusivity with a modified method. We prepare dense bulk samples that ensure diffusion-limitation. We utilize the scaling with $\sqrt{{t}_{\mathrm{relax}}+\tau }-\sqrt{{t}_{\mathrm{relax}}}$ (trelax: relaxation time; τ: pulse duration), avoiding problems with composition-dependent overpotentials. The equilibrium Nernst voltage is measured separately using small porous particles. This separation between the diffusion measurement and the titration procedure is critical for performing each measurement in a reliable setting. We report the chemical diffusion coefficients of LixNi1/3Mn1/3Co1/3O2 and their activation energy. We extract ionic conductivity and compare it with total conductivity to confirm ion-limitation in chemical diffusion. The measurements suggest that the time scale for diffusion in typical Li-ion battery particles could be much shorter than that of the intercalation/deintercalation processes at the particle surface (Biot number less than 0.1).

25 ENERGY STORAGE↗
DOE OSTI2026

A Scalable Reduced‐Order Model for the Steady Navier–Stokes Equations

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.

42 ENGINEERING↗
DOE OSTI2017

Stochastic control with rough paths

We study a class of controlled differential equations driven by rough paths (or rough path realizations of Brownian motion) in the sense of Lyons. It is shown that the value function satisfies a HJB type equation; we also establish a form of the Pontryagin maximum principle. Deterministic problems of this type arise in the duality theory for controlled diffusion processes and typically involve anticipating stochastic analysis. We make the link to old work of Davis and Burstein (Stoch Stoch Rep 40:203–256, 1992) and then prove a continuous-time generalization of Roger’s duality formula [SIAM J Control Optim 46:1116–1132, 2007]. The generic case of controlled volatility is seen to give trivial duality bounds, and explains the focus in Burstein–Davis’ (and this) work on controlled drift. Our study of controlled rough differential equations also relates to work of Mazliak and Nourdin (Stoch Dyn 08:23, 2008).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Theory and Applications of Macroscale Models in Porous Media

Systems dominated by heterogeneity over a multiplicity of scales, like porous media, still challenge our modeling efforts. The presence of disparate length- and time-scales that control dynamical processes in porous media hinders not only models predictive capabilities, but also their computational efficiency. Macrosopic models, i.e., averaged representations of pore-scale processes, are computationally efficient alternatives to microscale models in the study of transport phenomena in porous media at the system, field or device scale (i.e., at a scale much larger than a characteristic pore size). We present an overview of common upscaling methods used to formally derive macroscale equations from pore-scale (mass, momentum and energy) conservation laws. This review includes the volume averaging method, mixture theory, thermodynamically constrained averaging, homogenization, and renormalization group techniques. We apply these methods to a number of specific problems ranging from food processing to human bronchial system, and from diffusion to multiphase flow, to demonstrate the methods generality and flexibility in handling different applications. The primary intent of such an overview is not to provide a thorough review of all currently available upscaling techniques, nor a complete mathematical treatment of the ones presented, but rather a primer on some of the tools available for upscaling, the basic principles they are based upon, and their specific advantages and drawbacks, so to guide the reader in the choice of the most appropriate method for particular applications and of the most relevant technical literature.

42 ENGINEERING↗
DOE OSTI2024

Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks

Fractional and tempered fractional partial differential equations (PDEs) are effective models of long-range interactions, anomalous diffusion, and non-local effects. Traditional numerical methods for these problems are mesh-based, thus struggling with the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a promising solution due to their universal approximation, generalization ability, and mesh-free training. In principle, Monte Carlo fractional PINN (MC-fPINN) estimates fractional derivatives using Monte Carlo methods and thus could lift CoD. However, this may cause significant variance and errors, hence affecting convergence; in addition, MC-fPINN is sensitive to hyperparameters. In general, numerical methods and specifically PINNs for tempered fractional PDEs are under-developed. Herein, we extend MC-fPINN to tempered fractional PDEs to address these issues, resulting in the Monte Carlo tempered fractional PINN (MC-tfPINN). To reduce possible high variance and errors from Monte Carlo sampling, we replace the one-dimensional (1D) Monte Carlo with 1D Gaussian quadrature, applicable to both MC-fPINN and MC-tfPINN. We validate our methods on various forward and inverse problems of fractional and tempered fractional PDEs, scaling up to 100,000 dimensions. Our improved MC-fPINN/MC-tfPINN using quadrature consistently outperforms the original versions in accuracy and convergence speed in very high dimensions.

42 ENGINEERING↗
DOE OSTI2016

The effect of deadtime and electronic transients on the predelay bias in neutron coincidence counting

In neutron coincidence counting using the shift register autocorrelation technique, a predelay is inserted before the opening of the (R+A)-gate. Operationally the purpose of the predelay is to ensure that the (R+A)- and A-gates have matched effectiveness, otherwise a bias will result when the difference between the gates is used to calculate the accidentals corrected net reals coincidence rate. The necessity for the predelay was established experimentally in the early practical development and deployment of the coincidence counting method. The choice of predelay for a given detection system is usually made experimentally, but even today long standing traditional values (e.g., 4.5 µs) are often used. This, at least in part, reflects the fact that a deep understanding of why a finite predelay setting is needed and how to control the underlying influences has not been fully worked out. We attempt, in this paper, to gain some insight into the problem. One aspect we consider is the slowing down, thermalization, and diffusion of neutrons in the detector moderator. The other is the influence of deadtime and electronic transients. These may be classified as non-ideal detector behaviors because they are not included in the conventional model used to interpret measurement data. From improved understanding of the effect of deadtime and electronic transients on the predelay bias in neutron coincidence counting, the performance of both future and current coincidence counters may be improved.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Flow matching meets biology and life science: a survey

Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological research and discovery, enabling breakthroughs in molecule design, protein generation, catalysis discovery, drug discovery, and beyond. At the same time, biological applications have served as valuable testbeds for evaluating the capabilities of generative models. Recently, flow matching has emerged as a powerful and efficient alternative to diffusion-based generative modeling, with growing interest in its application to problems in biology and life sciences. This paper presents the first comprehensive survey of recent developments in flow matching and its applications in biological domains. We begin by systematically reviewing the foundations and variants of flow matching, and then categorize its applications into three major areas: biological sequence modeling, molecule generation and design, and peptide and protein generation. For each, we provide an in-depth review of recent progress. We also summarize commonly used datasets and software tools, and conclude with a discussion of potential future directions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Latent diffusion can map beam loss to two-dimensional phase-space projections

Beam loss monitors (BLMs) and beam current monitors (BCMs) are ubiquitous at particle accelerators around the world. These simple devices provide noninvasive high-level beam measurements but give no insight into the detailed 6D (𝑥,𝑦,𝑧,𝑝 𝑥 ,𝑝 𝑦 ,𝑝 𝑧 ) beam phase-space distributions or dynamics. We show that generative conditional latent diffusion models can learn intricate patterns to solve the extreme inverse problem of mapping waveforms of tens of BLMs or BCMs along an accelerator to detailed 2D projections of a charged particle beam’s 6D phase-space density. This transformational method can be used at any particle accelerator to transform simple noninvasive devices into detailed beam phase-space diagnostics. We demonstrate this concept via multiparticle simulations of the high-intensity beam in the kilometer-long Los Alamos Neutron Science Center linear proton accelerator.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Conductive Adhesives for Metallization of Interdigitated Back Contact Solar Cells

The metallization and interconnection of Si solar cells to form a module is usually a multi-step process: cells are metallized individually, soldered into strings, and subsequently assembled into a module. Furthermore, the metallization process for individual cells must be compatible with the doped surfaces being contacted, which creates additional challenges when moving from diffused junctions to heterojunctions and/or passivated contact structures. To mitigate these problems and streamline module manufacturing for the case of interdigitated back contact (IBC) cell modules, we developed an approach wherein unmetallized IBC cells are bonded directly to a circuitized backsheet using a conductive adhesive (CA) that only conducts out-of-plane. The metal/CA/silicon stack is bonded by hot-pressing at 100-200 degrees C.For a CA consisting of ethylene vinyl acetate (EVA) filled with Ag-coated spheres or indium powder we find that contact resistivities in the range of 1-10 Wcm 2 are obtained, regardless of which conductive filler and which hot pressing parameters are used. XPS revealed that about 1-2 nm EVA residue bonds to the Si in these samples, suggesting the formation of an insulating barrier that is responsible for the high contact resistance. Using indium powder alone, improved contact resistivities <0.6 Wcm 2 , high shunt resistances, and an implied open-circuit voltage loss below 10 mV were obtained. Furthermore, a cost estimate is presented, indicating that substantial cost savings of up to $10/m 2 , or about $0.05/W DC , are possible through CA-based IBC module manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2021

Randomized Sketching Algorithms for Low-Memory Dynamic Optimization

This paper develops a novel limited-memory method to solve dynamic optimization problems. The memory requirements for such problems often present a major obstacle, particularly for problems with PDE constraints such as optimal flow control, full waveform inversion, and optical tomography. In these problems, PDE constraints uniquely determine the state of a physical system for a given control; the goal is to find the value of the control that minimizes an objective. While the control is often low dimensional, the state is typically more expensive to store. This paper suggests using randomized matrix approximation to compress the state as it is generated and shows how to use the compressed state to reliably solve the original dynamic optimization problem. Concretely, the compressed state is used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the sketch. This approximate first- and second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region method that adaptively chooses the target rank using a posteriori error information and provably converges to a stationary point of the original problem. Numerical experiments with the sketched trust-region method show promising performance on challenging problems such as the optimal control of an advection-reaction-diffusion equation and the optimal control of fluid flow past a cylinder.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A geometric perspective on kinetic matter–radiation interaction and moment systems

Here, in this paper, we provide a geometric perspective on the kinetic interaction of matter and radiation, based on a pair bracket approach. We discuss the interaction of kinetic theories via dissipative brackets, with our fundamental example being the coupling of matter, described by the Boltzmann equation, and radiation, described by the radiation transport equation. We explore the transition from kinetic systems to their corresponding moment systems, provide a Hamiltonian description of such moment systems, and give a geometric interpretation of the moment closure problem for kinetic theories. As an application, we discuss in detail diffusion radiation hydrodynamics as an example of a pair bracket formulation on a space of moments corresponding to kinetic matter–radiation interaction. Additionally, using the variable moment closure framework of Burby [J. W. Burby, Variable-moment fluid closures with Hamiltonian structure, Sci. Rep. 13 (2023) 18286, doi:10.1038/s41598-023-45416-5], we show how to construct Hamiltonian moment closures for kinetic transport equations with arbitrary Hamiltonian. Using this general construction, we derive novel Hamiltonian moment closures for pure radiation transport.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

MEUMAPPS (C++ Version)

Many materials, metal alloys in particular, have features on the on micrometer or nanometer scale that have a large impact on the properties of the material. These features are known as the microstructure of the material. Understanding why and how the microstructure forms in a material is of fundamental scientific interest as well as of significant technological interest. The capability to predict microstructure evolution in a material allows the intentional design of microstructures and hence the intentional design of material properties. The phase-field method is one of the leading methods for predicting microstructure evolution. One of the most significant problems for phase-field models is their computational expense. Even limited phase-field simulations can easily require thousands of CPU core-hours to complete, which significantly limits their use. This code provides both a general framework for creating scalable, GPU-accelerated phase-field model applications as well as several applications themselves. The code is capable of using hundreds of GPUs efficiently, which greatly reduces the time required to perform simulations. The code is written with an emphasis on performance portability, that is the ability for the code to run efficiently on a number of different computing architectures without modification of the source code. The performance portability of this code is primarily enabled through the use of two libraries, Kokkos (performance portable data structures and execution patterns) and heFFTe (performance portable distributed 3D fast Fourier transforms). The code consists of a core library, applications, and tests. The core library includes shared functionality between applications. This includes interfaces with fast Fourier transform (FFT) libraries such as heFFTe, data structures based on Kokkos, file input and output capabilities, and a solver for infinitesimal strain mechanical equilibrium problems. Five applications are included in the code. The flagship application is the MEUMAPPS-SS application, which implements the Kim-Kim-Suzuki phase-field model for precipitation for an arbitrary number of phases and components in a metal alloy. Five simpler applications are also included that solve the Eshelby inclusion problem, Allen-Cahn equation, the coupled Allen-Cahn and diffusion equations, and the Cahn-Hilliard equation. The code includes two applications to solve the Cahn-Hilliard equation, one with constant-step-size first-order time integration and the second with adaptive high-order time integration.

DeWitt, Stephen [Oak Ridge National Lab. (ORNL), O↗
DOE OSTI2015

EOS7CA Version 1.0: TOUGH2 Module for Gas Migration in Shallow Subsurface Porous Media Systems

EOS7CA is a TOUGH2 module for mixtures of a non-condensible gas (NCG) and air with or without a gas tracer, an aqueous phase, and water vapor. The user can select the NCG as being CO 2 , N 2 , or CH 4 . EOS7CA uses a cubic equation of state with a multiphase version of Darcy’s Law to model flow and transport of gas and aqueous phase mixtures over a range of pressures and temperatures appropriate to shallow subsurface porous media systems. The limitation to shallow systems arises from the use of Henry’s Law for gas solubility which is appropriate for low pressures but begins to over-predict solubility starting at pressures greater than approximately 1 MPa (10 bar). The components modeled in EOS7CA are water, brine, NCG, gas tracer, air, and optional heat. The real gas properties module (ZEVCA) has options for Peng-Robinson, Redlich-Kwong, or Soave-Redlich-Kwong equations of state to calculate gas mixture density, enthalpy departure, and viscosity. Transport of the gaseous and dissolved components is by advection and Fickian molecular diffusion. This user guide provides instructions for use and two sample problems as verification and demonstration of EOS7CA.

03 NATURAL GAS↗
DOE OSTI2016

Voltammetry Method Evaluation

The purpose of the ANL MPACT Voltammetry project is to evaluate the suitability of previously developed cyclic voltammetry techniques to provide electroanalytical measurements of actinide concentrations in realistic used fuel processing scenarios. The molten salts in these scenarios are very challenging as they include high concentrations of multiple electrochemically active species, thereby creating a variety of complications. Some of the problems that arise therein include issues related to uncompensated resistance, cylindrical diffusion, and alloying of the electrodeposited metals. Improvements to the existing voltammetry technique to account for these issues have been implemented, resulting in good measurements of actinide concentrations across a wide range of adverse conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗