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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 OSTI2023

Molten Salt Tritium Transport Experiment: A Versatile Fluoride Salt Loop for Validation of Tritium Transport Phenomena

Tritium is generated in Molten Salt Reactors (MSRs) from neutron capture by lithium and other constituents of the molten salt FLiBe. Tritium is a unique radionuclide as it readily permeates through hot structural materials. Thus, any material in contact with tritium laden molten salt is a potential pathway for release. Understanding tritium transport and devising adequate control strategies is necessary for the safe operation of MSRs. The Molten Salt Tritium Transport Experiment (MSTTE) is a forced-convection fluoride salt loop with the capability to inject hydrogen isotopes into flowing molten salt and to measure transport phenomena such as permeation through metals and evolution from free-surfaces. MSTTE is designed to be versatile to test potential control technology in future campaigns. This report focuses on the current design and analysis of MSTTE. Custom designed and fabricated experiment components such as the hydrogen injection system, permeation test section, diagnostics, and gas distribution system are discussed in detail. System-level tritium transport modeling using the System Analysis Module investigates experiment parameters such as hydrogen source terms, salt flow rate, and salt temperature. Computational fluid dynamics of the salt flow in the permeation test section informs design choices to establish fully developed flow in the measured permeation zone.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Fingerprinting Brownian Motions of Polymers under Flow

Here, we present a quantitative approach to the self-dynamics of polymers under steady flow by employing a set of complementary reference frames and extending the spherical harmonic expansion technique to dynamic density correlations. Application of this method to nonequilibrium molecular dynamics simulations of polymer melts reveals a number of universal features. For both unentangled and entangled melts, the center-of-mass motions in the flow frame are described by superdiffusive, anisotropic Gaussian distributions, whereas the isotropic component of monomer self-dynamics in the center-of-mass frame is strongly suppressed. Spatial correlation analysis shows that the heterogeneity of monomer self-dynamics increases significantly under flow.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Chapman–Jouguet deflagration criteria and compressibility dynamics of turbulent fast flames for turbulence-induced deflagration-to-detonation transition

This work characterizes the compressibility dynamics in turbulent fast flames for a range of turbulent flame speeds. These turbulent fast flames experience increased effects of compressibility through the formation of strong shocks and may develop a runaway acceleration combined with a pressure buildup that leads to turbulence induced deflagration-to-detonation transition (tDDT). Simultaneous high-speed particle image velocimetry, OH* chemiluminescence, schlieren, and pressure measurements are used to examine the reacting flow field and flame dynamics. We examine flames with turbulent flame speeds ranging from 100 to 600 m/s. At lower turbulent flame speeds, the flame is not able to produce favorable background conditions for deflagration-to-detonation transition (DDT) onset, and thus flame compressibility and turbulence amplification are less dominant, resulting in a weaker acoustic coupling between the flame and compressed region. As the turbulent burning velocities exceed the Chapman–Jouguet deflagration speed, favorable background conditions are produced, as we observe flame-generated shocks and flame-generated turbulence with higher turbulent velocities and larger turbulent scales. At this regime, the flame is categorized to be at the runaway transition regime that leads to tDDT.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning

We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system. We present a collection of results on how to enforce constraints coming from the dynamical system in order to accelerate the training of deep neural networks to represent the flow map of the system as well as increase their predictive ability. In particular, we provide ways to enforce constraints during training for all three major modes of learning, namely supervised, unsupervised and reinforcement learning. In general, the dynamic constraints need to include terms which are analogous to memory terms in model reduction formalisms. Such memory terms act as a restoring force which corrects the errors committed by the learned flow map during prediction. For supervised learning, the constraints are added to the objective function. For the case of unsupervised learning, in particular generative adversarial networks, the constraints are introduced by augmenting the input of the discriminator. Finally, for the case of reinforcement learning and in particular actor-critic methods, the constraints are added to the reward function. In addition, for the reinforcement learning case, we present a novel approach based on homotopy of the action-value function in order to stabilize and accelerate training. We use numerical results for the Lorenz system to illustrate the various constructions.

Stinis, Panagiotis↗
DOE OSTI2020

Annular Flow Simulation Supported by Iterative In-Memory Mesh Adaptation

Various flow regimes exist in a boiling water reactor (BWR) as the steam quality increases in the uprising coolant flow, from bubbly flow, slug/churn flow, to annular flow. The annular flow is characterized by the presence of a fast-moving gas core and the surrounding liquid film flowing on the conduit wall. Additionally, entrained droplets can be observed in the gas core with ingested bubbles in the liquid film. The dynamics occurring on the wavy interface between the liquid film and gas core plays a crucial role in affecting the heat transfer rate and pressure drop within the BWR core. However, a fundamental understanding of annular flow is still lacking, partly due to the difficulty in obtaining detailed local data in annular flow experiments. In the current study, a novel simulation framework is developed for the annular flow by coupling a computational fluid dynamics flow solver with state-of-the-art meshing software. The gas-liquid interface is tracked with the level set method. Based on the computed flow solutions, the computational mesh is dynamically adapted in memory to meet the local mesh resolution requirement. This iterative simulation-adaptation framework can ensure the fine mesh resolution across the interface, which not only helps mitigate the mass conservation degradation known to level set methods but also improves the representation of dramatic interface topological changes such as wave breaking and droplet entrainment. The present investigation will shed light onto the complex interfacial processes involved in annular flow and generate much needed simulation data for annular flow modeling.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Quantum Solver Using Singular Value Decomposition for Computational Fluid Dynamics

Numerical solutions for fluid flow problems are challenging and have been focus of Computational Fluid Dynamics (CFD) research for past several decades. The advent of quantum computing promises exponential speedup in comparison to existing classical methods and alleviate computational constraints posed by CFD problems. Although solutions for most problems of interest in fluid dynamics using quantum computing are distant, recent advances in algorithms, software and hardware provide a path towards realizing this goal. Quantum linear solver algorithms (QLSA) such as Harrow–Hassidim–Lloyd (HHL) and Variational Quantum Linear Solver (VQLS) have been successfully implemented to solve for canonical problems such as Hele-Shaw flow. However, these algorithms still suffer to scale and address problems with ill-conditioned Jacobians. In the current paper, we alleviate these restrictions with a new quantum solver based on Singular Value Decomposition (SVD) and simulate flow past a 2D cylinder. The fidelity of the SVD based quantum solver in predicting the flow past 2D cylinder is computed along with an assessment of errors. Classical and quantum solutions for the flow are compared for different resolutions. Finally, we discuss variation in the solutions based on number of shots used.

Gottiparthi, Kalyan [ORNL] (ORCID:0000000213540255↗
DOE OSTI2020

DEMONSTRATION OF A DATA-DRIVEN PHYSICS-BASED APPROACH FOR COMPUTATIONALLY EFFICIENT CFD PREDICTION OF TWO-PHASE BUBBLY FLOW

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale physics-guided data-driven approach, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

42 ENGINEERING↗
DOE OSTI2020

Validation of blade-resolved computational fluid dynamics for a MW-scale turbine rotor in atmospheric flow

In this study, simulation results of two different computational fluid dynamics codes, Nalu-Wind and EllipSys3D, are presented for a wind turbine rotor in complex yawed and sheared inflow. The results are compared to measurements from the DanAero experiments, to validate computed pressures and azimuthal trends. Despite different code methodologies and grid setups, the codes agree well in computed pressures and integrated forces along the blade for all blade azimuthal positions, however with some discrepancy in the very yawed case. Additionally, both codes capture well the azimuthal trends and force levels seen in measurements. Investigation into discrepancies shows that expanding grids before the rotor, lead to smearing of the wind profiles, which is likely the main cause of the differences in the results between the codes. Additionally, omission of the ground constraint cause discrepancies in relative velocity seen by the passing blade, due to an over speeding beneath the rotor.

17 WIND ENERGY↗
DOE OSTI2022

Fluctuations and power-law scaling of dry, frictionless granular rheology near the hard-particle limit

Here the flow of frictionless granular particles is studied with stress-controlled discrete element modeling simulations for systems varying in size from 300 to 100,000 particles. The volume fraction and shear-stress ratio μ are relatively insensitive to system size for a wide range of inertial numbers I. Second-order effects in strain rate, such as normal stress differences, require large system sizes to accurately extract meaningful results, notably a nonmonotonic dependence in the first normal stress difference with strain rate. The rheological response represented by the μ(I) scalar model works well at describing the lower-order aspects of the rheology, except near the quasistatic limit of these stress-controlled flows. The pressure is varied over five decades, and a pressure dependence of the coordination number is observed, which is not captured by the inertial number. Large fluctuations observed for small systems N≤1000 near the quasistatic limit can lead to the arrest of flow resulting in challenges to fitting the data to rheological relationships. The inertial number is also insufficient for capturing the pressure-dependent behavior of property fluctuations. Fluctuations in the flow and microstructural properties are measured in both the quasistatic and inertial regimes, including shear stress, pressure, strain rate, normal stress differences, volume fraction, coordination number, and contact fabric anisotropy. The fluctuations in flow properties scale self-similarly with pressure and system size. A transition in the scaling of fluctuations of stress properties and contact fabric anisotropy are measured and proposed as a quantitative identification of the transition from inertial to quasistatic flow.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Comparative analysis of internal energy excitation and dissociation of nitrogen predicted by independently developed ab initio potential energy surfaces

In this article we present a comparative atomic level study analyzing the vibrational excitation and dissociation of molecular nitrogen due to N 2 ⁢( 1 Σ g ⁢ + )+N⁢( 4 S u ) and N 2 ⁢( 1 Σ g ⁢ + )+N 2 ⁢( 1 Σ g ⁢+ ) interactions governed by independently developed potential energy surfaces at the University of Minnesota and NASA Ames Research Center. Further, vibrational excitation was studied for N 2 +N 2 interactions from T=10000 to 25000 K and for N 2 +N from T=5000 to 30000 K. Nonequilibrium dissociation is studied from T=10000 to 30000 K under the quasi-steady-state condition for N 2 +N 2 and N 2 +N interactions. Finally, an inviscid Mach 20 dissociating nitrogen flow over a cylinder with a Knudsen number of 0.015 is carried out to study the impact of molecular interactions predicted by independently developed potential energy surfaces on a canonical hypersonic flow.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Rheology of concentrated fiber suspensions with a load-dependent friction coefficient

Here we investigate the effects of fiber aspect ratio, roughness, flexibility, and volume fraction on the rheology of concentrated suspensions in a steady shear flow using direct numerical simulations. We model the fibers as inextensible continuous flexible slender bodies with the Euler-Bernoulli beam equation governing their dynamics suspended in an incompressible Newtonian fluid. The fiber dynamics and fluid flow coupling is achieved using the immersed boundary method. In addition, the fiber surface roughness might lead to interfiber contacts, resulting in normal and tangential forces between the fibers, which follow Coulomb’s law of friction. The surface roughness is modeled as hemispherical protrusions on the fiber surfaces. Their deformation results in a normal load-dependent friction coefficient. Our simulations accurately predict the experimentally observed shear thinning in fiber suspensions. Furthermore, we find that the suspension viscosity η increases with increasing the volume fraction, roughness, fiber rigidity, and aspect ratio. The increase in η is the macroscopic manifestation of a similar increase in the microscopic contact contribution to the total stress with these parameters. In addition, we observe positive and negative first N 1 and second N 2 normal stress differences, respectively, with |N 2 | < |N 1 |, in agreement with previous experiments. Last, we propose a modified Maron-Pierce law to quantify the reduction in the jamming volume fraction by increasing the fiber aspect ratio and roughness. Our results and analysis establish the use of fiber surface tribology to tune the suspension flow behavior.

42 ENGINEERING↗
DOE OSTI2020

Kilometer-Scale Hydrologic Exchange Flows in a Gravel Bed River Corridor and Their Implications to Solute Migration

In this work, a well-characterized field site along a major river corridor was used to investigate the dynamic pathways and impacts of subsurface hydrogeologic structures on kilometer-scale hydrologic exchange flows between river water and groundwater. A aqueous uranium (U aq ) plume exists at the site that discharges to the Columbia River. We performed temporally intensive monitoring of specific conductance (SpC) and U aq concentrations within the plume at increasing distances from the river shoreline and at locations both within and outside a presumed subsurface pathway of lateral hydrologic exchange. Specific conductance and U aq were utilized as in-situ tracers of hydrologic exchange and associated groundwater-surface water mixing. Monitoring data was collected for an approximate two year period during which seasonal river elevations varied by more than 2 m causing distinct events of river water intrusion and retreat from the near-shore aquifer, and large temporal variations in well-water analyte concentrations. Statistical clustering analysis was applied to identify well groupings exhibiting common time series behaviors. PFLOTRAN was implemented within a modeling domain of 3.3 x 1.9 km with dynamic flow boundary conditions and accurate subsurface topography for the basal aquitard underlying the aquifer. In-silico calculations of river water tracer behavior were performed to assess predominant flow directions and velocities influenced by river stage variations and variable aquitard topography, and their collective influences on SpC and U aq concentrations. We demonstrate complicated multidirectional flow behaviors at the kilometer scale resulting from hydrologic exchange and subsurface structure that influence plume dynamics and determine the overall system behavior.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Controls From Above and Below: Snow, Soil, and Steepness Drive Diverging Trends of Subsurface Water and Streamflow Dynamics

ABSTRACT The importance of subsurface water dynamics, such as water storage and flow partitioning, is well recognised. Yet, our understanding of their drivers and links to streamflow generation has remained elusive, especially in small headwater streams that are often data‐limited but crucial for downstream water quantity and quality. Large‐scale analyses have focused on streamflow characteristics across rivers with varying drainage areas, often overlooking the subsurface water dynamics that shape streamflow behaviour. Here we ask the question: What are the climate and landscape characteristics that regulate subsurface dynamic storage, flow path partitioning, and dynamics of streamflow generation in headwater streams? To answer this question, we used streamflow data and a widely‐used hydrological model (HBV) for 15 headwater catchments across the contiguous United States. Results show that climate characteristics such as aridity and precipitation phase (snow or rain) and land attributes such as topography and soil texture are key drivers of streamflow generation dynamics. In particular, steeper slopes generally promoted more streamflow, regardless of aridity. Streams in flat, rainy sites (< 30% precipitation as snow) with finer soils exhibited flashier regimes than those in snowy sites (> 30% precipitation as snow) or sites with coarse soils and deeper flow paths. In snowy sites, less weathered, thinner soils promoted shallower flow paths such that discharge was more sensitive to changes in storage, but snow dampened streamflow flashiness overall. Results here indicate that land characteristics such as steepness and soil texture modify subsurface water storage and shallow and deep flow partitioning, ultimately regulating streamflow response to climate forcing. As climate change increases uncertainty in water availability, understanding the interacting climate and landscape features that regulate streamflow will be essential to predict hydrological shifts in headwater catchments and improve water resources management.

Kerins, Devon [Department of Civil and Environment↗
DOE OSTI2025

Dynamics of metastable contact soliton dissipative exchange flows in one-dimensional ferromagnetic channels

Dissipative exchange flows (DEFs) are large-amplitude boundary value solutions of ferromagnetic channels. In their low-injection limit, DEFs reduce to spin superfluids. However, in the strong injection limit, nonlinearities dominate close to the injection site and a soliton is formed; this solution has been termed a contact soliton dissipative exchange flow (CS-DEF). Here, in this work, we numerically investigate CS-DEF solutions in a moderate injection regime and a finite injection width. We find a solution where two metastable solitons coexist in the injection region. This solution is metastable in the sense that any perturbation to the system will eject one of the solitons out of the injection region. Moreover, soliton dynamics can be excited when two injection regions are separated by a certain distance. We find that the ensuing DEF between the solitons induces a steady-state dynamics in which metastable solitons are continually ejected and nucleated. Furthermore, and depending on the relative signs of the spin injections, the soliton dynamics possess a particular handedness and frequency related to the spin transfer torque delivered by the DEF. Our results provide insights into the transport of spin current by DEFs - where the interaction between DEFs and solitons suggests a mechanism for detaching contact-solitons from the injection boundary. Although this study focuses on the "nonlocal" interaction between solitons, it may lead to the investigation of new mechanisms for inserting solitons in a DEF, e.g., for discrete motion and transport of information over long distances.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Examining metastatic behavior within 3D bioprinted vasculature for the validation of a 3D computational flow model

Understanding the dynamics of circulating tumor cell (CTC) behavior within the vasculature has remained an elusive goal in cancer biology. To elucidate the contribution of hydrodynamics in determining sites of CTC vascular colonization, the physical forces affecting these cells must be evaluated in a highly controlled manner. To this end, we have bioprinted endothelialized vascular beds and perfused these constructs with metastatic mammary gland cells under physiological flow rates. By pairing these in vitro devices with an advanced computational flow model, we found that the bioprinted analog was readily capable of evaluating the accuracy and integrated complexity of a computational flow model, while also highlighting the discrete contribution of hydrodynamics in vascular colonization. This intersection of these two technologies, bioprinting and computational simulation, is a key demonstration in the establishment of an experimentation pipeline for the understanding of complex biophysical events.

42 ENGINEERING↗
DOE OSTI2021

Data-driven learning of Mori–Zwanzig operators for isotropic turbulence

Developing reduced-order models for turbulent flows, which contain dynamics over a wide range of scales, is an extremely challenging problem. In statistical mechanics, the Mori–Zwanzig (MZ) formalism provides a mathematically exact procedure for constructing reduced-order representations of high-dimensional dynamical systems, where the effects due to the unresolved dynamics are captured in the memory kernel and orthogonal dynamics. Turbulence models based on MZ formalism have been scarce due to the limited knowledge of the MZ operators, which originates from the difficulty in deriving MZ kernels for complex nonlinear dynamical systems. In this work, we apply a recently developed data-driven learning algorithm, which is based on Koopman's description of dynamical systems and Mori's linear projection operator, on a set of fully resolved isotropic turbulence datasets to extract the Mori–Zwanzig operators. With data augmentation using known turbulence symmetries, the extracted Markov term, memory kernel, and orthogonal dynamics are statistically converged and the generalized fluctuation–dissipation relation can be verified. The properties of the memory kernel and orthogonal dynamics, and their dependence on the choices of observables are investigated to address the modeling assumptions that are commonly used in MZ-based models. A series of numerical experiments are then constructed using the extracted kernels to evaluate the memory effects on prediction. The results show that the prediction errors are strongly affected by the choice of observables and can be further reduced by including the past history of the observables in the memory kernel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Progress in Normalizing Flows for 4d Gauge Theories

Normalizing flows have arisen as a tool to accelerate Monte Carlo sampling for lattice field theories. This work reviews recent progress in applying normalizing flows to 4-dimensional nonabelian gauge theories, focusing on two advancements: an architectural improvement referred to as learned active loops, and the application of correlated ensemble methods to QCD with N f = 2 dynamical fermions.

Abbott, Ryan [Massachusetts Institute of Technolog↗
DOE OSTI2022

Insensitivity of a turbulent laser-plasma dynamo to initial conditions

It has recently been demonstrated experimentally that a turbulent plasma created by the collision of two inhomogeneous, asymmetric, weakly magnetized, laser-produced plasma jets can generate strong stochastic magnetic fields via the small-scale turbulent dynamo mechanism, provided the magnetic Reynolds number of the plasma is sufficiently large. In this paper, we compare such a plasma with one arising from two pre-magnetized plasma jets whose creation is identical save for the addition of a strong external magnetic field imposed by a pulsed magnetic field generator. We investigate the differences between the two turbulent systems using a Thomson-scattering diagnostic, x-ray self-emission imaging, and proton radiography. The Thomson-scattering spectra and x-ray images suggest that the external magnetic field has a limited effect on the plasma dynamics in the experiment. Although the external magnetic field induces collimation of the flows in the colliding plasma jets and although the initial strengths of the magnetic fields arising from the interaction between the colliding jets are significantly larger as a result of the external field, the energies and morphologies of the stochastic magnetic fields post-amplification are indistinguishable. We conclude that, for turbulent laser-plasmas with supercritical magnetic Reynolds numbers, the dynamo-amplified magnetic fields are determined by the turbulent dynamics rather than the seed fields or modest changes in the initial flow dynamics of the plasma, a finding consistent with theoretical expectations and simulations of turbulent dynamos.

47 OTHER INSTRUMENTATION↗