X-ray Doppler Velocimetry: An imaging diagnostic of 3D fluid flow in turbulent plasma
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The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.
The essential fluid flow processes associated with the solar and Jovian atmospheres will be examined in a laboratory experiment scheduled for performance on Spacelab Missions One and Three. The experimental instrumentation required to generate and to record convective fluid flow is described. Details of the optical system configuration, the lens design, and the optical coatings are described. Measurement of thermal gradient fields by schlieren techniques and measurement of fluid flow velocity fields by photochromic dye tracers is achieved with a common optical system which utilizes photographic film for data recording. Generation of the photochromic dye tracers is described, and data annotation of experimental parameters on the film record is discussed.
Sandia National Laboratories' Jim Martin and Kyle Solis explain research on the effects of magnetic fields on fluid flows and how they stimulate vigorous flows. Fluid flow is a necessary phenomenon in everything from reactors to cooling engines in cars.
The growth of protein crystals suitable for x-ray crystal structure analysis is an important topic. The quality (perfection) of protein crystals is now being evaluated by mosaicity analysis (rocking curves) and x-ray topographic images as well as the diffraction resolution limit and overall data quality. In yet another study, use of hanging drop vapour diffusion geometry on the IML-2 shuttle mission showed, again via CCD video monitoring, growing apocrustacyanin C(sub 1) protein crystal executing near cyclic movement, reminiscent of Marangoni convection flow of fluid, the crystals serving as "markers" of the fluid flow. A review is given here of existing results and experience over several microgravity missions. Some comment is given on gel protein crystal growth in attempts to 'mimic' the benefits of microgravity on Earth. Finally, the recent new results from our experiments on the shuttle mission LMS are described. These results include CCD video as well as interferometry during the mission, followed, on return to Earth, by reciprocal space mapping at the NSLS, Brookhaven, and full X-ray data collection on LMS and Earth control lysozyme crystals. Diffraction data recorded from LMS and ground control apocrustacyanin C(sub 1) crystals are also described.
A pumped fluid heat rejection system (HRS) requires continuous flow of the working fluid to ensure that the components controlled by the HRS stay within their allowable temperature limits. An interruption of flow could result in violations of hardware qualification limits and mission failure. Some of these violations can happen within a few hours. Hence, quick detection of a flow fault to invoke mitigation measures is very critical. In typical pumped fluid HRS, dual or triple pumps are employed to switch the backup units in case the primary unit were to fail. The key metric for the selection of a fault detection system is that it should detect the fault much before the fault’s impact on the thermal health of the HRS controlled components is realized. A trade study was conducted to select such a system for the Europa Clipper Mission to Europa, a moon of Jupiter that is planned for a launch in 2023. Out of the several concepts studied, the most attractive one was a novel and simple concept that uses a low power film heater attached to a section of the HRS tubing. While the fluid flows at its nominal rate, the high thermal coupling of the flowing fluid leads to the tube being close to the fluid’s temperature. However, when the flow stops, the heater warms the small thermal mass of the tubing to a high temperature in a short span of time (~15 minutes). This large temperature rise would then imply that the flow must have stopped. This paper will describe the various concepts considered, the chosen concept, its implementation, and the results of developments tests to validate its performance.
Disclosed herein is a acoustic concentration of particles in a fluid flow that includes a substantially acoustically transparent membrane and a vibration generator that define a fluid flow path therebetween. The fluid flow path is in fluid communication with a fluid source and a fluid outlet and the vibration generator is disposed adjacent the fluid flow path and is capable of producing an acoustic field in the fluid flow path. The acoustic field produces at least one pressure minima in the fluid flow path at a predetermined location within the fluid flow path and forces predetermined particles in the fluid flow path to the at least one pressure minima.
Abstract Focused fluid flow is common in sedimentary basins worldwide, where flow structures often penetrate through sandy reservoir rocks, and clay‐rich caprocks. To better understand the mechanisms forming such structures, the impacts of the viscoelastic deformation and strongly nonlinear porosity‐dependent permeability of clay‐rich materials are assessed from an experimental and numerical modeling perspective. The experimental methods to measure the poroviscoelastic and transport properties of intact and remolded shale have been developed, and the experimental data is used to constrain the numerical simulations. It is demonstrated that viscoelastic deformation combined with nonlinear porosity‐dependent permeability triggers the development of localized flow channels, often imaged as seismic chimneys. The permeability inside a channel increases by several orders of magnitude compared to the background values. In addition, the propagation time scale and the channel size strongly depend on the material properties of the fluid and the rock. The time‐dependent behavior of the clay‐rich rock may play a key role in the long‐term integrity of the subsurface formations.
We report, in fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.
The fluid flow inside the space shuttle main engine (SSME) traverses through a complex geometrical configuration. The flow is compressible, viscous, and turbulent with pockets of separated regions. Several computer codes are being developed to solve three dimensional Navier-Stokes equations with different turbulence models for analyzing the SSME internal flow. The locally implicit scheme is a computationally efficient scheme which converges rapidly in multi-grid modes for elliptic problems. It has the promise of providing a rapidly converging algorithm for steady-state viscous flow problems.
Physical phenomena of fluid flows are often analyzed in computational fluid dynamics. With the progress in computer systems, complex 3D time-varying flow simulations are becoming feasible. There are many existing techniques for visualizing fluid flows; however, most of them do not consider the time-dependent nature of the flow. Hence, they may not provide adequate insights to the flow phenomena. New techniques for visualizing time-varying fluid flows are presented.
One of the fundamental differences of compressible fluid flows from incompressible fluid flows is the involvement of thermodynamics. This difference should be manifested in the design of numerical schemes. Unfortunately, the role of entropy, expressing irreversibility, is often neglected even though the entropy inequality, as a conceptual derivative, is verified for some first order schemes. In this paper, we refine the GRP solver to illustrate how the thermodynamical variation is integrated into the design of high resolution methods for compressible fluid flows and demonstrate numerically the importance of thermodynamic effects in the resolution of strong waves. As a by-product, we show that the GRP solver works for generic equations of state, and is independent of technical arguments.
Fluid transport in porous materials is commonly studied in geological samples (soil, sediments, etc.) or idealized systems, but the fluid flow through compacted granular materials, consisting of substantially strained granules, remains relatively unexplored. As a step toward filling this gap, we study a model of liquid transport in packings of deformable elastic shells using finite-element and lattice-Boltzmann methods. In this work, we find that the fluid flow abruptly vanishes as the porosity of the material falls below a critical value, and the flow obstruction exhibits features of a percolation transition. We further show that the fluid flow can be captured by a simplified permeability model in which the complex porous material is replaced by a collection of disordered capillaries, which are distributed and shaped by the percolation transition. To that end, we numerically explore the divergence of hydraulic tortuosity $τ_H$ and the decrease of a hydraulic radius Rh as the percolation threshold is approached. We interpret our results in terms of scaling predictions derived from the percolation theory applied to random packings of spheres.
The flow of fluids in nano-confinement has applications in separations, water purification, medical systems and in the recovery of fluids in petroleum systems. It is believed that fluids do not obey continuum laws when flowing in nanopores (or in confinement). This paper attempts to shed light on various nano confinement effects such as fluid-wall interactions, pore size and molecular geometry, using molecular dynamic simulation. Here, water, hexane, and methanol flow behaviors were simulated for pore diameters ranging from 1 to 8 nm. In addition to the density analysis of the confined fluids, the fluid flows in saturated nanopores were simulated by the sectional flow method. Water and methanol molecules were completely stabilized in the 1 nm pore. Hexane molecules could not enter the 1 nm pore, due to geometric considerations. For the 2 – 8 nm pores, all fluids showed reduced flow rate compared to the Hagen–Poiseuille flow, due to an interfacial molecular layer stabilized at the pore surface. Flow reduction observed in these studies is contrary to significant flow enhancements observed for fluid flow in carbon nanotubes of similar dimensions. Water flow showed almost constant stick length for all the pore sizes. Methanol flow had the largest stick length. Hexane flow was reduced because of overcrowding of molecules at the pore surface. The effect of confinement diminished with an increase in pore diameter.
Fluid flow control valve for regulating fluids in molecular quantities