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The effectiveness of four water-based machining fluids is studied as a function of their physical properties. Mathematical formulas are derived for optimal selection of machining fluids.
A new Monte Carlo simulation method is implemented which holds the number and kind of atoms constant rather than the number and kind of molecules for a molecular fluid mixture. Chemical reactions are allowed as a correlated move of atoms such as an interchange of atoms between molecules. Equilibrium chemical composition is determined as an average over the simulation along with equation of state quantities. Results are presented for N 2 + O 2 → 2NO at 30GPa and 3000K. The equilibrium composition is shown to be sensitive to the potentials between unlike species.
Here, we use physics-informed neural networks (PINNs) to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. For synthetic velocity data generated with the power-law viscosity model, the PINN-inferred viscosity model agrees with the analytical model for shear rates with large absolute values but deviates for shear rates near zero where the analytical model has an unphysical singularity. Once the viscosity model is learned the PINN method can solve the momentum conservation equation using only the boundary conditions.
Numerical difficulties, notably the non-monotonic behavior of the Wood speed of sound and the volume fraction positivity, associated with the reduced five-equation two-phase flow model of Kapila et al. (2001) [A.K. Kapila, R. Menikoff, J.B. Bdzil, S.F. Son, D.S. Stewart, 2001. Two-phase modeling of deflagration-to-detonation transition in granular materials: reduced equations, Physics of Fluids 13(10), 3002–3024] have been resolved in the past through the introduction of a frozen speed of sound and an algebraic approach for mechanical relaxation afforded by a pressure non-equilibrium six-equation model proposed by [R. Saurel, F. Petitpas, R.A. Berry, 2009. Simple and efficient relaxation methods for interfaces separating compressible fluids, cavitating flows and shocks in multiphase mixture, J. Comput. Phys. 228, 1678–1712]. By contrast, it is explored and demonstrated in this work that these difficulties can in fact still be resolved within the numerical scheme for solving the reduced five-equation model by numerically replacing the Wood speed of sound for the estimates of wave speeds in the approximate Riemann solver HLLC with the monotonic mixture speed of sound for a transport five-equation model. For shock interface (artificial mixture separating pure or nearly pure fluids) interaction problems, with the apparent advantage of monotonic behavior of the speed of sound in the interface, the effect of the numerical replacement is also confined to the interface. Differences other than the behavior of the speed of sound within the interface in the solutions due to the replacement diminish with increasing resolution when reasonable solution can be obtained with Wood speed of sound. For cavitating/expansion problems in physical fluid mixture, it is pointed out and explained why acoustics in the numerical solutions still propagate at the Wood speed of sound (therefore consistent with the reduced five-equation model) even though in some cases a much higher speed of sound like the numerical replacement above for solving the reduced five-equation model or the frozen speed of sound for solving a six-equation model is used for the estimates of wave speeds in the HLLC scheme. A variant of the five-equation two-phase flow model by Saurel et al. (2008) [R. Saurel, F. Petitpas, R. Abgrall, 2008. Modelling phase transition in metastable liquids: application to cavitating and flashing flows, J. Fluid Mech. 607, 313–350] is then constructed for liquid-vapor phase transition in cavitating flows. The relaxation toward thermo-chemical equilibrium during phase transition is achieved by solving a simple system of algebraic equations for the equilibrium state variables for better efficiency, following Pelanti and Shyue (2014) [M. Pelanti, K.-M. Shyue, 2014. A mixture-energy-consistent six-equation two-phase numerical model for fluid with interfaces, cavitation and evaporation waves. J. Comput. Phys. 259, 331–357]. Therefore, the current model retains both the simplicity afforded by the five-equation model and the efficiency of the algebraic relaxation solver. An alternative algebraic approach for handling the non-conservative term (the so-called K∇ · u term) in the reduced five-equation model for mechanical equilibrium of a liquid-vapor mixture is also explored by enforcing the thermal equilibrium at the same time. Finally, numerical results of sample tests in both one and two dimensions in the literature as well as that in three dimensions demonstrate the effectiveness and ability of the proposed model to simulate cavitating flows. An interesting mechanism of shock generation by acoustics in water due to phase transition is then found by the numerical simulations.
This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.
In this work, collisionless Landau fluid models are combined with collisional fluid models using a Padé approximation that is accurate in both collisionality limits. The collisionless models capture Landau damping through a nonlocal integro-differential sub-diffusion operator with ballistic characteristics. Collisional extensions of Landau fluid models are derived by analyzing the higher order moment equations which combine a Landau closure with collisional friction forces. The model derived here evolves fluid moments for density, parallel velocity, and anisotropic pressure and includes the frictional heat flux, the parallel thermal force and anisotropic electrical conductivity. Since anisotropies must vanish in the collisional limit, a simple closure can be derived if the friction force neglects pitch-angle scattering for the closure moments themselves. The resulting plasma physics model is potentially quite useful for applications in magnetic fusion and astrophysics.
Hall-magnetohydrodynamic (Hall-MHD) theory, two-fluid simulations, and kinetic simulations are used in this work to investigate the cross-field transport properties of Kelvin–Helmholtz instabilities in nonuniform low-beta collisionless plasmas. Hall-MHD analysis shows how the linear properties of the instability are modified by density gradients and magnetization. High-order accurate two-fluid and kinetic simulations, with complete dynamics of finite-mass electrons and ions, are applied to a suite of parameter cases to systematically assess the effects of diamagnetic drift, magnetization, charge separation, and finite Larmor motion. Initialization of exact two-species kinetic equilibria facilitates the study of isolated physical effects and enables detailed cross-comparisons between two-fluid and kinetic simulations, including for cases where ion gyroradii are comparable to gradient scale lengths. For nonuniform plasmas with significant space charge, the results of two-fluid and kinetic simulations are found to disagree with Hall-MHD predictions. Kelvin–Helmholtz instability growth rates, per unit shear, are shown to be smaller when ion diamagnetic drift and $E \times B$ drift are parallel and larger when the two drifts are antiparallel. The effect is attributed to polarization drift in the shear layer, which leads to redistribution of charge, alters the electric field that drives plasma advection, and consequently modifies growth rates. Instability-induced mass transport for different parameters is characterized in terms of the flux across the shear layer and a simplified diffusion model. Distribution functions from kinetic simulations are shown to deviate substantially from Maxwellian reconstructions, indicating the importance of kinetic physics during the nonlinear phase of the instability.
Physical properties of labyrinthine fluids, and caloric stimulation of semicircular canals
Physical, metallurgical, chemical, and thermodynamic surveys of high pressure tanks and plumbing systems of Apollo spacecraft
Some physical, analytical, and computational aspects of viscous flow are examined with reference to examples of computed flows. The discussion of the physical aspects covers the development of important scales used to reference flow phenomena in laminar and turbulent shear layers; the usefulness of the concepts of circulation and vorticity; and some relatively large-scale organized structures that have recently been identified in transitional and fully developed turbulent flows. Among the analytical aspects discussed are a compact presentation of the compressible Navier-Stokes equations, the Reynolds-averaged form of these equations, and a simplified description of some forms of turbulent models. Finally, results of a simulation of the onset of transition, direct turbulence simulations, and large-eddy simulations are given.
The existence of time-periodic stochastic motions of an incompressible fluid is obtained. Here the fluid is subject to a time-periodic body force and an additional time-periodic stochastic force that is produced by a rigid body moves periodically stochastically with the same period in the fluid.
Table-top laser–plasma ion accelerators have many exciting applications, many of which require ion beams with simultaneous narrow energy spread and high conversion efficiency. However, achieving these requirements has been elusive. We report the experimental demonstration of laser-driven ion beams with narrow energy spread and energies up to 18 MeV per nucleon and ~5% conversion efficiency (that is 4 J out of 80-J laser). Using computer simulations we identify a self-organizing scheme that reduces the ion energy spread after the laser exits the plasma through persisting self-generated plasma electric (~1012 V m -1 ) and magnetic (~104 T) fields. Furthermore, these results contribute to the development of next generation compact accelerators suitable for many applications such as isochoric heating for ion-fast ignition and producing warm dense matter for basic science.
The cost, size and availability of electron accelerators are dominated by the achievable accelerating gradient. Conventional high-brightness radio-frequency accelerating structures operate with 30–50 MeVm -1 gradients. Electron accelerators driven with optical or infrared sources have demonstrated accelerating gradients orders of magnitude above that achievable with conventional radio-frequency structures. However, laser-driven wakefield accelerators require intense femtosecond sources and direct laser-driven accelerators suffer from low bunch charge, sub-micron tolerances and sub-femtosecond timing requirements due to the short wavelength of operation. Here we demonstrate linear acceleration of electrons with keV energy gain using optically generated terahertz pulses. Terahertz-driven accelerating structures enable high-gradient electron/proton accelerators with simple accelerating structures, high repetition rates and significant charge per bunch. As a result, these ultra-compact terahertz accelerators with extremely short electron bunches hold great potential to have a transformative impact for free electron lasers, linear colliders, ultrafast electron diffraction, X-ray science and medical therapy with X-rays and electron beams.
In order to provide a comprehensive theoretical description of MgSiO 3 at extreme conditions, we combine results from path integral Monte Carlo and density functional molecular dynamics simulations and generate a consistent equation of state for this material. We consider a wide range of temperature and density conditions from 10 4 to 10 8 K and from 0.321 to 64.2 g/cm -3 (0.1- to 20-fold the ambient density). We study how the L and K shell electrons are ionized with increasing temperature and pressure. We derive the shock Hugoniot curve and compare with experimental results. Our Hugoniot curve is in good agreement with the experiments, and we predict a broad compression maximum that is dominated by the K shell ionization of all three nuclei while the peak compression ratio of 4.70 is obtained when the Si and Mg nuclei are ionized. To conclude, we analyze the heat capacity and structural properties of the liquid.
The development of accurate reduced descriptions and improved closures for magnetic reconnection is an important and a long‐standing challenge in plasma physics. The four‐fluid approach, and associated closures, that were investigated have the potential to improve the accuracy of plasma fluid models, capturing physical effects which would otherwise require a kinetic description. If successful, this approach could have an important impact for the modeling of laboratory and space plasmas. The major goals of this project were to develop new machine learning (ML) tools based on sparse and symbolic regression techniques, and to extract interpretable and generalizable reduced models (e.g., in the form of partial differential equations - PDEs) from data generated by first principles plasma simulations. Preserving interpretability of such data‐driven models is key to addressing the long‐standing theoretical and numerical challenges. Prior proof‐of‐principle studies have demonstrated the enormous potential of this approach, by recovering the well‐established hierarchy of plasma equations (from Vlasov to MHD) from data produced by particle‐in‐cell (PIC) simulations. Our goal in this project was to extend and apply these new tools to construct better kinetic closures for magnetic reconnection; to derive better models of particle injection and acceleration by this fundamental plasma process; and to use this understanding to accelerate the development of multi‐scale plasma algorithms. While our immediate focus was on the problem of magnetic reconnection, the tools that were will developed are general and applicable to other areas of plasma physics, and more broadly to many‐body phenomena. We anticipate that the development of these multi‐scale models will have a significant impact across different areas of plasma science, from fusion to space and astrophysical plasmas.
In this grant period, the focus has been on enhancement and application of the direct simulation Monte Carlo (DSMC) particle method for computing hypersonic flows of re-entry vehicles. Enhancement efforts dealt with modeling gas-gas interactions for thermal non-equilibrium relaxation processes and gas-surface interactions for prediction of vehicle surface temperatures. Both are important for application to problems of engineering interest. The code was employed in a parametric study to improve future applications, and in simulations of aeropass maneuvers in support of the Magellan mission. Detailed comparisons between continuum models for internal energy relaxation and DSMC models reveals that several discrepancies exist. These include definitions of relaxation parameters and the methodologies for implementing them in DSMC codes. These issues were clarified and all differences were rectified in a paper (Appendix A) submitted to Physics of Fluids A, featuring several key figures in the DSMC community as co-authors and B. Haas as first author. This material will be presented at the Fluid Dynamics meeting of the American Physical Society on November 21, 1993. The aerodynamics of space vehicles in highly rarefied flows are very sensitive to the vehicle surface temperatures. Rather than require prescribed temperature estimates for spacecraft as is typically done in DSMC methods, a new technique was developed which couples the dynamic surface heat transfer characteristics into the DSMC flow simulation code to compute surface temperatures directly. This model, when applied to thin planar bodies such as solar panels, was described in AIAA Paper No. 93-2765 (Appendix B) and was presented at the Thermophysics Conference in July 1993. The paper has been submitted to the Journal of Thermophysics and Heat Transfer. Application of the DSMC method to problems of practical interest requires a trade off between solution accuracy and computational expense and limitations. A parametric study was performed and reported in AIAA Paper No. 93-2806 (Appendix C) which assessed the accuracy penalties associated with simulations of varying grid resolution and flow domain size. The paper was also presented at the Thermophysics Conference and will be submitted to the journal shortly. Finally, the DSMC code was employed to assess the pitch, yaw, and roll aerodynamics of the Magellan spacecraft during entry into the Venus atmosphere at off-design attitudes. This work was in support of the Magellan aerobraking maneuver of May 25-Aug. 3, 1993. Furthermore, analysis of the roll characteristics of the configuration with canted solar panels was performed in support of the proposed 'Windmill' experiment. Results were reported in AIAA Paper No. 93-3676 (Appendix D) presented at the Atmospheric Flight Mechanics Conference in August 1993, and were submitted to Journal of Spacecraft and Rockets.
The NASA Lewis Research Center (LeRC) defines and develops advanced technology for high priority national needs. The work of the Center is directed toward new propulsion, power, and communications technologies for application to aeronautics and space, so that U.S. leadership in these areas is ensured. The end product is knowledge, usually in a report, that is made fully available to potential users--the aircraft engine industry, the energy industry, the automotive industry, the space industry, and other NASA centers. In addition to offices and laboratories for almost every kind of physical research in such fields as fluid mechanics, physics, materials, fuels, combustion, thermodynamics, lubrication, heat transfer, and electronics, LeRC has a variety of engineering test cells for experiments with components such as compressors, pumps, conductors, turbines, nozzles, and controls. A number of large facilities can simulate the operating environment for a complete system: altitude chambers for aircraft engines; large supersonic wind tunnels for advanced airframes and propulsion systems; space simulation chambers for electric rockets or spacecraft; and a 420-foot-deep zero-gravity facility for microgravity experiments. Some problems are amenable to detection and solution only in the complete system and at essentially full scale. By combining basic research in pertinent disciplines and generic technologies with applied research on components and complete systems, LeRC has become one of the most productive centers in its field in the world. This brochure describes a number of the facilities that provide LeRC with its exceptional capabilities.
Abstract We studied the role of electron physics in 3‐D two‐fluid 10‐moment simulation of Ganymede's magnetosphere. The model captures nonideal physics like the Hall effect, electron inertia, and anisotropic, nongyrotropic pressure effects. A series of analyses were carried out: (1) The resulting magnetic field topology and electron and ion convection patterns were investigated. The magnetic fields were shown to agree reasonably well with in situ measurements by the Galileo satellite. (2) The physics of collisionless magnetic reconnection were carefully examined in terms of the current sheet formation and decomposition of generalized Ohm's law. The importance of pressure anisotropy and nongyrotropy in supporting the reconnection electric field is confirmed. (3) We compared surface “brightness” morphology, represented by surface electron and ion pressure contours, with oxygen emission observed by the Hubble Space Telescope. The correlation between the observed emission morphology and spatial variability in electron/ion pressure was demonstrated. Potential extension to multi‐ion species in the context of Ganymede and other magnetospheric systems is also discussed.