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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Development of Advanced Casing Treatments for Flow Control

Under the Base R&T and Ultra Efficient Engine Technology programs, the NASA-Goddard Space Flight Center Compressor Branch is investigating flow control strategies required to increase the loading and efficiency of core compressors while maintaining current levels of operability. Flow-control strategies being studied include advanced casing treatments, wall jet injection, and blade-tip injection for compressor stability enhancement, directed jets for surface boundary layer control, and vortex-generating devices. The use of computational fluid dynamics (CFD) simulations to assess the effectiveness of flow-control devices and to guide their design is a key element in this research. CFD simulations serve to screen potential flow-control concepts at a lower cost than executing physical experiments in turbomachinery facilities. CFD simulations also provide guidance in designing physical experiments for those flow control concepts, which appear promising.

Keith, Theo G., Jr.↗
DOE OSTI2021

Striving to translate shale physics across ten orders of magnitude: What have we learned?

Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO 2 , or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.

58 GEOSCIENCES↗
NASA NTRS1979

Flow patterns near a conical sonic line

A study is made in the physical plane of the flow properties near a conical cross flow sonic line. The shapes of the Mach lines at regular and singular sonic points are presented. Through a study of the jump conditions at a singular sonic point, the possibility of a double sonic line is discussed. The properties of bubbles of supersonic cross flow are examined. Numerical results are presented to support some of the theoretical observations.

Salas, M. D.↗
DOE OSTI2016

Notes from 1999 on computational algorithm of the Local Wave-Vector (LWV) model for the dynamical evolution of the second-rank velocity correlation tensor starting from the mean-flow-coupled Navier-Stokes equations

These notes present an account of the Local Wave Vector (LWV) model of a turbulent flow defined throughout physical space. The previously-developed Local Wave Number (LWN) model is taken as a point of departure. Some general properties of turbulent fields and appropriate notation are given first. The LWV model is presently restricted to incompressible flows and the incompressibility assumption is introduced at an early point in the discussion. The assumption that the turbulence is homogeneous is also introduced early on. This assumption can be relaxed by generalizing the space diffusion terms of LWN, but the present discussion is focused on a modeling of homogeneous turbulence.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Representation and display of vector field topology in fluid flow data sets

The visualization of physical processes in general and of vector fields in particular is discussed. An approach to visualizing flow topology that is based on the physics and mathematics underlying the physical phenomenon is presented. It involves determining critical points in the flow where the velocity vector vanishes. The critical points, connected by principal lines or planes, determine the topology of the flow. The complexity of the data is reduced without sacrificing the quantitative nature of the data set. By reducing the original vector field to a set of critical points and their connections, a representation of the topology of a two-dimensional vector field that is much smaller than the original data set but retains with full precision the information pertinent to the flow topology is obtained. This representation can be displayed as a set of points and tangent curves or as a graph. Analysis (including algorithms), display, interaction, and implementation aspects are discussed.

Helman, James↗
NASA NTRS1980

An electrostatic analog for generating cascade grids

Accurate and efficient numerical simulation of flows through turbomachinery blade rows depends on the topology of the computational grids. These grids must reflect the periodic nature of turbomachinery blade row geometries and conform to the blade shapes. Three types of grids can be generated that meet these minimal requirements: through-flow grids, O-type grids, and C-type grids. A procedure which can be used to generate all three types of grids is presented. The resulting grids are orthogonal and can be stretched to capture the essential physics of the flow. A discussion is also presented detailing the extension of the generation procedure to three dimensional geometries.

Adamczyk, J. J.↗
NASA NTRS2012

Asymmetric Uncertainty Expression for High Gradient Aerodynamics

When the physics of the flow around an aircraft changes very abruptly either in time or space (e.g., flow separation/reattachment, boundary layer transition, unsteadiness, shocks, etc), the measurements that are performed in a simulated environment like a wind tunnel test or a computational simulation will most likely incorrectly predict the exact location of where (or when) the change in physics happens. There are many reasons for this, includ- ing the error introduced by simulating a real system at a smaller scale and at non-ideal conditions, or the error due to turbulence models in a computational simulation. The un- certainty analysis principles that have been developed and are being implemented today do not fully account for uncertainty in the knowledge of the location of abrupt physics changes or sharp gradients, leading to a potentially underestimated uncertainty in those areas. To address this problem, a new asymmetric aerodynamic uncertainty expression containing an extra term to account for a phase-uncertainty, the magnitude of which is emphasized in the high-gradient aerodynamic regions is proposed in this paper. Additionally, based on previous work, a method for dispersing aerodynamic data within asymmetric uncer- tainty bounds in a more realistic way has been developed for use within Monte Carlo-type analyses.

Pinier, Jeremy T↗
NASA NTRS1978

A scheme for computing surface layer turbulent fluxes from mean flow surface observations

A physical model and computational scheme are developed for generating turbulent surface stress, sensible heat flux and humidity flux from mean velocity, temperature and humidity at some fixed height in the atmospheric surface layer, where conditions at this reference level are presumed known from observations or the evolving state of a numerical atmospheric circulation model. The method is based on coupling the Monin-Obukov surface layer similarity profiles which include buoyant stability effects on mean velocity, temperature and humidity to a force-restore formulation for the evolution of surface soil temperature to yield the local values of shear stress, heat flux and surface temperature. A self-contained formulation is presented including parameterizations for solar and infrared radiant fluxes at the surface. Additional parameters needed to implement the scheme are the thermal heat capacity of the soil per unit surface area, surface aerodynamic roughness, latitude, solar declination, surface albedo, surface emissivity and atmospheric transmissivity to solar radiation.

Hoffert, M. I.↗
NASA NTRS1981

An efficient code for the simulation of nonhydrostatic stratified flow over obstacles

The physical model and computational procedure of the code is described in detail. The code is validated in tests against a variety of known analytical solutions from the literature and is also compared against actual mountain wave observations. The code will receive as initial input either mathematically idealized or discrete observational data. The form of the obstacle or mountain is arbitrary.

Pihos, G. G.↗
NASA NTRS1984

Analysis of physical-chemical processes governing SSME internal fluid flows

The efforts to adapt CHAM's computational fluid dynamics code, PHOENICS, to the analysis of flow within the high pressure fuel turbopump (HPFTP) aft-platform seal cavity of the SSME are summarized. In particular, the special purpose PHOENICS satellite and ground station specifically formulated for this application are listed and described, and the preliminary results of the first part two-dimensional analyses are presented and discussed. Planned three-dimensional analyses are also briefly outlined. To further understand the mixing and combustion processes in the SSME fuelside preburners, a single oxygen-hydrogen jet element was investigated.

Singhal, A. K.↗
NASA NTRS1976

Laser velocimetry applied to transonic and supersonic aerodynamics

As a further demonstration of the capabilities of laser velocity in compressible aerodynamics, measurements obtained in a Mach 2.9 separated turbulent boundary layer and in the transonic flow past a two-dimensional airfoil section are presented and compared to data realized by conventional techniques. In the separated-flow study, the comparisons were made against pitot-static pressure data. Agreement in mean velocities was realized where the pressure measurements could be considered reliable; however, in regions of instantaneous reverse velocities, the laser results were found to be consistent with the physics of the flow whereas the pressure data were not. The laser data obtained in regions of extremely high turbulence suggest that velocity biasing does not occur if the particle occurrence rate is low relative to the turbulent fluctuation rate. Streamwise turbulence intensities are also presented. In the transonic airfoil study, velocity measurements obtained immediately outside the upper surface boundary layer of a 6-inch chord MACA 64A010 airfoil are compared to edge velocities inferred from surface pressure measurements. For free-stream Mach numbers of 0.6 and 0.8, the agreement in results was very good. Dual scatter optical arrangements in conjunction with a single particle, counter-type signal processor were employed in these investigations. Half-micron-diameter polystyrene spheres and naturally occurring condensed oil vapor acted as light scatterers in the two respective flows. Bragg-cell frequency shifting was utilized in the separated flow study.

Johnson, D. A.↗
DOE OSTI2018

The confluences of ideas leading to, and the flow of ideas emerging from, individual-based modeling of riverine fishes

In this review article, we trace the history of events leading to the development of individual-based models (IBMs) to represent aquatic organisms in rivers and streams. As a metaphor, we present this history as a series of confluences between individual scientists (tributaries) sharing ideas. We describe contributions of these models to science and management. One iconic feature of river IBMs is the linkage between flow and the physical habitat experienced by individual animals, and the first model that focused on this linkage is briefly described. We continue by reviewing the contributions of riverine IBMs to eight broad areas of scientific inquiry. The first four areas include research to understand the effects of flow regimes on fish populations, species interactions (e.g., size-mediated competition and predation), fish movement and habitat selection, and contaminant and water quality impacts on populations. Next, we review research using IBMs to guide conservation biology of imperiled taxa through population viability analysis, including research to understand river fragmentation by dams and reconnection, to understand genetic outcomes for riverine metapopulations, and to anticipate the future effects of temperature and climate change. This rich body of literature has contributed to both theoretical insights (e.g., about animal behavior and life history) and applied insights (e.g., population-level effects of flow regimes, temperature, and the effects of hydropower and other industries that share rivers with aquatic biota). Lastly, we finish by exploring promising branches that lie ahead in the braided, downstream channel that represents future river modeling research.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Numerical simulations of high-speed flows about waveriders with sharp leading edges

A procedure is developed for the numerical simulation of stagnation-free inviscid supersonic and hypersonic flows about waveriders with sharp leading edges. The numerical approach involves the development of a specialized grid generator (named HYGRID), an algebraic solution-adaptive grid scheme, and a modified flow solving method. A comparison of the results obtained for several waverider geometries with exact solutions, other numerical solutions, and experimental results demonstrated the ability of the new procedure to produce stagnation-free Euler solutions about sharp-edged configurations and to describe the physics of the flow in these regions.

Jones, Kevin D.↗
DOE OSTI2022

Toward a machine-guided approach to energetic material discovery

In this article, we trained a machine learning (ML) model to connect microstructural details of an energetic material formulation to its performance for the purpose of guiding the discovery of new explosive formulations. Our hypothesis was that the algorithm would robustly learn the training data and produce an accurate surrogate model. Specifically, the algorithm learned the relationship between details of the void size distribution (VSD), initiating shock pressure, and the energetic material performance. We used realistic constraints on the VSD and a range of cases were ingested by a physically informed reactive flow model working within a hydrodynamic solver running on high-performance computing resources. The ML algorithm produced a surrogate model that accurately predicted known test points around the parameter space. In addition to the utility of the model and the process used for its development, we noted interesting comparisons between what we, the authors—subject matter experts, would heuristically conclude from the training data and the surrogate model predictions. We detected nuanced details that were missed by the surrogate model; however, these details are not important to an energetic material formulator. We concluded that the algorithm did indeed robustly learn the training data and produce an accurate surrogate model. We further concluded that the surrogate model is a powerful tool to guide the formulator in the absence of subject matter experts and limited-access computing resources.

42 ENGINEERING↗
DOE OSTI2021

Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. Furthermore, the results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

A numerical study of a class of TVD schemes for compressible mixing layers

At high Mach numbers the two-dimensional time-developing mixing layer develops shock waves, positioned around large-scale vortical structures. A suitable numerical method has to be able to capture the inherent instability of the flow, leading to the roll-up of vortices, and also must be able to capture shock waves when they develop. Standard schemes for low speed turbulent flows, for example spectral methods, rely on resolution of all flow-features and cannot handle shock waves, which become too thin at any realistic Reynolds number. The performance of a class of second-order explicit total variation diminishing (TVD) schemes on a compressible mixing layer problem was studied. The basic idea is to capture the physics of the flow correctly, by resolving down to the smallest turbulent length scales, without resorting to turbulence or sub-grid scale modeling, and at the same time capture shock waves without spurious oscillations. The present study indicates that TVD schemes can capture the shocks accurately when they form, but (without resorting to a finer grid) have poor accuracy in computing the vortex growth. The solution accuracy depends on the choice of limiter. However a larger number of grid points are in general required to resolve the correct vortex growth. The low accuracy in computing time-dependent problems containing shock waves as well as vortical structures is partly due to the inherent shock-capturing property of all TVD schemes. In order to capture shock waves without spurious oscillations these schemes reduce to first-order near extrema and indirectly produce clipping phenomena, leading to inaccuracy in the computation of vortex growth. Accurate simulation of unsteady turbulent fluid flows with shock waves will require further development of efficient, uniformly higher than second-order accurate, shock-capturing methods.

Sandham, N. D.↗
DOE OSTI2018

The mechanisms, dynamics, and implications of self-sealing and CO 2 resistance in wellbore cements

Here, this study analyzes the dynamics and mechanisms of the interactions of carbonated brine with hydrated-Portland-cement; in particular, the study focuses on self-sealing, a process whereby hydrated-Portland cement reacts with carbonated brine to for silica and calcium carbonate in sufficient quantities to seal the flow pathway. The analysis is based on a comprehensive set of reactive-transport simulations that explore the complex coupled dynamics between the fluid flow and mineral reactions that underlie self-sealing, and it relies heavily on the synthesis of the extensive body of work on wellbore integrity that has been conducted over the past decade. The analysis explores a large chemical and mineralogical diversity and a wide range in physical conditions and flow regimes, attempting to assess the robustness of the analysis. Self-sealing conditions arise over a wide range in cement properties and reservoir conditions. Although some properties and conditions promote a stronger self-sealing response, self-sealing occurs for a wide range of Ca:Si ratios in cement and for various reservoir fluid compositions. Self-sealing conditions move along a wellbore proportional to the flux of the leaking carbonated brine, and the reaction zone spreads out proportional to the fluid velocity, where volumetric flux and velocity are related by porosity (flux = velocity * porosity). However, self-sealing conditions can be maintained in a specific section of a wellbore by controlling the pressure drive and/or effective wellbore permeability, which in turn can limit the flux and velocity of any leaking fluid. Finally, the phases produced by hydrating Portland cement represent a carbonic cement that will react with a carbonated brine to produce end products (calcium carbonate and silica) that can maintain integrity in the presence of carbonic acid. Lastly, the attributes that make hydrated Portland cement phases a carbonic cement are required for self-sealing.

58 GEOSCIENCES↗