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NASA NTRS2005

Automated Extraction of Flow Features

Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process of most fluid handling devices. In order to efficiently and effectively use the results of a CFD simulation, visualization tools are often used. These tools are used in all stages of the CFD simulation including pre-processing, interim-processing, and post-processing, to interpret the results. Each of these stages requires visualization tools that allow one to examine the geometry of the device, as well as the partial or final results of the simulation. An engineer will typically generate a series of contour and vector plots to better understand the physics of how the fluid is interacting with the physical device. Of particular interest are detecting features such as shocks, re-circulation zones, and vortices (which will highlight areas of stress and loss). As the demand for CFD analyses continues to increase the need for automated feature extraction capabilities has become vital. In the past, feature extraction and identification were interesting concepts, but not required in understanding the physics of a steady flow field. This is because the results of the more traditional tools like; isc-surface, cuts and streamlines, were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of a great deal of interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snapshot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for co-processing environments). Methods must be developed to abstract the feature of interest and display it in a manner that physically makes sense.

Dorney, Suzanne↗

DOE OSTI2021

Therapeutic targeting of membrane-associated proteins in central nervous system tumors

The activity of the most complex system, the central nervous system (CNS) is profoundly regulated by a huge number of membrane-associated proteins (MAP). A minor change stimulates immense chemical changes and the elicited response is organized by MAP, which acts as a receptor of that chemical or channel enabling the flow of ions. Slight changes in the activity or expression of these MAPs lead to severe consequences such as cognitive disorders, memory loss, or cancer. CNS tumors are heterogeneous in nature and hard-to-treat due to random mutations in MAPs; like as overexpression of EGFRvIII/TGFβR/VEGFR, change in adhesion molecules α5β3 integrin/SEMA3A, imbalance in ion channel proteins, etc. Extensive research is under process for developing new therapeutic approaches using these proteins such as targeted cytotoxic radiotherapy, drug-delivery, and prodrug activation, blocking of receptors like GluA1, developing viral vector against cell surface receptor. The combinatorial approach of these strategies along with the conventional one might be more potential. Henceforth, our review focuses on in-depth analysis regarding MAPs aiming for a better understanding for developing an efficient therapeutic approach for targeting CNS tumors.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels

Modelling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. We show that when the kernel of these emulators is also learned from data (using kernel flows, a variant of cross-validation), then the resulting data-driven models are not only faster than equation-based models but are easier to train than neural networks such as the long short-term memory neural network. In addition, they are also more accurate and predictive than the latter. When trained on geophysical observational data, for example the weekly averaged global sea-surface temperature, considerable gains are also observed by the proposed technique in comparison with classical partial differential equation-based models in terms of forecast computational cost and accuracy. When trained on publicly available re-analysis data for the daily temperature of the North American continent, we observe significant improvements over classical baselines such as climatology and persistence-based forecast techniques. Although our experiments concern specific examples, the proposed approach is general, and our results support the viability of kernel methods (with learned kernels) for interpretable and computationally efficient geophysical forecasting for a large diversity of processes.

58 GEOSCIENCES↗

NASA NTRS1989

Application of a vectorized particle simulation in high-speed near-continuum flow

An efficient particle simulation technique, developed for use on vector architecture based supercomputers for studying hypersonic rarefied gas flows is employed to simulate the complex wake generated by Mach six flow over a 10 deg half-angle wedge for freestream Reynolds numbers of 1780 and 3560. Data obtained are compared against higher Reynolds number experimental results. Simulations utilized as many as 10 to the 5th computational cells and 10 to the 7th simulated particles having power-law interaction potentials. A code performance of 1.8-2.4 microsec of Cray-2 CPU time to process a single particle per timestep is achieved. Diffuse adiabatic and isothermal wedge surface models are used in this investigation. Although the wedge geometry is two-dimensional, the simulation incorporates a width-wise direction, resulting in a three-dimensional computation.

Woronowicz, Michael S.↗

NASA NTRS1997

Complex Volume Grid Generation Through the Use of Grid Reusability

This paper presents a set of surface and volume grid generation techniques which reuse existing surface and volume grids. These methods use combinations of data manipulations to reduce grid generation time, improve grid characteristics, and increase the capabilities of existing domain discretization software. The manipulation techniques utilize physical and computational domains to produce basis function on which to operate and modify grid character and smooth grids using Trans-Finite Interpolation, a vector interpolation method and parametric re-mapping technique. With these new techniques, inviscid grids can be converted to viscous grids, multiple zone grid adaption can be performed to improve CFD solver efficiency, and topological changes to improve modeling of flow fields can be done simply and quickly. Examples of these capabilities are illustrated as applied to various configurations.

Alter, Stephen J.↗

DOE OSTI2023

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗

NASA NTRS1994

Southern hemisphere low level wind circulation statistics from the Seasat scatterometer

Analyses of remotely sensed low-level wind vector data over the Southern Ocean are performed. Five-day averages and monthly means are created and the month-to-month variability during the winter (July-September) of 1978 is investigated. The remotely sensed winds are compared to the Australian Bureau of Meteorology (ABM) and the National Meteorological Center (NMC) surface analyses. In southern latitudes the remotely sensed winds are stronger than what the weather services' analyses suggest, indicating under-estimation by ABM and NMC in these regions. The evolution of the low-level jet and the major stormtracks during the season are studied and different flow regimes are identified. The large-scale variability of the meridional flow is studied with the aid of empirical orthogonal function (EOF) analysis. The dominance of quasi-stationary wave numbers 3,4, and 5 in the winter flows is evident in both the EOF analysis and the mean flow. The signature of an exceptionally strong blocking situation is evident in July and the special conditions leading to it are discussed. A very large intraseasonal variability with different flow regimes at different months is documented.

Levy, Gad↗

NASA NTRS1997

UFLIC: A Line Integral Convolution Algorithm for Visualizing Unsteady Flows

This paper presents an algorithm, UFLIC (Unsteady Flow LIC), to visualize vector data in unsteady flow fields. Using the Line Integral Convolution (LIC) as the underlying method, a new convolution algorithm is proposed that can effectively trace the flow's global features over time. The new algorithm consists of a time-accurate value depositing scheme and a successive feed-forward method. The value depositing scheme accurately models the flow advection, and the successive feed-forward method maintains the coherence between animation frames. Our new algorithm can produce time-accurate, highly coherent flow animations to highlight global features in unsteady flow fields. CFD scientists, for the first time, are able to visualize unsteady surface flows using our algorithm.

Shen, Han-Wei↗

DOE OSTI2023

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a collection of objects stored within an RDate file, stateWaterRightsHarmonized.RData. Each object either describes how one should interact with the other objects, or contains the harmonized water rights data. Here is each object described in detail: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. The column headers for each data frame are: waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. basinNum - The alpha-numeric identifier of the WMA the record belongs to. priorityDate - The priority date associated with the right. allocatedFlow - The maximum flow of the allocation in cubic feet per second (ft3s-1). source - Whether the right is for surface water or groundwater. origWaterUse - The original stated water use from the state. waterUse - The water use category under the unified use categories established here. rightsByState_surface: A list of the cleaned surface water rights collected from each state. See rightsByState_ground for more details on how the data is formatted. fullRightsRecs: A list of the combined groundwater and surface water records for each state. Essentially, these lists are the merging of rightsByState_ground and rightsByState_surface by state. See rightsByState_ground for more details on how the data is formatted. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page.

Economics↗

DOE OSTI2023

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use data sets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (a) a form of Nonlinear Vector Autoregression (NVAR), and (b) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures.

58 GEOSCIENCES↗

DOE OSTI2020

Rigidity of MHD equilibria to smooth incompressible ideal motion near resonant surfaces

In ideal MHD, the magnetic flux is advected by the plasma motion, freezing flux-surfaces into the flow. An MHD equilibrium is reached when the flow relaxes and force balance is achieved. We ask what classes of MHD equilibria can be accessed from a given initial state via smooth incompressible ideal motion. It is found that certain boundary displacements are formally not supported. This follows from yet another investigation of the Hahm–Kulsrud–Taylor (HKT) problem, which highlights the resonant behaviour near a rational layer formed by a set of degenerate critical points in the flux-function. When trying to retain the mirror symmetry of the flux-function with respect to the resonant layer, the vector field that generates the volume-preserving diffeomorphism vanishes at the identity to all order in the time-like path parameter.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1980

A vectorized code for calculating laminar and turbulent hypersonic flows about blunt axisymmetric bodies at zero and small angles of attack

A user's guide is provided for a computer code which calculates the laminar and turbulent hypersonic flows about blunt axisymmetric bodies, such as spherically blunted cones, hyperboloids, etc., at zero and small angles of attack. The code is written in STAR FORTRAN language for the CDC-STAR-100 computer. Time-dependent, viscous-shock-layer-type equations are used to describe the flow field. These equations are solved by an explicit, two-step, time asymptotic, finite-difference method. For the turbulent flow, a two-layer, eddy-viscosity model is used. The code provides complete flow-field properties including shock location, surface pressure distribution, surface heating rates, and skin-friction coefficients. This report contains descriptions of the input and output, the listing of the program, and a sample flow-field solution.

Kumar, A.↗

NASA NTRS1997

Illustrating Surface Shape in Volume Data via Principal Direction-Driven 3D Line Integral Convolution

The three-dimensional shape and relative depth of a smoothly curving layered transparent surface may be communicated particularly effectively when the surface is artistically enhanced with sparsely distributed opaque detail. This paper describes how the set of principal directions and principal curvatures specified by local geometric operators can be understood to define a natural 'flow' over the surface of an object, and can be used to guide the placement of the lines of a stroke texture that seeks to represent 3D shape information in a perceptually intuitive way. The driving application for this work is the visualization of layered isovalue surfaces in volume data, where the particular identity of an individual surface is not generally known a priori and observers will typically wish to view a variety of different level surfaces from the same distribution, superimposed over underlying opaque structures. By advecting an evenly distributed set of tiny opaque particles, and the empty space between them, via 3D line integral convolution through the vector field defined by the principal directions and principal curvatures of the level surfaces passing through each gridpoint of a 3D volume, it is possible to generate a single scan-converted solid stroke texture that may intuitively represent the essential shape information of any level surface in the volume. To generate longer strokes over more highly curved areas, where the directional information is both most stable and most relevant, and to simultaneously downplay the visual impact of directional information in the flatter regions, one may dynamically redefine the length of the filter kernel according to the magnitude of the maximum principal curvature of the level surface at the point around which it is applied.

Interrante, Victoria↗

NASA NTRS1993

PNS predictions of axisymmetric hypersonic blunt-body and afterbody flowfields

A new space-marching full-body PNS algorithm capable of treating the complete blunt-body and afterbody flowfields over typical wide-bodied configurations is developed and demonstrated. A hybrid differencing scheme involving Flux-Vector Splitting (FVS) across embedded shocks and flowfield discontinuities, and central differencing in smooth (shock-free) regions is used. It is demonstrated that this new full-body PNS scheme can be marched from the spherical stagnation point over the entire body using bow-shock capturing, and provides an efficient and effective way for predicting blunt-body flowfields for various reentry application. The Mach 20 flow over a 30-deg sphere cone is predicted and comparisons are made with an existing VSL scheme for the blunt-body region and an existing PNS scheme for the conical afterbody region. The predicted flowfield and surface-measurable quantities are in excellent agreement, and demonstrate the accuracy and efficiency of the new full-body PNS scheme.

Bhutta, Bilal A.↗

NASA NTRS1995

Model-Based Obstacle Detention From Image Sequences

This paper proposes a method for detecting obstacles on a runway from monocular image sequences. The surface of the runway is modeled as a plane and the model flow field corresponding to the runway is described by 8 coefficients. The set of 8 coefficients describing the initial model flow field is given by the data from Inertial Navigation System (INS). The uncertainties of this initial model flow field are estimated and used to obtain the accurate model flow field. The residual flow field after warping images by the accurate model flow field is computed by solving over determined gradient-based optical flow equations using a singular value decomposition (SVD). This SVD gives us a new insight of the uncertainties inherent in the optical flow computation. Those pixels with large residual flow vectors are considered as obstacles. Experimental results for two real image sequences are presented.

Sull, Sanghoon↗

DOE OSTI2026

Mountain Basin Controls on the Snow-to-Streamflow Signal: An AIC-Weighted Multiple Linear Regression Framework

A regression-based analysis quantifies how basin characteristics modulate the snow-to-streamflow signal. First, we use the ERA5-Land reanalysis gridded product (European Centre for Medium Range Weather Forecasts reanalysis 5 -Land component) for 4,655 hydrologic unit code - 10 (HUC10) mountain basins across the western United States (US) for water years 1987–2024. Linear regressions are performed for peak snow water equivalent (SWE) and annual streamflow for each mountain basin. Models use ordinary least squares in Python’s statsmodels package. After which, an Akaike Information Criterion (AIC)–weighted ensemble multiple linear regression (MLR) framework with 47 watershed traits is used to predict the linear regression coefficient of determination (r-squared) defining the ability of peak SWE to predict annual streamflow across all mountain basin. Predictor sets are constrained to avoid multicollinearity by excluding models with variance inflation factors (VIF) greater than 5. Mountain basin traits included in the MLR include seasonal climate, topography, vegetation type and structure, and bedrock geology. Accepted models are considered if their AIC is within 2.0 of the model with the minimum AIC, or best model. To compare predictor influence across acceptable models, we computed standardized regression coefficients. To evaluate structural redundancy among models, we constructed binary inclusion vectors for each acceptable model, denoting whether a predictor was present (1) or absent (0). Core predictor variables are defined as occurring in at least 67% of the acceptable models. For this regional analysis, only one model was found acceptable, with higher snow-to-streamflow translation (higher r-squared) occurring in colder mountain basins with higher relative winter precipitation, more snow accumulation and a lower fraction of annual precipitation that falls in the spring and summer. The second component of the data package uses previously published, high-resolution output from an integrated hydrological model of the East River watershed using the U.S. Geological Survey Groundwater and Surface water Flow model (GSFLOW, doi:10.15485/1998576). East River MLR expands upon the approach described above to explore the response of five streamflow metrics—annual streamflow, runoff efficiency, 7-day minimum flow, low-flow duration, and non-perennial stream fraction to snow system indicators including peak SWE, snow-covered area, snow disappearance date, and the fraction of basin area characterized by low-to-no snow, as well as seasonal precipitation and temperature, and annual hydrologic variables representing soil moisture, evapotranspiration (ET), the partitioning of incoming precipitation to evapotranspiration (ET/P), groundwater storage, and groundwater inflow to streams. MLR was done on all water years (P0: 1987-2024) and for each period as determined in the split analysis using pooled regression techniques (P1: 1987-2011 and P2: 2012-2024) to evaluate shifting predictor variable emphasis on streamflow generation. Results indicate that since 2012, peak SWE has lost statistical strength in its prediction of annual streamflow and runoff efficiency, and the indirect influence of spring temperature has emerged as critically important. Low-flow metrics remain largely influenced by soil moisture, vegetation water use and groundwater inflows with summer precipitation becoming a direct influence on minimum summer flow. Together, these data and Python-based analysis tools provide a framework for identifying the key watershed characteristics that control how streamflow responds to snow from year to year. The package also helps quantify uncertainty in statistical models and assess how snow–streamflow relationships vary across regions and over time. This dataset contains comma-separated values files (.csv), text files (.txt), python code files (.py), figure files (.png), and shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .xml). Further details on file contents and MLR execution can be found in the readme file and the FLMD files. Work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1999

Aeolian Sand Transport in the Planetary Context: Respective Roles of Aerodynamic and Bed-Dilatancy Thresholds

The traditional view of aeolian sand transport generally estimates flux from the perspective of aerodynamic forces creating the airborne grain population, although it has been recognized that "reptation" causes a significant part of the total airborne flux; reptation involves both ballistic injection of grains into the air stream by the impact of saltating grains as well as the "nudging" of surface grains into a creeping motion. Whilst aerodynamic forces may initiate sand motion, it is proposed here that within a fully-matured grain cloud, flux is actually governed by two thresholds: an aerodynamic threshold, and a bed-dilatancy threshold. It is the latter which controls the reptation population, and its significance increases proportionally with transport energy. Because we only have experience with terrestrial sand transport, extrapolations of aeolian theory to Mars and Venus have adjusted only the aerodynamic factor, taking gravitational forces and atmospheric density as the prime variables in the aerodynamic equations, but neglecting reptation. The basis for our perspective on the importance of reptation and bed dilatancy is a set of experiments that were designed to simulate sand transport across the surface of a martian dune. Using a modified sporting crossbow in which a sand-impelling sabot replaced the bolt-firing mechanism, individual grains of sand were fired at loose sand targets with glancing angles typical of saltation impact; grains were projected at about 80 m/s to simulate velocities commensurate with those predicted for extreme martian aeolian conditions. The sabot impelling method permitted study of individual impacts without the masking effect of bed mobilization encountered in wind-tunnel studies. At these martian impact velocities, grains produced small craters formed by the ejection of several hundred grains from the bed. Unexpectedly, the craters were not elongated, despite glancing impact; the craters were very close to circular in planform. High-speed photography showed them to grow in both diameter and depth after the impactor had ricochetted from the crater site. The delayed response of the bed was "explosive" in nature, and created a miniature ejecta curtain spreading upward and outward for many centimeters for impact of 100-300 micron-diameter grains into similar material. Elastic energy deposited in the bed by the impacting grain creates a subsurface stress regime or "quasi-Boussinesq" compression field. Elastic recovery of the bed occurs by dilatancy; shear stresses suddenly convert the grains from closed to open packing, and grains are consequently able to eject themselves forcefully from the impact site. Random jostling of the grains causes radial homogenization of stress vectors and a resulting circular crater. There is a great temptation to draw parallels with cratering produced by meteorite impacts, but a rigorous search for common modelling ground between the two phenomena has not been conducted at this time. For every impact of an aerodynamically energized grain, there are several hundred grains ejected into the wind for the high-energy transport that might occur on Mars. Many of these grains will themselves become subject to the boundary layer's aerodynamic lift forces (their motion will not immediately die and add to the creep population), and these grains will become indistinguishable from those lifted entirely by aerodynamic forces. As each grain impacts the bed, it will eject even more grains into the flow. A cascading effect will take place, but because it must be finite in its growth, damping will occur as the number of grains set in motion causes mid-air collisions that prevent much of the impact energy from reaching the surface of the bed -thus creating a dynamic equilibrium in a high-density saltation cloud. It is apparent that for a given impact energy, the stress field permits a smaller volume of grains to convert to open packing as the size of the bed grains increases, or as the energy of the "percussive" grain decreases (by decrease in velocity or mass). Thus, the mass of the "repercussive" grain population that is ejected from the impact site becomes a function of the scale of the stress field in relation to the scale of the bed material (self-similarity being applicable if both bed size and energy are simultaneously adjusted). In other words, in a very high energy aeolian system where an aerodynamically raised grain can ballistically raise many more grains, the amount of material lifted into the wind becomes largely a function of a dilatancy threshold. If this threshold is exceeded, grains are repercussively injected into the saltation cloud. The "dilatancy threshold" may be defined in terms of the saltation percussive force required to convert the bed, through elastic response, from a closed to an open packing system. If open packing cannot be created, the grains cannot escape from the impact site, even though the elastic deformation and percussive force may be able to reorganize the grains with respect to one another. As the crossbow experiments showed, for an ever-increasing bed grain size, a point is reached when no material can be moved because the energy of the percussive grain is insufficient to dilate the relatively coarse bed. Although this seems to be stating the obvious -- that too little energy will not cause the bed to splash -- the consequences of exceeding the "splash threshold" by dilatancy are not so obvious for high-energy aeolian transport. It is noted that the force required to elastically dilate the bed has to overcome Coulombic grain attractions such as dipole-dipole coupling, dielectric, monopole, contact-induced dipole attractions, van der Waals forces, molecular monolayer capillary forces, as well as the mechanical interlocking frictional resistance of the grains. On Mars, it is predicted that the dilatancy threshold may be the prime control of grain flux. Additional information is contained in the original.

Marshall, J. R.↗

NASA NTRS1986

HOST turbine heat transfer subproject overview

The experimental part of the turbine heat transfer subproject consists of six large experiments, which are highlighted in this overview, and three of somewhat more modest scope. One of the initial efforts was the stator airfoil heat transfer program. The non-film cooled and the showerhead film cooled data have already been reported. The gill region film cooling effort is currently underway. The investigation of secondary flows in a 90 deg curved duct, was completed. The first phase examined flows with a relatively thin inlet boundary layer and low free stream turbulence. The second phase studied a thicker inlet boundary layer and higher free stream turbulence. A comparison of analytical and experimental cross flow velocity vectors is shown for the 60 deg plane. Two experiments were also conducted in the high pressure facility. One examined full coverage film cooled vanes, and the other, advanced instrumentation. The other three large experimental efforts were conducted in a rotation reference frame. An experiment to obtain gas path airfoil heat transfer coefficients in the large, low speed turbine was completed. Single-stage data with both high and low-inlet turbulence were taken. The second phase examined a one and one-half stage turbine and focused on the second vane row. Under phase 3 aerodynamic quantities such as interrow time-averaged and rms values of velocity, flow angle, inlet turbulence, and surface pressure distribution were measured.

Gladden, Herbert J.↗