The discrete-element method for predicting skin friction for turbulent flow over rough surfaces considers the drag on the surface to be the sum of the skin friction on the flat part of the surface and the drag on the individual roughness elements that protrude into the boundary layer. The discrete-element method considers heat transfer from a rough surface to be the sum of convection through the fluid on the flat part of the surface and the convection from each of the roughness elements. The discrete-element method has been widely used and validated for roughness composed of sparse, ordered, and deterministic elements. Modifications made to the discrete-element roughness method to extend the validation to real surface roughness are detailed. These modifications include accounting for the deviation of the roughness element cross sections from circular configurations, determining the location of the computational "surface" that differs from the physical surface, and accounting for temperature changes along the height of the roughness elements. Two randomly-rough surfaces found on high-hour gas-turbine blades were characterized using a Taylor-Hobson Form Talysurf Series 2 profilometer. A method for using the three-dimensional profilometer output to determine the geometry input required in the discrete-element method for randomly-rough surfaces is presented. Two randomly-rough surfaces, two elliptical-analog surfaces, and two cone surfaces were generated for wind-tunnel testing using a three-dimensional printer. The analog surfaces were created by replacing each random roughness element from the original randomly-rough surface with an elliptical roughness element with the equivalent planorm area and eccentricity. The cone surfaces were generated by placing conical roughness elements on a flat plate to create surfaces with equivalent values of centerline-averaged height or root-mean-square (RMS) height as the randomly-rough surfaces. The results of the wind tunnel skin friction coefficient and Stanton number measurements and the discrete-element method predictions for each of the six surfaces are presented and discussed. For the randomly-rough surfaces studied, the discrete-element method predictions are within 7% of the experimentally measured skin friction coefficients. The discrete-element predictions are within 16% of the experimentally measured Stanton numbers for the randomly-rough surfaces.

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Porosity is a fundamental characteristic of rock critical to its mechanical and hydrologic behavior, yet a study of the open and accumulated healed porosity of nine core samples from Newberry Volcano shows that different measurement methods produce significantly different estimates of pore volume and structure. This study compares traditional 2D point count, petrographic image analysis, and 3D x-ray Micro Computed Tomography (micro CT) measurement of porosity primarily derived from fracture slip and dilation. The set of measurements quantifies the discrepancy among measurement methods and provides a basis for assessing how this uncertainty depends on geologic factors including the stage of fracture development, and the size and connectivity of the pores. This comparison reveals that detailed petrographic mapping provides the most accurate characterization of fracture porosity, and its history of development, owing to its high spatial resolution and accuracy of phase identification as well as insights afforded from mineralogic and textural relationships. However, this analysis lacks the three-dimensional characterization necessary to determine pore shape and interconnectedness, especially in highly anisotropic and heterogeneous fracture porosity. Micro CT does characterize the three dimensionality of pores, and thus although it consistently underestimates porosity due to non-uniqueness of phase densities and limitations in resolution, and is difficult to post process, this method can usefully augment the petrographic analysis. High resolution mapping of petrographic thin sections also provides a means to characterize the roughness of fracture surfaces across multiple cycles of slip, related dilation, and healing. Analysis of 19 slip events on a small, early stage fracture experiencing less than mm-scale slip, indicates that this roughness is preserved across multiple slip events and is consistently associated with dilation recorded by the accumulation of layers of precipitated cement. Initially, characteristic length scales intrinsic to rock such as the primary grain and pore size distribution of the > 0.2 mm size fraction significantly influence the roughness of fractures, until the dominant mechanism of fracture growth becomes linkage among macroscopic fractures. This correlation among primary rock characteristics such as grain size, fracture roughness, repeated fracture slip, and dilation provides a potential method to assess the key attributes promoting dilatant, self-propping fracture slip necessary for successful stimulation to generate an Enhanced Geothermal System. Comparison to more developed fractures characterized by the development of fault rock suggest such stimulation is most successful for fractures sustaining small slip of a few millimeters or less during single slip events. / Geology

Yes / Dynamic wetting failure was observed in the simple dip coating flow with a series of substrates, which had a rough side and a comparatively smoother side. When we compared the air entrainment speeds on both sides, we found a switch in behaviour at a critical viscosity. At viscosity lower than a critical value, the rough side entrained air at lower speeds than the smooth side. Above the critical viscosity the reverse was observed, the smooth side entraining air at lower speed than the rough side. Only substrates with significant roughness showed this behaviour. Below a critical roughness, the rough side always entrained air at lower speeds than the smooth side. These results have both fundamental and practical merits. They support the hydrodynamic theory of dynamic wetting failure and imply that one can coat viscous fluids at higher speeds than normal by roughening substrates. A mechanism and a model are presented to explain dynamic wetting failure on rough surfaces.

Roughness noise can be a significant contributor of sound in low Mach number, high Reynolds number flows. Only a small amount of experimental research has been conducted to analyze roughness noise because of its often low energy levels that are hard to isolate even in a laboratory setting. This study details efforts to scale the roughness noise while independently varying roughness size and edge velocity. Measurements were taken in the Virginia Tech Anechoic Wall Jet Facility for stochastic rough surfaces varying from hydrodynamically smooth to fully rough as well as deterministic rough surfaces including 1mm and 3mm hemispheres and a 2D wavy wall. Inner and outer variable normalizations were applied to recorded far field data in an attempt to find specific driving variables of the roughness noise. Also, a newly formulated derivation that attempts to scale the far field sound from a single point wall pressure measurement was used to collapse the far field noise. From the results, the inner and outer variable scalings were unable to collapse the noise generated by all velocities and roughness sizes. The changing spectral shapes of noise generated by rough surfaces with significantly varying wavenumber spectra make it impossible to scale the produced noise using the proposed inner and outer variable scalings. They use only one a single scaling value for the entire frequency range of each spectrum. The analyzed wall pressure normalization, which is inherently frequency dependent, produces a tight collapse within the uncertainty of the measurements for all rough surfaces studied except the larger hemispherical roughness which had individual elements that dominated the surrounding region of the wall pressure microphone. This indicates that the roughness generated noise is directly proportional to the wall pressure spectrum. The collapsed data displayed a slope of Ï ^2, the expected dipole efficiency factor. This is the clearest confirmation to date that the roughness noise source is of a dipole nature. / Master of Science