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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We study the spontaneous edge current of chiral superconductors with high chirality both in the absence and presence of Meissner screening. We compute the edge current from a self-consistent solution to a set of coupled equations: quasiclassical Eilenberger equation, superconducting gap equation, and Maxwell equation. We find that the spatial dependent chiral edge current is largely suppressed and has more nodes for higher chirality pairings. In the absence of Meissner screening, the integrated current at T=0 is zero for all higher chirality pairings; while it is substantial for chiral p-wave. This conclusion is consistent with previous studies. In contrast, at finite T, the integrated current is non-zero even for higher chiral pairings. It turns out that the spatial varying order parameter is crucial to understand this finite T behavior of the edge current. When Meissner screening is included, the magnitude of the edge currents is reduced for all chiral pairings; however, the reduction is much weaker in higher chirality cases. We conclude that the Meissner effect is not that important for higher chiral pairings. We also consider the effect of the rough surface on the edge current. The edge current of even chiral pairings is inverted by the strong surface roughness; however, that of the odd chiral pairings is not. The sub-dominant order parameters, induced by the surface, are the key to understanding this current inversion. / Thesis / Master of Science (MSc)

The problem of rough surface scattering and propagation over rough terrain in ducting environments has been receiving considerable attention in the literature. One popular method of modeling this problem is the parabolic wave equation (PWE) method. In this method, the Helmholtz wave equation is replaced by a PWE under the assumption of predominant forward propagation and scattering. The resulting PWE subjected to the appropriate boundary condition(s) is then solved, given an initial field distribution, using marching techniques such as the split-step Fourier algorithm. As is obvious from the assumption on which it is based, the accuracy of the PWE approximation deteriorates in situations involving appreciable scattering away from the near-forward direction, i.e. when the terrain under consideration is considerably rough. The backscattered field is neglected in all PWE-based models. An alternative and more rigorous method for modeling the problem under consideration is the boundary integral equation (BIE) method, which is formulated in two steps. The first step involves setting up an integral equation (the magnetic field integral equation, MFIE, or the electric field integral equation EFIE) governing currents induced on the rough surface by the incident field and solving for these currents numerically. The resulting currents are then used in the appropriate radiation integrals to calculate the field scattered by the surface everywhere in space. The BIE method accounts for all orders of multiple scattering on the rough surface and predicts the scattered field in all directions in space (including the backscattering direction) in an exact manner. In homogeneous media, the implementation of the BIE approach is straightforward since the kernel (Green's function or its normal derivative) which appears in the integral equation and the radiation integrals is well known. This is not the case, however, in inhomogeneous media (ducting environments) where the Green's function is not readily known. Due to this fact, there has been no attempt, up to our knowledge, at using the BIE (except under the parabolic approximation) to model the problem under consideration prior to the work presented in this thesis. In this thesis, a closed-form approximation of the Green's function for a two-dimensional ducting environment formed by the presence of a linear-square refractivity profile is derived using the asymptotic methods of stationary phase and steepest descents. This Green's function is then modified to more closely model the one associated with a physical ducting medium, in which the refractivity profile decreases up to a certain height, beyond which it becomes constant. This modified Green's function is then used in the BIE approach to study low grazing angle (LGA) propagation over rough surfaces in the aforementioned ducting environment. The numerical method used to solve the MFIE governing the surface currents is MOMI, which is a very robust and efficient method that does not require matrix storage or inversion. The proposed method is meant as a benchmark for people studying forward propagation over rough surfaces using the parabolic wave equation (PWE). Rough surface scattering results obtained via the PWE/split-step approach are compared to those obtained via the BIE/MOMI approach in ducting environments. These comparisons clearly show the shortcomings of the PWE/split-step approach. / Ph. D.

The aerodynamic flow and fluctuating surface pressure of a plane, turbulent, two-dimensional wall jet flow into still air over smooth and rough surfaces has been investigated in a recently constructed wall jet wind tunnel testing facility. The facility has been shown to produce a wall jet flow with Reynolds numbers based on the momentum thickness, Re<SUB>&delta</SUB> = &deltaU<SUB>m</SUB>/&nu, of between 395 and 1100 and nozzle exit Reynolds numbers, Re<SUB>j</SUB> = U<SUB>m</SUB>b/&nu, of between 16000 and 45000. The wall jet flow properties (&delta, &delta<SUP>*</SUP>, &theta, y<SUB>1/2</SUB>, U<SUB>m</SUB>, u<SUP>*</SUP>, etc.) were measured and characterized over a wide range of initial flow conditions and measurement locations relative to the wall jet source. These flow properties were measured for flow over a smooth flow surface and for flow over roughness patches of finite extent. The patches used in the current study varied in length from 305 mm to 914 mm (between 24 and 72 times the nozzle height, b) and were placed so that the leading edge of the patch was fixed at 1257 mm (x/b = 99) downstream of the wall jet source. These roughness patches were of a random sand grain roughness type and the roughness grain size was varied throughout this experiment. The tests covered roughness Reynolds numbers (k<SUP>+</SUP>) ranging from less than 2 to over 158 (covering the entire range of rough wall flow regimes from hydrodynamically smooth to fully rough). For the wall jet flows over 305 mm long patches of roughness, the displacement and momentum thicknesses were found to vary noticeably with the roughness grain size, but the maximum velocity, mixing layer length scale, y<SUB>/2</SUB>, and the boundary layer thickness were not seen to vary in a consistent, determinable way. Velocity spectra taken at a range of initial flow conditions and at several distinct heights above the flow surface showed a limited scaling dependency on the skin friction velocity near the flow surface. The spectral density of the surface pressure of the wall jet flow, which is not believed to have been previously investigated for smooth or rough surfaces, showed distinct differences with that seen in a conventional boundary layer flow, especially at low frequencies. This difference is believed to be due to the presence of a mixing layer in the wall jet flow. Both the spectral shape and level were heavily affected by the variation in roughness grain size. This effect was most notable in overlap region of the spectrum. Attempts to scale the wall jet surface pressure spectra using outer and inner variables were successful for the smooth wall flows. The scaling of the rough wall jet flow surface pressure proved to be much more difficult, and conventional scaling techniques used for ordinary turbulent boundary layer surface pressure spectra were not able to account for the changes in roughness present during the current study. An empirical scaling scheme was proposed, but was only marginally effective at scaling the rough wall surface pressure. / Ph. D.

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