In this thesis I investigate characteristics of turbulent flow in a channel where one of the walls has riblets, superhydrophobic microribs, or a hybrid surface with traditional riblets built on a superhydrophobic microrib surface. PIV measurements are used to find the velocity profile, the turbulent statistics, and shear stress profile in the rectangular channel with one wall having a structured test surface. Both riblets and superhydrophobic surfaces can each provide a reduction in the wall shear stress in a turbulent channel flow. Characterizing the features of the flow using particle image velocimetry (PIV) is the focus of this research. Superhydrophobicity results from the combination of a hydrophobic coating applied to a surface with microrib structures, resulting in a very low surface energy, such that the fluid does not penetrate in between the structures. The micro-rib structures are aligned in the streamwise flow direction. The riblets are larger than the micro-rib structure by an order of magnitude and protrude into the flow. All the test surfaces were produced on silicon wafers using photolithographic techniques. Pressure in the channel is maintained below the Laplace pressure for all testing, creating sustainable air pockets between the microribs. Velocity profiles, turbulent statistics, shear stress profiles, and friction factors are presented. Measurements were acquired for Reynolds numbers ranging from 4.5x10^3 to 2.0x10^4. Modest drag reductions were observed for the riblet surfaces. Substantial drag increase occurred over the superhydrophobic surfaces. The hybrid surfaces showed the greatest drag reduction. Turbulence production was strongly reduced during riblet and hybrid tests.

The realm of autonomous robotics has seen impressive advancements in recent years, with robots taking on essential roles in various sectors, including disaster response, environmental monitoring, agriculture, and healthcare. As these highly intelligent machines continue to integrate into our daily lives, the pressing imperative is to elevate and refine their performance, enabling them to adeptly manage complex tasks with remarkable efficiency, adaptability, and keen decision-making abilities, all while prioritizing safety-aware navigation, mapping, and control systems. Ensuring the safety-awareness of these robotic systems is of paramount importance in their development and deployment. In this research, bio-inspired neural networks, nature-inspired intelligence, deep learning, heuristic algorithm and optimization techniques are developed for safety-aware autonomous robots navigation, mapping and control. A bio-inspired neural network (BNN) local navigator coupled with dynamic moving windows (DMW) is developed in this research to enhance obstacle avoidance and refines safe trajectories. A hybrid model is proposed to optimize trajectory of the global path of a mobile robot that maintains a safe distance from obstacles using a graph-based search algorithm associated with an improved seagull optimization algorithm (iSOA). A Bat-Pigeon algorithm (BPA) is proposed to undertake adjustable speed navigation of autonomous vehicles in light of object detection for safety-aware vehicle path planning, which can automatically adjust the speed in different road conditions. In order to perform effective collision avoidance in multi-robot task allocation, a spatial dislocation scheme is developed by introduction of an additional dimension for UAVs at different altitudes, whereas UAVs avoid collision at the same altitude using a proposed velocity profile paradigm. A multi-layer robot navigation system is developed to explore row-based environment. A directed coverage path planning (DCPP) fused with an informative planning protocol (IPP) method is proposed to efficiently and safely search the entire workspace. A human-autonomy teaming strategy is proposed to facilitate cooperation between autonomous robots and human expertise for safe navigation to desired areas. Simulation, comparison studies and on-going experimental results of optimization algorithms applied for autonomous robot systems demonstrate their effectiveness, efficiency and robustness of the proposed methodologies.

Fluid suspended biological particles (bioparticles) flowing through a non-uniform electric field are actuated by the induced dielectrophoretic (DEP) force, known to be dependent upon the bioparticles’ dielectric phenotypes. In this work: a 10-1000 kHz DEP actuation potential applied to a co-planar microelectrode array (MEA) induces a DEP force, altering passing bioparticle trajectories as monitored using: (1) an optical assay, in which the lateral bioparticle velocities are estimated from digital video; and (2) a capacitive cytometer, in which a 1.478 GHz capacitance sensor measures the MEA capacitance perturbations induced by passing bioparticles, which is sensitive to the bioparticles’ elevations. The experimentally observed and simulated lateral velocity profiles of actuated polystyrene microspheres (PSS) and viable and heat shocked Saccharomyces cerevisiae cells verify that the bioparticles’ dielectric phenotypes can be inferred from the resultant trajectories due to the balance between the DEP force and the viscous fluid drag force.

Fluid suspended biological particles (bioparticles) flowing through a non-uniform electric field are actuated by the induced dielectrophoretic (DEP) force, known to be dependent upon the bioparticles’ dielectric phenotypes. In this work: a 10-1000 kHz DEP actuation potential applied to a co-planar microelectrode array (MEA) induces a DEP force, altering passing bioparticle trajectories as monitored using: (1) an optical assay, in which the lateral bioparticle velocities are estimated from digital video; and (2) a capacitive cytometer, in which a 1.478 GHz capacitance sensor measures the MEA capacitance perturbations induced by passing bioparticles, which is sensitive to the bioparticles’ elevations. The experimentally observed and simulated lateral velocity profiles of actuated polystyrene microspheres (PSS) and viable and heat shocked Saccharomyces cerevisiae cells verify that the bioparticles’ dielectric phenotypes can be inferred from the resultant trajectories due to the balance between the DEP force and the viscous fluid drag force.