Browsing by Author "McClure, James E."

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    Effect of Topology and Geometric Structure on Collective Motion in the Vicsek Model
    McClure, James E.; Abaid, Nicole (Frontiers, 2022-03-08)
    In this work, we explore how the emergence of collective motion in a system of particles is influenced by the structure of their domain. Using the Vicsek model to generate flocking, we simulate two-dimensional systems that are confined based on varying obstacle arrangements. The presence of obstacles alters the topological structure of the domain where collective motion occurs, which, in turn, alters the scaling behavior. We evaluate these trends by considering the scaling exponent and critical noise threshold for the Vicsek model, as well as the associated diffusion properties of the system. We show that obstacles tend to inhibit collective motion by forcing particles to traverse the system based on curved trajectories that reflect the domain topology. Our results highlight key challenges related to the development of a more comprehensive understanding of geometric structure's influence on collective behavior.
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    Multi-scale Investigations of Geological Carbon Sequestration in Deep Saline Aquifers
    Guo, Ruichang (Virginia Tech, 2022-05-25)
    Geological carbon dioxide (CO2) sequestration (GCS) in deep saline aquifers is viewed as a viable solution to dealing with the impact of anthropogenic CO2 emissions on global warming. The trapping mechanisms that control GCS include capillary trapping, structural trapping, dissolution trapping, and mineral trapping. Wettability and density-driven convection play an important role in GCS, because wettability significantly affects the efficiency of capillary trapping, and density-driven convection greatly decreases the time scale of dissolution trapping. This work focuses on the role of wettability on multiphase flow in porous media, density-driven convection in porous media, and their implications for GCS in deep saline aquifers. Wettability is a critical control over multiphase fluid flow in porous media. However, our understanding on the wettability heterogeneity of a natural rock and its effect on multiphase fluid flow in a natural rock is limited. This work innovatively models the heterogeneous wettability of a rock as a correlated random field. The realistic wetting condition of a natural rock can be reconstructed with in-situ measurements of wettability on the internal surfaces of the rock. A Bentheimer sandstone was used to demonstrate the workflow to model and reconstruct a wettability field. Relative permeability, capillary pressure-water saturation relation are important continuum-scale properties controlling multiphase flow in porous media. This work employed lattice Boltzmann method to simulate the displacement process. We found that pore-scale surface wettability heterogeneity caused noticeable local scCO2 and water redistributions under less water-wet conditions at the pore scale. At the continuum scale, the capillary pressure-water saturation curve under the heterogeneous wetting condition was overall similar to that under the homogeneous wetting condition. This suggested that the impact of local wettability heterogeneity on the capillary pressure-water saturation curve was averaged out at the entire-sample scale. The only difference was that heterogeneous wettability led to a negative entry pressure at the primary drainage stage under the intermediate-wet condition. The impact of pore-scale wettability heterogeneity was more noticeable on the relative permeability curves. Particularly, the variation of the scCO2 relative permeability curve in the heterogeneous wettability scenario was more significant than that in the homogenous wettability scenario. Results showed that higher wettability heterogeneity (i.e., higher standard deviation and higher correlation length) increased the variations in the CO2/brine relative permeability curves. Dissolution of CO2 into brine is a primary mechanism to ensure the long-term security of GCS. CO2 dissolved in brine increases the CO2-brine solution density and thus can cause downward convection. Onset of density-driven instability and onset of convective dissolution are two critical events in the transition process from a diffusion-dominated regime to a convection-dominated regime. In the laboratory, we developed an empirical correlation between light intensity and in-situ solute concentration. Based on the novel and well-controlled experimental methods, we measured the critical Rayleigh-Darcy number and critical times for the onset of density-driven instability and convective dissolution. To further investigate the impact of permeability heterogeneity on density-driven convection, a three-dimensional (3D) fluidics method was proposed to advance the investigation on density-driven convection in porous media. Heterogeneous porous media with desired spatial correlations were efficiently built with 3D-printed elementary porous blocks. In the experiments, methanol-ethylene-glycol (MEG), was used as surrogate fluid to CO2. The heterogeneous porous media were placed in a transparent tank allowing visual observations. Results showed that permeability structure controlled the migration of MEG-rich water. Permeability heterogeneity caused noticeable uncertainty in dissolution rates and uncertainty in dissolution rates increases with correlation length. To sum up, this work comprehensively employed novel experimental methods and large-scale direct simulations to investigate the sequestration of CO2 in saline aquifers at a pore scale and a continuum scale. The findings advanced our understanding on the role of wettability heterogeneity and permeability heterogeneity on GCS in deep saline aquifers.
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    Scalable Multi-Agent Systems in Restricted Environments
    Heintzman, Larkin Lee (Virginia Tech, 2023-02-15)
    Modern robotics demonstrates the reality of near sci-fi solutions regularly. Swarms of interconnected robotic agents have been proven to have benefits in scalability, robustness, and efficiency. In communication restricted environments, such teams of robots are often required to support their own navigation, planning, and decision making processes, through use of onboard processors and collaboration. Example scenarios that exhibit restriction include unmanned underwater surveys and robots operating in indoor or remote environments without cloud connectivity. We begin this thesis by discussing multi-agent state estimation and it's observability properties, specifically for the case of an agent-to-agent range measurement system. For this case, inspired by navigation requirements underwater, we derive several conditions under which the system's state is guaranteed to be locally weakly observable. Ensuring a state is observable is necessary to maintain an estimate of it via filters, thus observability is required to support higher level navigation and planning. We conclude this section by creating an observability-based planner to control a subset of the agents' inputs. For the next contribution, we discuss scalability for coverage maximizing path planners. Typically planning for many individual robots incurs significant computational complexity which increases exponentially with the number of agents, this is often exacerbated when the objective function is collaborative as in coverage optimization. To maintain feasibility while planning for a large team of robots, we call upon a powerful relation from combinatorics which utilizes the greedy selection algorithm and a matroid condition to create an efficient planner that maintains a fixed performance ratio when compared to the optimal path. We then introduce a motivating example of autonomously assisted search and rescues using multiple aerial agents, and derive planners and models to suit the application. The framework begins by estimating the likely locations of a lost person through a Monte Carlo simulation, yielding a heatmap covering the area of interest. The heatmap is then used in combination with parametrized agent trajectories and a machine learning optimization algorithm to maximize the search efficiency. The search and rescues use case provides an excellent computational testbed for the final portion of the work. We close by discussing a computation architecture to support multi-agent system autonomy. Modern robotic autonomy results, especially computer vision and machine learning algorithms, often require large amounts of processing to yield quality results. With general purpose computing devices reaching a progression barrier, one that is not expected to be solved in the near term, increasingly devices must be designed with their end purposes in mind. To better support autonomy in multi-agent systems, we propose to use a distributed cluster of embedded processors which allows the sharing of computation and storage resources among the component members with minimal communication overhead. Our proposed architecture is composed of mature softwares already well-known in the robotics community, Kubernetes and the robot operating system, allowing ease of use and interoperability with existing algorithms.