Tag: MAE

CPS: Synergy: Distributed coordination of smart devices to mitigate intermittency of renewable generation for a smarter and sustainable power grid

The ultimate goal of the project is to help the electric grid become more reliable even when a large amount of electricity is generated from green, but intermittent – sources such as solar and wind. To deal with this intermittency, inexpensive source of energy storage are required. Instead of investing in batteries, this project seeks to obtain cheap storage by manipulating power demand in consumer loads through intelligent decision-making algorithms. By varying power demand up and down from what a load would nominally consume, the load can be made to behave like a battery, effectively creating a source of Virtual Energy Storage (VES). This kind of virtual storage is cheaper than batteries since it is a software-based solution; little additional hardware is needed. Another aspect of the project is to develop decision-making algorithms to cope with operational issues faced by the power distribution networks (that deliver electricity to neighborhoods) due to increasing use of intermittent solar power.

Autonomous Control of Indoor Climate for Commercial Buildings

Buildings account for 45 percent of the total energy consumption in the United States (U.S.), and maintaining indoor climate, which includes heating, cooling, and ventilation, accounts for approximately half of that energy consumption. A low-cost option for reducing building energy usage is intelligent climate control, moving away from the prevalent “design for steady-state conditions” philosophy into one that exploits the constantly changing conditions a building operates in due to its occupants and the weather. The potential for intelligent climate control has been recognized for many years, especially for commercial buildings that have the requisite sensors and actuators. In particular, control algorithms that make decisions using real-time optimization have been shown to be highly promising. In spite of its promise, such “model-optimization” based control technologies have not been widely adopted by industry. The reason for this lack of translation to practice is the lack of autonomy of existing algorithms. Not only do they require expert human involvement in model creation, which have to be tuned for each building manually, they do not provide guarantees about the quality of real-time decisions. Addressing these weaknesses will lead to the wider adoption of intelligent building climate control technologies, which will contribute to the technological edge U.S. industries enjoy, and reduce the nation’s energy usage.

Virtual Diffraction Techniques used to Study Dislocation Loop Grain Boundary Interactions and Assess Slip Transfer Criteria (9.2 Mechanical Behavior of Materials)

The objective of this proposal is to develop a multiscale simulation approach to study dislocation – grain boundary interactions and to employ this approach to advance criteria for slip transfer across grain boundaries to consider the grain boundary damage state. In addition, the proposed simulation approach will allow for an analysis of the role of dislocation core structures on slip transfer. The hypothesis of this proposal is that the mechanisms by which dislocations are obstructed, absorbed, and/or transmitted through grain boundaries are sensitive to the damage state of the grain boundary (defined as a departure from equilibrium atomic structure) in addition to slip geometry. Such details are critically important to understand how grain boundaries promote strengthening or act as sources/sinks for damage during high rate plastic deformation.

A New Paradigm for Materials Discovery and Development for Lower Temperature and Isothermal Thermochemical H2 Production

In this project we aim to use high-throughput computational screening, coupled with experimental characterization, DFT techniques and thermodynamic modeling to identify novel and efficient second generation thermochemical redox materials that operate isothermally or near isothermally below 1400 °C. We predict that we can improve performance (i.e. increase efficiency and decrease operating temperature) using identified materials compared to the state of the art material, ceria, by operating at high pO2 (> 10-6 atm) and perturbing the system from equilibrium with either rapid changes in pressure or temperature (only small deviations, or “near” isothermal) or a combination of the two, using engineered structures (e.g. porous scaffolds) that afford enhanced surface driven kinetics without requiring bulk heating and cooling. We aim to leverage the expertise of University of Florida faculty from Mechanical Engineering and Aerospace Engineering (Jonathan Scheffe) and Materials Science Engineering (Juan Nino and Simon Phillpot) alongside four DOE HydroGEN nodes.

Turbulence informed models for shallow water simulations of turbidity currents

Turbidity currents at geophysical scale are highly nonlinear turbulent multiphase flows. The dynamics of these flows involve tight interactions of the phases over a wide range of length and time scales. Furthermore, the interactions of the turbidity currents with the bed at the bottom and with the ambient along the upper boundary are of great relevance in the development and dynamics of turbidity currents. Accurate prediction of these flows demands models that accurately account for all the basic physical processes involved. In this work we propose to use direct and large eddy simulation methodologies with immersed boundary method capabilities to examine the following key physical processes: (a) dependence of local bed erosion on instantaneous shear stress and pressure fluctuation, (b) turbulence modulation and suppression of turbulent production/transport by particle-induced density stratification, (c) entrainment of ambient fluid in the context of a self-stratified particle-laden interface, and (d) the formation/evolution of different types of bedforms under various flow conditions. The ultimate goal is to advance systematic improvements to Parker-type shallow water models (Parker et al, 1986) that incorporate the above physics and validate them against available data.

Cooperative Multi-Agent Systems in Strong Background Flows

Aerial and marine robots operate in a fluid medium which carries significant information not often utilized in path planning and cooperation of such robots. Motivated by this observation, we believe that modeling the macroscopic dynamics of a multi-agent system using the vast and historically grounded knowledge of fluid dynamics will open up many opportunities for a better understanding and subsequent enhancement of the behavior of such multi-agent systems. In addition, by including the dynamics of background flows, significant improvement in mobility, reachability and success rate can be expected in applications such as dynamical target tracking, foreign threat interception, and large-scale environmental sensing. Starting with the consideration of the underlying background flows and the sensor network as two tightly coupled dynamical systems, we leverage the fluid dynamical description of a system to understand the macroscopic motions of the sensor network as a continuum. We present designs for cooperative control strategies governing both local interactions within the multiagent systems and the emergent swarm behaviors. The proposed fluid-based swarm modeling and control scheme is well-suited for aerial or marine missions including multi-vehicle transportation, cooperative surveillance, collaborative target tracking and capturing, etc. More specifically, we will be focusing on the following research tasks:
-stability analysis and adaptive control of multi-agent systems modeled as continuous fluids;
-multi-agent system dynamics coupled with background ow dynamics;
-experimental evaluation with mobile aerial vehicle swarms

Dynamic network identification with applications to smart buildings

A dynamic network consists of interacting dynamic sub-systems. Such networks occur in many domains: living cells, financial markets, the Internet and the power grid are some examples. Heating, ventilation and air conditioning (HAVC) systems in buildings can also be modeled through dynamic networks since each room’s climate depends on that of nearby spaces. Knowledge of such dynamic network models is essential to design and deploy control strategies devoted to the improvement of energy efficiency and occupant comfort. Yet, in practice the structure and dynamics of these networks are either unknown or imprecisely known. For instance, information on the thermal interaction among rooms is difficult to obtain from laws of physics due to the complexity of the physical processes involved. The goal of this project is to formulate algorithms for the identification of dynamic sparse network models from measured data. The research results will support the study of advanced controls for HVAC systems to reduce their energy use and to provide demand-side flexibility to the power grid. Since buildings consume 75% of the nation’s electricity, improvement of energy efficiency through smart building control systems will contribute to the sustainability of the nation’s energy system.

Roll stall and the vortex-induced aerodynamic of low-aspect-ratio fliers

The design and development of highly-maneuverable aircraft has been a long-standing engineering challenge. This challenge presents itself in almost all flight regimes, from supersonic fighter jets down to low-speed, smaller-scale unmanned aircraft. Despite the very different operating conditions, there is at least one common feature among aircraft designs aiming to provide very agile, yet stable, flight. Namely, the planforms (the shape and layout of an airplane’s wing) of such aircraft are of low aspect ratio. Recent studies suggest that the aerodynamic and gust-response of such low aspect ratio fliers are significantly different than larger flyers and not well understood. This issue constitutes a critical gap in aerial vehicle development, and this research project addresses a critical gap in the development of reliable and fully controllable aerial drones. The researchers will also enhance course curricula with results from this research, and a course on unsteady low Reynolds number aerodynamics will be developed. A summer program is proposed that will enable local high school students to learn aerodynamics and flight concepts and to participate in a design/build/operate competition integrating fluid dynamics, aerodynamics, and aircraft design.

Switched Adaptive Control Methods for Electrical Stimulation Induced Cycling

This project will create new control methods to maximize the effectiveness of a commonly prescribed rehabilitation therapy for individuals with neurological conditions (NCs), including stroke, spinal cord injury, and traumatic brain injury. Functional Electrical Stimulation (FES) cycling uses an externally applied sequence of voltages to cause the individual’s leg muscles to contract to propel a recumbent cycle. The repetitive, coordinated motions of cycling can help restore limb function. An electric motor is available to augment the person’s own muscles, if needed. This project will determine how to switch between different muscle groups and the motor to ensure desired behaviors, despite differences in muscle strength and endurance between individuals. For example, the project will examine methods to enable an FES cycle to adapt to the individual attributes of a new participant within a known time interval. The results will be validated in populations of individuals with NCs to demonstrate clinical efficacy. This project will advance the national health by improving the quality of life for individuals with NCs. In addition to these direct benefits, the project will introduce top undergraduate students to advanced research methods in this critical area of biomechanics.