Enhancing efficiency of alternative energy conversion systems: a study of vibration and ocean thermal energy harvesters

Ouro-Koura, Habilou
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Tichy, John A.
Hella, Mona M.
Borca-Tasciuc, Theodorian
Deng, Zhiqun (Daniel)
Borca-Tasciuc, Diana-Andra
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Mechanical engineering
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This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.
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In all energy conversion systems and applications, efficiency is of major significance to engineers. This study focuses on two alternative energy conversion systems to improve their conversion efficiency. Part of this study is on electrostatic micro electromechanical systems (MEMS) used to harvest ambient kinetic energy or vibrations for applications such as powering Internet of Things (IoTs) sensors, while the other part is focused on marine thermal energy harvesting for powering Uncrewed Underwater Vehicles (UUVs).Electrostatic MEMS are used to harvest vibration energy from ambient sources such as wind, air ducts, human motion, and machinery. A typical electrostatic MEMS device consists of a spring-supported mass with a mobile electrode. This interacts with fixed electrode on the substrate to form a variable capacitor, converting mechanical energy into electrical energy when subjected to external vibrations. Despite the multitude of designs available for MEMS that harvest vibration energy, their maximum theoretical energy conversion limit remains largely unknown, hindering optimization efforts. This stands in contrast to other energy conversion systems like heat engines, which have a well-established theoretical limit in the form of Carnot efficiency. The concept of entropy served as the foundational element for deriving Carnot efficiency. This thesis explores the concept of mixing entropy, derived from statistical energy analysis, as an initial step towards discovering the efficiency limit of electrostatic MEMS. Specifically, mixing entropy is used to gauge the energy variation in an ideal electromechanical system at resonance. Nondimensional governing equations of the non-dissipative subsystems and the related different energy terms are developed. Multiple cases related to different initial conditions of the system are investigated. The results show that the maximum mixing entropy generated by the system coincides with the maximum energy transfer between the mechanical and electrical subdomains. A maximum defined effectiveness value of 4.4% for the system under consideration was obtained. This foundational research has the potential to be extended to more complex systems that include damping terms relevant to real-world applications. Such an expansion would pave the way for determining the maximum energy conversion limits for MEMS vibration energy harvesters. While the above approach focuses on laying down fundamental concepts, the second approach takes more of a practical route. Specifically, to increase the efficiency of electrostatic energy harvesters, an impact-based frequency-up conversion is studied. This technique uses a combination of electrodes’ impact along with the impact of a springless mass (microball) to a shuttle mass. Frequency up-conversion generates high-frequency vibrations from low-frequency excitation, typically through electrode impact that triggers free oscillation. The objective of this study is i) to understand the feasibility of combining two distinct power enhancement methods (electrode impact and springless mass); and ii) to understand the effect of ball sizes and materials. The results suggest that the two methods can be combined seamlessly to produce a system with enhanced performance. Furthermore, from testing different ball materials (tungsten carbide, zirconium dioxide, and silicon nitride) and sizes it was found that optimum combinations depend on the applied bias voltage, acceleration, or frequency conditions. The addition of microball increases the device bandwidth especially at low vibration peak-to-peak acceleration of 0.5 g (g is earth’s gravitational acceleration). The second part of this thesis targets the optimization of a system to be used in conjunction with a phase change material or PCM-based thermal engine to harvest ocean thermal energy. The oceans, largely unexplored, offer numerous resources and a rich biodiversity but present a harsh environment for exploration. UUVs serve as cost-effective tools for resource mapping, exploration, and scientific research. However, their mission durations are limited by battery life. To address this, researchers are turning to the ocean’s thermal energy as an alternative power source. Specifically, PCM-based systems can harness the ocean’s temperature gradient to generate power. As a UUV traverses different ocean depths, the PCM expands when absorbing heat from warmer surface water and contracts when releasing it in colder deep water. This volume change can pressurize a working fluid, which can either be used for propulsion through buoyancy changes or converted to electricity through a generator. While this offers a sustainable way to power the vehicle’s sensors and propulsion systems, the process’s efficiency is hampered by multiple stages of energy conversion. The goal of this thesis is to optimize this multi-stage conversion process in a real-world system. This exercise is first attempted on an ocean thermal energy using PCM to produce at least 8.1 kJ sufficient to power the SOLO-II float of the Argo program. A system-level numerical study is carried out simulating all key components. The optimized system is predicted to need less than 6 kg of PCM to produce the required power. The last part of the thesis expands on this type of system, investigating theoretically and experimentally a benchtop hydraulic-to-electric system for use with a PCM-based thermal engine. A numerical theoretical model is produced to simulate the performance of the hydraulic-to-electric system. Additionally, a predictive model using machine learning, specifically, the artificial neural networks (ANN), is developed for rapid assessment of the system energy based on available pressure and electrical load in use. Both the theoretical and ANN models are validated experimentally. The ANN model can monitor onboard overall energy conversion efficiency at a low computational energy demand with less than 15% relative error, allowing for better mission planning in real world UUV deployment. The maximum energy conversion efficiency of the benchtop model is 51%. The ANN model also has the potential for future expansion to include more system variables, paving the way for system-level energy efficiency optimization.
Rensselaer Polytechnic Institute, Troy, NY
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