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Description
Phononic crystals are artificially engineered materials that can forbid phonon propagation in a specific frequency range that is referred to as a “phononic band gap.” Phononic crystals that have band gaps in the GHz to THz range can potentially enable sophisticated control over thermal transport with “phononic devices”. Calculations of the phononic band diagram are the standard method of determining if a given phononic crystal structure has a band gap. However, calculating the phononic band diagram is a computationally expensive and time-consuming process that can require sophisticated modeling and coding. In addition to this computational burden, the inverse process of designing a phononic crystal with a specific band gap center frequency and width is a challenging problem that requires extensive trial-and-error work.
In this dissertation, I first present colloidal nanocrystal superlattices as a new class of three-dimensional phononic crystals with periodicity in the sub-20 nm size regime using the plane wave expansion method. These calculations show that colloidal nanocrystal superlattices possess phononic band gaps with center frequencies in the 102 GHz range and widths in the 101 GHz range. Varying the colloidal nanocrystal size and composition provides additional opportunities to fine-tune the phononic band gap. This suggests that colloidal nanocrystal superlattices are a promising platform for the creation of high frequency phononic crystals.
For the next topic, I explore opportunities to use supervised machine learning for expedited discovery of phononic band gap presence, center frequency and width for over 14,000 two-dimensional phononic crystal structures. The best trained model predicts band gap formation, center frequencies and band gap widths, with 94% accuracy and coefficients of determination (R2) values of 0.66 and 0.83, respectively.
Lastly, I expand the above machine learning approach to use machine learning to design a phononic crystal for a given set of phononic band gap properties. The best model could predict elastic modulus of host and inclusion, density of host and inclusion, and diameter-to-lattice constant ratio for target center and width frequencies with coefficients of determinations of 0.94, 0.98, 0.94, 0.71, and 0.94 respectively. The high values coefficients of determination represents great opportunity for phononic crystal design.
In this dissertation, I first present colloidal nanocrystal superlattices as a new class of three-dimensional phononic crystals with periodicity in the sub-20 nm size regime using the plane wave expansion method. These calculations show that colloidal nanocrystal superlattices possess phononic band gaps with center frequencies in the 102 GHz range and widths in the 101 GHz range. Varying the colloidal nanocrystal size and composition provides additional opportunities to fine-tune the phononic band gap. This suggests that colloidal nanocrystal superlattices are a promising platform for the creation of high frequency phononic crystals.
For the next topic, I explore opportunities to use supervised machine learning for expedited discovery of phononic band gap presence, center frequency and width for over 14,000 two-dimensional phononic crystal structures. The best trained model predicts band gap formation, center frequencies and band gap widths, with 94% accuracy and coefficients of determination (R2) values of 0.66 and 0.83, respectively.
Lastly, I expand the above machine learning approach to use machine learning to design a phononic crystal for a given set of phononic band gap properties. The best model could predict elastic modulus of host and inclusion, density of host and inclusion, and diameter-to-lattice constant ratio for target center and width frequencies with coefficients of determinations of 0.94, 0.98, 0.94, 0.71, and 0.94 respectively. The high values coefficients of determination represents great opportunity for phononic crystal design.
ContributorsSadat, Seid Mohamadali (Author) / Wang, Robert Y (Thesis advisor) / Huang, Huei-Ping (Committee member) / Ankit, Kumar (Committee member) / Wang, Liping (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2020
Description
Solar energy as a limitless source of energy all around the globe has been difficult to harness. This is due to the low direct solar-electric conversion efficiency which has an upper limit set to the Shockley-Queisser limit. Solar thermophotovoltaics (STPV) is a much more efficient solar energy harvesting technology as it has the potential to overcome the Shockley-Queisser limit, by converting the broad-spectrum solar irradiation into narrowband infrared spectrum radiation matched to the PV cell. Despite the potential to surpass the Shockley-Queisser limit, very few experimental results have reported high system-level efficiency.
The objective of the thesis is to study the STPV conversion performance with selective metafilm absorber and emitter paired with a commercial GaSb cell at different solar concentrations. Absorber and Emitter metafilm thickness was optimized and fabricated. The optical properties of fabricated metafilms showed good agreement with the theoretically determined properties. The experimental setup was completed and validated by measuring the heat transfer rate across the test setup and comparing it with theoretical calculations. A novel method for maintaining the gap between the emitter and PV cell was developed using glass microspheres. Theoretical calculations show that the use of the glass of microspheres introduces negligible conduction loss across the gap compared to the radiation heat transfer, which is confirmed by experimental heat transfer measurement. This research work will help enhance the fundamental understanding and the development of the high-efficiency solar thermophotovoltaic system.
The objective of the thesis is to study the STPV conversion performance with selective metafilm absorber and emitter paired with a commercial GaSb cell at different solar concentrations. Absorber and Emitter metafilm thickness was optimized and fabricated. The optical properties of fabricated metafilms showed good agreement with the theoretically determined properties. The experimental setup was completed and validated by measuring the heat transfer rate across the test setup and comparing it with theoretical calculations. A novel method for maintaining the gap between the emitter and PV cell was developed using glass microspheres. Theoretical calculations show that the use of the glass of microspheres introduces negligible conduction loss across the gap compared to the radiation heat transfer, which is confirmed by experimental heat transfer measurement. This research work will help enhance the fundamental understanding and the development of the high-efficiency solar thermophotovoltaic system.
ContributorsNayal, Avinash (Author) / Wang, Liping (Thesis advisor) / Wang, Robert (Committee member) / Milcarek, Ryan (Committee member) / Arizona State University (Publisher)
Created2020
Description
This thesis aims to design of wings for a laminate biped robot for providing locomotion stabilization during jump gliding. The wings are designed to collapse down during the jumping phase to maximize jump height and deployed back for gliding phase using anisotropic buckling in tape spring hinges. The project aims to develop a reliable dynamics model which can be utilized for design and evaluation of optimized systems for jump-gliding. The aerodynamic simulations are run on a vortex-lattice code which provides numeric simulations of the defined geometric bodies. The aerodynamic simulations assist in improving the design parameters such as planform, camber and twist to achieve the best possible Coefficient of Lift for maximizing glide distance. The aerodynamic simulation output is then plugged into a dynamics model built in Python, which is validated and correlated with experimental testing of a key wing designs. The experimental results are then utilized to improve the dynamics model and obtain better designs for improved performance. The simulation model informs the aerodynamic design of wings for sustaining glide for the biped platform and maximizing glide length to increase range.
ContributorsGadekar, Vipul (Author) / Takahashi, Timothy (Thesis advisor) / Aukes, Daniel (Thesis advisor) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2020
Description
Thermal Energy Storage (TES) is of great significance for many engineering applications as it allows surplus thermal energy to be stored and reused later, bridging the gap between requirement and energy use. Phase change materials (PCMs) are latent heat-based TES which have the ability to store and release heat through phase transition processes over a relatively narrow temperature range. PCMs have a wide range of operating temperatures and therefore can be used in various applications such as stand-alone heat storage in a renewable energy system, thermal storage in buildings, water heating systems, etc. In this dissertation, various PCMs are incorporated and investigated numerically and experimentally with different applications namely a thermochemical metal hydride (MH) storage system and thermal storage in buildings. In the second chapter, a new design consisting of an MH reactor encircled by a cylindrical sandwich bed packed with PCM is proposed. The role of the PCM is to store the heat released by the MH reactor during the hydrogenation process and reuse it later in the subsequent dehydrogenation process. In such a system, the exothermic and endothermic processes of the MH reactor can be utilized effectively by enhancing the thermal exchange between the MH reactor and the PCM bed. Similarly, in the third chapter, a novel design that integrates the MH reactor with cascaded PCM beds is proposed. In this design, two different types of PCMs with different melting temperatures and enthalpies are arranged in series to improve the heat transfer rate and consequently shorten the time duration of the hydrogenation and dehydrogenation processes. The performance of the new designs (in chapters 2 and 3) is investigated numerically and compared with the conventional designs in the literature. The results indicate that the new designs can significantly enhance the time duration of MH reaction (up to 87%). In the fourth chapter, organic coconut oil PCM (co-oil PCM) is explored experimentally and numerically for the first time as a thermal management tool in building applications. The results show that co-oil PCM can be a promising solution to improve the indoor thermal environment in semi-arid regions.
ContributorsAlqahtani, Talal (Author) / Phelan, Patrick E (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Mellouli, Sofiene (Committee member) / Wang, Robert (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2020
Description
As experiencing hot months and thermal stresses is becoming more common, chemically protective fabrics must adapt and provide protections while reducing the heat stress to the body. These concerns affect first responders, warfighters, and workers regularly surrounded by hazardous chemical agents. While adapting traditional garments with cooling devices provides one route to mitigate this issue, these cooling methods add bulk, are time limited, and may not be applicable in locations without logistical support. Here I take inspiration from nature to guide the development of smart fabrics that have high breathability, but self-seal on exposure to target chemical(s), providing a better balance between cooling and protection.
Natural barrier materials were explored as a guide, focusing specifically on prickly pear cacti. These cacti have a natural waxy barrier that provides protection from dehydration and physically changes shape to modify surface wettability and water vapor transport. The results of this study provided a basis for a shape changing polymer to be used to respond directly to hazardous chemicals, swelling to contain the agent.
To create a stimuli responsive material, a novel superabsorbent polymer was synthesized, based on acrylamide chemistry. The polymer was tested for swelling properties in a wide range of organic liquids and found to highly swell in moderately polar organic liquids. To help predict swelling in untested liquids, the swelling of multiple test liquids were compared with their thermodynamic properties to observe trends. As the smart fabric needs to remain breathable to allow evaporative cooling, while retaining functionality when soaked with sweat, absorption of water, as well as that of an absorbing liquid in the presence of water were tested.
Micron sized particles of the developed polymer were deposited on a plastic mesh with pore size and open area similar to common clothing fabric to establish the proof of concept of using a breathable barrier to provide chemical protection. The polymer coated mesh showed minimal additional resistance to water vapor transport, relative to the mesh alone, but blocked more than 99% of a xylene aerosol from penetrating the barrier.
Natural barrier materials were explored as a guide, focusing specifically on prickly pear cacti. These cacti have a natural waxy barrier that provides protection from dehydration and physically changes shape to modify surface wettability and water vapor transport. The results of this study provided a basis for a shape changing polymer to be used to respond directly to hazardous chemicals, swelling to contain the agent.
To create a stimuli responsive material, a novel superabsorbent polymer was synthesized, based on acrylamide chemistry. The polymer was tested for swelling properties in a wide range of organic liquids and found to highly swell in moderately polar organic liquids. To help predict swelling in untested liquids, the swelling of multiple test liquids were compared with their thermodynamic properties to observe trends. As the smart fabric needs to remain breathable to allow evaporative cooling, while retaining functionality when soaked with sweat, absorption of water, as well as that of an absorbing liquid in the presence of water were tested.
Micron sized particles of the developed polymer were deposited on a plastic mesh with pore size and open area similar to common clothing fabric to establish the proof of concept of using a breathable barrier to provide chemical protection. The polymer coated mesh showed minimal additional resistance to water vapor transport, relative to the mesh alone, but blocked more than 99% of a xylene aerosol from penetrating the barrier.
ContributorsManning, Kenneth (Author) / Rykaczewski, Konrad (Thesis advisor) / Burgin, Timothy (Committee member) / Emady, Heather (Committee member) / Green, Matthew (Committee member) / Thomas, Marylaura (Committee member) / Arizona State University (Publisher)
Created2020
Description
The operating temperature of photovoltaic (PV) modules has a strong impact on the expected performance of said modules in photovoltaic arrays. As the install capacity of PV arrays grows throughout the world, improved accuracy in modeling of the expected module temperature, particularly at finer time scales, requires improvements in the existing photovoltaic temperature models. This thesis work details the investigation, motivation, development, validation, and implementation of a transient photovoltaic module temperature model based on a weighted moving-average of steady-state temperature predictions.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
ContributorsPrilliman, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2020
Description
Electrostatic Discharge (ESD) is a unique issue in the electronics industry that can cause failures of electrical components and complete electronic systems. There is an entire industry that is focused on developing ESD compliant tooling using traditional manufacturing methods. This research work evaluates the feasibility to fabricate a PEEK-Carbon Nanotube composite filament for Fused Filament Fabrication (FFF) Additive Manufacturing that is ESD compliant. In addition, it demonstrates that the FFF process can be used to print tools with the required accuracy, ESD compliance and mechanical properties necessary for the electronics industry at a low rate production level. Current Additive Manufacturing technology can print high temperature polymers, such as PEEK, with the required mechanical properties but they are not ESD compliant and require post processing to create a product that is. There has been some research conducted using mixed multi-wall and single wall carbon nanotubes in a PEEK polymers, which improves mechanical properties while reducing bulk resistance to the levels required to be ESD compliant. This previous research has been used to develop a PEEK-CNT polymer matrix for the Fused Filament Fabrication additive manufacturing process
ContributorsChurchwell, Raymond L (Author) / Sugar, Thomas (Thesis advisor) / Rogers, Bradley (Committee member) / Morrell, Darryl (Committee member) / Arizona State University (Publisher)
Created2020
Description
Unmanned subsurface investigation technologies for the Moon are of special significance for future exploration when considering the renewed interest of the international community for this interplanetary destination. In precision agriculture, farmers demand quasi-real-time sensors and instruments with remote crop and soil detection properties to meet sustainability goals and achieve healthier and higher crop yields. Hence, there is the need for a robot that will be able to travel through the soil and conduct sampling or in-situ analysis of the subsurface materials on earth and in space. This thesis presents the design, fabrication, and characterization of a robot that can travel through the soil. The robot consists of a helical screw design coupled with a fin that acts as an anchor. The fin design is an integral part of the robot, allowing it to travel up and down the medium unaided. Experiments were performed to characterize different designs. It was concluded that the most energy-efficient speed from traveling down the medium is 20 rpm, while 60 rpm was the efficient speed for traveling up the medium. This research provides vital insight into developing subsurface robots enabling us to unearth the valuable knowledge that subsurface environment holds to help the agricultural, construction, and exploration communities.
ContributorsOkwae, Nana Kwame Kwame (Author) / Marvi, Hamidreza (Thesis advisor) / Tao, Jungliang (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2020
Description
Recent years, there has been many attempts with different approaches to the human-robot interaction (HRI) problems. In this paper, the multi-agent interaction is formulated as a differential game with incomplete information. To tackle this problem, the parameter estimation method is utilized to obtain the approximated solution in a real time basis. Previous studies in the parameter estimation made the assumption that the human parameters are known by the robot; but such may not be the case and there exists uncertainty in the modeling of the human rewards as well as human's modeling of the robot's rewards. The proposed method, empathetic estimation, is tested and compared with the ``non-empathetic'' estimation from the existing works. The case studies are conducted in an uncontrolled intersection with two agents attempting to pass efficiently. Results have shown that in the case of both agents having inconsistent belief of the other agent's parameters, the empathetic agent performs better at estimating the parameters and has higher reward values, which indicates the scenarios when empathy is essential: when agent's initial belief is mismatched from the true parameters/intent of the agents.
ContributorsChen, Yi (Author) / Ren, Yi (Thesis advisor) / Zhang, Wenlong (Committee member) / Yong, Sze Zheng (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this thesis, the problem of designing model discrimination algorithms for unknown nonlinear systems is considered, where only raw experimental data of the system is available. This kind of model discrimination techniques finds one of its application in the estimation of the system or intent models under consideration, where all incompatible models are invalidated using new data that is available at run time. The proposed steps to reach the end goal of the algorithm for intention estimation involves two steps: First, using available experimental data of system trajectories, optimization-based techniques are used to over-approximate/abstract the dynamics of the system by constructing an upper and lower function which encapsulates/frames the true unknown system dynamics. This over-approximation is a conservative preservation of the dynamics of the system, in a way that ensures that any model which is invalidated against this approximation is guaranteed to be invalidated with the actual model of the system. The next step involves the use of optimization-based techniques to investigate the distinguishability of pairs of abstraction/approximated models using an algorithm for 'T-Distinguishability', which gives a finite horizon time 'T', within which the pair of models are guaranteed to be distinguished, and to eliminate incompatible models at run time using a 'Model Invalidation' algorithm. Furthermore, due the large amount of data under consideration, some computation-aware improvements were proposed for the processing of the raw data and the abstraction and distinguishability algorithms.The effectiveness of the above-mentioned algorithms is demonstrated using two examples. The first uses the data collected from the artificial simulation of a swarm of agents, also known as 'Boids', that move in certain patterns/formations, while the second example uses the 'HighD' dataset of naturalistic trajectories recorded on German Highways for vehicle intention estimation.
ContributorsBhagwat, Mohit Mukul (Author) / Yong, Sze Zheng (Thesis advisor) / Berman, Spring (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2021