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Browsing by Author "Yang, Jiongzhi"

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    Comparison of Energy Efficiency, Eco-Friendliness, Cost, and Convenience of Phase-Change and Biosolar Materials in Solar Panels
    Vaughan, Clint; Richardson, Kelly; Yang, Jiongzhi (2019-05-08)
    Solar energy is a clean, renewable energy source that is a good alternative to nonrenewable energy sources. Currently, the two major materials utilized in solar panels are phase change materials (PCMs) and biosolar materials. The purpose of this study is to determine whether biosolar materials or phase change materials are better overall, in terms of energy efficiency, cost and convenience, and eco-friendliness in solar panels. Utilizing solar panels that implement phase-change materials or bio-solar materials, this study explores the energy efficiency, cost and convenience, and eco-friendliness, in a variety of different conditions and designs, for each type of material. To ensure that an overall finding on the better type of material can be found, this study uses a rating system, based on government regulations, industry standards, experimental data, and common scientific values. It is expected that there is higher energy efficiency with the utilization of phase-change materials than with bio-solar materials. However, it is expected that the bio-solar materials are more eco-friendly than the phase-change materials. Overall, it is expected that bio-solar materials are the better choice for solar panels because of their eco-friendliness, low cost, and similar energy efficiency to phase-change materials. The findings of this study can help to push communities to make an informed decision on a switch to renewable energy methods. More importantly, this study supports the use of clean, renewable energy with biosolar material solar panels, to combat rapid change in global climate and negative impacts of most nonrenewable energy sources.
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    Deep Reinforcement Learning for Multi-Phase Microstructure Design
    Yang, Jiongzhi; Harish, Srivatsa; Li, Candy; Zhao, Hengduo; Antous, Brittney; Acar, Pinar (2021-03-22)
    This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D printed periodic microstructures. These algorithms focus on improving the mechanical and thermal performance of engineering components by optimizing a microstructural architecture to meet different design requirements. Additionally, the machine learning solutions demonstrated equivalent results to the physics-based simulations while significantly improving the computational time efficiency. The outcomes of the project show promise to the automation of the design and manufacturing of microstructures to enable their fabrication in large quantities with the utilization of the 3-D printing technology.