Haluk Akay
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Balancing Repairability and Technical Requirements
A Reliability-Based Optimization Approach for PV Module Design
Master thesis
(2025)
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A.G. Timmers Verhoeven, O. Nejadseyfi, B.F. Fereidoonnezhad, Haluk Akay, Zola Fung-A-Jou
As the solar industry continues to grow, the accumulation of photovoltaic (PV) module waste highlights the pressing need for more circular and repairable designs. Conventional laminated modules are difficult to disassemble and recycle, which limits both material recovery and component reuse. While avoiding the lamination improves disassembly, it also compromises the module’s mechanical integrity. This trade-off demands a careful redesign to maintain structural requirements without sacrificing key criteria such as repairability, weight, and cost. This study presents a reliability based optimization approach to redesign the PV module while balancing repairability, weight, and cost constraints. A semi-quantitative relative repairability assessment method, tailored specifically for PV modules, was developed to quantify repairability impacts of design changes. Using Robustimzer software, reliability-base optimization was incoporated to account for uncertain scenarios during the life cycle of the product. Finite Element Analysis and prototype verification ensured compliance with IEC61215 mechanical load standards, achieving an optimized design for mass, repairability and costs simultaneously. As a case study, this methodology was applied to a laminate-free module developed by Biosphere Solar, demonstrating how repairability-focused design can be effectively balanced with structural and economic requirements. The proposed approach offers a scalable framework for advancing sustainable design practices in the PV industry.
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As the solar industry continues to grow, the accumulation of photovoltaic (PV) module waste highlights the pressing need for more circular and repairable designs. Conventional laminated modules are difficult to disassemble and recycle, which limits both material recovery and component reuse. While avoiding the lamination improves disassembly, it also compromises the module’s mechanical integrity. This trade-off demands a careful redesign to maintain structural requirements without sacrificing key criteria such as repairability, weight, and cost. This study presents a reliability based optimization approach to redesign the PV module while balancing repairability, weight, and cost constraints. A semi-quantitative relative repairability assessment method, tailored specifically for PV modules, was developed to quantify repairability impacts of design changes. Using Robustimzer software, reliability-base optimization was incoporated to account for uncertain scenarios during the life cycle of the product. Finite Element Analysis and prototype verification ensured compliance with IEC61215 mechanical load standards, achieving an optimized design for mass, repairability and costs simultaneously. As a case study, this methodology was applied to a laminate-free module developed by Biosphere Solar, demonstrating how repairability-focused design can be effectively balanced with structural and economic requirements. The proposed approach offers a scalable framework for advancing sustainable design practices in the PV industry.
Interpreting the results of Atomic Force Microscopy on soft samples is a challenging task due to the coupling of the tip-sample forcing with the unobservable surface motion. Current analysis methods for soft samples are either slow and brittle to noise or require surface deformation to be provided explicitly and cannot converge onto the global optimum in equational reclaim. In this paper a machine learning algorithm called a Mixture of Expert neural network was successfully used with the goal of model classification between hard and soft samples in addition to estimating dynamic properties of those samples. In addition a separate autoencoder based machine learning approach is explored for the purposes of reconstructing the unobservable dynamics of the system in equational form. These results are obtained with the hope that they are implemented in Atomic Force Microscopy software for in-situ sample analysis.
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Interpreting the results of Atomic Force Microscopy on soft samples is a challenging task due to the coupling of the tip-sample forcing with the unobservable surface motion. Current analysis methods for soft samples are either slow and brittle to noise or require surface deformation to be provided explicitly and cannot converge onto the global optimum in equational reclaim. In this paper a machine learning algorithm called a Mixture of Expert neural network was successfully used with the goal of model classification between hard and soft samples in addition to estimating dynamic properties of those samples. In addition a separate autoencoder based machine learning approach is explored for the purposes of reconstructing the unobservable dynamics of the system in equational form. These results are obtained with the hope that they are implemented in Atomic Force Microscopy software for in-situ sample analysis.