Sirvi Autor "Odubena, Joseph Theophilus" järgi

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Determination of surface elastic properties of nanoscale metal using copper and tungsten as an example
(Tartu Ülikool, 2023) Odubena, Joseph Theophilus; Zadin, Veronika, juhendaja; Kyritsakis, Andreas, juhendaja
High electric field (HEF) systems such as reactors and colliders operate in extreme environments and are susceptible to breakdown due to two primary reasons: slow-acting structural changes and general material failure. Macro and micro-level studies have shown that HEF systems undergo internal and external structural and mechanical property alterations, leading to damaging occurrences such as vac- uum arcs. This phenomenon is characterised by a low burning voltage of approximately 20V and a high current of 35-500A between two metallic electrodes in a vacuum. Anomalies like vacuum arcs can cause significant damage to HEF systems, ultimately leading to general system failure or break- down, which could have fatal consequences in fields like medicine, biology, renewable energy, and geophysics. Thus, conducting extensive research and developing theories to explain the failure process and pro- vide possible solutions is crucial. Many scientific studies and theories have highlighted fundamental irregularities in the mechanical properties of materials used in constructing HEF systems, primarily made of 98% Copper as the primary cause. This research aims to contribute to the pool of available data on the surface energy properties of Copper and Tungsten, which is critical data needed to study the HEF system failure phenomena accurately and provide compelling solutions. Unlike other very complex articles, this thesis provides a step-by-step process for generating accurate data on the surface energy properties of Copper and Tungsten using LAMMPS – a molecular dynam- ics software, and OVITO – a visualisation tool. While Copper is the primary material used in HEF systems, Tungsten appears to be a suitable alternative due to its material properties, and studying both materials is essential. The data generated will aid in providing a better understanding of HEF system failure and develop more effective solutions.