氩氢等离子体反应器的数字孪生
Digital Twin of an Argon-Hydrogen Plasma Reactor
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中文总结 AI 辅助
本文为氩氢等离子体反应器构建COMSOL数字孪生，结合AI模型实现数据同化与优化，旨在实现反应器全自主实时控制与工艺优化。
中文摘要 AI 辅助
主要概念验证围绕一个氩氢等离子体反应器展开，该反应器在一体化步骤中完成关键原材料的熔化、还原、雾化和淬火，以优质球形粉末作为可交付输出，并控制成分化学。反应器的每次使用均通过热电偶和压力传感器进行监测，这些传感器为真实世界实验提供每日数据源。反应器通过COMSOL Multiphysics建模，该软件作为提供多物理场模拟的核心求解器。COMSOL的使用辅以人工智能（AI）模型，以实现无缝数据同化和优化。本文介绍了反应室和收敛-发散喷嘴的COMSOL孪生模型，以及一个在Ti-6Al-4V（Ti64）上验证的自定义相变粒子追踪层。此外，我们强调了如何利用COMSOL模拟与基于AI的数字代理之间的协同作用，构建自洽的优化循环，旨在（i）反应器的全自主实时控制和（ii）工艺优化。
英文摘要
The principal proof of concept revolves around an argon-hydrogen plasma reactor that melts, reduces, atomizes and quenches critical raw material in one step, with premium spherical powder as the deliverable output and control of the composition chemistry. Each usage of the reactor is monitored through thermocouples and pressure sensors, which provide a daily data source of the real-world experiments. The reactor is modeled through COMSOL Multiphysics, which represents the core solver used to provide multiphysics simulations. The usage of COMSOL is complemented with Artificial Intelligence (AI) models, to enable seamless data assimilation and optimization. This paper presents the COMSOL twin of the reaction chamber and converging-diverging nozzle, together with a custom phase-change particle-tracing layer validated on Ti-6Al-4V (Ti64). Moreover, we highlight how the synergy between COMSOL simulations and AI-based digital surrogates can be leveraged to build self-consistent optimization loops geared toward (i) fully autonomous live control of the reactor and (ii) optimization of the process.
