等离子体约束状态分类在聚变发电厂中的应用：轮廓反射仪与集成诊断
Plasma Confinement State Classification in Fusion Power Plants: Profile Reflectometer and Ensemble Diagnostics
AI总结：
本研究开发了基于机器学习的等离子体约束状态分类器，利用轮廓反射仪和集成模型提高诊断准确性。
摘要：
随着聚变示范电厂（FPPs）被视为可实现的目标，许多工程挑战仍然存在。在反应堆环境中，预计可用的诊断手段并不多。生存性、可维护性和有限的端口空间大大限制了与FPP相关的诊断手段数量。一个剩余的挑战是开发工具和设备，从有限的诊断子集中提取用于控制FPP的等离子体状态信息。本工作是为解决这一挑战而进行的总体项目的一部分。用于FPP的特定诊断子集仍然存在争议。我们采取了开发基于机器学习的工具来处理不同的重要等离子体状态参数的方法，利用已知的FPP可行的诊断手段。之前我们开发了一个利用电子回旋辐射（ECE）诊断的等离子体约束模式分类器。在这里，我们进一步开发了一个基于轮廓反射仪（PR）的分类器，测试准确率为97%，以及一个集成模型，将ECE和PR模型结合成一个模型，实现了99%的测试准确率。
英文摘要：
As Fusion Pilot Plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Not many diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine-learning-based tools for different significant plasma state parameters, using already known FPP-viable diagnostics. Previously we developed a plasma confinement mode classifier utilizing the Electron Cyclotron Emission (ECE) diagnostic. Here, we expand on this by developing a Profile Reflectometer (PR) based classifier with 97\% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99\% test accuracy.
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