聚变反应堆中氚增殖比的快速回归
Fast Regression of the Tritium Breeding Ratio in Fusion Reactors
AI总结：
本研究训练代理模型快速回归聚变反应堆的氚增殖比，人工神经网络实现R²=0.985且预测速度提升8×10⁶倍，并提出Quality-Adaptive Surrogate Sampling算法加速建模过程。
摘要：
氚增殖比（TBR）是现代和下一代D-T燃料核聚变反应堆设计的关键量。TBR表示增殖包层中生成的氚燃料与反应堆运行期间消耗的燃料之间的比率，它以复杂的方式依赖于反应堆几何形状和材料特性。在本研究中，我们探索了代理模型的训练，以对英国原子能管理局正在使用的Monte Carlo TBR模型提供低成本但高质量的近似。我们研究了其特征空间降维的可能性，回顾了9类代理模型的潜在适用性，并进行了超参数优化。在此我们展示了这些模型的性能和扩展特性，其中最快的人工神经网络实现了R²=0.985和0.898 μs的平均预测时间，相对于昂贵的MC模型实现了8×10⁶的相对加速。我们进一步提出了一种新颖的自适应采样算法——Quality-Adaptive Surrogate Sampling，能够与任何单独研究的代理模型接口。我们在一个玩具TBR理论上的初步测试证明了该算法在加速代理建模过程方面的有效性。
英文摘要：
The tritium breeding ratio (TBR) is an essential quantity for the design of modern and next-generation D-T fueled nuclear fusion reactors. Representing the ratio between tritium fuel generated in breeding blankets and fuel consumed during reactor runtime, the TBR depends on reactor geometry and material properties in a complex manner. In this work, we explored the training of surrogate models to produce a cheap but high-quality approximation for a Monte Carlo TBR model in use at the UK Atomic Energy Authority. We investigated possibilities for dimensional reduction of its feature space, reviewed 9 families of surrogate models for potential applicability, and performed hyperparameter optimisation. Here we present the performance and scaling properties of these models, the fastest of which, an artificial neural network, demonstrated $R^2=0.985$ and a mean prediction time of $0.898\ μ\mathrm{s}$, representing a relative speedup of $8\cdot 10^6$ with respect to the expensive MC model. We further present a novel adaptive sampling algorithm, Quality-Adaptive Surrogate Sampling, capable of interfacing with any of the individually studied surrogates. Our preliminary testing on a toy TBR theory has demonstrated the efficacy of this algorithm for accelerating the surrogate modelling process.
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