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基于多诊断参数分析的一维内爆热斑离子温度时空分布计算方法

唐琦 刘品阳 宋仔峰 陈伯伦 刘中杰 杨家敏

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基于多诊断参数分析的一维内爆热斑离子温度时空分布计算方法

唐琦, 刘品阳, 宋仔峰, 陈伯伦, 刘中杰, 杨家敏

A method for calculating the temporal and spatial distribution of ion temperature in hot spots of one-dimensional implosions based on multi-diagnostic parameter analysis

TANG Qi, LIU Pinyang, SONG Zifeng, CHEN Bolun, LIU Zhongjie, YANG Jiamin
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  • 惯性约束聚变中,热斑离子温度是决定聚变增益的关键参数,热斑离子温度时空分布能够揭示热斑能量的沉积与耗散过程,针对此物理研究需求,提出了一种基于多诊断参数分析的一维内爆热斑离子温度时空分布计算方法。本文以冲击压缩内爆为例,分析了离子温度时空分布的特性,建立了离子温度时空分布数学模型。利用计算算例作为模拟实验给出了离子温度相关的多个关键诊断量,以此作为离子温度时空分布求解的约束。通过遗传算法计算出了模型中的待定参数,计算参数给出的离子温度时空分布与模拟实验基本相符,验证了本方法的有效性。本方法可以应用于近一维内爆实验热斑离子温度时空分布的计算,为更深入地了解内爆热斑的形成与演化过程提供了实验观测手段。
    In inertial confinement fusion (ICF), the ion temperature of hot spots is a critical parameter determining fusion gain, and its spatiotemporal distribution provides insights into energy deposition and dissipation processes. However, directly diagnosing such distributions remains challenging due to the extreme spatiotemporal scales of hot spots (~100 ps, ~100 μm). To address this challenge, this study proposes a computational method for reconstructing the spatiotemporal ion temperature distribution in one-dimensional implosion hot spots through multi-diagnostic parameter analysis.
    Using shock-compressed implosions as a case study, the physical process was simulated via the 1D radiation-hydrodynamics code Multi1D. Analysis revealed two key mechanisms: (1) The propagation of reflected shock waves governs the rapid temperature rise and spatiotemporal differences in peak temperatures, and (2) ion-ion and ion-electron thermal conduction dominates the slow temperature decline. These mechanisms were found to be universal across varying initial conditions. Based on these characteristics, a mathematical model with 10 parameters was developed to describe the spatiotemporal ion temperature distribution. The relationships between this distribution and experimental diagnostic quantities—including neutron yield, average ion temperature, time-dependent fusion reaction rates, and neutron imaging profiles—were rigorously derived.
    Using computational cases as simulated experiments, key diagnostic parameters related to ion temperature were generated as constraints. Genetic algorithms were employed to optimize the model parameters, and the resulting ion temperature distributions showed strong agreement with simulation results during the fusion phase, validating the method’s effectiveness.
    This approach provides a means to reconstruct ion temperature distributions in near-one-dimensional ICF experiments using conventional neutron diagnostics, circumventing the limitations of spatiotemporally resolved measurement techniques. While theoretically extensible to 2D/3D scenarios, challenges such as increased model complexity and insufficient multidimensional diagnostic data must be addressed. The method offers a valuable experimental tool for understanding hot spot formation and evolution, calibrating radiation-hydrodynamics codes, and optimizing implosion designs, with significant implications for achieving fusion ignition.
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