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中国物理学会期刊

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

CSTR: 32037.14.aps.74.20250018

A method of calculating spatiotemporal distribution of ion temperature in hot spots of one-dimensional implosions based on multi-diagnostic parameter analysis

CSTR: 32037.14.aps.74.20250018
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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 a distribution remains challenging due to the extreme spatiotemporal scales of hot spots (~100 ps, ~100 μm). To cope with this challenge, a computational method of reconstructing the spatiotemporal ion temperature distribution in one-dimensional implosion hot spots through multi-diagnostic parameter analysis is proposed in this work.
    Taking shock-compressed implosions for example, the physical process is simulated via the one-dimensional (1D) radiation-hydrodynamics code Multi1D. The analysis shows two key mechanisms. One is that the propagation of reflected shock waves governs the rapid temperature rise and spatiotemporal differences in peak temperatures, and the other is that ion-ion conduction and ion-electron thermal conduction dominate the slow temperature decline. These mechanisms are found to be universal under different initial conditions. Based on these characteristics, a mathematical model with 10 parameters is 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 rate, and neutron imaging profile, are rigorously derived.
    Using computational cases as simulated experiments, key diagnostic parameters related to ion temperature are generated as constraints. Genetic algorithm is employed to optimize the model parameters, and the resulting ion temperature distributions show excellent agreement with simulation results in the fusion phase, thus validating the effectiveness of the method.
    This approach provides a way to reconstruct the ion temperature distribution in near-one-dimensional ICF experiments by using traditional neutron diagnostics, thus bypassing the limitations of spatiotemporally resolved measurement techniques. Although theoretically extensible to 2D/3D scenarios, challenges such as increased model complexity and insufficient multidimensional diagnostic data must be addressed. This method provides a valuable experimental way for understanding formation and evolution of hot spots, calibrating radiation-hydrodynamics codes, and optimizing implosion designs, which is of great significance for achieving fusion ignition.

     

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