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.