five

A multi-rocket piston model to study three-dimensional asymmetries in implosions at the national ignition facility

收藏
DataCite Commons2025-01-31 更新2025-04-15 收录
下载链接:
https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/MUWZVI
下载链接
链接失效反馈
官方服务:
资源简介:
Ignition and gain greater than unity has been achieved in inertial confinement fusion (ICF) implosions at the National Ignition Facility (NIF). These accomplishments required implosions that produced high hotspot pres- sures that are inertially confined by a dense shell of DT fuel. However, even in the burning and igniting plasma regime, 3D asymmetries can reduce the coupling of shell kinetic energy to the hotspot harming the overall implosion performance and truncating burn. Likewise, the overall scale of the implosion can be minimized by maintaining a high efficiency of energy coupling from the imploding shell to the hotspot. Recent experiments commonly show signs of significant 3D asymmetry that manifest as high hotspot velocity or asymmetry in the self-emission and scattered neutron images. While modeling 3D asymmetries in implosion with full scale hy- drodynamic simulations is often performed, it is labor intensive and computationally costly. Therefore, 3D simulation is applied only in special cases like experiments of particular interest. To enable a wider survey of 3D post-shot analysis, an approximate but computationally inexpensive approach is applied by using multiple rocket-pistons discretizing the spherical implosion. These rocket-pistons are coupled together through the central hotspot pressure using the power balance equations. The approach is similar to that reported by Springer [Springer et al., Nuclear Fusion 59 (3) (2019)] with the inclusion of an approximate hohlraum model beginning at the rocket-implosion stage and post-processing of realistic synthetic diagnostic data at the stagnation and peak burn. This rocket piston tool can provide approximate 3D image and diagnostic data that can then be compared quantitively with data enabling new techniques in iterative, forward fitting, and machine learning to interpreting measurements.
提供机构:
Harvard Dataverse
创建时间:
2025-01-31
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作