状态方程和聚类算法在核汽化中的作用

Navjot K. Dhillon, Sejal Ahuja, Rajat Rana, Sakshi Gautam
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引用次数: 0

摘要

利用量子分子动力学(QMD)模型,研究了不同核状态方程碰撞中的核汽化,并系统比较了基于简单空间关联、空间动量关联、质量依赖结合能切割和模拟退火聚类算法(SACA)的不同聚类方法。通过研究气体/液体含量和汽化概率与入射能量的行为,预测了不同核状态方程,即软、硬和软动量依赖(SMD)相互作用对碰撞汽化开始能量的影响。这两个观测值很好地探测了核汽化的临界点。进一步探讨了不同聚类算法对核汽化起始能量的影响,并对不同聚类算法对[公式:见文]和[公式:见文]碰撞的计算结果与实验数据进行了比较。
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Role of equation of state and clusterization algorithms on nuclear vaporization
By using the Quantum Molecular Dynamics (QMD) model, a study on the nuclear vaporization in [Formula: see text] collision is presented for different nuclear equations of state along with a systematic comparison of different clusterization methods based on simple spatial correlations, spatial-momentum correlations, mass dependent binding energy cuts and Simulated Annealing Clusterization Algorithm (SACA). The effect of different nuclear equations of state i.e., Soft, Hard and Soft with Momentum Dependent (SMD) interactions on the energy of onset of vaporization for [Formula: see text] collisions is predicted by investigating gas/liquid content and probability of vaporization versus incident energy behavior. These two observables probe the critical point of nuclear vaporization very well. Further outcome of different clusterization algorithms on the energy of onset of nuclear vaporization is also probed and a comparison of calculations with the experimental data for [Formula: see text] and [Formula: see text] collisions is carried out for different clusterization algorithms.
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