3D非齐次不可压Navier-Stokes方程的粘性极限研究

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"陈鹏飞和肖跃龙的学术论文,探讨了3维非齐次不可压Navier-Stokes方程在旋度边界条件下的消失粘性极限问题" 这篇论文主要聚焦于三维非齐次不可压Navier-Stokes方程组在特定边界条件下的理论分析。Navier-Stokes方程是流体力学中的核心方程,用于描述流体的运动状态,尤其在处理粘性流体问题时不可或缺。不可压Navier-Stokes方程意味着流体的密度在整个过程中保持恒定,这是一个理想化的假设,常用于空气动力学和海洋学等领域。 在3D非齐次不可压Navier-Stokes方程中,"非齐次"指的是流体内部可能存在非均匀的分布,如温度、压力或密度的变化,这使得方程组变得复杂。"旋度边界条件"则是指流体旋度(即流体粒子旋转速度的向量)在边界上的行为,它对流场的动态特性有显著影响。这类边界条件在实际问题中常见,例如在壁面附近流体的涡旋生成和流动分离。 论文的核心工作是研究当粘性系数趋于零时,即"消失粘性极限",Navier-Stokes方程的行为。粘性是流体内部阻力的表现,其消失会导致理想流体模型,此时流体没有内摩擦。这一极限过程在理论分析中非常重要,因为它能帮助理解流体动力学的无粘性近似和湍流现象。 作者首先证明了在给定的光滑有界区域$R^3$中,带有旋度边界条件的3D非齐次不可压Navier-Stokes方程组的初边值问题具有局部强解的存在性。这意味着在有限的时间区间内,可以找到满足方程的唯一连续且可微的解。接着,他们进一步证明了随着粘性系数的减小,这些强解会收敛到一个解,这个解满足无粘性的Euler方程。他们还给出了强解的收敛率估计,这为理解和量化粘性效应在流体动力学中的影响提供了定量依据。 关键词包括非齐次不可压Navier-Stokes方程、旋度边界条件以及消失粘性极限,这些关键词揭示了论文的主要研究方向和技术难点。论文的贡献在于深化了对流体动力学基本理论的理解,特别是对于粘性流体模型向理想流体模型过渡的数学描述,对于工程应用和数值模拟等领域有着重要的理论指导意义。

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

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