具有粗糙数据的一维可压缩Navier-Stokes系统的全局适定性

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2023-09-21 DOI:10.1016/j.matpur.2023.09.012
Ke Chen , Ly Kim Ha , Ruilin Hu , Quoc-Hung Nguyen
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引用次数: 0

摘要

在本文中,我们用粗糙的初始数据研究了气体动力学中一维可压缩Navier-Stokes系统的全局适定性问题。首先,Liu和Yu(2022)[30]利用BV空间中的初速度数据建立了一维等熵cNSE的全局适定性理论。然后,Wang等人将其扩展到BV空间中具有初始速度和温度数据的1d全cNSE。(2022)[31]。利用W2γ,1空间中的初速度数据改进了刘和余的全局适定性结果;对于任意γ>;0任意小。我们的基本思想是基于建立一维抛物方程的各种“端点”平滑估计。
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Global well-posedness of the 1d compressible Navier–Stokes system with rough data

In this paper, we study the global well-posedness problem for the 1d compressible Navier–Stokes systems (cNSE) in gas dynamics with rough initial data. First, Liu and Yu (2022) [30] established the global well-posedness theory for the 1d isentropic cNSE with initial velocity data in BV space. Then, it was extended to the 1d full cNSE with initial velocity and temperature data in BV space by Wang et al. (2022) [31]. We improve the global well-posedness result of Liu and Yu with initial velocity data in W2γ,1 space; and of Wang–Yu–Zhang with initial velocity data in L2W2γ,1 space and initial data of temperature in W˙23,65W˙2γ1,1 for any γ>0 arbitrarily small. Our essential ideas are based on establishing various “end-point” smoothing estimates for the 1d parabolic equation.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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