The Kuznetsov and Blackstock Equations of Nonlinear Acoustics with Nonlocal-in-Time Dissipation

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED Applied Mathematics and Optimization Pub Date : 2024-04-16 DOI:10.1007/s00245-024-10130-9
Barbara Kaltenbacher, Mostafa Meliani, Vanja Nikolić
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Abstract

In ultrasonics, nonlocal quasilinear wave equations arise when taking into account a class of heat flux laws of Gurtin–Pipkin type within the system of governing equations of sound motion. The present study extends previous work by the authors to incorporate nonlocal acoustic wave equations with quadratic gradient nonlinearities which require a new approach in the energy analysis. More precisely, we investigate the Kuznetsov and Blackstock equations with dissipation of fractional type and identify a minimal set of assumptions on the memory kernel needed for each equation. In particular, we discuss the physically relevant examples of Abel and Mittag–Leffler kernels. We perform the well-posedness analysis uniformly with respect to a small parameter on which the kernels depend and which can be interpreted as the sound diffusivity or the thermal relaxation time. We then analyze the limiting behavior of solutions with respect to this parameter, and how it is influenced by the specific class of memory kernels at hand. Through such a limiting study, we relate the considered nonlocal quasilinear equations to their limiting counterparts and establish the convergence rates of the respective solutions in the energy norm.

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具有非局部时间耗散的非线性声学库兹涅佐夫和布莱克斯托克方程
在超声波学中,如果在声运动控制方程系统中考虑到古尔廷-皮普金类型的一类热通量定律,就会产生非局部准线性波方程。本研究扩展了作者之前的工作,纳入了具有二次梯度非线性的非局部声波方程,这就要求在能量分析中采用新的方法。更确切地说,我们研究了具有分数型耗散的库兹涅佐夫方程和布莱克斯托克方程,并确定了每个方程所需的最小记忆核假设集。我们特别讨论了与物理相关的阿贝尔核和米塔格-勒夫勒核。我们针对内核所依赖的一个小参数(可理解为声扩散率或热弛豫时间)均匀地进行了好求分析。然后,我们分析解的极限行为与该参数的关系,以及它如何受到当前记忆核的特定类别的影响。通过这种极限研究,我们将所考虑的非局部准线性方程与它们的极限对应方程联系起来,并确定了各自解在能量规范中的收敛速率。
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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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