用幂级数直接求和法求解自由对称体的欧拉-泊松方程

IF 3.1 3区 工程技术 Q2 MECHANICS Archive of Applied Mechanics Pub Date : 2025-02-19 DOI:10.1007/s00419-025-02774-y
Guilherme Corrêa Silva
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引用次数: 0

摘要

欧拉-泊松方程通过一阶微分方程控制的旋转矩阵和角速度分量来描述刚体方向的时间演化。根据Cauchy-Kovalevskaya定理,可以将这些方程的解表示为演化参数的幂级数。在这项工作中,我们为自由对称刚体的情况导出了这些级数的和。利用运动积分,直接对这些级数的项求和,我们得到了自由对称体的欧拉-泊松方程的初等函数的通解。这种方法避免了对欧拉角等标准参数化的需要,允许直接的、封闭形式的解。结果与前人的研究结果一致,为求解欧拉-泊松方程提供了新的思路。
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General solution to Euler–Poisson equations of a free symmetric body by direct summation of power series

Euler–Poisson equations describe the temporal evolution of a rigid body’s orientation through the rotation matrix and angular velocity components, governed by first-order differential equations. According to the Cauchy–Kovalevskaya theorem, these equations can be solved by expressing their solutions as power series in the evolution parameter. In this work, we derive the sum of these series for the case of a free symmetric rigid body. By using the integrals of motion and directly summing the terms of these series, we obtain the general solution to the Euler–Poisson equations for a free symmetric body in terms of elementary functions. This method circumvents the need for standard parametrizations like Euler angles, allowing for a direct, closed-form solution. The results are consistent with previous studies, offering a new perspective on solving the Euler–Poisson equations.

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来源期刊
CiteScore
4.40
自引率
10.70%
发文量
234
审稿时长
4-8 weeks
期刊介绍: Archive of Applied Mechanics serves as a platform to communicate original research of scholarly value in all branches of theoretical and applied mechanics, i.e., in solid and fluid mechanics, dynamics and vibrations. It focuses on continuum mechanics in general, structural mechanics, biomechanics, micro- and nano-mechanics as well as hydrodynamics. In particular, the following topics are emphasised: thermodynamics of materials, material modeling, multi-physics, mechanical properties of materials, homogenisation, phase transitions, fracture and damage mechanics, vibration, wave propagation experimental mechanics as well as machine learning techniques in the context of applied mechanics.
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