天体力学任务的多步出网格法分析

IF 0.7 Q4 ASTRONOMY & ASTROPHYSICS
L. Olifer, V. Choliy
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引用次数: 0

摘要

摘要有时候,需要对天体轨迹进行高精度的预测。最常见的方法是解析解开普勒方程或者用龙格-库塔或亚当斯积分器数值解运动方程。对于低轨道卫星,迫切需要计算重力势和其他影响运动的力。结果,运动方程的右侧变得更大,经典积分器将不太有效。另一方面,有一种结合了龙格-库塔法和亚当斯法的多步出网格法。MOG方法是基于使用m个解的在网值和n × m个离网导数估计。该方法可以提供最大可能阶的稳定积分器O (hm+mn+n−1)。本研究的主要课题是在考虑地球位势模型(例如EGM2008 (Pavlis at al., 2008))并考虑大气阻力或太阳辐射压力等其他扰动的情况下,实现和分析求解卫星运动方程的MOG方法。对低轨道和不同偏心率(从0.1到0.9)的卫星进行了模拟。将MOG积分器的结果与Runge-Kutta积分器和Adams积分器的结果进行比较。结果表明,在处理高阶时,MOG方法比同阶的经典方法具有更好的精度和较少的右值估计。这使得它比“经典”方法有一些优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Analysis of Multistep-Out-of-Grid Method for Celestial Mechanics Tasks
Abstract Occasionally, there is a necessity in high-accurate prediction of celestial body trajectory. The most common way to do that is to solve Kepler’s equation analytically or to use Runge-Kutta or Adams integrators to solve equation of motion numerically. For low-orbit satellites, there is a critical need in accounting geopotential and another forces which influence motion. As the result, the right side of equation of motion becomes much bigger, and classical integrators will not be quite effective. On the other hand, there is a multistep-out-of-grid (MOG) method which combines Runge-Kutta and Adams methods. The MOG method is based on using m on-grid values of the solution and n × m off-grid derivative estimations. Such method could provide stable integrators of maximum possible order, O (hm+mn+n−1). The main subject of this research was to implement and analyze the MOG method for solving satellite equation of motion with taking into account Earth geopotential model (ex. EGM2008 (Pavlis at al., 2008)) and with possibility to add other perturbations such as atmospheric drag or solar radiation pressure. Simulations were made for satellites on low orbit and with various eccentricities (from 0.1 to 0.9). Results of the MOG integrator were compared with results of Runge-Kutta and Adams integrators. It was shown that the MOG method has better accuracy than classical ones of the same order and less right-hand value estimations when is working on high orders. That gives it some advantage over ”classical” methods.
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CiteScore
1.00
自引率
11.10%
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