نوع مقاله : مقالة‌ تحقیقی‌ (پژوهشی‌)

نویسندگان

1 مهندسی مکانیک، دانشکده فنی، دانشگاه تهران، تهران، ایران.

2 پژوهشکده سامانه‌های ماهواره، پژوهشگاه فضایی ایران، تهران، ایران

3 دانشگاه صنعتی خواجه نصیرالدین طوسی، دانشکده هوافضا، تهران، ایران

چکیده

در ماهواره‌های مخابراتی زمین آهنگ برای انجام ماموریت‌های از تراستر استفاده شده و نیاز به سوخت مصرفی می‌باشد. در این مقاله بر اساس یک روش جدید و بدون نیاز به روابط ریاضی حاکم بر دینامیک ماهواره و تنها بر اساس داده‌های موجود برای ماهواره‌های پیشین به تعیین جرم سوخت ماهواره‌های مخابراتی پرداخته می‌شود. برای این منظور از روش حداقل مربعات خطا و شبکه عصبی مصنوعی استفاده شده و دو روش از نظر دقت مقایسه شده و دو مدل ریاضی برای تشخیص جرم سوخت ماهواره بر اساس پارامترهای طراحی آن ارائه شده است. با مقایسه خروجی مدل برای چندین ماهواره واقعی و مقایسه با جرم سوخت واقعی آن‌ها مشخص شد که روابط ارائه شده دارای دقت بالای 95 درصد است و برای امکان‌سنجی و طراحی مفهومی پروژه‌های ماهواره بسیار مناسب و کارا می‌باشند. هم‌چنین برای مدل خطی به دست آمده تحلیل حساسیت انجام شده و در نهایت روش ارائه شده برای به محاسبه حجم ماهواره نیز مورد ارزیابی قرار گرفته است

کلیدواژه‌ها

عنوان مقاله [English]

GEO satellite station keeping mass design based on data mining

نویسندگان [English]

  • ehsan maani 1
  • Peyman Nikpey 2
  • Ehsan Zabihian 3

1 Department of Mechanical Engineering, University of Tehran, Tehran, Iran.

2 Satellite Research Institute, Tehran, Iran

3 Aerospace Engineering Department, K. N. Toosi University of Technology, Tehran

چکیده [English]

n GEO communicational satellites, thrusts are being used for many missions such as station keeping, longitude change maneuver and actuators desaturation. These types of actuators need fuel and its estimation requires many complicated calculations. In this paper, a novel method based on availed data for previous satellites is proposed for estimation of satellite fuel mass and it does not need mathematical modeling of satellite dynamics. Two methods, least square method and artificial intelligence, are used to this aim and two mathematical model are proposed for satellite fuel mass estimation. By applying the models to several previous satellites, it is shown that the models have lower than 5% average error. The proposed method in this paper is quick and accurate and can be utilized for GEO satellites feasibility study and conceptual design

کلیدواژه‌ها [English]

  • GEO satellite
  • Fuel mass
  • Neural Networks
  • Artificial intelligence
  • Data mining
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