Thermal Control Design Conception of the Amazonia-1 Satellite

Silva, Douglas Felipe da; Muraoka, Issamu; Garcia, Ézio Castejon

doi:10.5028/jatm.v6i2.320

Cited by 16 publications

(7 citation statements)

References 4 publications

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“…Amazonia-1 satellite comprehends the Multi-Mission Platform (MMP) and payload module. MMP is an innovative satellite platform developed by INPE acting as a service module and it is composed of a thermal subsystem [14], Telemetry, Tracking and Command (TT&C) subsystem, propulsion subsystem, power supply subsystem (PSS), mechanical subsystem, and Attitude Control and Data Handling (ACDH). ACDH is composed of an Attitude and Orbit Control System (AOCS) and On-Board Data Handling (OBDH).…”

Section: Amazonia-1mentioning

confidence: 99%

The Application of Machine learning to Amazonia-1 satellite power subsystem telemetry prediction

Barbosa

Ferreira

Júnior

2023

J. Phys.: Conf. Ser.

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This article presents the data acquisition, exploratory data analysis, model training, evaluation, and use of hyperparameters in a machine learning model that will be used to predict telemetry data from the Amazonia-1 satellite. The Amazonia-1 satellite was launched in 2021, it uses the Multi-Mission Platform as a service module and has a Wide Field Imager imaging camera. Its power subsystem has 715 telemetries with distinct data types that will be used as dependent and independent variables. The amount of telemetry data generated daily is large, making manual analysis of this data unfeasible. The ensemble XGBoost machine learning algorithm is used to predict the values of the dependent variable D008 “Battery Module 1 Voltage” that belongs to the electric power subsystem. For the evaluation and performance Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2 are used. The final learning model resulted in the coefficient of determination (R2) with 99.99%, MAE of 0.005749, and RMSE of 0.007727. After the cross-validation step, RMSE reached 0.006888. The execution time was 57 minutes and 32 seconds. Based on these numbers, we can consider that the machine learning model built reached a good result, especially when used with cross-validation.

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Section: Amazonia-1mentioning

confidence: 99%

The Application of Machine learning to Amazonia-1 satellite power subsystem telemetry prediction

Barbosa

Ferreira

Júnior

2023

J. Phys.: Conf. Ser.

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“…They suggest using heater as a thermal control system for eclipse term lasting longer than one-hour. Silva et al [34] present thermal control design and analysis of Amazonia-1, a Brazilian remote sensing satellite by applying passive means and heats. The thermal model is developed based on a lumped parameter method assuming the nodes that are isothermal and the simulations are performed by using Thermal Desktop, RadCAD and SINDA/FLUINT that are software packages of C&R Technologies.…”

Section: Numerical Approachesmentioning

confidence: 99%

Advances in Thermal Modeling and Analysis of Satellites

Atar

Aktaş

2022

Gazi University Journal of Science

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“…Ukuran radiator tergantung pada besarnya daya yang dikeluarkan, temperatur panas yang dibuang (ditentukan oleh bagian yang dikontrol), dan temperatur lingkungan sekitar [3]. Banyak para engineer termal satelit telah melakukan penelitian tentang radiator termal yang menjadi referensi dalam mendesain maupun menganalisis sistem kontrol termal, di antaranya Silva dkk, [4] yang mempelajari tentang penggunaan isolasi pada sistem termal satelit Amazonia-1 untuk melindungi panel struktur bagian luar dari bahaya radiasi matahari, dan menggunakan radiator pada empat sisi panel untuk menolak disipasi panas internal satelit. Moffitt B.A., dkk [5] melakukan penelitian tentang sistem termal aktif dan pasif dengan menggunakan optical properties termal radiator jenis Z-93 Paint untuk membuang panas luar dan panas internal satelit Combat Sentinel (CSSAT).…”

Section: Pendahuluanunclassified

Radiator Thermal Sebagai Sistem Penolak Panas Pada Satelit Bentuk Silinder

Fauzi

Budiantoro

2020

Transmisi

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Panas terjadi pada satelit karena pengaruh lingkungan antariksa, khususnya sinar matahari sebagai sumber energi panel surya satelit. Panel surya menghasilkan energi yang disimpan oleh baterai satelit, yang dibutuhkan untuk operasional satelit dalam keadaan eclipse. Sinar matahari dapat menyebabkan menurunnya kinerja satelit jika energi yang dihasilkan melebihi batas kapasitas simpan daya baterai, sehingga mengancam keberlangsungan kinerja satelit. Jumlah energi yang dibutuhkan satelit dapat diatur, salah satunya dengan menganalisis bagian krusial sisi satelit. Makalah ini membahas penggunaan jumlah panas per satuan luas dari permukaaan panel satelit sebagai sistem penolak panas (radiator), yang berfungsi membuang panas berlebih yang dihasilkan oleh komponen elektronik dan radiasi dari eksternal satelit di ruang antariksa. Kemampuan dari 4 jenis bahan sebagai termal radiator dievaluasi. Hasil studi memperlihatkan bahwa sistem penolak panas dengan bahan lapisan radiator jenis OSR Aluminium mempunyai kenaikan jumlah panas yang dibuang atau ditolak ke ruang hampa yang tinggi sebesar 143,90 W/m2 pada suhu -50°C dan 776,95 W/m2 pada suhu +50°C di musim panas (summer solstice), sedangkan jumlah panas yang dibuang ke ruang hampa dengan jumlah yang terendah terjadi pada bahan lapisan radiator jenis White Paint SG-121FD sebesar -170,68 W/m2 pada suhu -50°C dan 517,07 W/m2 pada suhu +50°C di musim dingin (winter solstice).

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Thermal Control Design Conception of the Amazonia-1 Satellite

Cited by 16 publications

References 4 publications

The Application of Machine learning to Amazonia-1 satellite power subsystem telemetry prediction

The Application of Machine learning to Amazonia-1 satellite power subsystem telemetry prediction

Advances in Thermal Modeling and Analysis of Satellites

Radiator Thermal Sebagai Sistem Penolak Panas Pada Satelit Bentuk Silinder

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