Estimation and Prediction of Long-duration Aerodynamic Drag Effect on Floating LEO Debris using the EDAC-adapted Model
Victor U. J. NWANKWO, Jens BERDERMANN, Timothy N. KODIKARA, Frank HEYMANN William DENIG
Abstract. The reliable estimation of atmospheric drag-induced orbital decay is quite challenging due to the limited representation of solar and non-solar forcing mechanisms in atmospheric density models and the complex operational dynamics of some spacecraft. Such uncertainties widen the gap between modelled estimates and actual orbital parameters and also hamper the ability of mission designers and/or operators to accurately estimate the remaining lifetime of spacecraft in orbit. Medium- to long-term estimation or prediction of a defunct spacecraft orbit is even more challenging when the object is no longer subject to ground commands. In this work, we present a robust atmospheric drag model adapted to the ephemeris data-assisted calibration (EDAC) simulation method, to improve the simulation and prediction of short-, medium- and long-term evolutions of the orbital decay of LEO objects, as functions of specific solar-geophysical indices. We show that the simulated long-term evolution of atmospheric drag-induced orbital decay compares well with the historical data of objects and confirms its validity using independent data. We present the EDAC-adapted model as a novel and effective approach to atmospheric drag prediction.
Keywords
Aerodynamic Drag, Ephemeris Data Assisted Calibration, LEO Debris, Orbital Decay
Published online 7/20/2026, 9 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Victor U. J. NWANKWO, Jens BERDERMANN, Timothy N. KODIKARA, Frank HEYMANN William DENIG, Estimation and Prediction of Long-duration Aerodynamic Drag Effect on Floating LEO Debris using the EDAC-adapted Model, Materials Research Proceedings, Vol. 69, pp 1131-1139, 2026
DOI: https://doi.org/10.21741/9781644904251-195
The article was published as article 195 of the book CEAS – AIDAA Conference 2025
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] De Lafontaine, J. and Garg, S.C., 1982. A review of satellite lifetime and orbit decay prediction. Proceedings of the Indian Academy of Sciences Section C: Engineering Sciences, 5(3), pp.197-258. https://doi.org/10.1007/BF02897683
[2] Bigazzi, A., Cauli, C., and Berrilli, F., 2020. Lower-thermosphere response to solar activity: an empirical-mode-decomposition analysis of GOCE 2009-2012 data, Ann. Geophys., 38, 789-800, 2020. https://doi.org/10.5194/angeo-38-789-2020
[3] He, C., Yang, Y., Carter, B., Zhang, K., Hu, A., Li, W., Deleflie, F., Norman, R., & Wu, S. (2020). Impact of thermospheric mass density on the orbit prediction of LEO satellites. Space Weather, 18, e2020SW002336. https://doi.org/10.1029/2019SW002336
[4] Nwankwo, V. U. J., Denig, W., Ajakaiye, M. P., Akanni, J. F., Raulin, J.-P., Correia, E., Enoh, J. E., Fatokun, J., & Anekwe, P. I. (2021). Atmospheric drag effects on modelled low Earth orbit (LEO) satellites during the July 2000 Bastille Day event in contrast to an interval of geomagnetically quiet conditions. Annales Geophysicae, 39, 397-411. https://doi.org/10.5194/angeo-39-397-2021
[5] Chen, J., Du, J. and Sang, J., 2019. Improved orbit prediction of LEO objects with calibrated atmospheric mass density model. Journal of Spatial Science, 64(1), pp.97-110. https://doi.org/10.1080/14498596.2017.1371089
[6] Bernstein, V., Pilinski, M. and Sutton, E.K., 2021. Assessing thermospheric densities derived from orbital drag data. In Proceedings of the 31st AAS/AIAA Space Flight Mechanics Meeting. AAS Publications Office.
[7] Doornbos, E. and Klinkrad, H., 2006. Modelling of space weather effects on satellite drag. Advances in Space Research, 37(6), pp.1229-1239. https://doi.org/10.1016/j.asr.2005.04.097
[8] Vallado, D.A. and Finkleman, D., 2014. A critical assessment of satellite drag and atmospheric density modeling. Acta Astronautica, 95, pp.141-165. https://doi.org/10.1016/j.actaastro.2013.10.005
[9] Sagnieres, L. and Sharf, I., 2017, April. Uncertainty characterization of atmospheric density models for orbit prediction of space debris. In 7th European Conference on Space Debris (Vol. 1, pp. 18-21). ESA Space Debris Office Darmstadt, Germany.
[10] Kumar, R., Singh, R., Chinnappan, A.K. and Appar, A., 2022. Simulation of the orbital decay of a spacecraft in low Earth orbit due to aerodynamic drag. The Aeronautical Journal, 126(1297), pp.565-583. https://doi.org/10.1017/aer.2021.83
[11] Chen, X., Tang, C., Dai, W., Hu, X., Chen, L., Zhang, Z., Zhu, X. and Li, M., 2025. Modelling and prediction of atmospheric drag coefficients in LEO satellite orbit determination and prediction with Bi-LSTM approach. Advances in Space Research, 75(3), pp.2874-2888. https://doi.org/10.1016/j.asr.2024.10.063
[12] Oliveira, D.M., Zesta, E., Mehta, P.M., Licata, R.J., Pilinski, M.D., Tobiska, W.K. and Hayakawa, H., 2021. The current state and future directions of modeling thermosphere density enhancements during extreme magnetic storms. Frontiers in Astronomy and Space Sciences, 8, p.764144. https://doi.org/10.3389/fspas.2021.764144
[13] Bruinsma, S. L., et al. (2021). The DTM-2020 thermosphere model: Evaluation with independent data. J. Space Weather Space Clim., 11, 1-20. https://doi.org/10.1051/swsc/2021032
[14] Cimmino, N., Opromolla, R. and Fasano, G., 2023. Machine learning-based approach for ballistic coefficient estimation of resident space objects in LEO. Advances in Space Research, 71(12), pp.5007-5025. https://doi.org/10.1016/j.asr.2023.02.007
[15] Pilinski, M.D., McNally, R.L., Bowman, B.A., Palo, S.E., Forbes, J.M., Davis, B.L., Moore, R.G., Kemble, K., Koehler, C. and Sanders, B., 2016. Comparative analysis of satellite aerodynamics and its application to space-object identification. Journal of Spacecraft and Rockets, 53(5), pp.876-886. https://doi.org/10.2514/1.A33482
[16] Wang, X., Ren, T., Wang, R., Luo, B., Aa, E., Cai, L., Li, M., Miao, J., Liu, S. and Gong, J., 2024. Estimates of spherical satellite drag coefficients in the upper thermosphere during different geomagnetic conditions. Space Weather, 22(11), p.e2024SW003974. https://doi.org/10.1029/2024SW003974
[17] Walterscheid, R.L., Chen, M.W., Chao, C.C., Gegenheimer, S., Cabrera‐Guzman, J. and McVey, J., 2023. Comparative accuracies of models for drag prediction during geomagnetically disturbed periods: A first principles model versus empirical models. Space weather, 21(5), p.e2022SW003332. https://doi.org/10.1029/2022SW003332
[18] Nwankwo, V.U.J., Berdermann, J., Heymann, F., Kodikara, T.N. and Fernandez-Gomez I., 2025a. Model-driven assessment of space weather impact levels on the trajectory of floating debris in low earth orbit. Proceedings of the 9th ESA European Conference on Space Debris, Bonn, Germany.
[19] Picone, J. M., Hedin, A. E., Drob, D. P., & Aikin, A. C., 2002. NRLMSISE-00 empirical model of the 719 atmosphere: Statistical comparisons and scientific issues. J Geophys. Res.: Space Physics, 107(A12), SIA72015. https://doi.org/10.1029/2002JA009430
[20] Moe, M.M., Wallace, S.D. and Moe, K., 1995. Recommended drag coefficients for aeronomic satellites. The Upper Mesosphere and Lower Thermosphere: A Review of Experiment and Theory, 87, 349-356. https://doi.org/10.1029/GM087p0349
[21] Moe, K., Moe, M.M. and Wallace, S.D., 1998. Improved satellite drag coefficient calculations from orbital measurements of energy accommodation. J. Spacecr. Rockets, 35(3), 266-272. https://doi.org/10.2514/2.3350
[22] Moe, K. and Moe, M. M., 2005. Gas-surface interactions and satellite drag coefficients. Planet. Space Sci., 53(8), 793-801. https://doi.org/10.1016/j.pss.2005.03.005
[23] Pardini, C., Anselmo, L., Moe, K. and Moe, M.M., 2010. Drag and energy accommodation coefficients during sunspot maximum. Advances in Space Research, 45(5), pp.638-650. https://doi.org/10.1016/j.asr.2009.08.034
[24] Ray, V., Scheeres, D.J. and Alnaqbi, S., 2021. Decorrelating density and drag-coefficient through attitude variations. 2021 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS).
[25] Boniface, C. and Bruinsma, S., 2021. Uncertainty quantification of the DTM2020 thermosphere model. Journal of Space Weather and Space Climate, 11, p.53. https://doi.org/10.1051/swsc/2021034
[26] Mehta, P.M., Paul, S.N., Crisp, N.H., Sheridan, P.L., Siemes, C., March, G. and Bruinsma, S., 2023. Satellite drag coefficient modeling for thermosphere science and mission operations. Advances in Space Research, 72(12), pp.5443-5459. https://doi.org/10.1016/j.asr.2022.05.064
