Speaker
Description
Dear Clean Space Committee,
My name is Emre Ergene, and I am currently pursuing a Master of Science degree in Space Engineering at Politecnico di Milano. I am conducting my thesis research in collaboration with Ecosmic. The focus of my research is to estimate the Ballistic Coefficient (BC) of various objects, primarily consisting of space debris. The Ballistic Coefficient is a critical parameter, expressed as BC = A/m * C_D, and it plays a significant role in the re-entry analysis of these objects.
Through my literature review, I have identified four methodologies to estimate this parameter. One of the methods involves the use of Two-Line Elements (TLE), specifically those available from Spacetrack. In this approach, atmospheric drag acceleration is the primary equation, enabling the estimation of BC. However, recognizing the variability in atmospheric density values depending on the atmospheric model employed, my research has expanded to include a comparative analysis of different atmospheric models (Jacchia, MSIS, NRLMSIS) to ensure the accuracy of my calculations.
Given that atmospheric drag acceleration is only applicable to Low Earth Orbit (LEO) objects, this approach is limited to such objects. Consequently, I have explored an alternative approach for other orbital regimes, known as the Orbit Propagation approach. In this method, various perturbations are considered and propagated between TLE pairs, again sourced from Spacetrack, with BC used as a free parameter until a fit between the propagation results and the TLE data is achieved. This approach is also utilized in DISCOSweb, and I intend to compare these two methods to assess their reliability.
While the aforementioned approaches are post-processing methods, real-time processing can be achieved through the Orbit Determination approach, which also incorporates ground-based measurements.
Given the availability of TLE data for many objects through Spacetrack, some of which have known reference BC values, the final approach I am considering is the application of Machine Learning. This approach is particularly promising, as machine learning algorithms excel in situations involving unpredictability, such as the calculation of atmospheric density—a scenario similar to weather forecasting where AI has demonstrated significant success.
I believe that my work aligns closely with the objectives of Clean Space Days, as the analysis of space debris and the estimation of their characteristics are essential for Active Debris Removal operations. Additionally, understanding the re-entry timelines of these objects will contribute to a clearer understanding of the evolution of LEO and other orbital regions.
Thank you for considering my application.
Yours sincerely,
Emre Ergene
