DRAMA 3.0 - Upgrade of ESA’s debris risk assessment and mitigation analysis tool suite

Braun, Vitali; Funke, Quirin; Lemmens, Stijn; Sanvido, Silvia

doi:10.1016/j.jsse.2020.07.020

Cited by 7 publications

(3 citation statements)

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“…ESA developed DRAMA to assist with threshold selection (Debris Risk Assessment and Mitigation Analysis) [26]. We primarily employ its ARES (Assessment of Risk Event Statistics) module for the assessment in this paper, and we use DRAMA to calculate the ACPL using collision probability obtained through the above equation, adding the Starlink constellation to the race group of DRAMA to obtain the degree to which the target satellite is affected by Starlink.…”

Section: Acpl Assessment Modelmentioning

confidence: 99%

Impact of Mega Constellations on Geospace Safety

et al. 2022

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The extent of the impact of mega constellations on the low−orbiting geospace environment, which has not yet been assessed in more concrete quantitative terms, is an extremely important issue to consider as mega constellations are built. Satellite safety and lifetime can clearly represent the situation of space targets, and thus can reflect the impact of mega constellations on geospace security. Three target satellites with different characteristics were selected and the Accepted Collision Probability Level (ACPL) was calculated to obtain the impact of Starlink on satellite mission lifetime. Upon considering Starlink without early avoidance control, the lifetimes of the three target satellites were shortened by 56.21%, 99.09%, and 99.82%, respectively. After 10 revolutions of early avoidance control, two were shortened to 92.166% and 91.99%, while the lifetime of JILIN−01 was extended by 155.44%. After joining Starlink, the total risk became larger; even if the target satellite avoided control far more frequently than before joining Starlink, it will face a worse geospace environment. Adopting the most aggressive orbit avoidance control cannot avoid the deterioration of the geospace environment from the perspective of satellite lifetime, which is an irreversible and deteriorating process.

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Section: Acpl Assessment Modelmentioning

confidence: 99%

Impact of Mega Constellations on Geospace Safety

et al. 2022

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“…The analysis tool DRAMA (Debris Risk Assessment and Mitigation Analyses) [5,6] was used for the calculation of maximum lifetime after end of operations and to confirm the maximum possible orbital altitude. The simulation resulted in a natural orbital lifetime of 17 years at an orbital altitude of 576 km (using the latest Two Line Elements (TLE) of the highest Starlink satellite [7]).…”

Section: Space Debris Mitigation Guidelinesmentioning

confidence: 99%

InnoCube—A Wireless Satellite Platform to Demonstrate Innovative Technologies

Grzesik

Baumann²,

Walter³

et al. 2021

Aerospace

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A new innovative satellite mission, the Innovative CubeSat for Education (InnoCube), is addressed. The goal of the mission is to demonstrate “the wireless satellite”, which replaces the data harness by robust, high-speed, real-time, very short-range radio communications using the SKITH (SKIpTheHarness) technology. This will make InnoCube the first wireless satellite in history. Another technology demonstration is an experimental energy-storing satellite structure that was developed in the previous Wall#E project and might replace conventional battery technology in the future. As a further payload, the hardware for the concept of a software-based solution for receiving signals from Global Navigation Satellite Systems (GNSS) will be developed to enable precise position determination of the CubeSat. Aside from technical goals this work aims to be of use in the teaching of engineering skills and practical sustainable education of students, important technical and scientific publications, and the increase of university skills. This article gives an overview of the overall design of the InnoCube.

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“…In non-destructive collision events, fragmentation does not happen. While there has been exhaustive research on modelling destructive collisions 5,6 , on risk assessment due to small space debris 7 , and reconnecting fragments in space to their parent satellite body 8 , the extraction of trajectory information of untracked space debris observing a non-destructive collision has not been studied extensively.…”

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confidence: 99%

Tracking an untracked space debris after an inelastic collision using physics informed neural network

Singh,

Kumar

et al. 2024

Sci Rep

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With the sustained rise in satellite deployment in Low Earth Orbits, the collision risk from untracked space debris is also increasing. Often small-sized space debris (below 10 cm) are hard to track using the existing state-of-the-art methods. However, knowing such space debris’ trajectory is crucial to avoid future collisions. We present a Physics Informed Neural Network (PINN)—based approach for estimation of the trajectory of space debris after a collision event between active satellite and space debris. In this work, we have simulated 8565 inelastic collision events between active satellites and space debris. To obtain the states of the active satellite, we use the TLE data of 1647 Starlink and 66 LEMUR satellites obtained from space-track.org. The velocity of space debris is initialized using our proposed velocity sampling method, and the coefficient of restitution is sampled from our proposed Gaussian mixture-based probability density function. Using the velocities of the colliding objects before the collision, we calculate the post-collision velocities and record the observations. The state (position and velocity), coefficient of restitution, and mass estimation of un-tracked space debris after an inelastic collision event along with the tracked active satellite can be posed as an optimization problem by observing the deviation of the active satellite from the trajectory. We have applied the classical optimization method, the Lagrange multiplier approach, for solving the above optimization problem and observed that its state estimation is not satisfactory as the system is under-determined. Subsequently, we have designed Deep Neural network-based methods and Physics Informed Neural Network (PINN) based methods for solving the above optimization problem. We have compared the performance of the models using root mean square error (RMSE) and interquartile range of the predictions. It has been observed that the PINN-based methods provide a better estimation performance for position, velocity, mass and coefficient of restitution of the space debris compared to other methods.

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DRAMA 3.0 - Upgrade of ESA’s debris risk assessment and mitigation analysis tool suite

Cited by 7 publications

References 0 publications

Impact of Mega Constellations on Geospace Safety

Impact of Mega Constellations on Geospace Safety

InnoCube—A Wireless Satellite Platform to Demonstrate Innovative Technologies

Tracking an untracked space debris after an inelastic collision using physics informed neural network

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