The magnetosphere sustained by the rotation of the Earth's liquid iron core traps charged particles, mostly electrons and protons, into structures referred to as the Van Allen belts. These radiation belts, in which the density of charged energetic particles can be very destructive for sensitive instrumentation, have to be crossed on every orbit of satellites traveling in elliptical orbits around the Earth, as is the case for ESA's INTEGRAL and XMM-Newton missions. This paper presents the first working version of the 5DRBM-e model, a global, data-driven model of the radiation belts for trapped electrons. The model is based on in-situ measurements of electrons by the radiation monitors on board the INTEGRAL and XMM-Newton satellites along their long elliptical orbits for respectively 16 and 19 years of operations. This model, in its present form, features the integral flux for trapped electrons within energies ranging from 0.7 to 1.75 MeV. Cross-validation of the 5DRBM-e with the well-known AE8min/max and AE9mean models for a low eccentricity GPS orbit shows excellent agreement, and demonstrates that the new model can be used to provide reliable predictions along widely different orbits around Earth for the purpose of designing, planning, and operating satellites with more accurate instrument safety margins. Future work will include extending the model based on electrons of different energies and proton radiation measurement data.
During eclipses, the spacecraft cannot rely on solar energy and must exploit the power coming from the on-board batteries. Critical for the payload is to maintain its temperature inside a specific predetermined range to avoid degradation and malfunctioning. For XMM-Newton, this is done through the Temperature Closed Loop (TCL) unit, which automatically turns on and off the heaters when the instruments reach the minimum and maximum temperatures, respectively. A possible scenario is the case when more than a single instrument reaches the minimum temperature simultaneously, thus causing a peak in the demand of power from the batteries and threatening their integrity. Such a circumstance may impact the overall mission and a better battery management is therefore required. Here we propose a solution that uses machine learning to optimize the battery consumption during eclipses. We show that after training the algorithm with past data, we are able to predict the temperature profiles of the instruments with good accuracy. These predictions provide the relevant insight to determine the optimal times at which the heaters should be turned on and off, thus improving and optimizing the current eclipse operations. The approach presented here helps overcome the issue of excessively stressing the batteries and, despite being tailored for XMM-Newton, can be extended and applied to other missions as well.
The flagship of European X-ray astronomy, ESA's XMM-Newton space observatory, was launched in 1999 with an expected lifetime of up to ten years. Through an operational change of the AOCS system, we extended its potential lifetime until the late 20ies of this century. Spacecraft and instruments are performing without major degradation. Because of the highly elliptical orbit, the spacecraft is permanently visible from the ground station network. For this and other reasons, the originally chosen on-board autonomy design is very limited. Fifteen years of inflight experience created a deep understanding of the spacecraft behavior including elaborated strategies and procedures to recover from reoccurring non-critical anomalies on board. Based on this experience, an automated ground failure detection system has been developed to provide reliable fault isolation and adapted reaction. We present here the system put in place to conduct automatically some of the nominal XMM-Newton operations and share our experience with the automatic contingency recovery tool after half a year of usage. The on-ground Failure Detection, Isolation and Recovery (FDIR) system is able to read various sorts of anomaly messages, either from the spacecraft (telemetry being out-of-limit), from the ground segment (link loss) or from the mission control software (an application stopped). Upon detection of anomaly, the automation system starts the execution of a procedure to confirm its consistency and persistency to avoid over reaction to minor events. Once the anomaly has been confirmed and identified, the recovery is kicked off under the supervision of the operator or autonomously. The targeted level of automation shall release the operator from activities that were so far manually performed, let him concentrate on other activities running in parallel, reduce the stress in contingency situations and lessen the probability of human error.
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