The objective of the activity is to develop and test a cyber-physical modelling for a Digital Twin of AOCS sensors. A multi-physic model-based Digital Twin can be used to perform on-board self-calibration employing data-driven techniques and simplify the ground verification of new satellite platforms by having a direct dispersion of the different parameters, hence reducing complex Monte Carlodispersions. Targeted Improvements: Enabling on-board auto-calibration for simpler ground and flight operations. Description: Advances in Model Based Engineering and Digital Twin (DT) development has led to growth in the interest of multi-physic modelling of space systems, including, for example, AOCS sensors. The AOCS sensor DTs include functional and performance behaviour, complemented by physical behaviour (thermal, power, electronic, etc.), as well as failure and degradation modes. This detailed modelling is based on analytic and background supplier models. The emergence of artificial intelligence (AI)/machine learning (ML) combined techniques, potentially augmented with analytical a priori knowledge, offer the potential to improve the overall accuracy and validity of this modelling, together with a specific interest for statistical interpretation in the case of mass production (sensors for constellations,for example, gyros, star trackers, sun sensors). These new engineering techniques aimed at better fidelity in the AOCS sensor modelling offer the prospect of extending the benefits to on-board performance, for example, by adapting these techniques to the ultimateAOCS sensor calibration: self-calibration on board the spacecraft (e.g. constellation or low cost platform). The following techniques and use cases will be studied:-Digital Twin for AOCS sensors in communication satellite constellation use cases: architecture; interoperability needs, multi-physic modelling trade-off and key components; use of data-driven techniques (AI/ML) to populate the DTmodels; statistical interpretation and validation of AOCS sensor performance (mass manufacturing, ground self-calibration based on sample measurements and statistic extrapolation).-Use of ground models and knowledge for on-board self-calibration; data fusion techniques for on-board use to consolidate ground self-calibration with flight measurements, autonomously; autonomous verification of on-board self-calibration (fault detection).This study will bring tools, methods and processes for increasing the end-to-end autonomous handling of AOCS sensors in the platform development and verification chain. The activity includes predictive maintenance monitoring to inform on the overall health status and to prevent anomalies/failures. The outcome aims at providing digital data and modelsto improve knowledge of the units in flight, allowing correction and improvement by the supplier and better integration in MBSE or digital production. The DT will be tested against real hardware behaviour, up to the auto-calibration performance verification. The contractor shall select AOCS equipment for the purposes of this study. The targeted units will include AOCS complex sensors, such asequipment including software (e.g. star tracker) and/or physical hardware (e.g. gyroscope, inertial measurement unit). Procurement Policy: C(1) = Activity restricted to non-prime contractors (incl. SMEs). For additional information please go to:…

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