DIGITAL TWIN OF ATTITUDE AND ORBIT SENSORS - EXPRO+

Objective:To develop and test a model for a digital twin of attitude and orbit control system (AOCS) sensors as well as toassess compatibility of this multi-physics model-based digital twin with on-board self-calibration employing datadriven techniques and withthe simplification of ground verification processes of new satellite platforms.Description:Advances in model based engineering and digital twin (DT) development has led to growth in the interest of multiphysic modelling of space systems, including, for example, AOCS sensors. The AOCS sensor DTs includefunctional 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 suppliermodels. The emergence of artificial intelligence (AI) / machine learning (ML) combined techniques, potentiallyaugmented with analytical a priori knowledge, offer the potential to improve the overall accuracy and validity ofthis 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 ultimate AOCS 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, preferably from sensors used on platform series: architecture; interoperabilityneeds, multi-physic modelling trade-off and key components; use of data-driven techniques (AI/ML) to populate the DT models; statistical interpretation and validation of AOCS sensor performance (mass manufacturing, groundself-calibration based on sample measurements and statisticextrapolation).- 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 activity will bring tools, methods, and processes for increasing the end-to-end autonomous handling of AOCS sensorsin the platform development and verification chain. It includes predictive maintenance monitoring to inform on the overall health status and to prevent anomalies/failures. This activity aims at providing digital data and models to improve knowledge of the units in flight, allowing correction and improvement by the supplier and better integration in model-based system engineering (MBSE) or digital production.This activity encompasses the following tasks:- Define the system into which the deliverable items will be integrated, establish the end user requirements andquantify the advantages of utilising the new items with respect to state-of-the-art alternatives.- Define a complete, self-standing and traceable set of technical requirements for the deliverable items.- Select the technical baseline that offers the best potential to achieve the activity objectives.- Detailed design and demonstrate, by appropriate analyses, simulations, tests or other means, that it cansatisfy the technical requirements.- Establish all detailed plans necessary to successfully implement and test the deliverable items and verify theircompliance to the technical specifications.- Implement the deliverable items, quantify their performances by means of a suitable test campaign anddemonstrate compliance to the technical requirements.