In this work, a multi-fidelity framework for the simulation of
small satellites is investigated. Taking into account the concept
of digital twin, our work focuses on handling a constant
stream of live data. Towards this end, current multi-fidelity
modelling methods and low fidelity surrogate models for time
series were surveyed. A multi-fidelity approach is used to
combine a low fidelity surrogate model with a high fidelity
model. As a high fidelity model, a previously investigated
finite element model is assumed. As a low fidelity model,
auto-regressive and recurrent neural network-based models
are investigated. Through cokriging, the low fidelity data is
corrected by the high fidelity data through a comprehensive
correction, where the parameters are given through Gaussian
processes in order to perform uncertainty quantification. As
an application, the thermal simulation of a small satellite, and
the use of this framework in conjunction with sparse telemetry
data is proposed. This online, statistical approach aims to
provide a tool for performing fault detection.
How to Cite
Multifidelity, Digital Twin, Thermal Simulation, Deep Learning, Uncertainty Quantification
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