In this work, the concept of an Artificial Intelligence-based (AI) Digital Twin (DT) of an aircraft system is introduced, with the goal to improve the corresponding MRO Operations. More specifically, the current study aims to obtaining knowledge on the optimal placement of sensors in an ideal Power Electronics Cooling System (PECS) of a modern airliner, aiming to improve input data as a basis for an AI-based DT. The three main fluid parameters to be measured directly or indirectly at various physical locations at the PECS are mass flow rate, temperature and static pressure. The physics-based model can then be combined with a Machine Learning (ML) model, such as a Random Forest (RF), with a multitude of decision trees. Following, the AI system determines whether the PECS operations is considered normal, aiming to optimize the performance of the system and to maximize the Useful Remaining Life (URL). The suggested AI-DT approach is based both on data-driven and physics-based models, an approach which results in increased reliability and availability, reducing possible Aircraft on Ground (AOG) events. Subsequently, the enhanced prediction capability results in the optimization of the maintenance processes and in reduced operational costs.
In PowerMatching City, the leading Dutch smart grid project, 40 households participated in a field laboratory designed for sustainable living. The participating households were equipped with various decentralized energy sources (PV and micro combined heat-power units), hybrid heat pumps, smart appliances, smart meters, and an in-home display. Stabilization and optimization of the network was realized by trading energy on the market. To reduce peak loads on the smart grid and to be able to make optimal use of the decentralized energy sources, two energy services were developed jointly with the end users: Smart Cost Savings enabled users to keep the costs of energy consumption as low as possible, and Sustainable Together enabled them to become a sustainable community. Furthermore, devices could be controlled automatically, smartly, or manually to optimize the energy use of the households. Quantitative and qualitative studies were conducted to provide insight into the experiences and behaviours of end users. In this chapter, these experiences and behaviours are described. The chapter argues that end users: (1) prefer to consume self-produced energy, even when it is not the most efficient strategy to follow, (2) prefer feedback on costs over feedback on sustainability, and (3) prefer automatic and smart control, even though manual control of appliances felt most rewarding. Furthermore, we found that experiences and behaviours were fully dependent on trust between community members, and on trust in both technology (ICT infrastructure and connected appliances) and the participating parties.
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