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RaPiD models: Reciprocal Physics and Data models


Description:

This project aims to provide more specific, accurate and timely decision support in operation of safety-critical systems, by combining physics-based modelling with data-driven machine learning and probabilistic uncertainty assessment. The underlying idea is to combine well-established and robust physics-based full order models (FOM), that are made effective by reduced order modelling (ROM), and use of probabilistic data-driven models to both increase the accuracy as well as focus simulation efforts where the information gained produce the most value with respect to the relevant decision context. Creating hi-fidelity insights for better real-time decision-support in complex and high-risk systems requires innovative and new ways of combining data-driven and physics-based modelling, beyond current approaches (e.g. hybrid modelling, multi-fidelity modelling, etc). Integrating these model domains in a way that ensures both that the computational speed and the overall uncertainty is acceptable is the biggest research challenge of the project. This will enable the asset owners to make faster and better safety-critical decisions and reduce (overly conservative) restrictions on operating windows, maintenance and inspection intervals, etc. Increasing uptime, more optimal production, and avoiding safety critical incidents, if only by a few percent, has a huge value for the asset owners. “Conventional finite-elements simulations appear to be conservative compared with what we observe from sensor data. However, models based on sensor data do not capture safety-critical behaviour which we have not yet experienced. In effect, neither model is able to give proper decision support and we are left with conservatism to offset the uncertainty”. Paraphrased statement from asset owner operating on the NCS


Project leader: Simen Eldevik

Started: 2020

Ends: 2023

Category: Næringsliv

Sector: Næringsliv

Budget: 7060000

Institution: DNV AS

Address: Bærum