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KSP MAPLES (Multi-fidelity and Probabilistic Lifetime Estimation for Slender Marine Structures)

Model and reduce the uncertainties in lifetime prediction of marine risers based on targeted probability levels using probablistic and multi-fidelity modelling relying on measurement data.

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The oceans, with their relentless waves, strong currents, and floating platforms, pose significant challenges for slender marine structures like risers and dynamic power cables.

One critical issue is Vortex-Induced Vibrations (VIV), which can affect their safety and longevity. VIV occurs when water flows around the structures, forming vortices that cause rhythmic vibrations, potentially leading to fatigue and damage over time.

Many of these structures, designed and installed before 2004, are nearing the end of their service lives, posing the challenge: how can their operational lifespan be extended safely without full replacement?

Traditional approaches separate the effects of waves and VIV, resulting in uncertainty in safety factors. Overly conservative factors lead to costly designs, while underestimation can cause failures. These methods also fail to account for the combined effects of waves, currents, and floating platform motions, which often act simultaneously in real-world conditions.

One advancement is the use of time-domain VIV prediction models, which simulate how structures behave under real-world conditions, providing a clearer picture of their performance over time. However, current technologies still have limitations, leading to higher-than-expected probabilities of failure.

The MAPLES project aims to tackle these challenges through probabilistic and multi-fidelity modeling.

By combining data from both laboratory experiments and real-world measurements, MAPLES seeks to improve the accuracy of lifetime predictions, reducing uncertainty and enabling safer, more cost-effective designs. This approach will enhance the understanding of the underlying physics, optimize safety factors, and make operations more efficient, reducing the need for expensive over-design.

Graphics of methodology.

Key facts

Funding

RCN 353114 (14.4 MNOK)

Funding partners

Project duration

2025 - 2029