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We aim to use a multiscale sequential Bayesian inference approach. It is multiscale because we have a continuous-time discrete-state pure jump process base microscopic model and then two levels of approximation. First, a chemical Langevin diffusion, which is a continuous-time, continuous state stochastic approximation of the base model. Finally, by neglecting the brownian noise we arrive at a deterministic mean-field approximation.
![KAUST CEMSE AMCS STOCHNUM Tanker](/sites/default/files/2020-06/KAUST-CEMSE-AMCS-STOCHNUM-Tanker.jpg)
![KAUST CEMSE AMCS STOCHNUM Pure Jump Processes](/sites/default/files/2020-06/KAUST-CEMSE-AMCS-STOCHNUM-Pure-Jump-Processes.jpg)
Cylinder liners of diesel engines used for marine propulsion are naturally subjected to a wear process and may fail when their wear exceeds a specified limit. Since failures often represent high economical costs, it is utterly important to predict and avoid them.
![KAUST CEMSE AMCS STOCHNUM Master Equation Solution](/sites/default/files/2020-06/KAUST-CEMSE-AMCS-STOCHNUM-Master-Equation-Solution.jpg)
![KAUST CEMSE AMCS STOCHNUM Fobj2contour](/sites/default/files/2020-06/KAUST-CEMSE-AMCS-STOCHNUM-Fobj2contour_0.jpg)
In this application, we model the wear process using a pure jump process, and therefore the inference goal is to estimate the coefficients and amplitudes of the jump intensities. We found that using a Gaussian approximation based on moment expansions it is possible to accurately estimate the jump intensities and amplitudes. We obtained results equivalent to the state of the art but using a simpler and less expensive approach.
Collaborators
Alvaro Moraes (KAUST)
Fabrizio Ruggeri (Politecnico di Milano)
Raul Tempone (KAUST)
Pedro Vilanova (KAUST)
Publications
- A. Moraes, F. Ruggeri, R. Tempone and P. Vilanova. Multiscale Modeling of Wear Degradation in Cylinder Liners. Multiscale Modeling & Simulation, 12(1):396-409, 2014.