Nilanjan Saha

TL;DR: In this paper, the authors present derivative-free weak and strong solutions of stochastically driven nonlinear oscillators of engineering interest using higher order forms of the locally transversal linearization (LTL) method.

TL;DR: In this paper, a simple method is proposed for predicting the extreme response of uncertain structures subjected to stochastic excitation, taking advantage of the regularity of the tail region of the mean upcrossing rate function.

Abstract: This paper describes the stochastic dynamic response of National Renewable Energy Laboratory 5 MW offshore fixed-based wind turbine (OWT) under various soil conditions – medium dense sand, stiff clay and layered profiles in 20 m depth of water. The aerodynamic and hydrodynamic OWT loads are derived using the force-controlled approach. Usually the OWT generates power in an operational regime and survives at extreme wind speeds. Therefore, two met-ocean conditions adhering to the irregular Joint North Sea Wave Project spectrum are considered – one in an operational regime (average wind speed Vw = 12 m/s, significant wave height Hs = 4 m and peak spectral period Tp = 10 s) and another in a near cut-out regime (Vw = 22 m/s, Hs = 10 m, Tp = 14 s). The soil is modelled by way of a non-linear ground-to-spring model. For each sea state, time domain stochastic responses are calculated and the ensemble average response is calculated from 50 Monte-Carlo simulations. The change in ensemble average response due to cha...

TL;DR: In this paper, the authors explored a recently developed method for statistical response load (load effect) extrapolation for application to extreme response of wind turbines during operation, which is in the present implementation restricted to cases where the Gumbel distribution is the appropriate asymptotic extreme value distribution.

TL;DR: In this paper, a particle filter (PF) for state and parameter estimations of nonlinear engineering dynamical systems, modelled through stochastic differential equations (SDEs), is proposed to address a possible loss of accuracy in the estimates due to the discretization errors due to numerical integration of the SDEs.