The course aims to provide the students with knowledge required in order to establish statistical and probabilistic models associated with uncertainties, as well as formulate and solve the equations of motion for dynamic systems in both frequency and time domains. MATLAB and Python will be introduced. The focus is on application within marine, offshore and subsea technology.
Contents
Properties of various statistical models and their physical applications. Statistical models using the principle of statistical inference. Monte Carlo simulation. Transformation of variables. Signal processing in both frequency and time domains (digital filters, energy estimation, de-trending, finding data errors). Direct stiffness method. Equations of motions for dynamic systems. Impact loading. Vibration and resonance. Programming in MATLAB and Python.
Learning outcomes
The learning outcome includes the following aspects:
- To have the insights related to properties of various statistical models and their physical applications, and establish statistical models using the principle of statistical inference.
- To be familiar with different techniques within probabilistic analysis, including Monte Carlo simulations and transformation of variables.
- To be able to process signals in both frequency and time domains and carry out data analysis including digital filtering and Fourier analysis.
- To be able to establish the equations of motions for dynamic systems and solve the motions in both frequency and time domains.
- To understand the important phenomenon in dynamic responses of simple systems, including impact loadings and resonant motions.
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