Jinhui Yan

TL;DR: In this article, the authors propose data-mining as an effective solution to understand the underlying physical mechanisms of additive manufacturing (AM) processes and material compositions, structures and properties in end-use products with arbitrary shapes.

TL;DR: A computational fluid–structure interaction (FSI) framework for the simulations of the interaction between free-surface flow and floating structures, such as offshore wind turbines, is proposed, which has good efficiency, accuracy and robustness characteristics.

TL;DR: In this article, a physics-informed neural network (PINN) framework was proposed to predict the temperature and melt pool dynamics during metal additive manufacturing (AM) processes with only a moderate amount of labeled data sets.

TL;DR: In this paper, a structural model of the wind turbine was developed, which makes use of the recently proposed rotation-free Kirchhoff-love shell and beam/cable formulations, and a moving-domain finite-element-based ALE-VMS was employed for the aerodynamics in combination with the sliding-interface formulation to handle the VAWT mechanical components in relative motion.

TL;DR: In this paper, a computational free-surface flow framework that enables 3D, time-dependent simulation of horizontal-axis tidal-stream turbines (HATTs) is presented and deployed using a complex-geometry HATT.