Yu-Hung Chang

My principal research interests lie in the field of the application of a data-driven method to solve problems in fundamental fluid mechanics, thermodynamics, and statistics fields. I am currently working on interdisciplinary studies combined with statistics, machine-learning, and computational fluid dynamics to develop a paradigm of design method of new generation by using a simple swirl injector as an example. This method can predict spatiotemporal evolving flow dynamics in very short turnaround time, which can help engineers and scientists to study further primary issues of fluid mechanics inside of a swirl injector design, which can be applied for auto engines, home appliance, fire hose, or any other appliances with a swirl injector. With fully trained mechanical engineering, aerospace engineering, and statistics backgrounds, I have particular expertise and interest in data-driven algorithm, including machine learning, for solving engineering problems. In the future, I would like to extend my research with engineering, applied math and statistics background to solve problems in other engineering fields.