Yuxuan Chen

Graduate Research Assistant

Contact Information:

Scott Campus (Omaha)

ychen80@huskers.unl.edu

Degree:
Ph.D. - AREN

Expected Graduation Date:

May 2024

Advisor:
David Yuill

About Me:

I am a PhD student in Architectural Engineering, and my current research focuses on machine learning-based fault detection and diagnosis (FDD) in air-conditioners and fault prevalence of commercial building systems. Besides, I am also interested in thermo science and engineering, especially in the field of renewable energy.

Education

Arizona State University, Tempe, AZ, Mechanical Engineering, M.S., 2020

Guangzhou University, Guangzhou, China, Building Environment & Energy Engineering, B.E., 2018

Teaching Experience

Teaching Assistant: (2021 Spring) AREN 3100 - HVAC Fundamentals

Teaching Assistant: (2020 Fall) MENG 2230 - Engineering Statics

Research Area

Fault detection and diagnosis (FDD) in air-conditioners; Fault prevalence in commercial building systems; Machine learning-based FDD in air conditioners

Society & Membership

Student Member - American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE)

Sample Publications

Chen, Y. and Phelan, P., 2021, “Predicting Peak Energy Demand for An Office Building Using Artificial Intelligence (AI) Approaches,” Proceedings of the 2021 ASME Power Conference. https://doi.org/10.1115/POWER2021-64492

Hu, Y., Yuill, D. P., Rooholghodos, S. A., Ebrahimifakhar, A., and Chen, Y., 2021, “Impacts of Simultaneous Operating Faults on Cooling Performance of A High Efficiency Residential Heat Pump,” Energy and Buildings 242, pp. 110975. https://doi.org/10.1016/j.enbuild.2021.110975

Wu, J. and Chen, Y., 2020, “Broadband Radiative Cooling and Decoration for Passively Dissipated Portable Electronic Devices by Aperiodic Photonic Multilayers,” Annalen der Physik 532(5), pp. 2000001. https://doi.org/10.1002/andp.202000001