Aung Myat

Assistant Professor

Tennessee State University

United States

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About

Dr. Aung Myat is an Assistant Professor at Tennessee State University, USA. His research focuses on innovative HVAC&R systems, including nanofluid-enhanced technologies and advanced cooling load prediction models for tropical climates. He has published several recent articles exploring experimental and numerical studies related to passive displacement dual cooling coil systems and deep learning applications in energy efficiency.

Articles (10)

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2025

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2025

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

Collaborators (2)

Peng Cheng Wang

Singapore Institute of Technology

SINGAPORE

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Steve Kardinal Jusuf

Associate Professor

Singapore Institute of Technology

SINGAPORE

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Aung Myat

Research Interests

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Articles (10)

Nanofluid-Enhanced HVAC&R Systems (2015–2025): Experimental, Numerical, and AI-Driven Insights with a Strategic Roadmap

Experimental study on the application of a Passive Displacement Dual Coil Cooling system in a tropical climate

Enhancing Day-Ahead Cooling Load Prediction in Tropical Commercial Buildings Using Advanced Deep Learning Models: A Case Study in Singapore

Enhancing Day-Ahead Cooling Load Prediction in Tropical Commercial Buildings Using Advanced Deep Learning Models: A Case Study in Singapore

A hybrid model based on multivariate fast iterative filtering and long short-term memory for ultra-short-term cooling load prediction

Experimental investigation of the cooling capacity and thermal performance of the passive displacement dual cooling coil system in tropical climate

Mathematical modelling of passive displacement dual cooling coil (PDDCC) system in a tropical climate

Mathematical modelling of passive displacement dual cooling coil (PDDCC) system in a tropical climate

Experimental investigation of the cooling capacity and thermal performance of the passive displacement dual cooling coil system in tropical climate

Enhancing Day-Ahead Cooling Load Prediction in Tropical Commercial Buildings Using Advanced Deep Learning Models: A Case Study in Singapore

Collaborators (2)

Peng Cheng Wang

Steve Kardinal Jusuf

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