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Dang Dinh Nguyen

Research Professor

Kyungpook National University

South Korea

Research Interests

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Professors in South Korea Professors in Fluid Mechanics Fluid Mechanics professors in South Korea

About

Dr. Dang Dinh Nguyen is a Research Professor at Kyungpook National University in South Korea. His research areas encompass artificial intelligence applications in optimization, particularly in microfluidic and microbial fuel cells, as well as the use of deep learning for assessing CAD software performance and solar air heater efficiency. His recent publications highlight advancements in explainable AI and data-driven approaches to optimize various energy systems and technologies.

Articles (8)

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2026

Year:

2025

Year:

2025

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2024

Year:

2024

Year:

2023

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2022

Year:

2022

Collaborators (3)

Hang-Nga Mai

Research professor

Kyungpook National University

SOUTH KOREA

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Wael Att

Professor and Chair

Tufts University School of Dental Medicine

UNITED STATES

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Du-Hyeong Lee

Professor

The University of Iowa

UNITED STATES

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Dang Dinh Nguyen

Research Interests

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Articles (8)

Achieving High Power and Energy Efficiency for Microfluidic Fuel Cells with Flow-through Porous Electrodes at the Desired Cell Voltage and Fuel Flow Rate with Deep Learning-based Multi-target and Criteria Optimization

Artificial Intelligence–Assisted Optimization of Microfluidic and Microbial Fuel Cells: an Updated Review

A generalized correlation for predicting microdroplet sizes in a squeezer T-junction microfluidic device with various squeezer types

Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance

Smart optimization and investigation of a PCMs-filled helical finned-tubes double-pass solar air heater: An experimental data-driven deep learning approach

Guiding the optimization of membraneless microfluidic fuel cells via explainable artificial intelligence: Comparative analyses of multiple machine learning models and investigation of key operating parameters

Efficient stacking of glucose/oxygen microfluidic biofuel cells using a single-streamflow channel

Deep learning–based optimization of a microfluidic membraneless fuel cell for maximum power density via data-driven three-dimensional multiphysics simulation

Collaborators (3)

Hang-Nga Mai

Wael Att

Du-Hyeong Lee

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