Chen Yang | Engineering | Research Excellence Award

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Prof. Chen Yang | Engineering | Research Excellence Award

School of Energy and Power, Chongqing University  |  China

Prof. Chen Yang  research centers on advanced energy systems, renewable energy utilization, and thermal power engineering, with strong emphasis on modeling, optimization, and dynamic control of complex thermo-energy systems, supported by a research record of 1,004 citations across 868 documents, 98 publications, and an h-index of 18. His contributions span ultra-supercritical circulating fluidized bed boilers, nuclear power reactor secondary systems, compressed air energy storage, and hybrid solid oxide fuel cell–gas turbine systems, advancing the efficiency, reliability, and safety of large-scale power generation. He has developed multi-physics and multi-scale reduced-order modeling techniques to address nonlinear dynamics, uncertainty, cooperative simulation, and system stability challenges, enabling enhanced operational performance under transient and abnormal working conditions. His work integrates mechanistic models with artificial intelligence, including neural networks and time-series methods, to achieve online simulation, intelligent prediction, fault early warning, and predictive control in energy systems. He has also contributed to thermodynamic coupling analysis, waste heat utilization strategies, and multi-objective optimization frameworks for green energy systems. Through these innovations, his research significantly supports sustainable power technology development, promotes intelligent and resilient energy infrastructures, and contributes to low-carbon energy transformation and modern energy system advancement.

Citation Metrics (Scopus)

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Citations
1004

Documents
98

h-index
18

🟦 Citations    🟥 Documents    🟩 h-index


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Featured Publications

Mehran Pourgholi | Engineering | Best Researcher Award

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Assist. Prof. Dr. Mehran Pourgholi | Engineering | Best Researcher Award

Islamic Azad University | Iran

Assist. Prof. Dr. Mehran Pourgholi, an accomplished researcher in Civil and Structural Engineering, has made notable contributions to the fields of system identification, structural health monitoring, inverse problems, and stochastic subspace methods. His research focuses on enhancing the reliability of modal analysis and vibration-based damage detection in large-scale structures such as dams and steel buildings. Dr. Pourgholi integrates advanced computational approaches including entropy-based model selection, optimization algorithms, and error analysis frameworks to improve accuracy in structural system modeling. His collaborative work with experts from the University of Tabriz and Islamic Azad University has produced high-impact studies published in leading international journals such as the Journal of Vibration and Control, Mechanical Systems and Signal Processing, and Engineering Reports. According to Google Scholar, Dr. Pourgholi has 77 citations (68 since 2020), an h-index of 4, and an i10-index of 2, while Scopus records 51 citations across 46 documents with an h-index of 3. His influential publications on stochastic subspace identification and modal analysis have advanced understanding of dynamic behavior in civil structures. Recognized for his scholarly excellence, he has been honored with the Best Researcher Award for his significant impact in the field of structural system identification and vibration analysis.

Publication Profile

Scopus | Orcid | Google Scholar 

Featured Publications

Tarinejad, R., & Pourgholi, M. (2018). Modal identification of arch dams using balanced stochastic subspace identification. Journal of Vibration and Control, 24(10), 2030–2044.

Pourgholi, M., Mohammadzadeh Gilarlue, M. M., Vahdaini, T., & Azarbonyad, M. (2023). Influence of Hankel matrix dimension on system identification of structures using stochastic subspace algorithms. Mechanical Systems and Signal Processing, 186, 109893.

Pourgholi, M., Tarinejad, R., Khabir, M. E., & Mohammadzadeh Gilarlue, M. M. (2023). System identification of Karun IV Dam using balanced stochastic subspace algorithm considering the uncertainty of results. Journal of Vibration and Control, 29(23–24), 5342–5356.

Tarinejad, R., Pourgholi, M., & Yaghmaei-Sabegh, S. (2016). Signal processing of dynamic tests results using subspace identification based on orthogonal decomposition technique (SI-ORT). Modares Mechanical Engineering, 15(10), 104–116.

Pourgholi, M., Ghannadi, M., & Gavgani, S. S. (2024). Modal analysis of earthquake records for dams using stochastic subspace based on error analysis. Engineering Reports, 6(8), e12822