Assist Prof Dr. Serdal Damarseçkin | Innovative Leadership | Best Researcher Award|

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Assist Prof Dr. Serdal Damarseçkin | Innovative Leadership | Best Researcher Award|

Şırnak University, Turkey

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Early Academic Pursuits 🎓

Serdal Damarseçkin’s academic journey began with a strong foundation in physics. He earned his bachelor’s degree from Yüzüncü Yıl University, where he completed a program in the Department of Physics between 1999 and 2003. Demonstrating an early passion for understanding the complexities of the universe, Damarseçkin continued his studies at the same university, where he pursued his master’s degree. His master’s thesis, titled “Some Quantum Mechanical Solutions for the Two-Body Interacting Problem with a Screened Coulomb Potential”, reflects his early interest in complex quantum systems, which would later feed into his research in particle physics.

His pursuit of academic excellence culminated in his doctoral studies at CERN, in partnership with Çukurova University, between 2011 and 2018. His PhD thesis, “Search for New Particles Decaying to Dijet at √𝑠 = 13 TeV Proton-Proton Collisions with Data Scouting Technique at CMS”, marked the beginning of his significant contributions to high-energy physics, especially in the field of new physics.

Professional Endeavors in Particle Physics 🚀

Damarseçkin’s career has been largely shaped by his involvement with CERN, where he participated in groundbreaking projects within the Compact Muon Solenoid (CMS) experiment, one of the largest and most complex particle detectors at the Large Hadron Collider (LHC). His research focused on the search for new physics beyond the Standard Model, investigating two-jet resonances, dark matter, extra dimensions, and potential supersymmetric particles.

In addition to his work on new particle searches, Damarseçkin contributed to detector calibration and development. From 2013 to 2017, he was involved in the installation and upgrade of the CMS detector, a critical task in ensuring the precision and reliability of experimental data. His expertise in CMS HCAL (hadron calorimeter) calibration with test beam data from 2014 to 2017 further highlights his technical proficiency in refining the tools used to explore fundamental particles.

Contributions and Research Focus 🔬

Damarseçkin’s contributions to particle physics are deeply rooted in his exploration of two-jet resonance phenomena at √𝑠 = 13 TeV proton-proton collisions, carried out with the data scouting technique. His technical notes reflect this focus, showcasing key studies such as:

Searches for dijet resonances in pp collisions at √𝑠 = 13 TeV using up to 36 fb⁻¹ of data.
Calo scouting at 13 TeV, a pioneering technique that allows for more efficient analysis of large datasets, crucial in searching for rare phenomena like new particle decays.
In addition to particle physics, Damarseçkin’s research interests extend to the field of renewable energy. His international publications demonstrate his engagement in hydrogen energy research, a key area in sustainable energy development. His articles on hydrogen production and solar energy systems—such as the paper on hydrogen production from ZnCl2 salt and the integration of solar ponds with parabolic trough collectors—illustrate his cross-disciplinary approach to scientific inquiry, bridging the gap between physics and energy sustainability.

Accolades and Recognition 🏆

Damarseçkin’s work in high-energy physics and renewable energy has garnered recognition within the scientific community. His research on new particles and energy systems has led to several international publications in highly regarded journals. The paper on hydrogen production from ZnCl2 salt, published in the International Journal of Hydrogen Energy in 2024, is a testament to his contribution to advancing hydrogen energy technologies. His ability to address both fundamental physics questions and real-world energy challenges positions him as a versatile and impactful researcher.

Impact and Influence 🌍

Serdal Damarseçkin’s work at CERN has not only contributed to the discovery of new particles but also advanced the methodologies used in particle physics experiments. His involvement in the calibration and development of the CMS detector ensured the precision of one of the world’s most advanced scientific instruments, impacting countless research projects that rely on the LHC’s data.

In renewable energy, his focus on hydrogen energy—one of the most promising clean energy solutions—illustrates his commitment to addressing global energy challenges. His innovative approach to integrating solar energy technologies with hydrogen production reflects a forward-thinking vision for sustainable energy systems.

Legacy and Future Contributions ✨

Looking ahead, Damarseçkin’s legacy will likely be shaped by his dual contributions to both high-energy physics and renewable energy. His work on the search for new particles in proton-proton collisions remains a cornerstone of efforts to expand our understanding of the universe’s fundamental forces and particles. Simultaneously, his research into sustainable energy systems holds significant promise in the fight against climate change and the global transition to clean energy.

As Damarseçkin continues his research, he will undoubtedly leave a lasting impact in both fields, fostering a new generation of scientists who will build upon his contributions to physics and energy research. His interdisciplinary expertise positions him to be a leader in the quest for both new scientific discoveries and innovative solutions to global challenges.

📝Notable Publications

Assoc Prof Dr. Shaohua Wu | Innovative Leadership | Best Researcher Award

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Assoc Prof Dr. Shaohua Wu | Innovative Leadership | Best Researcher Award

Dalian University of Technology, China

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🎓 Early Academic Pursuits

Shaohua Wu began his academic journey at Tianjin University, where he earned a B.S. in Thermal Energy and Dynamic Engineering (2007-2011) and later completed an M.A. in Power Machinery and Engineering (2011-2013). His early academic foundation laid the groundwork for his deep engagement with energy systems and thermodynamics. His education was marked by honors, such as receiving the Best Master Thesis Award in 2014.

Wu further pursued a Ph.D. at the National University of Singapore (NUS) (2015-2018), under the supervision of Prof. Wenming Yang, with a joint doctoral training at the University of Cambridge (2016-2018) under Prof. Markus Kraft and Dr. Jethro Akroyd. This dual-institution experience provided him with a solid interdisciplinary foundation in mechanical and chemical engineering.

💼 Professional Endeavors

Shaohua Wu has held various academic positions, starting as a Research Associate at Cambridge CARES (2018-2019) before becoming a Research Fellow at NUS (2019-2020). In 2021, he took on the role of Associate Professor at Dalian University of Technology, where he currently leads the Multiphase Flow Group as a principal investigator. His leadership in this role has allowed him to mentor over 10 researchers, contributing to high-fidelity numerical algorithms and AI-driven multiscale simulations for propulsion and power systems.

🔬 Contributions and Research Focus

Wu’s research spans first-principles-based modeling and simulation of reactive flows, including combustion, multiphase flows, and their multiscale interactions. He specializes in Computational Fluid Dynamics (CFD) and population balance modeling (PBM), and has integrated machine learning algorithms into these fields for predictive and computational efficiency. Some of his key research topics include:

Multiphase systems in propulsion and power generation (reciprocating engines, gas turbines).
AI-driven multivariate PBM for predicting particle behavior.
Chemical kinetics simulation using AI for mechanism construction and reduction.

🌍 Impact and Influence

Shaohua Wu’s work has had a significant impact on the fields of combustion, particle dynamics, and energy system optimization. His development of next-generation simulation software, such as the Kinetics & SRM Engine Suite, has been used for optimizing internal combustion engines and reactors. He has also contributed to soot particle modeling and reduction technologies that help lower emissions in various energy sectors. With numerous patents, his innovations extend to real-world applications, such as vehicle exhaust purification devices and ABS braking systems for motorcycles.

📖 Academic Citations

Shaohua Wu’s work has been widely recognized and cited in leading journals, including Journal of Aerosol Science, Energy and AI, Applied Energy, and Chemical Engineering Science. His contributions to reactive flow simulations, particle dynamics, and CFD have earned him a notable presence in the academic community, particularly through high-impact publications and as a reviewer for prestigious journals.

💻 Technical Skills

Wu is highly proficient in a range of programming languages (Fortran, C/C++, Python, MATLAB) and commercial/open-source software (ANSYS Fluent, OpenFOAM, CHEMKIN, KIVA). His expertise extends to mathematics and statistical algorithms for CFD, machine learning, and optimization, with experience in:

Deep learning algorithms (CNN, RNN, GAN, GNN, PINN)
CFD-related algorithms (Finite Volume Method, Multigrid method, SIMPLE algorithm)
Optimization algorithms (Genetic Algorithm, Particle Swarm Optimization)

🎓 Teaching Experience

With over six years of teaching, Shaohua Wu has lectured extensively at the Dalian University of Technology, focusing on courses such as Engineering Thermodynamics, Computational Fluid Dynamics (CFD), and Big Data and Machine Learning in Energy. His dedication to teaching has earned him excellent student ratings. He also taught and assisted courses at NUS, such as Heat and Mass Transfer and Numerical Algorithms for Scientific Computing.

🏆 Legacy and Future Contributions

Shaohua Wu has made substantial strides in particle dynamics, population balance modeling, and the AI-driven simulation of reactive flows, contributing to both academic and industrial advancements. His future work aims to integrate AI with CFD to develop smarter, more efficient energy and propulsion systems, focusing on energy sustainability. Wu’s efforts in deep learning-based chemical kinetics could revolutionize fuel modeling and lead to cleaner combustion technologies.

With his current projects, including research on soot particle dynamics and deep learning-driven solvers, Shaohua Wu is poised to make lasting contributions to energy research, helping drive the development of more sustainable energy technologies.

📝Notable Publications

Selective catalytic reduction of nitric oxide with ammonia over zirconium-doped copper/ZSM-5 catalysts

Authors: F Bin, C Song, G Lv, J Song, S Wu, X Li
Journal: Applied Catalysis B: Environmental
Volume: 150
Pages: 532-543
Year: 2014

Extension of moment projection method to the fragmentation process

Authors: S Wu, EKY Yapp, J Akroyd, S Mosbach, R Xu, W Yang, M Kraft
Journal: Journal of Computational Physics
Volume: 335
Pages: 516-534
Year: 2017

Three-dimensional MP-PIC simulation of the steam gasification of biomass in a spouted bed gasifier

Authors: S Yang, F Fan, Y Wei, J Hu, H Wang, S Wu
Journal: Energy Conversion and Management
Volume: 210
Pages: 112689
Year: 2020

A moment projection method for population balance dynamics with a shrinkage term

Authors: S Wu, EKY Yapp, J Akroyd, S Mosbach, R Xu, W Yang, M Kraft
Journal: Journal of Computational Physics
Volume: 330
Pages: 960-980
Year: 2017

Numerical study on the effective utilization of high sulfur petroleum coke for syngas production via chemical looping gasification

Authors: Z Li, H Xu, W Yang, S Wu
Journal: Energy
Volume: 235
Pages: 121395
Year: 2021