Prof. Dr. Jinju Sun | Computational Methods | Best Researcher Award

Prof. Dr. Jinju Sun | Computational Methods | Best Researcher Award

Professor | Xi'an Jiaotong University | China

Prof. Dr. Jinju Sun is a distinguished scholar in the School of Energy and Power Engineering at Xi’an Jiaotong University, renowned for her pioneering contributions to fluid mechanics, turbomachinery, and multiphase flow systems through advanced Computational Methods. Her educational journey spans cryogenic engineering to a PhD in turbomachinery and engineering mechanics, which laid the foundation for her expertise in Computational Methods applied to turbomachinery optimization, Lattice Boltzmann modeling, and Vortex Method simulations. Throughout her professional career, she has served as a researcher, lecturer, and professor, advancing research through numerous national and international collaborations emphasizing Computational Methods in fluid dynamics and green energy system design. She has received prestigious honors, including the Donald Julius Groen Prize and the Arthur Charles Main Award from the Institution of Mechanical Engineers (UK), in recognition of her outstanding achievements utilizing Computational Methods for energy system modeling and flow optimization. Her research interests include cryogenic liquid turbines, compressor instabilities, and innovative Computational Methods for fluid-structure interaction and multiphase flow behavior. She has authored numerous high-impact publications and holds multiple international patents that demonstrate her excellence in Computational Methods-based innovation. Prof. Dr. Jinju Sun’s research skills encompass CFD modeling, LBM, topology optimization, and Computational Methods-driven analysis for turbomachinery and green energy systems. In conclusion, her dedication to advancing Computational Methods in engineering has positioned her as a global leader driving innovation, sustainability, and scientific excellence in modern energy and power engineering.

Profile: ORCID

Featured Publications

1. Qu, Y., Sun, J., Song, P., & Wang, J. (2025). Enhancing efficiency and economic viability in Rectisol system with cryogenic liquid expander. Asia-Pacific Journal of Chemical Engineering.

2. Ge, Y., Peng, J., Chen, F., Liu, L., Zhang, W., Liu, W., & Sun, J. (2023). Performance analysis of a novel small-scale radial turbine with adjustable nozzle for ocean thermal energy conversion. AIP Advances.

3. Fu, X., & Sun, J. (2023). Three-dimensional color-gradient lattice Boltzmann model for simulating droplet ringlike migration under an omnidirectional thermal gradient. International Journal of Thermal Sciences.

4. Song, P., Sun, J., Wang, S., & Wang, X. (2022). Multipoint design optimization of a radial-outflow turbine for Kalina cycle system considering flexible operating conditions and variable ammonia-water mass fraction. Energies.

5. Song, P., Wang, S., & Sun, J. (2022). Numerical investigation and performance enhancement by means of geometric sensitivity analysis and parametric tuning of a radial-outflow high-pressure oil–gas turbine. Energies.

Dr. Yu Peng | Artificial Neural Networks | Best Researcher Award

Dr. Yu Peng | Artificial Neural Networks | Best Researcher Award

Associate Research Fellow | East China University of Science and Technology | China

Dr. Yu Peng, Associate Research Fellow at the School of Materials Science and Engineering, East China University of Science and Technology, has built an impressive academic and research career with a strong focus on materials chemistry and energy-related applications, where Artificial Neural Networks play a pivotal role in his innovative work. With a B.Sc. in Pharmacy from Nanchang University and a Ph.D. in Physical Chemistry under joint supervision at ShanghaiTech University and the Chinese Academy of Sciences, he deepened his expertise before advancing as a Post-doctor and then securing his current academic position. Dr. Yu Peng’s professional experience demonstrates his dedication to exploring polar and ferroelectric materials, two-dimensional photoelectric materials, photocatalytic hydrogen production, and photocatalytic biomass conversion, where Artificial Neural Networks are consistently applied to enhance predictive modeling, structural optimization, and performance analysis. His research has earned prestigious awards such as the Carbon Future Young Investigator Honorable Mention Award and multiple national scholarships, reflecting his outstanding contributions to science. Widely published in leading journals, his work bridges experimental material synthesis with Artificial Neural Networks modeling to design high-performance perovskites and hybrid semiconductors. His research skills encompass advanced materials characterization, computational simulations, and integration of Artificial Neural Networks in analyzing photoelectric and catalytic behaviors. Dr. Yu Peng’s career reflects a fusion of theoretical insight and practical applications, and his achievements in Artificial Neural Networks demonstrate his commitment to driving forward sustainable energy materials, innovative photocatalysts, and advanced optoelectronic devices, making him a recognized contributor to the global research community.

Profile: ORCID

Featured Publications

1. Zhang, J., Zhang, Y., Peng, Y., Wang, M. M., Zhu, Y., Wang, X., Tang, Y. Y., Ding, P. C., Liu, P. F., & Yang, H. G. (2025). Template-free synthesis of single-crystal SrTiO₃ nanocages for photocatalytic overall water splitting. Chemical Communications.

2. Peng, Y., Li, L., Xu, Y., Wang, X., & Hou, Y. (2025). Two-dimensional multilayered ferroelectric with polarization-boosted photocatalytic hydrogen evolution. Catalysts.

3. Peng, Y., Zhang, Y., Wang, X., Sui, X. Y., Lin, M. Y., Zhu, Y., Jing, C., Yuan, H. Y., Yang, S., Liu, P. F., et al. (2024). Polar aromatic two-dimensional Dion–Jacobson halide perovskites for efficient photocatalytic H₂ evolution. Angewandte Chemie.

4. Peng, Y., Zhang, Y., Wang, X., Sui, X. Y., Lin, M. Y., Zhu, Y., Jing, C., Yuan, H. Y., Yang, S., Liu, P. F., et al. (2024). Polar aromatic two-dimensional Dion–Jacobson halide perovskites for efficient photocatalytic H₂ evolution. Angewandte Chemie International Edition.

5. Liu, D., Zheng, Y., Sui, X. Y., Wu, X. F., Zou, C., Peng, Y., Liu, X., Lin, M., Wei, Z., Zhou, H., et al. (2024). Universal growth of perovskite thin monocrystals from high solute flux for sensitive self-driven X-ray detection. Nature Communications.

Dr. Bahadir Kopcasiz | Computational Methods | Best Researcher Award

Dr. Bahadir Kopcasiz | Computational Methods | Best Researcher Award

Assistant Professor | Istanbul Gelisim University | Turkey

Dr. Bahadir Kopcasiz is an accomplished academic whose expertise centers on Computational Methods, with strong emphasis on nonlinear partial differential equations, soliton theory, symbolic and semi-analytical analysis, and advanced mathematical modeling. He earned his Ph.D. in Mathematics from Bursa Uludag University, preceded by a Master’s in Mathematics from Yeditepe University and a Bachelor’s from Karadeniz Technical University, building a solid foundation for his contributions in Computational Methods. Currently serving as an Assistant Professor at Istanbul Gelisim University, he actively teaches courses such as Differential Equations, Statistics, Probability, and Numerical Analysis, integrating Computational Methods into both undergraduate and graduate programs. His research primarily focuses on soliton solutions in nonlinear Schrödinger-type systems, dynamical structures in quantum physics, and the development of innovative Computational Methods to study complex dynamical systems, with numerous publications in high-impact journals including Archives of Computational Methods in Engineering, Nonlinear Dynamics, and Symmetry. He has also presented extensively at international conferences, showcasing advancements in Computational Methods for applied physics and engineering. Among his recognitions, he received the Best Researcher Award at the International Research Awards on Composite Materials and academic incentive awards from Istanbul Gelisim University, which highlight his outstanding scholarly contributions in Computational Methods. His research skills are distinguished by mastery of symbolic computation, semi-analytical modeling, and integration of Computational Methods with machine learning for dynamic system optimization, as evidenced by his involvement in national projects. In conclusion, Dr. Bahadir Kopcasiz exemplifies excellence in academia through his dedication to advancing Computational Methods, innovative problem-solving, impactful publications, and mentorship, establishing himself as a valuable contributor to mathematics, physics, and engineering research. His Google Scholar citations 337, h-index 12, i10-index 14, showcasing measurable research impact.

Profiles: Google Scholar | ORCID

Featured Publications

1. Kopçasız, B., & Yaşar, E. (2022). The investigation of unique optical soliton solutions for dual-mode nonlinear Schrödinger’s equation with new mechanisms. Journal of Optics, 1–15.

2. Kopçasız, B., & Yaşar, E. (2022). Novel exact solutions and bifurcation analysis to dual-mode nonlinear Schrödinger equation. Journal of Ocean Engineering and Science.

3. Kopçasız, B., & Yaşar, E. (2024). Dual-mode nonlinear Schrödinger equation (DMNLSE): Lie group analysis, group invariant solutions, and conservation laws. International Journal of Modern Physics B, 38(02), 2450020.

4. Kopçasız, B. (2024). Qualitative analysis and optical soliton solutions galore: Scrutinizing the (2+1)-dimensional complex modified Korteweg–de Vries system. Nonlinear Dynamics, 112(23), 21321–21341.

5. Kopçasız, B., Seadawy, A. R., & Yaşar, E. (2022). Highly dispersive optical soliton molecules to dual-mode nonlinear Schrödinger wave equation in cubic law media. Optical and Quantum Electronics, 54(3), 194.