Assoc. Prof. Dr. Osama Hussein Galal | Stochastic Fluid Dynamics | Excellence in Research

Associated Professor | Fayoum University | Egypt

Assoc. Prof. Dr. Osama Hussein Galal is a distinguished academic specializing in Stochastic Fluid Dynamics, whose professional journey reflects exceptional expertise in Engineering Mathematics and Physics. His academic and research trajectory demonstrates profound engagement with Stochastic Fluid Dynamics in analyzing uncertainty quantification, fractional-order systems, and fluid flow modeling. Over his extensive academic tenure, he has served as an educator, researcher, consultant, and supervisor, contributing significantly to Stochastic Fluid Dynamics applications in non-Newtonian fluid analysis, stochastic differential equations, and advanced computational mechanics. His professional experience extends to engineering consultancy and structural design, where he integrated Stochastic Fluid Dynamics methodologies for enhanced prediction accuracy in complex engineering systems. Assoc. Prof. Dr. Osama Hussein Galal has guided numerous postgraduate dissertations focusing on Stochastic Fluid Dynamics and uncertainty modeling in power systems, beam analysis, and transmission lines. His research interest revolves around integrating Stochastic Fluid Dynamics with machine learning, renewable energy modeling, and fractional calculus applications. Recognized for his scholarly contributions, he has received several awards for excellence in teaching, research supervision, and scientific publications. His research skills encompass analytical modeling, stochastic simulation, and the mathematical treatment of Stochastic Fluid Dynamics in engineering contexts, establishing him as a leading voice in the field. Through his numerous publications in reputed international journals, he has advanced global understanding of Stochastic Fluid Dynamics and its engineering implications. His career exemplifies the fusion of theoretical rigor and practical innovation, positioning him as a prominent figure in modern computational and stochastic analysis. Google Scholar profile of 102 Citations, 6 h-index, 5 i10-index.

Profiles: Google Scholar | ORCID

Featured Publications

1. Hatata, A., Galal, O. H., Said, N., & Ahmed, D. (2021). Prediction of biogas production from anaerobic co-digestion of waste activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network. Renewable Energy, 178, 226–240.

2. Galal, O. H., El-Tahan, W., El-Tawil, M. A., & Mahmoud, A. A. (2008). Spectral SFEM analysis of structures with stochastic parameters under stochastic excitation. Structural Engineering and Mechanics: An International Journal, 28(3), 281–294.

3. Galal, O. H., El-Tawil, M. A., & Mahmoud, A. A. (2002). Stochastic beam equations under random dynamic loads. International Journal of Solids and Structures, 39(4), 1031–1040.

4. Galal, O. H. (2013). A proposed stochastic finite difference approach based on homogenous chaos expansion. Journal of Applied Mathematics, 2013(1), 950469.

5. El-Beltagy, M. A., Wafa, M. I., & Galal, O. H. (2012). Upwind finite-volume solution of Stochastic Burgers’ equation. Scientific Research Publishing.

Prof. Dr. Murat Barut | Motor Control | Best Researcher Award

Professor | Nigde Omer Halisdemir University | Turkey

Prof. Dr. Murat Barut, a distinguished Professor in Electrical and Electronics Engineering, has made significant contributions in the field of Motor Control, integrating advanced estimation techniques, artificial intelligence, and control algorithms into electrical drive systems. His educational background spans from Electronics Engineering at Erciyes University to dual doctorates in Control and Computer Engineering and Electric and Computer Engineering from prestigious universities in Türkiye and the USA. His professional journey includes academic and research roles at Nigde University, Istanbul Technical University, and the University of Alaska Fairbanks, where he focused extensively on Motor Control applications for induction and synchronous machines. Prof. Dr. Murat Barut’s research interests center on speed-sensorless estimation, position-sensorless operation, Extended Kalman Filter design, artificial intelligence-based modeling, and high-performance Motor Control systems. He has led and participated in multiple funded projects dedicated to real-time Motor Control algorithm development and FPGA implementations. Recognized with honors such as the Siemens Excellence Award and the Most Influential Scientist Award, he continues to advance Motor Control research with innovative methodologies. His professional skills encompass estimation theory, adaptive control, power electronics, and signal processing — all directed toward efficient Motor Control of electrical drives. Prof. Dr. Murat Barut has contributed as a reviewer and editor in various IEEE and SCI-indexed journals, reinforcing his reputation in the global Motor Control community. His career exemplifies excellence in engineering education, innovation, and leadership, with a strong record of scholarly impact demonstrated through a Google Scholar profile of 2011 citations, 20 h-index, and 27 i10-index.

Featured Publications

1. Barut, M., Bogosyan, S., & Gokasan, M. (2007). Speed-sensorless estimation for induction motors using extended Kalman filters. IEEE Transactions on Industrial Electronics, 54(1), 272–280.

2. Barut, M., Bogosyan, S., & Gokasan, M. (2008). Experimental evaluation of braided EKF for sensorless control of induction motors. IEEE Transactions on Industrial Electronics, 55(2), 620–632.

3. Zerdali, E., & Barut, M. (2017). The comparisons of optimized extended Kalman filters for speed-sensorless control of induction motors. IEEE Transactions on Industrial Electronics, 64(6), 4340–4351.

4. Barut, M., Demir, R., Zerdali, E., & Inan, R. (2011). Real-time implementation of bi input-extended Kalman filter-based estimator for speed-sensorless control of induction motors. IEEE Transactions on Industrial Electronics, 59(11), 4197–4206.

5. Yildiz, R., Barut, M., & Zerdali, E. (2020). A comprehensive comparison of extended and unscented Kalman filters for speed-sensorless control applications of induction motors. IEEE Transactions on Industrial Informatics, 16(10), 6423–6432.*