Ali Jabbary
Urmia University, Mechanical Engineering, Faculty Member
- Iterative Methods, Newton-Raphson algorithm, Orbital Mechanics, Numerical Methods, Computational Fluid Dynamics (CFD) modelling and simulation, Engineering, and 13 moreTechnology, Education, Music, Computer Science, Architecture, Artificial Intelligence, Environmental Sustainability, Data Mining, Mathematics, Renewable Energy, Software Engineering, Psychology, and Physicsedit
- M.D. Mechanical Engineering in Energy Conversion Field • Professional in the CFD field and fluid flow simulation • Expert in Fuel Cell designs engineering and development • Advanced Programming in Python, C/C++, MATLAB, and Java • Fluency in English Language and Advanced English Texts • Experienc... moreM.D. Mechanical Engineering in Energy Conversion Field
• Professional in the CFD field and fluid flow simulation
• Expert in Fuel Cell designs engineering and development
• Advanced Programming in Python, C/C++, MATLAB, and Java
• Fluency in English Language and Advanced English Texts
• Experienced in SOLIDWORKS software
• Machine Learning and Deep Learning research and development
• Image enhancement + 2D to 3D image transform using AI
• Providing support chatbots
• Web automation + Data Scraping + Data Mining
• Android App development
• Stock/Cryptocurrency price prediction using deep neural networks
• Natural Language Processing using deep learning
• Task Automation with Python
• Arduino and Raspberry Pi programming for robotics
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We are currently researching new technologies and up-to-date science on renewable energy-based systems, PEM fuel cells, and fuel cell-based hybrid systems.
We apply new techniques such as the Homotopy perturbation method and Homotopy analysis method to solve nonlinear PDE equations in multi-physics problems, such as the stagnation point flow phenomenon in the presence of a magnetic field.
We use artificial intelligence and deep learning methods for various researches, including fluid flow prediction, stock/cryptocurrency price prediction, and image/video quality enhancement.
We utilize natural language processing with artificial intelligence for advanced text analysis.
We are also working on signal processing combined with artificial intelligence and Arduino to troubleshoot mechanical and electrical devices and predict possible future failures.edit - Nima Ahmadiedit
This article presents new PEM fuel cell models with hexagonal and pentagonal designs. After observing cell performance improvement in these models, we optimized them. Inlet pressure and temperature were used as input parameters, and... more
This article presents new PEM fuel cell models with hexagonal and pentagonal designs. After observing cell performance improvement in these models, we optimized them. Inlet pressure and temperature were used as input parameters, and consumption and output power were the target parameters of the multi-objective optimization algorithm. Then we used artificial intelligence techniques, including deep neural networks and polynomial regression, to model the data. Next, we employed the RSM (Response Surface Method) method to derive the target functions. Furthermore, we applied the NSGAII multi-objective genetic algorithm to optimize the targets. Compared to the base model (Cubic), the optimized Pentagonal and Hexagonal models averagely increase the output current density by 21.819% and 39.931%, respectively.
Research Interests:
This article presents new PEM fuel cell models with hexagonal and pentagonal designs. After observing cell performance improvement in these models, we optimized them. Inlet pressure and temperature were used as input parameters, and... more
This article presents new PEM fuel cell models with hexagonal and pentagonal designs. After observing cell performance improvement in these models, we optimized them. Inlet pressure and temperature were used as input parameters, and consumption and output power were the target parameters of the multi-objective optimization algorithm. Then we used artificial intelligence techniques, including deep neural networks and polynomial regression, to model the data. Next, we employed the RSM (Response Surface Method) method to derive the target functions. Furthermore, we applied the NSGAII multi-objective genetic algorithm to optimize the targets. Compared to the base model (Cubic), the optimized Pentagonal and Hexagonal models averagely increase the output current density by 21.819% and 39.931%, respectively.
Research Interests:
ABSTRACT A numerical 3D procedure is presented based on the Finite Volume Method to solve the governing equations of Proton Exchange Membrane Fuel Cell (PEMFC) with rhombus design. We evaluated these equations in both the anode and... more
ABSTRACT A numerical 3D procedure is presented based on the Finite Volume Method to solve the governing equations of Proton Exchange Membrane Fuel Cell (PEMFC) with rhombus design. We evaluated these equations in both the anode and cathode gas channels. In the present research, we examined the impact of rhombus design on the output characteristics of PEMFC under appropriate operating conditions and verified the outputs with experimental data. The water accumulation has a significant effect on fuel cell performance. We studied different aspects of the fuel cell to obtain the water accumulation and characteristics’ distribution of fluid flow in the gas channel and their influence on the performance of PEMFC. The current intensity and power density are the most critical elements of a fuel cell. Model B has increased the current density by one compared to the base model. The cell power consumption has been reduced by 1/4 and 1/8 ratios. The pressure drop in the presented models has been significantly reduced and controlled. The electrical power generated by Model B is 1.5 higher than the base model. Proton Exchange Membrane Fuel Cell (PEMFC) governing equations.
Research Interests:
A numerical 3D procedure is presented based on the Finite Volume Method to solve the Proton Exchange Membrane Fuel Cell (PEMFC) governing equations with rhombus design. We evaluated these equations in both the anode and cathode gas... more
A numerical 3D procedure is presented based on the Finite Volume Method to solve the Proton Exchange Membrane Fuel Cell (PEMFC) governing equations with rhombus design. We evaluated these equations in both the anode and cathode gas channels. In the present research, we examined the impact of rhombus design on the output characteristics of PEMFC under appropriate operating conditions and verified the outputs with experimental data. Water accumulation has a significant effect on fuel cell performance. We studied different aspects of the fuel cell to obtain the water accumulation and characteristics' distribution of fluid flow in the gas channel and their influence on the performance of PEMFC. The current intensity and power density are the most critical elements of a fuel cell. Model B has increased the current density by one A/cm^2 compared to the base model. The cell power consumption has been reduced by 1/4 and 1/8 ratios. The pressure drop in the presented models has been significantly reduced and controlled. The electrical power generated by Model B is 1.5 w/cm^2 higher than the base model. Proton Exchange Membrane Fuel Cell (PEMFC) governing equations. ARTICLE HISTORY