Electronic Cooling
Effective thermal management is essential for the reliable operation of electronic devices, spacecraft systems, and avionics. Heat transfer enhancement methods are generally classified into active and passive categories.
Recent research in this field has focused on:
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Investigating the influence of geometrical fin parameters, such as inter-fin spacing and Reynolds number, on heat transfer enhancement through experimental and numerical techniques.
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Optimizing the position and geometry of vortex promoters embedded in channels to maximize convective cooling efficiency over finned surfaces.
Reverse Engineering
Reverse engineering enables the rapid reconstruction of CAD models for complex geometries and facilitates the optimization of mechanical components to achieve targeted performance levels.
Research in this area emphasizes:
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Integrating non-contact scanning technologies with numerical modeling to replicate and improve existing designs, particularly in fan systems.
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Validating CFD-generated geometries through experimental testing, ensuring robust correlation between simulation and physical performance outcomes.
Machine Learning
Artificial intelligence and machine learning techniques, including Multiple Linear Regression, Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), have emerged as powerful tools in thermal-fluid systems modeling.
Key contributions in this area include:
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Predicting laminar combined convection characteristics in enclosures based on geometrical parameters and Reynolds number variations.
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Developing accurate data-driven heat transfer correlations and comparing the predictive accuracy of traditional statistical models with AI-based methods.
Solar Chimney
Solar chimney power plants (SCPPs) are considered a sustainable and cost-effective method for harnessing solar energy through buoyancy-driven natural convection.
Current studies in this field explore:
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The impact of chimney height, collector dimensions, and inlet geometry on airflow dynamics and thermal efficiency using CFD simulations.
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Performance evaluation and design optimization for scalable and environmentally friendly solar energy solutions, particularly in regions with high solar radiation.
Francis turbines remain a cornerstone technology in hydropower generation due to their adaptability across a wide range of flow rates and hydraulic heads.
Ongoing research in this domain addresses:
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CFD-based optimization of runner geometry, guide vanes, and other critical components for efficient operation under full and partial load conditions.
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Generation of numerical hill charts and analysis of rotor–stator interactions to improve overall hydraulic performance.
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Rehabilitation strategies for aging hydropower turbines to restore or enhance energy output.
Turbomachine Design
Reverse Engineering
Modern aerospace systems require precise aerodynamic, aeroacoustic, and thermal design for safe and efficient performance under complex operating conditions.
Research in this field—guided by high-fidelity numerical modeling and experimental validation—includes:
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Numerical and experimental analysis of aeroacoustic behavior in cavity flows and bio-inspired airfoils, focusing on noise reduction strategies.
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Supersonic cavity flow studies using URANS-based CFD models to investigate unsteady pressure fluctuations and resonance phenomena.
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Machine learning-assisted prediction of aerodynamic noise in rod-airfoil configurations, providing efficient and accurate alternatives to traditional simulations.
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Ongoing work on icing phenomena in airfoils, assessing the impact of ice accretion on aerodynamic performance and safety using CFD and AI-based modeling.
