Some of my recent research works
Large Eddy Simulation of Mixing-Controlled Hydrogen Injection Effects on Jet A-1 Spray Flame Structure and Emissions in a Swirl-Stabilized Combustor Using OpenFOAM
Hydrogen can reduce carbon emissions in gas turbines, but its role in spray combustion is still not fully clear. This is especially true in swirl-stabilized, non-premixed systems, where fuel and air mix before burning. In these systems, processes like turbulence, droplet evaporation, hydrogen mixing, and chemical reactions happen together in very small and fast scales, making them hard to study experimentally. In this study, hydrogen is injected separately into a combustor with Jet A-1 spray fuel. This creates local changes in the fuel–air mixture, affecting flame formation and stability. Large Eddy Simulation (LES) in OpenFOAM is used to analyze different hydrogen levels and their impact on combustion. The liquid fuel is modeled using a Lagrangian spray method with breakup and evaporation, while the gas phase uses a compressible LES model to capture flow features like shear layers and recirculation. A turbulence–chemistry model is also applied to represent mixing-controlled combustion.
Research under consideration for publication
Crude Oil Fouling in Heat Exchangers
Fouling in crude preheat trains is a major cause of thermal inefficiency in petroleum refineries. The limited fundamental understanding of its causes and mechanisms led to ineffective fouling mitigation techniques. It is believed that asphaltenes precipitation and deposition is the major cause of fouling. This research attempts to simulate the deposition of asphaltenes from crude oil in a multi-pass shell and tube heat exchanger through discrete-phase CFD simulations. The effects of bulk velocity, temperature gradients in the radial direction and particle sizes on asphaltenes deposition are investigated. In an effort to understand the effect of various forces on the rate of deposition of asphaltenes, forces such as gravity, drag, Saffman lift and thermophoretic are applied on the asphaltenes particles. The deposition velocities of the asphaltenes particles are determined based on solving the balance of these forces.
Research Published in Applied Thermal Engineering, ScienceDirect
https://www.sciencedirect.com/science/article/abs/pii/S1359431118347471
Combustion performance prediction in oil and gas plants using integrated neural network models and SAP S4HANA sensor analytics
The efficiency of oil combusting and gas facilities deals with energy expenses as well as operational costs and the overall carbon footprint. Other approaches use simplistic dynamic control and process automation, but these approaches are neither flexible enough to account for drifts and dynamics in real-time, nor performant enough for process deviation in real-time to respond. This research details a transformer internal combustion engine with a scalable, platform agnostic AI architecture for combustion IT forecasting combined with real-time hardware integration from enterprise systems like SAP S/4HANA, Oracle, and Siemens MindSphere, using hybrid GRU machine learning with dense GRU neural networks. The process unites level balance thermodynamic models with sequential learning to physically integrated geo-temporal coupling learning. Operational data spanning 6 months and 3 plants (6.5 million samples) was used to train the system. The system beat conventional machine learning approaches with RMSE 2.1–2.4, MAE 1.7–1.9, and R2 > 0.91 for combustion-efficiency forecasting.
Research Published in Scientific Reports, Nature
Investigation of cyclone separator performance and the effect of inlet velocity, impurity concentration, and geometrical parameters on biofuel purification
As the global pursuit of sustainable and clean energy intensifies, biofuels have emerged as a viable alternative to fossil fuels. However, effective impurity removal during biofuel production remains a key technical hurdle, affecting both process efficiency and fuel quality. This research focuses on optimizing biofuel purification by evaluating the performance of cyclone separators using computational fluid dynamics (CFD) simulations. Specifically, it investigates the impact of inflow velocity, impurity volume fraction, and critical geometric parameters such as barrel length, cone length, and vortex finder diameter on separation efficiency. By analyzing both individual and interactive effects of these parameters, the research provides detailed insights into optimizing cyclone design for enhanced impurity removal.
Research Published in Biofuels, Taylor & Francis
https://www.tandfonline.com/doi/full/10.1080/17597269.2026.2622760
Effects of wavy structure, ambient conditions and solar intensities on flow and temperature distributions in a mini solar flat plate collector using computational fluid dynamics
Mini solar flat plate collectors have gained traction due to their cost-effectiveness, high efficiency in converting solar radiation to heat energy, and versatility in residential and commercial applications, offering long lifespans with minimal maintenance. Optimizing the thermal performance of solar flat plate collectors using numerical simulations helps enhance their efficiency, making them even more appealing for small-scale heating and hot water applications. This research investigated the flow behavior and temperature distribution of air within a small-sized solar collector (286 × 800 × 70 mm) and optimizes its design for achieving the highest outlet air temperature. The optimization process included varying geometric parameters, such as the wavy structure hole diameter, and considering a range of operating conditions, including ambient conditions and solar intensities.
Research Published in Biofuels, Taylor & Francis
https://www.tandfonline.com/doi/full/10.1080/19942060.2023.2236179