Research
My scientific interest has been numerical modelling of multiphase non-Newtonian flow applied in (but not limited to) water managing and, more specifically, anaerobic digestion for wastewater treatment. After using and developing finite-volume Computational Fluid dynamics (CFD) models, I have focussed my attention to developing new models and methodologies for the Lattice-Boltzmann method, and i am now part of the OpenLB developer team. I am now investigating the possibility of linking CFD to artificial intelligence.
Advancing the Lattice-Boltzmann Method (LBM)
As a numerical modeller, I am dedicated to advancing the Lattice-Boltzmann method (LBM), an efficient computational approach to Computational Fluid Dynamics. LBM operates on a mesoscopic scale, modeling fluid flow by tracking the evolution of (virtual) particle probability distribution on a lattice grid. This method is particularly adept at handling computationally-intensive simulations involving hundreds of CPUs or multiple GPU cards at high-performance numerical facilities. I am part of the development team of OpenLB (www.openlb.net), a leading open-source library for high-performance LBM modelling. My contributions include the development of code for non-Newtonian flow, flow in porous media and multiphase modelling.
My work in LBM also includes investigations of the theoretical aspects of LBM modelling of multiphase flow and flow in porous media.
Computational Fluid Dynamics (CFD) in Environmental Engineering
In my research, I employ Computational Fluid Dynamics (CFD) techniques, with a particular emphasis on the Lattice-Boltzmann Method (LBM), to address complex challenges in environmental engineering, focusing on water processes and treatment.
Key Applications:
Gas Mixing in Anaerobic Digestion: Anaerobic digesters are critical components in wastewater treatment, facilitating the breakdown of organic matter to produce biogas. Effective mixing within these digesters ensures uniform distribution of substrates and microorganisms, enhancing process efficiency. I utilize CFD, particularly LBM, to simulate gas-induced mixing patterns, aiming to optimize digester design and operation. For instance, my research has involved developing Euler–Lagrange models to assess flow fields in full-scale, gas-mixed anaerobic digesters, providing insights into improving mixing strategies.
Hyporheic Exchange: This process involves the mixing of surface water and groundwater in the hyporheic zone, impacting nutrient cycling and water quality in aquatic ecosystems. Accurately modeling hyporheic exchange is vital for understanding contaminant transport and ecological health. I apply LBM to simulate the complex interactions between surface flows and porous media, enhancing predictions of hyporheic exchange dynamics. This approach allows for detailed analysis of flow patterns and solute transport mechanisms within the hyporheic zone.
Artificial Intelligence (AI) for Computational Fluid Dynamics (CFD)
My latest research focuses on integrating Artificial Intelligence (AI)—specifically Machine Learning—with Computational Fluid Dynamics (CFD). I have developed an AI model capable of generating time sequences of fluid flow patterns from an initial CFD-produced prompt. Unlike traditional CFD methods, this model does not require explicit inputs for properties like viscosity or turbulence; instead, it learns these behaviors from past data.
By combining transformers (similar to those in ChatGPT) with AI-driven image reconstruction techniques (U-Net), my model extends CFD-generated simulations, producing realistic, time-evolving flow patterns with high fidelity.
Research grants
Optimised design of hydraulic flocculators treating water using natural seeds as coagulants
ROYAL SOCIETY
March 2025 - March 2027
Retrofittable Propulsion System for Electric Vessels with Hydrogen Range Extender (RESTORE).
INNOVATE UK (UK)
June 2024 - March 2025
Smart Energy Generation and Recharging Systems for Marinas and Green Corridors (SMARTGEN)
INNOVATE UK (UK)
November 2024 - March 2025