About
Dr
Simon Rodriguez
Postdoctoral Researcher
UCD
Simon Rodriguez is a postdoctoral researcher at I-Form, specialising in numerical simulation and computational modelling. He earned his undergraduate degree in Gas Engineering in Venezuela and spent seven years at Intevep, the Venezuelan National Oil and Gas Research Center, where he applied both numerical and experimental methods to engineering challenges across water, oil, and gas facilities. In 2019, he was awarded the prestigious Chevening Scholarship to pursue a Master's degree in Computational Fluid Dynamics (CFD) at Cranfield University. He subsequently completed his PhD at University College Dublin (UCD), where he developed the One-Step SelfSim algorithm that enhances numerical simulations by integrating measured strain data into material model formulations. He also created PythonPal (PythonPal4Foam), a computational tool that enables seamless integration between Python and OpenFOAM, supporting machine learning-enhanced simulations. At I-Form, his research focuses on leveraging multi-scale simulation codes to optimise additive manufacturing (AM) processes, aiming to reduce reliance on physical experiments and improve process efficiency.
Technical Summary
Simon’s research focuses on optimising additive manufacturing (AM) processes through the integration of advanced multi-scale simulation codes, aiming to reduce reliance extensive physical experiments. These simulation codes—developed at I-Form and in the broader research community—operate at macro, meso, and micro scales, each capturing distinct but interconnected aspects within the AM process. His work is centred on developing a robust methodology to efficiently integrate these models, enabling seamless data flow and a more comprehensive understanding of AM process dynamics.
A key aspect of his research is identifying optimal process parameters—such as laser power, scan speed, and layer thickness—that ensure manufactured parts meet structural and performance requirements. By linking simulation outputs with manufacturing outcomes, Simon contributes to the development of a predictive framework that minimises trial-and-error in AM.
As part of his PhD at UCD, Simon developed PythonPal (PythonPal4Foam), a tool that enables direct execution of Python code within OpenFOAM without requiring translation between C++ and Python. This integration allows researchers to combine OpenFOAM’s high-performance simulation capabilities with Python’s advanced machine learning ecosystem, while ensuring negligible computational overhead. His expertise in numerical simulation, high-performance computing, and computational mechanics is now being applied to AM process optimisation, supporting I-Form’s mission to advance efficient, cost-effective, and high-quality AM technologies.