黑料福利网

Recently 5 young scientist from our department have been promoted to the next level on the scientific ladder. Antoni Forner Cuenca has become Full Professor. Ghislaine Vantomme, Fernanda Neiro D鈥橝ngelo, Frank Peters and Nikolay Kosinov have been promoted to Associate Professor. Part of a research transition program in the department of Chemical Engineering and Chemistry, is encouraging the scientific development of its staff. In a series of portraits we highlight their current research line and their personal drive to contribute to the department鈥檚 vision 鈥楥hemistry for a better world鈥�.

Frank Peters is an Associate Professor at the research group Multiscale Modelling of Multiphase Flows where he works with multiple generations of dedicated teams of PhD and postdoctoral researchers, guiding them from foundational modeling skills to expertise in advanced computational methods. Being a 黑料福利网 alumnus who obtained his MSc in Physics here, after obtaining his PhD in Delft and spending time as a postdoc in Japan and at the University of Amsterdam, Peters returned to Eindhoven in 2004 as an Assistant Professor in the group for Chemical Reactor Technology of Piet Kerkhof.

Frank, what are you working on in your research line?

鈥淚n general, I aim to build computational engines to power the next generation of sustainable chemical technologies. My work is based on two pillars: a deep understanding of the physics of chemical processes, like flow, heat and mass transfer, and advanced computational methods, ranging from high-performance computing to AI-driven modeling.

I am particularly proud of a breakthrough we achieved in packed bed modeling. In this project, we successfully bridged the gap between fundamental physics and application by combining detailed particle-resolved Computational Fluid Dynamics with experimental validation using MRI flow imaging. We used these insights to develop reduced Pore-Network Models (PNM). This example demonstrates exactly what I strive for: transforming computationally expensive, high-fidelity simulations into fast, accurate tools that are suitable for agile process development.鈥�

Why did you get into your particular research field?

鈥淥riginally educated as an applied physicist, my career has always been defined by a clear theme: my deep passion for modeling. By crossing traditional disciplines, I managed to build a unique and versatile skillset.

My PhD work in rheology combined flow computations with kinetic modeling. Later, I moved into material science, utilizing molecular and coarse-grained simulation techniques. Today, I apply this broad expertise to multiphase flows. This cross-disciplinary background allows me to see the bigger picture, which is essential as modeling increasingly becomes the cornerstone of process design and control in chemical engineering.鈥�

What experience or connection outside 黑料福利网 has proven to be the most valuable for your growth?

鈥淚n my early career, I have been fortunate to experience a diverse range of research environments that shaped my growth. My journey took me from the industrial KSLA Shell research lab to the vibrant group of Frans Nieuwstadt in Delft for my PhD. Later, I worked in Masao Doi鈥檚 group in Nagoya, Japan, and in Berend Smit鈥檚 group in Amsterdam.

Currently, industrial collaborations allow me to test my models against real-world data, effectively bridging the gap between fundamental research and practical implementations.鈥�

What impact for society do you hope to create with your research?

鈥淚n the first place, I want to contribute directly to the Energy and Materials transition. To move toward a sustainable, circular economy, we must rethink how we design and control chemical processes. My computational tools enable real-time optimization, which helps minimize energy consumption and rwaste of resources. Through open science, I ensure these tools are widely accessible, thus accelerating the adoption of sustainable industrial practices.

Second, I want to address the changing nature of scientific research itself. AI is rapidly evolving to solving societal problems, and because computational research is so closely linked to software development鈥攚here AI agents are already excelling鈥攐ur field is facing a fundamental shift. The question is: will these tools make the human scientist super-productive, or superfluous? I hope to help answer this question by redefining how we teach. We need to determine what constitutes 鈥檅asic knowledge鈥� for the next generation and how to foster critical computational thinking in an era in which students can increasingly depend on AI.鈥�

In what role do you see yourself in 10 years from now?

鈥淐urrently, through courses like Particle-Based Simulations, Multiphase Reactor Modeling, and a new Molecular Simulations class, I focus on building strong foundational modeling skills and computational thinking. But looking forward, the rise of AI brings massive uncertainty. We don't know if 鈥榰nderstanding the physics will remain the primary bottleneck, or if AI will eventually handle that for us.

I believe the future role of the scientist may shift toward setting goals, generating ideas, and being creative, using AI as a very capable assistant. While the specifics are unclear, 鈥榯he change is inevitable. My goal is to ensure that by incorporating these tools into our research and education now, we, as a department and as a university, keep up with this revolution and stay at the forefront.鈥�