Research

Research Interests

My research interests are centered on the area of Fluid Mechanics with a particular focus on complex multiphysics flows to (i) advance their physics and engineering understanding, (ii) develop multiscale modeling approaches, and (iii) create new technological innovations in the fields of energy & propulsion, transportation & aerospace, biomedical, and manufacturing technologies.

As manifested in the Horizon Europe Program and the United States’ science and technology strategy, expressed through the Office of Science and Technology Policy, one of the main objectives is to provide innovations to (i) help transition to a climate-neutral economy; (ii) develop biomedical technologies to improve the quality of healthcare services; (iii) construct the next-generation industry, where nanoengineering, biotechnology and advanced manufacturing are some of the basic pillars; and (iv) lead the transformation in the fields of transportation and space exploration.

Fluid Mechanics systems presenting coupled processes active at multiple scales are encountered in a vast number of fundamental and applied contexts, like for example: the motion of small-scale (Kolmogorov-size) turbulent eddies over several meter long drag-reducing coatings on aircraft, nanoscale electrochemical reactions interplaying with microscale flow in fuel cells, or blood flow in the cardiovascular system. In this regard, the study of micro- and nanoscale flow phenomena and their interaction with larger scales is growing at a rapid pace as it has been recognized that the ability to control fluids at such small scales is leading to advances in basic research and technological innovations. However, many research challenges arise in such problems: (i) detailed understanding of the flow mechanisms, (ii) how to effectively model and coarse-grain multiphysics phenomena, (iii) the creation of validation databases, and (iv) the development of efficient experimental/computational approaches for engineering design and optimization.

The study of complex flows greatly benefits from the combination of interconnected theoretical, computational and experimental approaches. This manifold methodology provides a robust framework to corroborate the phenomena observed, validate the modeling assumptions utilized, and facilitates the exploration of wider parameter spaces and extraction of more sophisticated insights. These analyses are typically encompassed within the field of Predictive Science & Engineering, which has attracted attention in the Fluid Mechanics community and is expected to exponentially grow as computational studies transition from (mostly) physics simulations to active vectors for scientific discovery and technological innovation with the advent of Exascale computing.

Fluid Mechanics problems are often characterized by high-dimensional, non-linear phenomena. Although the equations of fluid motion derived from first principles provide a detailed partial differential equation (PDE) model, it is often difficult to utilize it in engineering applications. However, advances in experimental techniques and the ever-increasing fidelity of computational simulations are leading to a significant abundance of data. As a result, data science and reduced-order modeling (ROM) are becoming increasingly popular approaches for flow analysis, design and optimization. In the domain of data assimilation methods, machine learning provides advanced capabilities to extract flow features. On the other hand, ROM is especially interesting for obtaining efficient models for design and optimization. These rapidly developing methods offer a paradigm shift in our ability to measure, predict and manipulate flows.

Ongoing (selected) Research Activities