Originally from Verona, I hold a Master of Science in Aerospace Engineering from Politecnico di Milano, with a thesis on the stability of wall-bounded flows with spanwise forcing under the supervision of Prof. Maurizio Quadrio. I then obtained a PhD in computational fluid dynamics from KTH Royal Institute of Technology in Stockholm, under the supervision of Prof. Philipp Schlatter. There, I conducted space-adaptive direct numerical simulations to study transitional and turbulent shear flows. I subsequently spent two years as a researcher at MIT, working with Prof. Wim van Rees on vortex dynamics and machine-learning-based prediction of unsteady aerodynamic loads. I am currently a Postdoctoral Fellow at the University of Cambridge, where I work with Prof. Matthew Colbrook on a causal-spectral framework for vortex-gust wing interaction.
Daniele Massaro
Massachusetts Institute of Technology
Dept. of Mechanical Engineering
77 Massachusetts Avenue
Cambridge, MA 02139
USA
danmas@mit.edu
I’ve always had wide-ranging interests, from philosophy to the natural sciences. Although I ended up following a scientific career, I’ve kept a real love of literature and the humanities, and I still lose myself in classic novels whenever I can (you’ll find a few recommendations below). At Politecnico di Milano, I built a solid theoretical foundation, particularly through courses in functional and complex analysis, while also studying rotary-wing dynamics and spacecraft orbits. But it was fluid mechanics that really captured me: there’s something rather elegant about the way it brings together mathematics, physics and engineering.
My work on turbulence began with my Master's thesis on stability and transition delay. During my PhD, I delved into wall turbulence and coherent structures, using high-order spectral element methods and adaptive mesh refinement for direct numerical simulations. To gain a deeper understanding, we combined established techniques such as Proper Orthogonal Decomposition with cutting-edge approaches such as Shannon transfer entropy from information theory, investigating mechanisms in transitional and turbulent flows.
At MIT, I explored vortex-dominated flows, drawing on MIT's long-standing tradition in artificial intelligence to develop AI models for predicting complex aerodynamic interactions.
I am always keen to learn and to collaborate, especially on topics ranging from classical turbulence theory to modern AI modelling of complex flow systems.
Братья Карамазовы (The Karamazov Brothers), Fyodor Dostoevsky, 1879-1880.
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Il fu Mattia Pascal, Luigi Pirandello, 1904.
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El ingenioso hidalgo don Quixote de la Mancha, Miguel de Cervantes, 1605 & 1615.
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We perform the direct numerical simulation of the tubulent flow around a 3D Flettner rotor, wherein the rotor exploits the Magnus effect for harvesting the wind energy, saving up a significant amount of fuel.
The video was an entry at the Gallery of Fluid Motion 2022, an exhibition at the Annual Meeting of the American Physics Society, Division of Fluid Dynamics.
Our research on the instability mechanism in a 180°-bend in pipe flow was featured on the Science X network , as it clarifies how the transition from laminar to turbulent flow occurs in curved pipes, similar to that in the aortic arch. Understanding this transition is important, as turbulence in the aorta may be linked to various heart diseases.
The image is from the cartoon series Once Upon a Time...Life, where blood cells are depicted as animated characters to illustrate bodily functions in an engaging, accessible way.
No copyright infringement intended. All rights belong to their respective owners.