Cogito, ergo sum.

I’ve always had this nagging suspicion that the world is a lot more interesting than the "clean" versions we see in textbooks. People love to simplify things—they take a complex, swirling mess and turn it into a neat little equation. But if the equation ignores the actual physics, what's the point? My career has been one long attempt to stop ignoring the mess and start modeling it properly. Whether it’s the roar of a jet engine or the quiet pulse of blood in your brain, I’m looking for the truth in the turbulence.

Early Work On Turbulent Reacting Flow

In the early days, I was obsessed with fire—specifically, how you keep a flame stable inside a gas turbine. The industry was using "black-box" models that assumed turbulence was the same in every direction (Isotropic Turbulence). I knew that was wrong. If you’ve ever seen a swirling flow, you know it has a "soul"—a Central Recirculation Zone—that holds the flame together.

In 2009, I was fortunate to be among early adopters of OpenFOAM for building custom solvers. I wanted to see the aerodynamic structures that the low-fidelity models were effectively "deleting." You can’t build a hydrogen engine if you’re lying to yourself about how the air moves. This was my first lesson in Scientific Integrity: the model must serve the physics, not the other way around.

The Transition to Hemodynamics

Then I got interested in the pipes inside our own heads. For decades, the medical world has treated blood like it was just red water. They call it the Newtonian Assumption. But blood is a crowded, goopy suspension of cells. When it flows through an intracranial aneurysm, it doesn't behave like water at all.

I applied the same high-resolution Large Eddy Simulation (LES) standards I used for aerospace to the human circulatory system. I found that blood flow in the brain isn't always smooth; it’s often defined by high-frequency turbulent fluctuations. By developing Quasi-Mechanistic Viscosity Models, I showed that the "simplified" math was leading to dangerous miscalculations of rupture risks. We weren't just doing bad math; we were doing bad medicine.

Ongoing Research and Development

Now, I’m chasing something even deeper. Everyone uses Kolmogorov’s theory of turbulence—a beautiful piece of math from the 40s. But nature isn't always a fan of oversimplified assumptions. My recent work shows that physiologic blood flow exhibit "Non-Kolmogorov" turbulence scaling. This unique spectral signature is how the flow "talks" to your cells, a defining process for endothelial mechanobiology.

But there’s a bigger problem in science right now: the "crisis of validation." People are pumping out simulations faster than they can think about them. I’ve been working on the Computational Reynolds Number, a way to define the limits of what a computer can actually "know" about a dissipative system. I call my current philosophical approach Operational Structuralism. It’s about building a framework where a simulation isn't just a pretty picture, but a rigorous, verifiable piece of knowledge.