I build mathematical, stochastic, and computational frameworks to understand why turbulent flows generate sound—and what can be done about it. From vortex particle methods to porous-material acoustics, the approach is always physics-first.
Bridging the Navier–Stokes equations and acoustics—the central problem of aeroacoustics.
My research sits at the intersection of fluid mechanics, mathematical physics, and numerical methods. The central question driving my work: how does the chaotic, multiscale structure of turbulence produce sound—and can we predict, model, and control it without solving everything at once?
I pursue this across three connected tracks: mathematical modelling (stochastic Markov-chain turbulence, vortex particle methods, Lighthill's analogy), computational simulation (LES, DES, FW-H, ODT), and physical experiments (wind tunnel, airfoil-turbulence interaction, porous coatings). The result is a research programme that can move between a tensor identity and a wind turbine blade without losing rigour.
Explore the research →Stochastic vortex particle methods for predicting airfoil noise from incoming turbulence. Time-domain BEM, validated against experiment.
Read more →ODT as a reduced-order model for jet self-noise. Markov-chain representations of turbulent velocity fluctuations and their acoustic signatures.
Read more →How porous coatings suppress vortex shedding and far-field noise in cylinders and bluff bodies. Permeability, thickness, and geometric effects.
Read more →LES, DES, DNS, and FW-H acoustic analogies for airframe, fan, and wind-turbine noise. Tool development in SU2, OpenFOAM, and Python.
Read more →Self-similarity, far-downstream velocity statistics, and acoustic emission from turbulent round jets at high Reynolds number.
Read more →Turbulent inflow and trailing-edge noise from wind turbine blades. Experimental and numerical validation at DLR Braunschweig.
Read more →My research develops physics-based mathematical and computational frameworks for understanding, predicting, and controlling the noise generated by turbulent flows. The problems are fundamental; the tools are often novel.
Markov chains, Langevin equations, Monte Carlo sampling, and ODT for turbulence statistics without resolving all scales.
Lagrangian, meshless solvers for Euler and Navier–Stokes equations. Synthetic turbulence generation.
LES, DES, DNS using OpenFOAM, SU2. Scale-resolving simulations for aeroacoustic analysis.
Lighthill, FW-H, BEM. Connecting flow solutions to far-field sound radiation in a mathematically rigorous way.
This is the thread that runs from my doctoral thesis to the present. I developed a stochastic, time-domain approach using vortex particles as both a turbulence representation and an acoustic source model. The key insight is that a Lagrangian description of turbulence—individual vortex elements with prescribed statistics—is both physically transparent and computationally tractable.
The resulting framework produces broadband noise predictions that compare well with high-fidelity LES at orders-of-magnitude lower cost. It has been extended to handle realistic turbulent spectra, anisotropic inflow, and experimental validation against wind-tunnel measurements.
More recently at DLR, the vortex particle method has been augmented with a look-up table approach to further accelerate turbulent inflow and leading-edge interaction noise prediction for aircraft high-lift devices and wind turbine applications.
One-Dimensional Turbulence (ODT) is a stochastic closure model developed by Kerstein. Unlike classical eddy-viscosity approaches, ODT directly simulates turbulent advection via stochastic rearrangement events on a 1D domain—preserving the physical mechanisms of the energy cascade without the cost of 3D DNS.
My work during the Cambridge postdoc (DFG Walter Benjamin Fellowship) applied ODT to investigate turbulent jet dynamics: self-similar velocity statistics, far-downstream fluctuations, and—crucially—their acoustic emission via Lighthill's equation. This is a principled way to bridge the gap between low-order turbulence models and the acoustic field, with explicit probabilistic treatment of source fluctuations.
The goal is a framework where turbulence is not parametrised but modelled as a stochastic process with physical fidelity, producing acoustic predictions with quantifiable uncertainty.
Porous coatings offer a passive, structurally simple route to noise control. Owls—nature's model—use porous wing structures to fly nearly silently. The aeroacoustic engineering challenge is to understand the mechanism precisely enough to design effective coatings without trial and error.
My computational studies (DES, FW-H) on porous-coated cylinders and staggered cylinder configurations show that coating permeability and thickness act on distinct physical mechanisms: permeability suppresses vortex shedding regularity while thickness affects the mean velocity distribution and acoustic source distribution.
This work was conducted in collaboration with experimental colleagues and has direct application to airframe landing-gear noise and wind turbine strut noise.
Complementing the mathematical modelling track, I maintain strong capability in high-fidelity computation. This includes LES/DES in OpenFOAM and SU2, acoustic post-processing via FW-H, and grid-generated turbulence simulations to benchmark turbulence modelling assumptions.
A recent focus at DLR has been assessing Navier–Stokes turbulence model fidelity for grid-generated turbulence interacting with an airfoil—a critical validation case for wind turbine and aircraft inflow noise predictions. The question of where and how turbulence models fail is as important as what they predict correctly.
Organized thematically. Each entry includes a brief note on scientific significance. For full citation lists, see Google Scholar [TO BE LINKED] or download my CV.
Invited seminars, conference presentations, and workshops.
Research code developed as part of my scientific programme. Most is open-source on GitHub.
Generates turbulent inflow conditions using Gaussian-profile vortex elements (vortons). Produces statistically correct energy spectra for use in leading-edge noise simulations.
GitHub →A 2D meshless Lagrangian solver for the incompressible Euler equations using the vortex particle method. Foundational tool for the time-domain leading-edge noise framework.
GitHub →Computes directivity patterns of radiated sound pressure from airfoils using the FW-H acoustic analogy. Post-processes CFD surface data to far-field sound predictions.
GitHub →I believe computational results should be reproducible. Where journal policies and data agreements permit, the code and data associated with published papers are made available via GitHub or data repositories. If you are trying to reproduce a specific result and cannot find the relevant code, please get in touch.
I am a research scientist at the Institute of Aerodynamics and Flow Technology at DLR Braunschweig, where I work in the aeroacoustics group of Dr. Michaela Herr and Prof. Jan Delfs on turbulent noise from aircraft high-lift systems and wind turbines.
My research is unified by a single question: how does the chaotic, multi-scale structure of turbulence give rise to sound—and can we model this process mathematically, without losing physical meaning? This has led me to develop Lagrangian vortex particle methods, stochastic Markov-chain turbulence models, and one-dimensional turbulence approaches as computational and theoretical tools for aeroacoustic prediction.
Before DLR, I held a DFG Walter Benjamin Postdoctoral Fellowship at the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge (2021–2023), working on the mathematics of jet noise with Prof. Lorna Ayton and Prof. Marten Klein. The Cambridge period deepened my connection between mathematical physics and turbulence modelling—particularly through ODT and Lighthill's acoustic analogy.
My PhD (summa cum laude, BTU Cottbus & TU Berlin, 2016–2019) established the time-domain stochastic vortex particle method for predicting leading-edge noise from turbulence–airfoil interaction—work that was recognised with the BTU Best Thesis Award 2020 and the DEGA Young Scientist Award 2019.
I studied Computational Mechanics at MIPT Moscow (M.Sc., 9.5/10) and Mechanical Engineering at VIT Vellore (B.Tech., honours), where I first encountered the frustrating beauty of turbulence. Between degrees, I spent time at Rolls-Royce Aeroengines working on fan and airframe noise, and led R&D aeroacoustics collaborations at ebm-papst—experience that grounds my theoretical work in engineering reality.
I have supervised four master's theses, lectured an advanced CFD course at BTU Cottbus for three years, and review for seven journals including Journal of Fluid Mechanics and Physics of Fluids.
Institute of Aerodynamics and Flow Technology. Wind turbine and aircraft aeroacoustics.
DAMTP. Jet noise, ODT, Lighthill's analogy. With Prof. Ayton & Prof. Klein.
Numerical Flow and Gas Dynamics. Jet turbulence modelling with Prof. Schmidt.
Stochastic vortex particle method for leading-edge noise. Supervisors: Sarradj, Schmidt, Harlander.
Grade: 9.5/10. Aerodynamics, CFD, aeroacoustics, numerical methods.
Honours in Automotive Engineering. SAE Revathy Iyer Award; NASA N+3 competition.
I am based at DLR Braunschweig and am always open to scientific discussion, collaboration, or speaking invitations.
I am particularly interested in working with researchers and groups on:
If you are interested in pursuing a master's thesis or internship in computational aeroacoustics or turbulence modelling, please send a brief description of your background and interests alongside your CV. I am particularly interested in students with a strong mathematical background and familiarity with Python or C++.