DLR Braunschweig  ·  ORCID 0009-0000-1911-9317

The mathematics
of turbulent
noise

I develop stochastic, Lagrangian, and computational frameworks to understand why turbulent flows generate sound — and how to make them quieter. Physics-first, always.

35+ publications Scholar ↗
Governing equations
Lighthill's analogy
∂²ρ′/∂t² − c₀²∇²ρ′ = ∂²Tᵢⱼ/∂xᵢ∂xⱼ
// quadrupole turbulence sources → acoustic field
Stochastic vortex particle
dωₖ/dt = (ωₖ·∇)u + ν∇²ωₖ + σdW
// Lagrangian + Itô noise → leading-edge sound
ODT turbulence model
∂u/∂t = −u∂u/∂x + ℒ{u}
// stochastic rearrangement → energy cascade
Lattice Boltzmann (2026)
∂fᵢ/∂t + cᵢ·∇fᵢ = Ω(fᵢ)
// mesoscopic → turbulence distortion + coherence
35+Total publications
142+Citations
4k+Reads
DFGWalter Benjamin Fellow
7Journals reviewed for
Turbulence distortion LBM simulation
LBM simulation · AIAA/CEAS 2026
Latest paper 32nd AIAA/CEAS · 2026
Turbulence distortion and coherence effects in airfoil leading-edge noise: insights from Lattice Boltzmann simulations
S. Sharma, M. Soni, A. Suryadi, M. Herr  ·  AIAA 2026-3445
Lattice Boltzmann simulations reveal how turbulence distorts and loses coherence as it interacts with an airfoil leading edge — mesoscopic physics inaccessible to standard CFD that directly controls the broadband noise spectrum. A new computational frontier in aeroacoustics.
Read paper ↗ doi:10.2514/6.2026-3445
1 / 3
AWB Wind Tunnel · LES
∂²ρ′
—————
∂t²
Multi-scale turbulence: grid → decay → distortion → airfoil → far-field noise
LES + AWB experiments · WTN 2025
2025 11th Wind Turbine Noise Conf. · Copenhagen
Multi-scale turbulence structures in grid-generated turbulence for leading-edge noise prediction
S. Sharma, A. Suryadi, M. Herr  ·  pp. 430–440
LES combined with AWB wind-tunnel experiments characterises how turbulence decays, distorts, and regenerates through airfoil interaction. Vortical structure visualisation and spectral analysis link multi-scale flow dynamics directly to far-field SPL spectra and directivity patterns.
Read paper ↗ doi:10.11581/08042886-dea0-4511-b4bd-6c5403125735
2 / 3
Stochastic · Lagrangian
dωₖ/dt =
 (ωₖ·∇)u
 + ν∇²ωₖ
 + σ dW
Itô noise drives stochastic vortex dynamics → acoustic radiation
Stochastic BEM · JSV 2020
Core paper Journal of Sound and Vibration · 2020
Stochastic modelling of leading-edge noise in time-domain using vortex particles
S. Sharma, E. Sarradj, H. Schmidt  ·  JSV 488:115656
The doctoral thesis core result. A time-domain stochastic framework demonstrates that a Lagrangian vortex approach can rival high-fidelity LES for leading-edge noise prediction — at orders of magnitude lower computational cost. Best Thesis 2020, DEGA Young Scientist Award 2019.
Read paper ↗ doi:10.1016/j.jsv.2020.115656
3 / 3
Scientific identity

Turbulence is not just a flow problem. It is a problem in stochastic mechanics, spectral theory, and statistical physics.

How does the chaotic, multiscale structure of turbulence produce sound — and can we predict, model, and control it without solving everything at once?

My research sits at the intersection of fluid mechanics, mathematical physics, and numerical methods. The approach spans three tracks: mathematical modelling (stochastic Markov-chain turbulence, vortex particle methods, Lighthill's and Lattice Boltzmann analogies), computational simulation (LES, DES, LBM, FW-H), and physical experiments (Acoustic Wind Tunnel Braunschweig, porous coatings, airfoil–turbulence interaction).

The result is a programme that can move between a tensor identity and a wind turbine blade without losing rigour — or between a Markov chain and a microphone array without losing contact with experiment.

Current position

RoleResearch Scientist
InstituteDLR — Inst. of Aerodynamics & Flow Technology
LocationBraunschweig, Germany
Since2023

Profiles

ScholarGoogle Scholar ↗
ResearchGateProfile ↗
GitHubSparsh-Sharma ↗
ORCID0009-0000-1911-9317 ↗
Research threads

Six connected problems

Leading-edge noise

Turbulence–airfoil interaction noise

Stochastic vortex particle methods for predicting broadband airfoil noise from incoming turbulence. Time-domain BEM validated against AWB experiments.

Explore →
Stochastic methods

ODT & stochastic turbulence modelling

One-dimensional turbulence as reduced-order model for jet self-noise. Markov-chain representations coupled to Lighthill's analogy with quantifiable uncertainty.

Explore →
Passive noise control

Porous materials for flow & noise control

How porous coatings and cross-sectional geometry control vortex shedding noise. Permeability, thickness, and shape as aeroacoustic design parameters.

Explore →
Lattice Boltzmann · 2026

Turbulence distortion & LBM simulations

Turbulence distortion and coherence effects in airfoil leading-edge noise via LBM. New computational frontier at DLR for mesoscopic aeroacoustics.

Explore →
Jet aeroacoustics

Turbulent jets & jet noise prediction

Self-similarity, far-downstream velocity statistics, and acoustic emission from turbulent round jets at high Reynolds number using ODT and LES.

Explore →
Wind energy

Wind turbine aeroacoustics

Turbulent inflow noise from wind turbine blades. AWB experiments, multi-scale LES, and noise prediction within DLR's HGF energy programme.

Explore →
Selected publications

Key papers

Full annotated list → Publications · Google Scholar · ResearchGate

2026
32nd AIAA/CEAS Aeroacoustics New · LBM
Turbulence distortion and coherence effects in airfoil leading-edge noise: insights from Lattice Boltzmann simulations
S. Sharma, M. Soni, A. Suryadi, M. Herr
A new computational frontier. LBM captures the mesoscopic-scale turbulence–airfoil coupling inaccessible to standard CFD — coherence effects and distortion physics that directly govern the broadband noise spectrum.
AIAA ↗
2025
11th Wind Turbine Noise Conf. · Copenhagen New
Multi-scale turbulence structures in grid-generated turbulence for leading-edge noise prediction
S. Sharma, A. Suryadi, M. Herr
LES + AWB wind-tunnel experiments showing how turbulence decays, distorts, and regenerates through airfoil interaction — linking multi-scale flow dynamics directly to SPL spectra and far-field directivity.
doi:10.11581/08042886-dea0-4511-b4bd-6c5403125735
DLR elib ↗
2020
Journal of Sound and Vibration
Stochastic modelling of leading-edge noise in time-domain using vortex particles
S. Sharma, E. Sarradj, H. Schmidt
The doctoral thesis core result. Establishes the time-domain stochastic vortex particle method as a viable, physics-based alternative to high-fidelity LES — methodologically novel, orders of magnitude cheaper.
doi:10.1016/j.jsv.2020.115656
Elsevier ↗
2019
Physical Review Fluids
Two-dimensional isotropic turbulent inflow conditions for the vortex particle method
S. Sharma, E. Sarradj
Foundational paper. Derives mathematically consistent isotropic turbulent inflow statistics for Lagrangian solvers — the bedrock on which the entire leading-edge noise programme rests.
doi:10.1103/PhysRevFluids.4.022701
APS ↗
Research programme

Turbulence as a mathematical object,
sound as its physical consequence

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 — stochastic Lagrangian methods, mesoscopic Lattice Boltzmann, one-dimensional turbulence, and high-fidelity LES, always anchored in experiment.

Stochastic methods

Markov chains, Langevin equations, Monte Carlo, ODT for turbulence statistics without resolving all scales.

Vortex particle / LBM

Lagrangian meshless Euler/N-S solvers. Lattice Boltzmann for mesoscopic turbulence–sound coupling.

High-fidelity CFD

LES, DES, DNS in OpenFOAM and SU2. Scale-resolving for aeroacoustic source analysis and model validation.

Acoustic analogies

Lighthill, FW-H, BEM. Connecting flow solutions to far-field radiation in a mathematically rigorous way.

Theme 01

Leading-edge noise & turbulence–airfoil interaction

When a turbulent flow meets an airfoil's leading edge, how do we predict the radiated sound without resolving every vortex?

This thread runs from my doctoral thesis through the most recent DLR work. I developed a stochastic, time-domain approach using vortex particles as both a turbulence representation and an acoustic source model. A Lagrangian description of turbulence — individual vortex elements with prescribed statistics — is physically transparent and computationally tractable.

The framework produces broadband noise predictions comparing well with high-fidelity LES at orders-of-magnitude lower cost, validated against AWB wind-tunnel measurements. A look-up table extension (DLR, 2024) scales this to operational aircraft and wind turbine applications.

The most recent work (WTN 2025, AIAA/CEAS 2026 LBM paper) explores turbulence distortion and coherence effects through airfoil interaction using Lattice Boltzmann simulations — a new frontier where mesoscopic physics governs the broadband noise spectrum.

Key methods
  • Lagrangian vortex particle method
  • Stochastic turbulence synthesis
  • Time-domain BEM
  • FW-H acoustic analogy
  • Lattice Boltzmann (ProLB / in-house)
  • LES (OpenFOAM) + AWB experiments
Representative papers
  • Sharma & Sarradj, Phys. Rev. Fluids (2019)
  • Sharma et al., JSV (2020)
  • Sharma & Herr, J. Phys.: Conf. Ser. (2024)
  • Sharma et al., WTN (2025)
  • Sharma, Soni et al., AIAA/CEAS (2026)
Theme 02

Stochastic turbulence modelling & one-dimensional turbulence

Can a one-dimensional stochastic process — retaining turbulent intermittency, energy cascades, and self-similarity — serve as a faithful reduced-order model for aeroacoustic prediction?

One-Dimensional Turbulence (ODT) directly simulates turbulent advection via stochastic rearrangement events on a 1D domain, preserving the physics of the energy cascade without 3D DNS cost. My DFG Walter Benjamin work at Cambridge applied ODT to turbulent jet dynamics: self-similar velocity statistics, far-downstream fluctuations, and acoustic emission via Lighthill's equation.

Key result (GAMM 2023): ODT-resolved acoustic sources at high Reynolds number — bridging the gap between low-order turbulence models and the acoustic field with explicit probabilistic treatment of source fluctuations and quantifiable uncertainty.

Key methods
  • One-dimensional turbulence (ODT)
  • Lighthill's acoustic analogy
  • Markov chain turbulence modelling
  • Statistical self-similarity analysis
  • FW-H post-processing
Key papers
  • Sharma, Klein & Schmidt, Phys. Fluids (2022)
  • Medina Méndez, Sharma et al., PAMM (2023)
  • Sharma, Ayton et al., INTER-NOISE (2023)
  • Sharma et al., GAMM (2023)
Collaborators
  • Prof. Marten Klein (BTU Cottbus)
  • Dr. Lorna Ayton (Cambridge)
  • Prof. Heiko Schmidt (BTU Cottbus)
Theme 03

Porous materials for passive flow & noise control

How do porous coating microstructure, thickness, permeability, and bluff-body cross-sectional geometry interact to suppress vortex shedding noise?

Porous coatings offer a passive, structurally simple route to noise control. Owls fly nearly silently using porous wing structures. The 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 show permeability and thickness act on distinct mechanisms. The optimal combination is Reynolds-number dependent. Recent work extends to non-circular cross-sections: trapezoidal cylinders (AIAA/CEAS 2023), and now square cylinders with porous coatings — both experimental and numerical investigations presented at AIAA/CEAS 2026. Applicable to airframe landing-gear and wind turbine strut noise.

Key methods
  • DES (SU2, OpenFOAM)
  • FW-H acoustic analogy
  • Porous medium modelling
  • Open-jet wind tunnel
  • Microphone array beamforming
Key papers
  • Sharma, Geyer & Arcondoulis, JSV (2023)
  • Geyer, Velpula & Sharma, AIAA/CEAS (2023)
  • Geyer, Sharma & Mousavi, AIAA/CEAS (2026)
  • Sharma & Geyer, AIAA/CEAS (2026)
  • Mousavi, Geyer & Sharma (in prep.)
Theme 04

High-fidelity CFD & AWB turbulence characterisation

How accurately can scale-resolving simulations predict aeroacoustic sources — and where do turbulence closures break down?

Complementing the modelling track: LES/DES in OpenFOAM and SU2, FW-H post-processing, and grid-generated turbulence simulations benchmarked against AWB wind-tunnel data. A dedicated study (STAB/DGLR 2024/2026) characterises airflow turbulence in the AWB using turbulence grids — providing the experimental ground truth against which all simulation approaches are validated.

Facility
  • Acoustic Wind Tunnel Braunschweig (AWB)
  • DLR Turbulence grid experiments
Key papers
  • Sharma, Suryadi & Herr, WTN (2025)
  • Sharma, Suryadi & Herr, AIAA/CEAS (2024)
  • Sharma et al., STAB/DGLR proc. (2026)
Academic work

Publications

Organized by year, annotated for significance. Live profiles: Google Scholar · ResearchGate · ORCID 0009-0000-1911-9317

35+Total publications
142+Citations
4,099+Reads
9Journal articles
7Journals reviewed for
2026
32nd AIAA/CEASNew · LBM

Turbulence distortion and coherence effects in airfoil leading-edge noise: insights from Lattice Boltzmann simulations

S. Sharma, M. Soni, A. Suryadi, M. Herr  — AIAA 2026-3445
New computational frontier. LBM captures mesoscopic turbulence–airfoil coupling inaccessible to standard CFD — coherence effects and distortion physics that govern the broadband noise spectrum directly.
32nd AIAA/CEASNew

Experimental investigation of aerodynamic noise and wake flow produced by porous-coated square cylinders

T.F. Geyer, S. Sharma, T. Mousavi  — AIAA 2026-3429
32nd AIAA/CEASNew

Numerical investigation of aerodynamic noise and wake flow produced by porous-coated square cylinders

S. Sharma, T.F. Geyer  — AIAA 2026-3430
Companion numerical study to the experimental paper above. DES + FW-H analysis of porous square cylinders, extending the permeability/geometry control framework to square cross-sections.
STAB/DGLR Symposium 2024 Proc.

Characterizing airflow turbulence in the Acoustic Wind Tunnel Braunschweig (AWB) using turbulence grids

S. Sharma, A. Suryadi, M. Herr  — Springer Nature, Vol. 156, pp. 447
Journal article (in prep.)

Turbulence distortion effects in airfoil leading-edge noise: insights from Lattice Boltzmann simulations

S. Sharma, A. Suryadi, M. Soni, M. Herr
Journal version of the AIAA/CEAS 2026 paper — extended analysis of turbulence distortion and coherence effects.
2025
11th Wind Turbine Noise Conf.

Multi-scale turbulence structures in grid-generated turbulence for leading-edge noise prediction

S. Sharma, A. Suryadi, M. Herr  — Copenhagen, pp. 430–440
LES + AWB experiments characterising how turbulence decays, distorts, and regenerates through airfoil interaction. Confirms grid-generated turbulence drives leading-edge noise through coherent-structure interactions.
doi:10.11581/08042886-dea0-4511-b4bd-6c5403125735
2024
J. Phys.: Conf. Ser.

Efficient prediction of turbulent inflow and leading-edge interaction noise using a vortex particle method with look-up table approach

S. Sharma, M. Herr
Extends the vortex particle framework to operational scale via a look-up table. Dramatically reduced wall time while preserving accuracy for wind turbine and aircraft inflow noise.
IOP Publishing link →
30th AIAA/CEAS

Assessment of turbulence modeling in Navier–Stokes simulations for grid-generated turbulence and airfoil interaction

S. Sharma, A. Suryadi, M. Herr  — AIAA 2024-3324
Comprehensive LES investigation assessing where turbulence models succeed and fail for aeroacoustic wind-tunnel predictions of airfoil–turbulence interaction noise.
2023
Journal of Sound and Vibration

On the influence of porous coating thickness and permeability on passive flow and noise control of cylinders

S. Sharma, T.F. Geyer, E.J.G. Arcondoulis  — JSV, p. 117563
Systematic parametric study isolating permeability and thickness as distinct aeroacoustic control parameters. Reynolds-number-dependent optimal coating design — a practically critical finding for airframe and strut noise.
doi:10.1016/j.jsv.2022.117563
PAMM

Toward the use of a reduced-order and stochastic turbulence model for assessment of far-field sound radiation: Low Mach number jet flows

J.A. Medina Méndez, S. Sharma, H. Schmidt, M. Klein  — PAMM 23(3):e202300186
doi:10.1002/pamm.202300186
GAMM Annual Meeting

Estimation of ODT-resolved acoustic sources in high Reynolds number turbulent jets

S. Sharma, L. Ayton, M. Klein, H. Schmidt
INTER-NOISE · Chiba, Japan

A theoretical study of self-noise generation in turbulent jets using one-dimensional turbulence and Lighthill's acoustic analogy

S. Sharma, L. Ayton, M. Klein, J.A. Medina Méndez  — pp. 282–293
AIAA AVIATION 2023

Experimental investigation of tonal noise radiation from trapezoidal cylinders in cross-flow

T.F. Geyer, R.V. Velpula, S. Sharma  — AIAA 2023-3496
Non-circular cross-sections at incidence 0°–180°. Strong geometry and angle-of-attack effects on tonal noise — opening new passive control strategies for bluff-body aeroacoustics.
2022
Physics of Fluids

Features of far-downstream asymptotic velocity fluctuations in a round jet: A one-dimensional turbulence study

S. Sharma, M. Klein, H. Schmidt  — Phys. Fluids 34
ODT reproduces self-similar turbulent statistics in the far field of a round jet — validating the stochastic framework as a physics-faithful alternative to DNS for jet noise source characterisation.
doi:10.1063/5.0101270
28th AIAA/CEAS · Southampton

Effect of coating thickness on aerodynamic noise reduction by porous-coated cylinders

S. Sharma, T.F. Geyer, E.J.G. Arcondoulis
28th AIAA/CEAS · Southampton

Numerical investigation of aerodynamic noise generation by porous-coated staggered cylinders

S. Sharma, T.F. Geyer
2021
Frequenz

Schallminderung an einer umströmten Tandem-Zylinder-Anordnung durch poröses Material

T.F. Geyer, S. Sharma, B. Calisci  — Frequenz 101–103
German-language journal publication on noise reduction in tandem cylinder arrangements using porous material — connects computational and experimental work with industrial noise-reduction practice.
Applied Acoustics

Effect of geometric parameters on the noise generated by rod–airfoil configuration

S. Sharma, T.F. Geyer, J. Giesler  — Appl. Acoustics 177:107908
AIAA AVIATION 2021

Experimental validation of a lower-order model for leading-edge noise based on vortex method

S. Sharma, T.F. Geyer, E. Sarradj, H. Schmidt
AIAA AVIATION 2021

Modelling turbulent jets at high Reynolds number using one-dimensional turbulence

S. Sharma, M. Klein, H. Schmidt
2019–2020
Journal of Sound and Vibration

Stochastic modelling of leading-edge noise in time-domain using vortex particles

S. Sharma, E. Sarradj, H. Schmidt  — JSV 488:115656
Core doctoral thesis result. Methodologically novel: a physics-based time-domain stochastic framework that rivals LES for leading-edge noise prediction at a fraction of the cost.
doi:10.1016/j.jsv.2020.115656
Physical Review Fluids

Two-dimensional isotropic turbulent inflow conditions for the vortex particle method

S. Sharma, E. Sarradj  — Phys. Rev. Fluids 4:022701
Foundational paper. Derives mathematically consistent 2D isotropic turbulent inflow statistics for Lagrangian solvers — the bedrock on which the entire vortex particle noise programme rests.
doi:10.1103/PhysRevFluids.4.022701
25th AIAA/CEAS · Reston

Numerical investigation of noise generation by rod–airfoil configuration using DES (SU2) and the FW-H analogy

S. Sharma, T.F. Geyer, E. Sarradj, H. Schmidt
2017–2018
AIAA/CEAS 2018

Detached Eddy Simulation of the flow noise generation of cylinders with porous cover

T.F. Geyer, S. Sharma, E. Sarradj
ISUAAAT15 · Oxford

Fluctuating inflow condition for time-domain BEM for airfoil–turbulence interaction noise

S. Sharma, E. Sarradj  — 2018
INTER-NOISE 2018 · Chicago

Time domain BEM for leading-edge noise subjected to linear vorticity

S. Sharma, T.F. Geyer, E. Sarradj
DAGA 2018 · Munich

Low-fidelity stochastic approach for airfoil–turbulence interaction noise

S. Sharma, E. Sarradj, H. Schmidt  — pp. 1184–1187
DAGA 2017 · Kiel

Unsteady lift due to the interaction of incidence turbulence with an airfoil

S. Sharma, E. Sarradj, H. Schmidt
Doctoral thesis
BTU Cottbus · summa cum laude · Best Thesis 2020

Stochastic modelling of leading-edge noise in time-domain using vortex particles

S. Sharma — Supervisors: Prof. E. Sarradj, Prof. H. Schmidt, Prof. U. Harlander
Best Thesis Prize 2020 (BTU) and DEGA Young Scientist Award 2019. Open access via BTU repository.
doi:10.26127/BTUOpen-5085
Talks & presentations

Scientific talks

Invited seminars
Mathematics of noise in a turbulent jet
Applied Mathematics Seminar · University of Cambridge, UK · 2022
Invited
Dominant sources of noise in a turbulent jet
Fluid Mechanics Seminar · University of Cambridge, UK · 2022
Invited
Lighthill's equation and one-dimensional turbulence
Research Seminar · TU Berlin, Germany · 2021
Invited
Stochastic modelling of leading-edge noise
Aeroacoustics Seminar · IIT Jammu, India · 2019
Invited
Conference presentations
Turbulence distortion and coherence effects in airfoil leading-edge noise: LBM simulations
32nd AIAA/CEAS Aeroacoustics Conference · 2026
Conference
Multi-scale turbulence structures in grid-generated turbulence for leading-edge noise prediction
11th Wind Turbine Noise Conference · Copenhagen, Denmark · June 2025
Conference
Assessment of turbulence modeling for grid-generated turbulence and airfoil interaction
30th AIAA/CEAS Aeroacoustics Conference · 2024
Conference
Self-similarity indications in a turbulent jet using one-dimensional turbulence
14th European Fluid Mechanics Conference (EFMC14) · Athens · 2022
Conference
Fluctuating inflow condition for time-domain BEM for airfoil–turbulence interaction noise
ISUAAAT15 · University of Oxford, UK · 2018
Conference
Fluctuating inflow condition for time-domain BEM for airfoil–turbulence interaction noise
XNOISE · TU Vienna, Austria · 2017
Conference
Leading-edge noise
Engineering Acoustics Seminar · TU Berlin, Germany · 2017
Seminar
Open code & tools

Software & code

Research code on github.com/Sparsh-Sharma

Turbulence synthesis

Synthetic Vorton Turbulence Model

PythonNumPy

Generates turbulent inflow conditions using Gaussian-profile vortex elements. Produces statistically correct energy spectra for leading-edge noise simulations.

GitHub →
Lagrangian solver

Vortex Particle Solver (Euler Equations)

PythonC++

2D meshless Lagrangian solver for incompressible Euler equations. Core tool for the time-domain leading-edge noise framework.

GitHub →
Aeroacoustics

FW-H Acoustic Solver

Python

Computes directivity patterns of radiated sound pressure from airfoils using the Ffowcs Williams–Hawkings acoustic analogy.

GitHub →
Reproducibility

I believe computational results should be reproducible. Where journal policies and data agreements permit, code and data associated with published papers are made available. If you are trying to reproduce a specific result and cannot find the code, get in touch.

About

Dr. Sparsh Sharma

DLR Braunschweig
Research Scientist
Inst. of Aerodynamics & Flow Technology
Awards & Fellowships
DFG Walter Benjamin Fellowship2021
Young Scientist Award — DEGA2019
Best Thesis Award — BTU2020
Revathy Iyer Award — SAE2014
NASA N+3 Best Design2013
Research areas
TurbulenceAeroacousticsVortex methodsLES / DESLBMODTStochastic modellingLeading-edge noisePorous materialsJet noiseWind energyFW-HBEM
Reviewer for
  • Journal of Fluid Mechanics
  • Physics of Fluids
  • Journal of Sound and Vibration
  • Aerospace Science and Technology
  • Applied Acoustics
  • Int. J. Heat and Fluid Flow
  • J. Wind Eng. & Ind. Aerodyn.
Memberships
  • AIAA
  • DEGA
  • SAE
Supervised theses
  • B. Calisci (2020) — vortex shedding noise
  • S. Kumar (2021) — SGS modelling, jet noise
  • N. Krishna (2021) — wall-mounted wings
  • D. Bhatt (2022) — fan aeroacoustics

Scientific biography

I am a research scientist at the Institute of Aerodynamics and Flow Technology at DLR Braunschweig, working in the aeroacoustics: 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 mathematically, without losing physical meaning? This has led me to develop Lagrangian vortex particle methods, stochastic Markov-chain turbulence models, one-dimensional turbulence (ODT) approaches, and most recently Lattice Boltzmann simulations — each chosen for what it reveals about the turbulence–sound coupling, not for computational fashion.

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 Dr. Lorna Ayton and Prof. Marten Klein. The Cambridge period deepened the 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 — recognised with the BTU Best Thesis Award 2020 and DEGA Young Scientist Award 2019. I worked at Rolls-Royce Aeroengines UTC on applied aeroacoustics between academic positions.

2023–now
Current
Research Scientist, DLR Braunschweig
Wind turbine & aircraft aeroacoustics. AWB experiments, LES, LBM, stochastic methods.
2021–2023
DFG Walter Benjamin Fellow, University of Cambridge
DAMTP. Jet noise, ODT, Lighthill's analogy. With Dr. Ayton & Prof. Klein.
2020–2021
Postdoctoral Researcher, BTU Cottbus
Jet turbulence modelling (ODT) with Prof. Schmidt.
2016–2019
PhD (summa cum laude), BTU Cottbus & TU Berlin
Stochastic vortex particle method for leading-edge noise. Best Thesis 2020. DEGA Award 2019.
Teaching

Computational Fluid Dynamics (advanced postgraduate lecture course), BTU Cottbus — winter terms 2020/21, 2021/22, 2022/23. Workshop on CFD and Aeroacoustic Analogies (with Dr. T. Geyer), December 2020. Teaching assistant, Engineering Mathematics, BTU Cottbus, 2020–2021.

Get in touch

Contact

Based at DLR Braunschweig. Always open to scientific discussion, collaboration proposals, or speaking invitations.

Open to collaboration on

  • Turbulence–structure interaction noise (airfoil, blade, landing gear)
  • Wind energy aeroacoustics and noise mitigation
  • Stochastic turbulence modelling and reduced-order methods
  • Lattice Boltzmann methods for aeroacoustics
  • Machine learning emulators for turbulent noise
  • Porous material design for flow and noise control
  • High-fidelity LES/DNS for noise source characterisation

For prospective students

Interested in a master's thesis or internship in computational aeroacoustics or turbulence modelling? Send a brief note on your background and interests with your CV. Strong mathematical background and Python or C++ experience are particularly welcome.