About

Charles Meneveau is the Louis M. Sardella Professor in Mechanical Engineering and an associate director of the Institute for Data-Intensive Science and Engineering (IDIES) at Johns Hopkins University. His expertise lies in the multiscale aspects of turbulence, large-eddy simulations, and wind farm fluid dynamics. He employs computational and theoretical tools for his research, focusing on subgrid-scale modeling, downscaling methods, fractal geometry, and their applications to large eddy simulation (LES). His research has advanced the understanding of small-scale motions in turbulent flows and led to the development of sophisticated subgrid-scale models, such as the Lagrangian dynamic model, which has been implemented in various research and open-source CFD codes like OpenFoam. Currently, his LES research aims to improve wall models and subgrid-scale models for velocity gradients, with applications in turbulent multiphase flows.

Research topics

• Computer Science
• Physics
• Artificial Intelligence
• Mechanics
• Classical mechanics
• Mathematics
• Meteorology
• Simulation
• Risk analysis (engineering)
• Telecommunications
• Medicine
• Materials science
• Mathematical optimization
• Ecology
• Statistical physics
• Geology
• Theoretical physics
• Algorithm
• Thermodynamics

Selected publications

• Reconstructing velocity and pressure from sparse noisy particle tracks using Physics-Informed Neural Networks

  arXiv (Cornell University) · 2022 · 6 citations

  • Computer Science
  • Artificial Intelligence
  • Physics

  Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid mechanics. However, reconstructing the full and structured Eulerian velocity and pressure fields from sparse and noisy particle tracks obtained experimentally remains a significant challenge. We introduce a new method for this reconstruction, based on Physics-Informed Neural Networks (PINNs). The method uses a Neural Network regularized by the Navier-Stokes equations to interpolate the velocity data and simultaneously determine the pressure field. We compare this approach to the state-of-the-art Constrained Cost Minimization method [1]. Using data from direct numerical simulations and various types of synthetically generated particle tracks, we show that PINNs are able to accurately reconstruct both velocity and pressure even in regions with low particle density and small accelerations. PINNs are also robust against increasing the distance between particles and the noise in the measurements, when studied under synthetic and experimental conditions.

  PublisherOA PDFDOI

• A vortex sheet based analytical model of the curled wake behind yawed wind turbines

  Journal of Fluid Mechanics · 2021 · 60 citations

  Senior authorCorresponding
  • Mechanics
  • Physics
  • Classical mechanics

  Motivated by the need for compact descriptions of the evolution of non-classical wakes behind yawed wind turbines, we develop an analytical model to predict the shape of curled wakes. Interest in such modelling arises due to the potential of wake steering as a strategy for mitigating power reduction and unsteady loading of downstream turbines in wind farms. We first estimate the distribution of the shed vorticity at the wake edge due to both yaw offset and rotating blades. By considering the wake edge as an ideally thin vortex sheet, we describe its evolution in time moving with the flow. Vortex sheet equations are solved using a power series expansion method, and an approximate solution for the wake shape is obtained. The vortex sheet time evolution is then mapped into a spatial evolution by using a convection velocity. Apart from the wake shape, the lateral deflection of the wake including ground effects is modelled. Our results show that there exists a universal solution for the shape of curled wakes if suitable dimensionless variables are employed. For the case of turbulent boundary layer inflow, the decay of vortex sheet circulation due to turbulent diffusion is included. Finally, we modify the Gaussian wake model by incorporating the predicted shape and deflection of the curled wake, so that we can calculate the wake profiles behind yawed turbines. Model predictions are validated against large-eddy simulations and laboratory experiments for turbines with various operating conditions.

  DOI

• Two-point stress–strain-rate correlation structure and non-local eddy viscosity in turbulent flows

  Journal of Fluid Mechanics · 2021 · 42 citations

  Senior authorCorresponding
  • Mechanics
  • Materials science
  • Physics

  Abstract

  DOI

• A mathematical framework for estimating risk of airborne transmission of COVID-19 with application to face mask use and social distancing

  Physics of Fluids · 2020 · 141 citations

  • Computer Science
  • Artificial Intelligence
  • Physics

  A mathematical model for estimating the risk of airborne transmission of a respiratory infection such as COVID-19 is presented. The model employs basic concepts from fluid dynamics and incorporates the known scope of factors involved in the airborne transmission of such diseases. Simplicity in the mathematical form of the model is by design so that it can serve not only as a common basis for scientific inquiry across disciplinary boundaries but it can also be understandable by a broad audience outside science and academia. The caveats and limitations of the model are discussed in detail. The model is used to assess the protection from transmission afforded by face coverings made from a variety of fabrics. The reduction in the transmission risk associated with increased physical distance between the host and susceptible is also quantified by coupling the model with available and new large eddy simulation data on scalar dispersion in canonical flows. Finally, the effect of the level of physical activity (or exercise intensity) of the host and the susceptible in enhancing the transmission risk is also assessed.

  DOI

Recent grants

• Dynamics of macro-vortices in horizontal axis turbine wind farms

  NSF · $400k · 2020–2024

• Multiscale Interactions in Turbulent Flows: Experiments and Simulation

  NSF · $335k · 2006–2010

• CDS&E: Studying Multiscale Fluid Turbulence via Open Numerical Laboratories

  NSF · $380k · 2015–2019

• Frameworks: Advanced Cyberinfrastructure for Sustainable Community Usage of Big Data from Numerical Fluid Dynamics Simulations

  NSF · $4.0M · 2021–2027

• Studying turbulent scale and space interactions using active grid wind tunnel and DNS database experiments

  NSF · $290k · 2010–2014

Frequent coauthors

• M. B. Parlange

  University of Rhode Island

  125 shared

• Dennice F. Gayme

  63 shared

• Johan Meyers

  KU Leuven

  56 shared

• Joseph Katz

  Johns Hopkins University

  51 shared

• Richard J. A. M. Stevens

  Max Planck Society

  40 shared

• Marcelo Chamecki

  University of California, Los Angeles

  37 shared

• Tamer A. Zaki

  Johns Hopkins University

  36 shared

• Luciano Castillo

  Purdue University West Lafayette

  36 shared

Education

• PhD, Mechanical Engineering

  Yale University

  1989

Awards & honors

• Member of the National Academy of Engineering

Similar researchers at Johns Hopkins University

• Richard Huganirh-210
• Steven L. Salzbergh-191
• Arturo Casadevallh-152
• Peter Campochiaroh-139
• Alan Yuilleh-134