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Overview

The PPMLES (Perturbed-Parameter ensemble of MUST Large-Eddy Simulations) dataset contains the main outputs of 200 large-eddy simulations (LES) of microscale pollutant dispersion that replicate the MUST field experiment1 for varying meteorological forcing parameters.

The goal of the PPMLES dataset is to provide a comprehensive dataset to better understand the complex interactions between the atmospheric boundary layer (ABL), the urban environment, and pollutant dispersion. It was originally used to assess the impact of the meteorological uncertainty on microscale pollutant prediction and to build a surrogate model that can replace the costly LES model2. The total computational cost of the PPMLES dataset is estimated to be about 6 million core hours.

For each sample of meteorological forcing parameters (inlet wind direction and friction velocity), the AVBP solver code34 was used to perform LES at very high spatio-temporal resolution (1e-3s time step, 30cm discretization length) to provide a fine representation of the pollutant concentration and wind velocity statistics within the urban-like canopy.

In particular, the PPMLES dataset includes:

  • the time-averaged pollutant concentration (c) and fluctuations (crms) fields for each sample,
  • the time-averaged wind velocity components (u, v, w) and turbulent kinetic energy fields for each sample,
  • the estimated aleatory uncertainty of each field,
  • the time series of the pollutant concentration (c) and wind velocity components (u, v, w) predicted for each sample at 93 locations.
Figure 1: Examples of pollutant concentration and wind velocity fields for two samples of the dataset

How to cite?

Lumet, E., Jaravel, T., and Rochoux, M. C. (2024). PPMLES – Perturbed-Parameter ensemble of MUST Large-Eddy Simulations. Dataset. Zenodo. DOI: 10.5281/zenodo.11394347.

@misc{lumet2024data,
    author = {Lumet, Eliott and Jaravel, Thomas and Rochoux, M{\'e}lanie C.},
    title = {{PPMLES – Perturbed-Parameter ensemble of MUST Large-Eddy Simulations}},
    Howpublished = {Dataset. Zenodo},
    year = {2024},
    doi = {10.5281/zenodo.11394347}}

  1. Yee, E., and Biltoft, C. A. (2004). Concentration fluctuation measurements in a plume dispersing through a regular array of obstacles. Boundary-Layer Meteorology, 111(3): 363-415. DOI: 10.1023/B:BOUN.0000016496.83909.ee ↩︎

  2. Lumet, E., Rochoux, M. C., Jaravel, T., and Lacroix, S. (2024). Uncertainty-Aware Surrogate Modeling for Urban Air Pollutant Dispersion Prediction. Submitted to Building and Environment. Preprint URL: https://ssrn.com/abstract=4920879 . Accessed: 2024-08-30. ↩︎

  3. Schonfeld, T. and Rudgyard, M. (1999). Steady and unsteady flow simulations using the hybrid flow solver AVBP. AIAA journal, 37(11):1378–1385. DOI: 10.2514/2.636. ↩︎

  4. Gicquel, L. Y., Gourdain, N., Boussuge, J.-F., Deniau, H., Staffelbach, G., Wolf, P., and Poinsot, T. (2011). High performance parallel computing of flows in complex geometries. Comptes Rendus Mécanique, 339(2):104–124. DOI: 10.1016/j.crme.2010.11.006. ↩︎