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Enabling probabilistic retrospective transport modeling for accurate source detection.

W Steven Rosenthal1, Paul W Eslinger1, Brian T Schrom1

  • 1Pacific Northwest National Laboratory, MSIN K7-90, 902 Battelle Boulevard, Richland, WA, 99354, USA.

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A new surrogate model accelerates radionuclide transport simulations, improving the prediction of atmospheric emissions and background levels. This enhances source identification and atmospheric monitoring capabilities.

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Area of Science:

  • Atmospheric Science
  • Nuclear Science
  • Computational Modeling

Background:

  • Predicting radionuclide emissions is computationally intensive, requiring extensive atmospheric transport model (ATM) simulations.
  • Current methods face limitations due to the high effort and time needed for complex gas/aerosol transport modeling.

Purpose of the Study:

  • To develop a high-performance surrogate model to accelerate ATM simulations for radionuclide transport.
  • To enhance the efficiency of predicting radionuclide source emissions and atmospheric background levels.

Main Methods:

  • Developed a surrogate model, the Atmospheric Transport Model Surrogate (ATaMS), for the HYSPLIT4 ATM.
  • Employed model reduction, code optimization, and high-performance computing for accelerated simulations.
  • Utilized a pre-computed grid of short-duration transport simulations as surrogate model parameters.

Main Results:

  • The ATaMS surrogate model significantly accelerates transport simulations compared to direct HYSPLIT4 simulations.
  • The model efficiently predicts radionuclide plume paths and identifies potential contributing sources.
  • Demonstrated improved scaling on high-performance computing systems.

Conclusions:

  • The ATaMS provides a computationally efficient tool for radionuclide transport modeling.
  • This surrogate modeling approach accelerates workflows for probabilistic source prediction.
  • Enables more efficient estimation of radionuclide atmospheric background concentrations.