The main goals of the PhD will be twofold:
- Research on sound numerical simulation methods for the extension of the present model to non-neutral atmospheric stratification and including vegetation and terrain effects. This also includes the development of specific model reduction techniques for these simulation types to guarantee reasonable calculation times in view of the future development of an operational model.
- The validation of the numerical simulations by acquiring a sound and complete experimental dataset around a nuclear facility.
Model development and numerical simulations
The simulation framework that will be used for this PhD-study is a state-of-the-art Pseudo-spectral LES solver SP-WIND, which is currently under development at KU Leuven . This higher-order CFD code is optimized for the fast and accurate evaluation of the wind field in the atmospheric boundary layer under different types of stratification. As such, the code is ideal for the characterization of the wind field and the continuation of the dispersion research at the near-range. However, as a first step, SP-WIND needs to be extended to be able to simulate the transport of radioactive gases and to account for vegetation and terrain effects. Secondly, continued research on model reduction is essential to account for these new developments. Finally, an optimal estimation methodology for the model parameters needs to be established to simulate the transition between different weather patterns using the reduced model. It goes without saying that a comprehensive literature survey is essential to complete each of these steps successfully.
Validation of numerical simulations will be a key issue to convince the CFD and nuclear community to further develop and use these type of models for performing assessments in the near range. The collection of a high quality dataset is in this context very important. The release of small but measureable quantities (below any health concern) of radionuclides from nuclear installations such as the BR1 (Ar-41) and the Institute of Radioelements (Xe-133, ..) can be used to create such a dataset. Particularly interesting of these installation is that release quantities are well known and existing fence monitoring around these installations gives continuously data on radionuclide fluency rates and/or dose rates. Additional detectors (radiological and meteorological) could be deployed to complete the dataset. However, thorough calibration (cloud calibration) and data collection issues (representative data and locations, …) should be resolved. The calibration of a "small" detector in combination with a large and irregular 3D source (the cloud) is challenging. Monte Carlo simulations, often used as part of detector calibrations for specific geometries, are extremely inefficient in such a case. Apart from testing the models developed within the context of this Ph.D., this dataset will be unique for the international community to test atmospheric dose and dispersion models at the near range.
 Meyers, J. and Meneveau, C., “Optimal turbine spacing in fully developed wind-farm boundary layers,” Wind Energy,Vol. 15, 2012, pp. 305–317.