EURAD2-HERMES

EURAD-HERMES aims at the development of high-fidelity numerical models for simulations of strongly coupled THMC processes in repository near-field, repository design optimisation and interpretation of mock up experiments using a combination of physics-based models and accelerated computing assisted with machine learning and artificial intelligence (Churakov et al. 2024, Prasianakis et al. 2025). Within the HERMES framework, novel numerical schemes are being developed and tested against benchmarks as well as automated workflows are introduced, forming the link to the ModelHub .

References

• S. V. Churakov et al. (2024): Position paper on high fidelity simulations for coupled processes, multi-physics and chemistry in geological disposal of nuclear waste. Environmental Earth Sciences, No. 17, vol. 83, p. 521, DOI:10.1007/s12665-024-11832-7, Publisher link Copy BibTeX

  @article{Churakov2024,
   author = {Churakov, S. V. and Claret, F. and Idiart, A. and Jacques, D. and Govaerts, J. and Kolditz, O. and Prasianakis, N. I. and Samper, J.},
   doi = {10.1007/s12665-024-11832-7},
   journal = {Environmental Earth Sciences},
   number = {17},
   pages = {521},
   title = {Position paper on high fidelity simulations for coupled processes, multi-physics and chemistry in geological disposal of nuclear waste},
   url = {https://link.springer.com/article/10.1007/s12665-024-11832-7},
   volume = {83},
   year = {2024}
  }

• N. I. Prasianakis et al. (2025): Geochemistry and machine learning: methods and benchmarking. Environmental Earth Sciences, No. 5, vol. 84, p. 121, DOI:10.1007/s12665-024-12066-3 Copy BibTeX

  @article{Prasianakis2025,
   author = {Prasianakis, N. I. and Laloy, E. and Jacques, D. and Meeussen, J. C. L. and Miron, G. D. and Kulik, D. A. and Idiart, A. and Demirer, E. and Coene, E. and Cochepin, B. and Leconte, M. and Savino, M. E. and Samper-Pilar, J. and De Lucia, M. and Churakov, S. V. and Kolditz, O. and Yang, C. and Samper, J. and Claret, F.},
   doi = {10.1007/s12665-024-12066-3},
   journal = {Environmental Earth Sciences},
   number = {5},
   pages = {121},
   title = {Geochemistry and machine learning: methods and benchmarking},
   url = {https://doi.org/10.1007/s12665-024-12066-3},
   volume = {84},
   year = {2025}
  }

• Mostafa Mollaali et al. (2025): Variational Phase-Field Fracture Approach in Reactive Porous Media. International Journal for Numerical Methods in Engineering, No. 1, vol. 126, p. e7621, DOI:10.1002/nme.7621 Copy BibTeX

  @article{Mollaali2025,
   author = {Mollaali, Mostafa and Yoshioka, Keita and Lu, Renchao and Montoya, Vanessa and Vilarrasa, Victor and Kolditz, Olaf},
   doi = {10.1002/nme.7621},
   journal = {International Journal for Numerical Methods in Engineering},
   number = {1},
   pages = {e7621},
   title = {Variational Phase-Field Fracture Approach in Reactive Porous Media},
   url = {https://doi.org/10.1002/nme.7621},
   volume = {126},
   year = {2025}
  }