Rincón, M. J., Caspersen, A., Thorenfeldt Ingwersen, N., Reclari, M.
& Abkar, M. (2024).
Flow investigation of two-stand ultrasonic flow meters in a wide dynamic range by numerical and experimental methods. Flow Measurement and Instrumentation,
96, Article 102543.
https://doi.org/10.1016/j.flowmeasinst.2024.102543
E. S. Chen, P., Bin, Y., I. A. Yang, X., Shi, Y.
, Abkar, M. & I. Park, G. (2023).
A priori screening of data-enabled turbulence models. Physical Review Fluids,
8(12), Article 124606.
https://doi.org/10.1103/PhysRevFluids.8.124606
Legaard, C. M., Schranz, T., Schweiger, G., Drgona, J., Falay, B.
, Gomes, C., Iosifidis, A., Abkar, M. & Larsen, P. G. (2023).
Constructing Neural Network Based Models for Simulating Dynamical Systems.
ACM Computing Surveys,
55(11), 1-34. Article 236.
https://doi.org/10.1145/3567591
Amarloo, A., Cinnella, P.
, Iosifidis, A., Forooghi, P. & Abkar, M. (2023).
Data-driven Reynolds stress models based on the frozen treatment of Reynolds stress tensor and Reynolds force vector.
Physics of Fluids,
35(7), Article 075154.
https://doi.org/10.1063/5.0160977
Rincón, M. J., Amarloo, A., Reclari, M., I.A. Yang, X.
& Abkar, M. (2023).
Progressive augmentation of Reynolds stress tensor models for secondary flow prediction by computational fluid dynamics driven surrogate optimisation.
International Journal of Heat and Fluid Flow,
104, Article 109242.
https://doi.org/10.1016/j.ijheatfluidflow.2023.109242
Eidi, A.
, Zehtabiyan-Rezaie, N., Chiassi, R., Yang, X. I. A.
& Abkar, M. (2022).
Data-driven quantification of model-form uncertainty in Reynolds-averaged simulations of wind farms.
Physics of Fluids,
34(8), Article 085135.
https://doi.org/10.1063/5.0100076
D. Huang, X. L. ., Jain, N.
, Abkar, M., Kunz, R. F. & Yang, X. I. A. (2021).
Determining a priori a RANS model’s applicable range via global epistemic uncertainty quantification.
Computers & Fluids,
230, Article 105113.
https://doi.org/10.1016/j.compfluid.2021.105113