Publications
Journals
- Kruljevic, B., Doan, N.A.K., Breda, P., Pfitzner, M., Langella, I. (2023). A priori and a posteriori analysis of flamelet modeling for large-eddy simulations of a non-adiabatic backward-facing step. Physics of Fluids. [https://pubs.aip.org/aip/pof/article/35/5/055114/2887954/A-priori-and-a-posteriori-analysis-of-flamelet]
- Shahzad, H., Hickel, S. and Modesti, D. (2023). Turbulence and added drag over acoustic liners.. Journal of Fluid Mechanics 965 (2023): A10.. [https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/turbulence-and-added-drag-over-acoustic-liners/42D25924000FC25F8158E5594A3D8208]
- Bernardini, M., Modesti, D., Salvadore. F., Sathyanarayana, S., Della Posta, G. and Pirozzoli, S. (2023). STREAmS-2.0: Supersonic turbulent accelerated Navier-Stokes solver version 2.0.. Computer Physics Communications 285 (2023): 108644.. [https://www.sciencedirect.com/science/article/pii/S0010465522003630]
- Pirozzoli, S. and Modesti, D. (2023). "Direct numerical simulation of one-sided forced thermal convection in plane channels. .Journal of Fluid Mechanics 957 (2023): A31.. [https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/direct-numerical-simulation-of-onesided-forced-thermal-convection-in-plane-channels/10F46B236A056CB9A056AEB2DC06C0BD]
- Barner, N.M., Ghafourpour, L.D., Güverte, M.S., Modesti, D. and Hulshoff, S. J. (2022). Climate Impact Mitigation Potential of Novel Aircraft Features. Aerospace — Open Access Aeronautics and Astronautics Journal. [https://research.tudelft.nl/en/publications/climate-impact-mitigation-potential-of-novel-aircraft-features]
- Modesti, D. and Pirozzoli, S. (2022). Direct numerical simulation of forced thermal convection in square ducts up to. Journal of Fluid Mechanics. [https://research.tudelft.nl/en/publications/direct-numerical-simulation-of-forced-thermal-convection-in-squar]
- Modesti, D., Sathyanarayana, S., Salvadore, F. and Bernardini, M. (2022). Direct numerical simulation of supersonic turbulent flows over rough surfaces. Journal of Fluid Mechanics. [https://research.tudelft.nl/en/publications/direct-numerical-simulation-of-supersonic-turbulent-flows-over-ro]
- Shahzad, H., Hickel, S. & Modesti, D. (2022). Permeability and Turbulence Over Perforated Plates. Flow, Turbulence and Combustion. [https://research.tudelft.nl/en/publications/permeability-and-turbulence-over-perforated-plates]
- Chantriaux, F., Quenouille, T., Doan, N.A.K., Swaminathan, N., Hardalupas, Y. and Taylor, A.M.K.P. (2022). Multiscale analysis of turbulence-flame interaction based on measurements in premixed flames. Combustion and Flame. [https://research.tudelft.nl/en/publications/multiscale-analysis-of-turbulence-flame-interaction-based-on-meas]
- Jigjid, K., Minamoto, Y., Khoa Doan, N.A. and Tanahashi, M. (2022). SGS Reaction rate modelling for MILD combustion based on machine-learning combustion mode classification: Development and a priori study. Proceedings of the Combustion Institute. [https://research.tudelft.nl/en/publications/sgs-reaction-rate-modelling-for-mild-combustion-based-on-machine-]
- Sitte, M.P. & Doan, N.A.K. (2022). Velocity reconstruction in puffing pool fires with physics-informed neural networks. Physics of Fluids. [https://research.tudelft.nl/en/publications/velocity-reconstruction-in-puffing-pool-fires-with-physics-inform]
- Modesti, D., Endrikat, S., Hutchins, N. and Chung, D. (2021). Dispersive stresses in turbulent flow over riblets. Journal of Fluid Mechanics. [https://research.tudelft.nl/en/publications/dispersive-stresses-in-turbulent-flow-over-riblets]
- Yang, R., Modesti, D., Zhao, Y.X., Wang, Q.C., Wang, Z.G. and Pirozzoli, S. (2021). Influence of corner angle in streamwise supersonic corner flow. Physics of Fluids. [https://research.tudelft.nl/en/publications/influence-of-corner-angle-in-streamwise-supersonic-corner-flow]
- Iavarone, S., Péquin, A., Chen, Z.X., Doan, N.A.K., Swaminathan, N. and Parente, A. (2021). An a priori assessment of the Partially Stirred Reactor (PaSR) model for MILD combustion. Proceedings of the Combustion Institute. [https://research.tudelft.nl/en/publications/an-a-priori-assessment-of-the-partially-stirred-reactor-pasr-mode]
- Lesjak, M. and Doan, N.A.K. (2021). Chaotic systems learning with hybrid echo state network/proper orthogonal decomposition based model. Data-Centric Engineering. [https://research.tudelft.nl/en/publications/chaotic-systems-learning-with-hybrid-echo-state-networkproper-ort]
- Doan, N.A.K., Bansude, S., Osawa, K., Minamoto, Y., Lu, T., Chen, J.H. and Swaminathan, N. (2021). Identification of combustion mode under MILD conditions using Chemical Explosive Mode Analysis. Proceedings of the Combustion Institute. [https://research.tudelft.nl/en/publications/identification-of-combustion-mode-under-mild-conditions-using-che]
- Tathawadekar, N., Doan, N.A.K., Silva, C.F. & Thuerey, N. (2021). Modeling of the nonlinear flame response of a Bunsen-type flame via multi-layer perceptron. Proceedings of the Combustion Institute. [https://research.tudelft.nl/en/publications/modeling-of-the-nonlinear-flame-response-of-a-bunsen-type-flame-v]
- Doan, N.A.K., Polifke, W. & Magri, L. (2021). Short- And long-term predictions of chaotic flows and extreme events: A physics-constrained reservoir computing approach. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. [https://research.tudelft.nl/en/publications/short-and-long-term-predictions-of-chaotic-flows-and-extreme-even]
- Bernardini, M., Modesti, D., Salvadore, F. and Pirozzoli, S. (2020). STREAmS: A high-fidelity accelerated solver for direct numerical simulation of compressible turbulent flows. Computer Physics Communications. [https://research.tudelft.nl/en/publications/streams-a-high-fidelity-accelerated-solver-for-direct-numerical-s]
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Doan, N.A.K., Polifke, W., Magri, L. (2020). Physics-informed echo state networks. Journal of Computation Science. [https://www.sciencedirect.com/science/article/pii/S1877750320305408]
Conferences
- Niebler, K., Bonnaire, P., Doan, N. A. K., Silva, C.F. (2022). Towards reconstruction of acoustic fields via physics-informed neural networks. Internoise 2022. [https://www.researchgate.net/publication/372418893_Towards_reconstruction_of_acoustic_fields_via_physics-informed_neural_networks]
- Doan, N.A.K., Polifke, W., Magri, L. (2020). Learning hidden states in a chaotic system: A physics-informed echo state network approach. Computational Science – ICCS 2020 - 20st International Conference (online). [https://link.springer.com/chapter/10.1007/978-3-030-50433-5_9]
- Doan, N.A.K., Polifke, W. and Magri, L., Paszynski, M., Kranzlmüller, D., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (2021). Auto-Encoded Reservoir Computing for Turbulence Learning. Computational Science – ICCS 2021 - 21st International Conference, Krakow, Poland. [https://research.tudelft.nl/en/publications/auto-encoded-reservoir-computing-for-turbulence-learning]
- Tathawadekar, N., Silva, C., Sitte, M.P., Doan, N.A.K. (2022). Physical quantities reconstruction in reacting flows with deep learning. Internoise 2022. [https://www.ingentaconnect.com/contentone/ince/incecp/2023/00000265/00000006/art00074]
Others
- Doan, N. A. K. (2022). Direct numerical simulations of flameless combustion. Fundamentals of Low Emission Flameless Combustion and Its Applications. [https://www.sciencedirect.com/science/article/pii/B9780323852449000022]
- Minamoto, Y., Doan, N. A.K. and Swaminathan, N., Bai, X.-S., Haugen N., Fureby C., Brethouwer G. (2021). MILD Combustion. Advanced Turbulent Combustion Physics and Applications. [https://research.tudelft.nl/en/publications/mild-combustion]
- Magri, L., Doan, N.A.K. (2020). Physics-informed data-driven prediction of turbulent reacting flows with lyapunov analysis and sequential data assimilation. Data Analysis for Direct Numerical Simulations of Turbulent Combustion: From Equation-Based Analysis to Machine Learning. [https://link.springer.com/chapter/10.1007/978-3-030-44718-2_9]