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The following publications used DAFoam in their studies. If we miss yours, please feel free to edit this page and submit a pull request.

  1. E. Ntantis, V. Xezonakis. Aerodynamic design optimization of a NACA 0012 airfoil: An introductory adjoint discrete tool for educational purposes. International Journal of Mechanical Engineering Education, 2024

  2. L. Fang, P. He. Field inversion machine learning augmented turbulence modeling for time-accurate unsteady flow. Physics of Fluids, 2024.

  3. L. Fang, P. He. A Duality-Preserving Adjoint Method for Segregated Navier-Stokes Solvers. Journal of Computational Physics, 2024.

  4. O. Bidar, S. Anderson, N. Qin. Sensor placement for data assimilation of turbulence models using eigenspace perturbations, Physics of Fluids, 2024.

  5. C. Wu, Y. Zhang. Development of a Generalizable Data-driven Turbulence Model: Conditioned Field Inversion and Symbolic Regression. arXiv:2402.16355, 2024.

  6. L. Fang, P. He. A Segregated Time-Accurate Adjoint Method for Field Inversion of Unsteady Flow. AIAA SciTech forum, 2024.

  7. S. Zoppelt, H. Koyuncuoglu. P. He. High-fidelity Aerostructural Optimization Benchmark for Aircraft Propellers in Hover. AIAA SciTech forum, 2024.

  8. O. Bidar, P. He, S. Anderson, N. Qin. Aerodynamic Shape Optimisation Using a Machine Learning-Augmented Turbulence Model. AIAA SciTech forum, 2024.

  9. J. Park, B. Knight, Y. Liao, M. Mangano, B. Pacini, K. Maki, J. Martins, J. Sun, and Y. Pan. CFD-based Design Optimization of Ducted Hydrokinetic Turbines, Scientific Reports, 2023.

  10. M.H. Negahban, M. Bashir, V. Traisnel, R.M. Botez. Seamless morphing trailing edge flaps for UAS-S45 using high-fidelity aerodynamic optimization. Chinese Journal of Aeronautics, 2023.

  11. S. Batay, A. Baidullayeva, Y. Zhao, D. Wei. Aero-Structural Design Optimization of Wind Turbine Blades. Preprints, 2023.

  12. F. Cao, Z. Tang, C. Zhu X. Zhao. An Efficient Hybrid Multi-Objective Optimization Method Coupling Global Evolutionary and Local Gradient Searches for Solving Aerodynamic Optimization Problems. Mathematics, 2023.

  13. C. Wu, Y. Zhang. Enhancing the shear-stress-transport turbulence model with symbolic regression: A generalizable and interpretable data-driven approach. Physical Review Fluids, 2023.

  14. F. Yeganehdoust, H. Karbasian, B. Vermeire. Aerodynamic Optimization of eVTOL Rotor Profiles. Proceedings of the Canadian Society for Mechanical Engineering International Congress, 2023.

  15. B. Pacini, M. Prajapati, K. Duraisamy, J.R.R.A. Martins, P. He. Towards Mixed-Fidelity Aero-Structural-Acoustic Optimization for Urban Air Mobility Vehicle Design. In: AIAA Aviation Forum, 2023.

  16. H. M. Hajdik, B. Pacini, A. Yildirim, B. J. Brelje, J.R.R.A. Martins. Combined systems packaging and aerodynamic shape optimization of a full aircraft configuration. In: AIAA Aviation Forum, 2023.

  17. S. Batay, B. Kamalov, D. Zhangaskanov, Y. Zhao, D. Wei, T. Zhou, X. Su. Adjoint-Based High-Fidelity Concurrent Aerodynamic Design Optimization of Wind Turbine. Fluids, 2023

  18. H. U. Koyuncuoglu, P. He. CFD Based Multi-Component Aerodynamic Optimization for Wing Propeller Coupling. In: AIAA Scitech Forum, 2023.

  19. P. He, H.U. Koyuncuoglu, H. Hu, A. Dhulipalla, H.Y. Hu, H. Hu. High-fidelity Aerodynamic and Aerostructural Optimization of UAV Propellers Using the Adjoint Method. In: AIAA Scitech Forum, 2023.

  20. B Pacini M. Prajapati, K. Duraisamy, J.R.R.A. Martins, P. He. Multipoint Aerostructural Optimization for Urban Air Mobility Vehicle Design. In: AIAA Scitech Forum, 2023.

  21. B Pacini M. Prajapati, K. Duraisamy, J.R.R.A. Martins, P. He. Understanding Distributed Propulsion on the NASA Tiltwing Concept Vehicle with Aerodynamic Shape Optimization. In: AIAA Scitech Forum, 2023.

  22. M. H. Negahban, M. Bashir, R.M. Botez. Free-Form Deformation Parameterization on the Aerodynamic Optimization of Morphing Trailing Edge. Applied Mechanics, 2023.

  23. M. H. Negahban, M. Bashir, R.M. Botez. Aerodynamic Optimization of a Novel Synthetic Trailing Edge and Chord Elongation Morphing: Application to the UAS-S45 Airfoil. In: AIAA Scitech Forum, 2023.

  24. A. Ghasemi, A. Elham. Efficient multi‑stage aerodynamic topology optimization using an operator‑based analytical differentiation. Structural and Multidisciplinary Optimization, 2022.

  25. L. Jofre, A. Doostan. Rapid aerodynamic shape optimization under uncertainty using a stochastic gradient approach. Structural and Multidisciplinary Optimization, 2022.

  26. H. U. Koyuncuoglu, P. He. Simultaneous wing shape and actuator parameter optimization using the adjoint method. Aerospace Science and Technology, 2022.

  27. Y.L. Lamer, J. Morlier, E. Benard, P. He. Aeroelastic analysis of high aspect ratio and strut-braced wings. In: 33th Congress of the International Council of the Aeronautical Sciences. 2022.

  28. O. Bidar, P. He, S. Anderson, N. Qin. An open-source adjoint-based field inversion tool for data-driven RANS modelling, In: AIAA Aviation Forum, 2022. AIAA 2022-4125. https://doi.org/10.2514/6.2022-4125

  29. O. Bidar, P. He, S. Anderson, N. Qin. Turbulent mean flow reconstruction based on sparse multi-sensor data and adjoint-based field inversion, In: AIAA Aviation Forum, 2022. AIAA 2022-3900. https://doi.org/10.2514/6.2022-3900

  30. H. U. Koyuncuoglu, P. He. Coupled Wing-Propeller Aerodynamic Optimization Using the Adjoint Method. In: AIAA Scitech Forum, 2022.

  31. C. Wu, Y. Zhang. Flow Topology Optimization at High Reynolds Numbers Based on Modified Turbulence Models. arXiv:2207.11711, 2022.

  32. N.N. Kozyulin, M.S. Bobrov, M.Y. Hrebtov. Adjoint shape optimization of a duct for a wall jet film cooling setup. J. Phys.: Conf. Ser. 2119, 2021.

  33. Kiet T. Tran, Ping He. Unsteady aerodynamic optimization of airfoils considering shape and propeller parameters. In: AIAA Aviation Forum, 2021.

  34. J. Ho, A. West. Field Inversion and Machine Learning for Turbulence Modelling Applied to Three-Dimensional Separated Flows. In: AIAA Aviation Forum, 2021.

  35. Ping He, Joaquim R. R. A. Martins. A hybrid time-spectral approach for aerodynamic optimization with unsteady flow. In: AIAA Scitech Forum, 2021. https://doi.org/10.2514/6.2021-0278

  36. Ping He, Charles A. Mader, Joaquim R.R.A. Martins, Kevin J. Maki. DAFoam: An open-source adjoint framework for multidisciplinary design optimization with OpenFOAM. AIAA Journal, 2020. https://doi.org/10.2514/1.J058853

  37. Ping He, Alton J. Luder, Charles A. Mader, Joaquim R.R.A. Martins, Kevin J. Maki. A time-spectral adjoint approach for aerodynamic shape optimization under periodic wakes. In: AIAA Scitech Forum, 2020. AIAA-2020-2114. https://doi.org/10.2514/6.2020-2114

  38. Ping He, Grzegorz Filip, Kevin J. Maki, Joaquim R. R. A. Martins. Design optimization for self-propulsion of a bulk carrier hull using a discrete adjoint method. Computers & Fluids, 192, pp. 104259, 2019. http://dx.doi.org/10.1016/j.compfluid.2019.104259

  39. Gaetan K. W. Kenway, Charles A. Mader, Ping He, Joaquim R. R. A. Martins. Effective adjoint approaches for computational fluid dynamics. Progress in Aerospace Sciences, 110, pp. 100542, 2019. http://dx.doi.org/10.1016/j.paerosci.2019.05.002

  40. Ping He, Charles A. Mader, Joaquim R. R. A. Martins, Kevin J. Maki. Aerothermal optimization of a ribbed U-bend cooling channel using the adjoint method. International Journal of Heat and Mass Transfer, 140, 152-172, 2019. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2019.05.075

  41. Ping He, Charles A. Mader, Joaquim R. R. A. Martins, Kevin J. Maki. An object-oriented framework for rapid discrete adjoint development using OpenFOAM. In: AIAA Scitech Forum, 2019. AIAA-2019-1210. http://dx.doi.org/10.2514/6.2019-1210

  42. Ping He, Charles A. Mader, Joaquim R. R. A. Martins, Kevin J. Maki. Aerothermal optimization of internal cooling passages using the adjoint method, In: 2018 Joint Thermophysics and Heat Transfer Conference, 2018. AIAA Aviation Forum, AIAA-2018-4080. http://dx.doi.org/10.2514/6.2018-4080

  43. Ping He, Grzegorz Filip, Joaquim R. R. A. Martins, Kevin J. Maki. Hull form hydrodynamic design using a discrete adjoint optimization method, In: 13th International Marine Design Conference, 2018

  44. Ping He, Charles A. Mader, Joaquim R. R. A. Martins, Kevin J. Maki. An aerodynamic design optimization framework using a discrete adjoint approach with OpenFOAM. Computers & Fluids, 168, pp. 285-303, 2018. http://dx.doi.org/10.1016/j.compfluid.2018.04.012