The following publications used DAFoam in their studies. If we miss yours, please feel free to edit this page and submit a pull request.
<67> Eric Segalerba. High fidelity simulations of the nasal flow with heat transfer and adjoint based optimization. PhD Thesis. University of Genoa, 2025.
<66> B. Pacini, M. Prajapati, K. Duraisamy, J.R.R.A. Martins, P. He. Investigating the Benefit of Aerodynamic Shape Optimization for a Wing with Distributed Propulsion. Meccanica, 2025.
<65> S. Zhang, C. Wu, Y. Zhang. Numerical Simulation of Three-dimensional High-Lift Configurations Using Data-Driven Turbulence Model. arXiv.2503.14859, 2025.
<64> U. Padmanaban, B. Ganapathisubramani, and S. Symon. Three-dimensional variational data assimilation of separated flows using time-averaged experimental data. arXiv.2503.19689, 2025.
<63> P. He, C. Psenica, L. Fang, M. Leader. Aerothermal Shape Optimization of Actively-Cooled Battery Packs Using Conjugate Heat Transfer. AIAA SciTech Forum, 2025.
<62> C. Psenica, L. Fang, S. Zoppelt, P. He. Conjugate Heat Transfer Shape Optimization of a U-bend Pipe With a Discrete Adjoint Method. AIAA SciTech Forum, 2025.
<61> Z. Li, L. Fang, A. Sharma, P. He. Field Inversion Machine Learning for Accurate Predictions of Time-Resolved Unsteady Flows Over Airfoils. AIAA SciTech Forum, 2025.
<60> C. Wu, Y. Zhang. Field Inversion and Machine Learning Based on the Rubber-Band Spalart-Allmaras Model. Theoretical and Applied Mechanics Letters. 2024.
<59> J. Park, M. Mangano, S. Seraj, B. Pacini, Y. Liao, B. Knight, K. Naik, K. Maki, J. Martins, J. Sun, and Y. Pan. CFD-based design optimization of a 5 kW ducted hydrokinetic turbine with practical constraints, arXiv.2411.13492, 2024.
<58> E. Ntantis, V. Xezonakis. Improving Transonic Performance with Adjoint-Based NACA 0012 Airfoil Design Optimization. Results in Engineering, 2024
<57> Z. Ding, Q. Liu, H Luo, M. Yang, Y. ZHang, S. Wang, Y. Luo, S. Chen. A preoperative planning procedure of septal myectomy for hypertrophic obstructive cardiomyopathy using image-based computational fluid dynamics simulations and shape optimization. Scientific Reports, 2024.
<56> J. Ho, N Pepper, T. Dodwell. Probabilistic Machine Learning to Improve Generalisation of Data-Driven Turbulence Modelling. Computers & Fluids, 2024.
<55> J. Caudron. Aerostructural optimization of an aircraft wing assisted by dimensionality reduction methods. MS Thesis. University of Liège, 2024.
<54> U. C. Padmanaban, B. Ganapathisubramani, C. Vanderwel, S. Symon. Towards passive scalar reconstruction using data assimilation. 14th UK Conference on Wind Engineering, 2024.
<53> M.H. Negahban, M. Bashir, C. Priolet, R.M. Botez.Novel Twist Morphing Aileron and Winglet Design for UAS Control and Performance. Drones. 2024.
<52> S. Batay, A. Baidullayeva, E. Sarsenov, Y. Zhao, T. Zhou, E. Ng, T. Kadylulu. Integrated Aerodynamic Shape and Aero-Structural Optimization: Applications from Ahmed Body to NACA 0012 Airfoil and Wind Turbine Blades. Fluids, 2024.
<51> J. Henry. Modeling and Optimization of Embedded Active Flow Control Systems. PhD Thesis. Illinois Institute of Technology, 2024.
<50> R. Halder. Data-Driven Surrogate Models for Computational Fluid Dynamics. PhD Thesis. University of Michigan, 2024.
<49> K. Hoang. High-fidelity aerostructural optimization of high aspect ratio wings, MS Thesis. Iowa State University, 2024.
<48> C. Thompson, U. Padmanaban, B. Ganapathisubramani, S. Symon. The effect of variations in experimental and computational fidelity on data assimilation approaches. Theoretical and Computational Fluid Dynamics, 2024.
<47> C. Wu, Y. Zhang. Flow Topology Optimization at High Reynolds Numbers Based on Modified Turbulence Models. Aerospace, 2024.
<46> 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
<45> L. Fang, P. He. Field inversion machine learning augmented turbulence modeling for time-accurate unsteady flow. Physics of Fluids, 2024.
<44> L. Fang, P. He. A Duality-Preserving Adjoint Method for Segregated Navier-Stokes Solvers. Journal of Computational Physics, 2024.
<43> O. Bidar, S. Anderson, N. Qin. Sensor placement for data assimilation of turbulence models using eigenspace perturbations, Physics of Fluids, 2024.
<42> C. Wu, Y. Zhang. Development of a Generalizable Data-Driven Turbulence Model: Conditioned Field Inversion and Symbolic Regression. AIAA Journal, 2024.
<41> L. Fang, P. He. A Segregated Time-Accurate Adjoint Method for Field Inversion of Unsteady Flow. AIAA SciTech forum, 2024.
<40> S. Zoppelt, H. Koyuncuoglu. P. He. High-fidelity Aerostructural Optimization Benchmark for Aircraft Propellers in Hover. AIAA SciTech forum, 2024.
<39> O. Bidar, P. He, S. Anderson, N. Qin. Aerodynamic Shape Optimisation Using a Machine Learning-Augmented Turbulence Model. AIAA SciTech forum, 2024.
<38> 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.
<37> 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.
<36> S. Batay, A. Baidullayeva, Y. Zhao, D. Wei. Aero-Structural Design Optimization of Wind Turbine Blades. Preprints, 2023.
<35> U. Toman. High-fidelity aerodynamic and aerostructural optimization of UAV propellers, MS Thesis. Iowa State University, 2023.
<34> 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.
<33> 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.
<32> F. Yeganehdoust, H. Karbasian, B. Vermeire. Aerodynamic Optimization of eVTOL Rotor Profiles. Proceedings of the Canadian Society for Mechanical Engineering International Congress, 2023.
<31> F. Chen, X. Cheng, K. Zhang, Y. Chen, X. Zhang. Unsteady Aerodynamic Shape Optimization of a Vertical Axis Wind Turbine Under the Framework of DAFoam. 2023 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2023) Proceedings.
<30> 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.
<29> 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.
<28> 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
<27> H. U. Koyuncuoglu, P. He. CFD Based Multi-Component Aerodynamic Optimization for Wing Propeller Coupling. In: AIAA Scitech Forum, 2023.
<26> 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.
<25> 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.
<24> 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.
<23> M. H. Negahban, M. Bashir, R.M. Botez. Free-Form Deformation Parameterization on the Aerodynamic Optimization of Morphing Trailing Edge. Applied Mechanics, 2023.
<22> 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.
<21> A. Ghasemi, A. Elham. Efficient multi‑stage aerodynamic topology optimization using an operator‑based analytical differentiation. Structural and Multidisciplinary Optimization, 2022.
<20> L. Jofre, A. Doostan. Rapid aerodynamic shape optimization under uncertainty using a stochastic gradient approach. Structural and Multidisciplinary Optimization, 2022.
<19> H. U. Koyuncuoglu, P. He. Simultaneous wing shape and actuator parameter optimization using the adjoint method. Aerospace Science and Technology, 2022.
<18> 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.
<17> 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.
<16> 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.
<15> H. U. Koyuncuoglu, P. He. Coupled Wing-Propeller Aerodynamic Optimization Using the Adjoint Method. In: AIAA Scitech Forum, 2022.
<14> 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.
<13> Kiet T. Tran, Ping He. Unsteady aerodynamic optimization of airfoils considering shape and propeller parameters. In: AIAA Aviation Forum, 2021.
<12> J. Ho, A. West. Field Inversion and Machine Learning for Turbulence Modelling Applied to Three-Dimensional Separated Flows. In: AIAA Aviation Forum, 2021.
<11> Ping He, Joaquim R. R. A. Martins. A hybrid time-spectral approach for aerodynamic optimization with unsteady flow. In: AIAA Scitech Forum, 2021.
<10> O. Bidar. Towards statistical inference to improve turbulence RANS closures for multi-element aerofoils. MS Thesis, The University of Sheffield, 2020.
<09> 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.
<08> 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.
<07> 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.
<06> 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.
<05> 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.
<04> 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.
<03> 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.
<02> 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
<01> 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.