| Machine learning methods in CFD for turbomachinery: A review J Hammond, N Pepper, F Montomoli, V Michelassi International Journal of Turbomachinery, Propulsion and Power 7 (2), 16, 2022 | 27 | 2022 |
| Machine learning for the development of data-driven turbulence closures in coolant systems J Hammond, F Montomoli, M Pietropaoli, RD Sandberg, V Michelassi Journal of Turbomachinery 144 (8), 081003, 2022 | 14 | 2022 |
| Topology optimisation of turbulent flow using data-driven modelling J Hammond, M Pietropaoli, F Montomoli Structural and Multidisciplinary Optimization 65 (2), 49, 2022 | 9 | 2022 |
| Error quantification for the assessment of data-driven turbulence models J Hammond, Y Frey Marioni, F Montomoli Flow, Turbulence and Combustion 109 (1), 1-26, 2022 | 4 | 2022 |
| Towards digital design of gas turbines F Montomoli, S Antorkas, M Pietropaoli, A Gaymann, J Hammond, ... Journal of the Global Power and Propulsion Society 2021 (May), 1-12, 2021 | 2 | 2021 |
| Failure domain analysis using Sliced-Normal distributions J Hammond, LG Crespo, F Montomoli | | 2023 |
| Robust data-driven turbulence closures for improved heat transfer prediction in complex geometries J Hammond, M Pietropaoli, F Montomoli International Journal of Heat and Fluid Flow 98, 109072, 2022 | | 2022 |
Prof Francesco MontomoliImperial College London, UK在 imperial.ac.uk 的电子邮件经过验证
Prof Francesco MontomoliImperial College London, UK在 imperial.ac.uk 的电子邮件经过验证