Open Access
Issue |
E3S Web Conf.
Volume 197, 2020
75th National ATI Congress – #7 Clean Energy for all (ATI 2020)
|
|
---|---|---|
Article Number | 11006 | |
Number of page(s) | 11 | |
Section | Turbomachinery | |
DOI | https://doi.org/10.1051/e3sconf/202019711006 | |
Published online | 22 October 2020 |
- Delibra, G., Borello, D., Hanjalic, K., Rispoli, F., An les insight into convective mechanism of heat transfer in a wall-bounded pin matrix, 2010 14th International Heat Transfer Conference, IHTC 14 Volume 2, 2010, Pages 807-815 2010 14th International Heat Transfer Conference, IHTC 14; Washington, DC; United States; 8 August 2010 through 13 August 2010. https://doi.org/10.1115/IHTC14-23205 [Google Scholar]
- Delibra, G., Borello, D., Hanjalic, K., Rispoli, F., Hybrid LES/RANS of internal flows: A case for more advanced RANS, Notes on Numerical Fluid Mechanics and Multidisciplinary Design Volume 130, 2015, Pages 19-35. https://doi.org/10.1007/978-3-319-15141-0_2 [CrossRef] [Google Scholar]
- Oztop Hakan F., Khudheyer S. Mushatet, and İlker Yılmaz. “Analysis of turbulent flow and heat transfer over a double forward-facing step with obstacles.” International Communications in Heat and Mass Transfer 39.9 (2012): 1395-1403. https://doi.org/10.1016/j.icheatmasstransfer.2012.07.011 [CrossRef] [Google Scholar]
- H. G. Weller, G. Tabor, H. Jasak, C. Fureby, A tensorial approach to computational continuum mechanics using object-oriented techniques, COMPUTERS IN PHYSICS, VOL. 12, NO. 6, NOV/DEC 1998. https://doi.org/10.1063/1.168744 [Google Scholar]
- Delibra, G., Borello, D., Hanjalić, K., Rispoli, F., URANS of flow and endwall heat transfer in a pinned passage relevant to gas-turbine blade cooling, International Journal of Heat and Fluid Flow Volume 30, Issue 3, June 2009, Pages 549-560. https://doi.org/10.1016/j.ijheatfluidflow.2009.03.015 [CrossRef] [Google Scholar]
- Launder, B. E. and Sharma, B. I. (1974), “Application of the Energy-Dissipation Model of Turbulence to the Calculation of Flow Near a Spinning Disc”, Letters in Heat and Mass Transfer, Vol. 1, No. 2, pp. 131-138. https://doi.org/10.1016/0094-4548(74)90150-7 [CrossRef] [Google Scholar]
- Goodfellow Ian, Yoshua Bengio, and Aaron Courville. “Deep Learning”. MIT press, 2016. http://www.deeplearningbook.org [Google Scholar]
- Wu JL, Xiao H, Paterson EG. 2018b. Physics-informed machine learning approach for augmenting turbulence models: a comprehensive framework. Phys. Rev. Fluids 3: 074602. https://doi.org/10.1103/PhysRevFluids.3.074602 [CrossRef] [Google Scholar]
- G. Angelini, A. Corsini, G. Delibra, M. Giovannelli, Identification of Losses in Turbomachinery with Machine Learning, GT2020-15337. [Google Scholar]
- A. Corsini, G. Delibra, M. Giovannelli, S. Traldi, Machine Learnt Synthetic Turbulence for LES Inflow Conditions, GT2020-15338. [Google Scholar]
- A. Corsini, G. Delibra, M. Giovannelli, G. Lucherini, S. Minotti, S. Rossin, L. Tieghi, Prediction of Ventilation Effectiveness for LM9000 Package With Machine Learning, GT2020-14916. [Google Scholar]
- Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). New York, NY, USA: ACM. https://doi.org/10.1145/2939672.2939785 [Google Scholar]
- Bergstra, J. and Bengio, Y., Random search for hyper-parameter optimization, The Journal of Machine Learning Research (2012). https://jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.