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Results for computational fluid dynamics

Publications & Outputs

  1. Machine learning-enabled prediction of wind turbine energy yield losses due to general blade leading edge erosion

    Cappugi, L., Castorrini, A., Bonfiglioli, A., Minisci, E. & Campobasso, S., 1/10/2021, In: Energy Conversion and Management. 245, 17 p., 114567.

    Research output: Contribution to journalJournal articlepeer-review

  2. Assessing wind turbine energy losses due to blade leading edge erosion cavities with parametric CAD and 3D CFD

    Castorrini, A., Cappugi, L., Bonfiglioli, A. & Campobasso, S., 28/09/2020, In: Journal of Physics: Conference Series. 1618, 11 p., 052015.

    Research output: Contribution to journalJournal articlepeer-review

  3. Cross-comparative analysis of loads and power of pitching floating offshore wind turbine rotors using frequency-domain Navier-Stokes CFD and blade element momentum theory

    Ortolani, A., Persico, G., Drofelnik, J., Jackson, A. & Campobasso, S., 28/09/2020, In: Journal of Physics: Conference Series. 1618, 11 p., 052016.

    Research output: Contribution to journalJournal articlepeer-review

  4. Rapid Estimate of Wind Turbine Energy Loss due to Blade Leading Edge Delamination Using Artificial Neural Networks

    Campobasso, S., Cavazzini, A. & Minisci, E., 26/06/2020, In: Journal of Turbomachinery. 142, 7, 11 p., 071002.

    Research output: Contribution to journalJournal articlepeer-review

  5. Investigation of the Pressure Drop Across Packed Beds of Spherical Beads: Comparison of Empirical Models With Pore-Level Computational Fluid Dynamics Simulations

    Otaru, A. J. & Kennedy, A. R., 8/04/2019, In: Journal of Fluids Engineering. 141, 7, 9 p., 071305.

    Research output: Contribution to journalJournal articlepeer-review

  6. Conduit dynamics and post-explosion degassing on Stromboli: a combined UV camera and 1 numerical modelling treatment

    Pering, T. D., McGonigle, A. J. S., James, M. R., Tamburello, G., Aiuppa, A., Delle Donne, D. & Ripepe, M., 28/05/2016, In: Geophysical Research Letters. 43, 10, p. 5009-5016 8 p.

    Research output: Contribution to journalJournal articlepeer-review

  7. Linear Frequency Domain and Harmonic Balance Predictions of Dynamic Derivatives

    Da Ronch, A., McCracken, A., Badcock, K., Widhalm, M. & Campobasso, S., 2013, In: Journal of Aircraft. 50, 3, p. 694-707 14 p.

    Research output: Contribution to journalJournal articlepeer-review

  8. A parallel 3D unstructured implicit RANS solver for compressible and incompressible CFD simulations

    Bonfiglioli, A., Campobasso, S., Carpentieri, B. & bollhoefer, M., 2012, Parallel Processing and Applied Mathematics: 9th International Conference, PPAM 2011, Torun, Poland, September 11-14, 2011. Revised Selected Papers, Part II. Wyrzykowski, R., Dongarra, J., Karczewski, K. & Waśniewski, J. (eds.). Berlin: Springer, p. 312-322 11 p. (Lecture Notes in Computer Science; vol. 7203).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  9. Wake-Tracking and Turbulence Modelling in Computational Aerodynamics of Wind Turbine Airfoils

    Campobasso, S., Zanon, A., Minisci, E. & Bonfiglioli, A., 12/2009, In: Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy. 223, 8, p. 939-951 13 p.

    Research output: Contribution to journalJournal articlepeer-review

  10. Integrating CFD and prototyping technologies in the investigation of multi-element profiles for a high-lift variable pitch vertical-axis tidal power generator

    Quayle, S. & Rennie, A., 2007, In: International Journal of Agile Systems and Management. 2, 2, p. 222-236 15 p.

    Research output: Contribution to journalJournal articlepeer-review