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Settore Macchine a fluido e Sistemi per l'Energia e l'Ambiente

Area di ricerca: Area 09 - Ingegneria Industriale

Settore: ING IND08 - Macchine a fluido e ING IND09 - Sistemi per l'energia e l'ambiente

Ricercatori:
Vinicio Magi (professore ordinario, SSD: ING-IND/08)
Annarita Viggiano (professore associato, SSD: ING-IND/08)
Aldo Bonfiglioli (professore associato, SSD: ING-IND/08)
Alessio Castorrini (RTDa, SSD: ING-IND/09)

Linee di ricerca:
Le principali linee di ricerca del settore Macchine a Fluido sono: a) sviluppo di strategie di combustione innovative per l'efficientamento dei motori endotermici; b) tecniche per il recupero del calore dei gas di scarico prodotti dalla combustione; c) utilizzo di idrogeno e di combustibili rinnovabili, quali biodiesel, etanolo, syngas, in sistemi energetici e propulsivi; d) caratterizzazione fluidodinamica di getti turbolenti; e) progettazione ed analisi di motori endotermici per l’aviazione; f) analisi ed ottimizzazione di espansori scroll per l'impiego in impianti ORC.
Nell’ambito dei sistemi per l'energia e l'ambiente e delle turbomacchine, le principali linee di ricerca sono: a) sviluppo di metodologie multiscala per la simulazione del vento e la stima della risorsa eolica; b) utilizzo di modelli fluidodinamici e tecniche numeriche avanzate per la stima dei carichi aerodinamici sulle pale eoliche, e per lo studio della propagazione delle scie dei rotori eolici; c) sviluppo di metodologie automatiche e modelli surrogati basati su CFD e machine learning, per la valutazione rapida della perdita energetica delle turbine eoliche associata al danneggiamento delle pale; d) sviluppo di algoritmi di tipo “shock-fitting” per la simulazione di flussi comprimibili con urti.

Elenco delle pubblicazioni degli ultimi 5 anni

- Rivista

  2. Cappugi, L., Castorrini, A., Bonfiglioli, A., Minisci, E., & Campobasso, M. S. (2021). Machine learning-enabled prediction of wind turbine energy yield losses due to general blade leading edge erosion. Energy Conversion and Management, 245, https://doi.org/10.1016/j.enconman.2021.114567.
  4. Campobasso, M. S., Castorrini, A., Cappugi, L., & Bonfiglioli, A. (2021). Experimentally validated three‐dimensional computational aerodynamics of wind turbine blade sections featuring leading edge erosion cavities. Wind Energy, https://doi.org/10.1002/we.2666.
  6. Castorrini, A., Gentile, S., Geraldi, E., & Bonfiglioli, A. (2021). Increasing spatial resolution of wind resource prediction using NWP and RANS simulation. Journal of Wind Engineering and Industrial Aerodynamics, 210, https://doi.org/10.1016/j.jweia.2020.104499.
  8. Castorrini, A., Venturini, P., Corsini, A., & Rispoli, F. (2021). Machine learnt prediction method for rain erosion damage on wind turbine blades. Wind Energy; 1– 18. https://doi.org/10.1002/we.2609
  10. Faruoli, M., Coclite, A., Viggiano, A., Caso, P., Magi, V., A comprehensive numerical analysis of the scavenging process in a uniflow two-stroke diesel engine for general aviation, (2021) Energies, 14 (21), art. no. 7361, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118808783&doi=10.3390%2fen14217361&partnerID=40&md5=62470314de9208991321177e416c221e, DOI: 10.3390/en14217361
  12. Bonelli, F., Viggiano, A., Magi, V., High-speed turbulent gas jets: an LES investigation of Mach and Reynolds number effects on the velocity decay and spreading rate, (2021) Flow, Turbulence and Combustion, 107 (3), pp. 519-550, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100042156&doi=10.1007%2fs10494-021-00242-5&partnerID=40&md5=b0a1f0055e3d1b3643d001a3ac6b6f67, DOI: 10.1007/s10494-021-00242-5
  14. Cantiani, A., Viggiano, A., Magi, V., On Direct Injection of Supercritical Water into Spark Ignition Engines as a Strategy for Heat Recovery, (2021) Energy Technology, 9 (8), art. no. 2100198, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108793988&doi=10.1002%2fente.202100198&partnerID=40&md5=ec2715c025d3998ae39a9ccf51fc5c97, DOI: 10.1002/ente.202100198
  16. D’Amato, M., Viggiano, A., Magi, V., On the turbulence-chemistry interaction of an HCCI combustion engine, (2020) Energies, 13 (22), art. no. 5876, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099035640&doi=10.3390%2fen13225876&partnerID=40&md5=0417b527a00baef55cf53a403ac6e626, DOI: 10.3390/en13225876
  18. Fiore, M., Magi, V., Viggiano, A., Internal combustion engines powered by syngas: A review, (2020) Applied Energy, 276, art. no. 115415, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087930941&doi=10.1016%2fj.apenergy.2020.115415&partnerID=40&md5=519763d9b7377c5114f13f73017b594c, DOI: 10.1016/j.apenergy.2020.115415
  20. Coclite, A., Faruoli, M., Viggiano, A., Caso, P., Magi, V., Liquid-Cooling System of an Aircraft Compression Ignition Engine: A CFD Analysis, (2020) Fluids, 5 (2), art. no. 71, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085989452&doi=10.3390%2ffluids5020071&partnerID=40&md5=e27347ead8656374221798c24e40802a, DOI: 10.3390/fluids5020071
  22. Jebakumar, A.S., Magi, V., Abraham, J., Lattice-Boltzmann simulations of flow past stationary particles in a channel, (2019) Numerical Heat Transfer; Part A: Applications, 76 (5), pp. 281-300, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068182603&doi=10.1080%2f10407782.2019.1630240&partnerID=40&md5=756edf3e782ded06d65257e6ad0f6020, DOI: 10.1080/10407782.2019.1630240
  24. Yen, M., Magi, V., Abraham, J., Modeling the effects of hydrogen and nitrogen addition on soot formation in laminar ethylene jet diffusion flames, (2019) Chemical Engineering Science, pp. 116-129, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051365493&doi=10.1016%2fj.ces.2018.07.061&partnerID=40&md5=b79843a67ddafd4d7978b29da573b88c, DOI: 10.1016/j.ces.2018.07.061
  26. Jebakumar, A.S., Premnath, K.N., Magi, V., Abraham, J., Fully-resolved direct numerical simulations of particle motion in a turbulent channel flow with the lattice-Boltzmann method, (2019) Computers and Fluids, 179, pp. 238-247, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056178805&doi=10.1016%2fj.compfluid.2018.11.003&partnerID=40&md5=3a276ecda3aaa0a6e7a894f672d01852, DOI: 10.1016/j.compfluid.2018.11.003
  28. Faruoli, M., Viggiano, A., Magi, V., A new approach to simulate Stirling engine regenerators as porous media under low reynolds conditions, (2019) International Journal of Heat and Technology, 37 (4), pp. 958-965, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077325967&doi=10.18280%2fijht.370404&partnerID=40&md5=9abf0c720d55697823130e4b969f40a4, DOI: 10.18280/ijht.370404
  30. Jebakumar, A.S., Magi, V., Abraham, J., Lattice-Boltzmann simulations of particle transport in a turbulent channel flow, (2018) International Journal of Heat and Mass Transfer, 127, pp. 339-348, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050128121&doi=10.1016%2fj.ijheatmasstransfer.2018.06.107&partnerID=40&md5=8040977cb7360e97654f4ecd4f96cfdd, DOI: 10.1016/j.ijheatmasstransfer.2018.06.107
  32. Fanelli, E., Lovaglio, N., Cornacchia, G., Braccio, G., Magi, V., Power generation in externally fired air turbine feed by biomass derived syngas, (2018) Modelling, Measurement and Control B, 87 (3), pp. 197-206, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056875270&doi=10.18280%2fmmc-b.870312&partnerID=40&md5=0fdf27b98546e4b5e12e2fd70b35a42e, DOI: 10.18280/mmc-b.870312
  34. Cantiani, A., Viggiano, A., Fanelli, E., Cornacchia, G., Braccio, G., Magi, V., CFD analysis of biodiesel combustion applied to industrial burners, (2018) Modelling, Measurement and Control C, 79 (3), pp. 61-69, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056844985&doi=10.18280%2fmmc-c.790301&partnerID=40&md5=5406bebb2c6bad5dd6d8c56050451ff7, DOI: 10.18280/mmc-c.790301
  36. Faruoli, M., Viggiano, A., Magi, V., An investigation of thermo-fluid dynamic performance of a Stirling engine regenerator by means of OpenFOAM, (2018) Modelling, Measurement and Control B, 87 (3), pp. 151-158, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056842152&doi=10.18280%2fmmc-b.870306&partnerID=40&md5=d474daa07bd59ab8b60edab698823906, DOI: 10.18280/mmc-b.870306
  38. Genco, A., Viggiano, A., Magi, V., How to enhance the energy efficiency of HVAC systems, (2018) Mathematical Modelling of Engineering Problems, 5 (3), pp. 153-160, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054753018&doi=10.18280%2fmmep.050304&partnerID=40&md5=b4fd970417227c849365adce7d87e05a, DOI: 10.18280/mmep.050304
  40. Yen, M., Magi, V., Abraham, J., Modeling Soot Formation in Turbulent Jet Flames at Atmospheric and High-Pressure Conditions, (2018) Energy and Fuels, 32 (8), pp. 8857-8867, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050748341&doi=10.1021%2facs.energyfuels.8b01946&partnerID=40&md5=742a99105d0f1bbe9d2aa6fd6fecf41e, DOI: 10.1021/acs.energyfuels.8b01946
  42. Genco, A., Viggiano, A., Viscido, L., Sellitto, G., Magi, V., Dynamic analysis of HVAC for industrial plants with different airflow control systems, (2018) Thermal Science and Engineering Progress, 6, pp. 330-345, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044962741&doi=10.1016%2fj.tsep.2017.12.004&partnerID=40&md5=e950bc01ff83d62ce7e7801eb8133655, DOI: 10.1016/j.tsep.2017.12.004
  44. Genco, A., Viggiano, A., Viscido, L., Sellitto, G., Magi, V., Optimization of microclimate control systems for air-conditioned environments, (2017) International Journal of Heat and Technology, 35 (Special Issue 1), pp. 236-243, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030241232&doi=10.18280%2fijht.35Sp0133&partnerID=40&md5=1084619f55435d24523f95ca62021e67, DOI: 10.18280/ijht.35Sp0133
  46. Wang, Z., Magi, V., Abraham, J., Turbulent Flame Speed Dependencies in Lean Methane-Air Mixtures under Engine Relevant Conditions, (2017) Combustion and Flame, 180, pp. 53-62, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014920606&doi=10.1016%2fj.combustflame.2017.02.023&partnerID=40&md5=950d52809b491677bc0093da4d9edb43, DOI: 10.1016/j.combustflame.2017.02.023
  48. Assonitis, A., Paciorri, R., Bonfiglioli, A. Numerical simulation of shock/boundary-layer interaction using an unstructured shock-fitting technique (2021) Computers and Fluids, 228, art. no. 105058. DOI: 10.1016/j.compfluid.2021.105058
  50. Zou, D., Bonfiglioli, A., Paciorri, R., Liu, J. An embedded shock-fitting technique on unstructured dynamic grids (2021) Computers and Fluids, 218, art. no. 104847. DOI: 10.1016/j.compfluid.2021.104847
  52. Ciallella, M., Ricchiuto, M., Paciorri, R., Bonfiglioli, A. Extrapolated Shock Tracking: Bridging shock-fitting and embedded boundary methods (2020) Journal of Computational Physics, 412, art. no. 109440. DOI: 10.1016/j.jcp.2020.109440
  54. Paciorri, R., Bonfiglioli, A. Accurate detection of shock waves and shock interactions in two-dimensional shock-capturing solutions (2020) Journal of Computational Physics, 406, art. no. 109196. DOI: 10.1016/j.jcp.2019.109196
  56. Campobasso, M.S., Yan, M., Bonfiglioli, A., Gigante, F.A., Ferrari, L., Balduzzi, F., Bianchini, A. Low-speed preconditioning for strongly coupled integration of Reynolds-averaged Navier–Stokes equations and two-equation turbulence models (2018) Aerospace Science and Technology, 77, pp. 286-298. DOI: 10.1016/j.ast.2018.03.015

- Capitoli di libri

  2. Carpentieri, Bruno, Bonfiglioli, Aldo (2018). Multilevel Variable-Block Schur-Complement-Based Preconditioning for the Implicit Solution of the Reynolds-Averaged Navier-Stokes Equations Using Unstructured Grids. In: Carpentieri Bruno. (a cura di): Adela Ionescu, Computational Fluid Dynamics Basic Instruments and Applications in Science. p. 43-72, Londra:IntechOpen Limited, ISBN: 978-953-51-3790-0, doi: 10.5772/intechopen.72043
  4. Paciorri, Renato, Bonfiglioli, Aldo (2017). Basic Elements of Unstructured Shock-Fitting: Results Achieved and Future Developments. In: (a cura di): M. Onofri R. Paciorri, Shock Fitting Classical Techniques, Recent Developments, and Memoirs of Gino Moretti. SHOCK WAVE AND HIGH PRESSURE PHENOMENA, p. 59-84, Springer, ISBN: 978-3-319-68426-0, ISSN: 2197-9529, doi: 10.1007/978-3-319-68427-7_3
  6. Campoli, L., Quemar, P., Bonfiglioli, A., Ricchiuto, M. (2017). Shock-Fitting and Predictor-Corrector Explicit ALE Residual Distribution. In: (a cura di): M. Onofri R. Paciorri, Shock Fitting Classical Techniques, Recent Developments, and Memoirs of Gino Moretti. SHOCK WAVE AND HIGH PRESSURE PHENOMENA, p. 113-129, Springer, ISBN: 978-3-319-68426-0, ISSN: 2197-9529, doi: 10.1007/978-3-319-68427-7_5

- Atti di Convegno

  2. Castorrini, A., Cappugi, L., Bonfiglioli, A., & Campobasso, M. S. (2020, September). Assessing wind turbine energy losses due to blade leading edge erosion cavities with parametric CAD and 3D CFD. In Journal of Physics: Conference Series (Vol. 1618, No. 5, p. 052015). IOP Publishing.
  4. Assonitis, A., Paciorri, R., Bonfiglioli, A. Numerical simulation of shock boundary layer interaction using shock fitting technique (2020) Lecture Notes in Mechanical Engineering, pp. 124-134. DOI: 10.1007/978-3-030-41057-5_10
  6. Leto, A., Bonfiglioli, A. Preliminary Design of a Radial Turbine for Methane Expander Rocket-Engine (2017) Energy Procedia, 126, pp. 738-745.  DOI: 10.1016/j.egypro.2017.08.221
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