A. De Iorio

15.1k total citations
9 papers, 88 citations indexed

About

A. De Iorio is a scholar working on Fluid Flow and Transfer Processes, Aerospace Engineering and Mechanical Engineering. According to data from OpenAlex, A. De Iorio has authored 9 papers receiving a total of 88 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Fluid Flow and Transfer Processes, 3 papers in Aerospace Engineering and 3 papers in Mechanical Engineering. Recurrent topics in A. De Iorio's work include Advanced Combustion Engine Technologies (5 papers), Particle physics theoretical and experimental studies (3 papers) and Aerodynamics and Fluid Dynamics Research (3 papers). A. De Iorio is often cited by papers focused on Advanced Combustion Engine Technologies (5 papers), Particle physics theoretical and experimental studies (3 papers) and Aerodynamics and Fluid Dynamics Research (3 papers). A. De Iorio collaborates with scholars based in Italy, France and Denmark. A. De Iorio's co-authors include Vincenzo De Bellis, Luigi Teodosio, Fabio Bozza, Stefano Fontanesi, Adolfo Senatore, A. Cagnotta, Emma Frosina, Dario Buono, Stefano Morisi and Luigi Maresca and has published in prestigious journals such as SAE technical papers on CD-ROM/SAE technical paper series, Applied Sciences and Physical review. D.

In The Last Decade

A. De Iorio

9 papers receiving 80 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
A. De Iorio Italy	5	60	43	26	25	18	9	88
L. Duong Vietnam	6	46 0.8×	76 1.8×	14 0.5×	24 1.0×	61 3.4×	17	132
Michael Guenthner Germany	6	60 1.0×	31 0.7×	31 1.2×	2 0.1×	11 0.6×	14	72
D. Moya Spain	4	9 0.1×	18 0.4×	13 0.5×	3 0.1×	27 1.5×	7	72
Yuhuai Li China	8	167 2.8×	107 2.5×	66 2.5×	7 0.3×	54 3.0×	25	212
N. Hinton United Kingdom	5	183 3.0×	161 3.7×	31 1.2×	5 0.2×	96 5.3×	8	219
Jamie Turner United States	9	309 5.2×	186 4.3×	172 6.6×	24 1.0×	54 3.0×	10	338
J. Hodges United States	5	60 1.0×	83 1.9×	21 0.8×	1 0.0×	8 0.4×	8	107
Pierre-Arnaud Beau France	5	117 1.9×	313 7.3×	3 0.1×	4 0.2×	31 1.7×	6	327
George Giannakopoulos Switzerland	9	263 4.4×	309 7.2×	22 0.8×	16 0.6×	147 8.2×	20	343
Andrea Aniello France	10	204 3.4×	243 5.7×	11 0.4×	4 0.2×	145 8.1×	15	264

Countries citing papers authored by A. De Iorio

Since Specialization

Citations

This map shows the geographic impact of A. De Iorio's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by A. De Iorio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. De Iorio more than expected).

Fields of papers citing papers by A. De Iorio

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. De Iorio. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by A. De Iorio. The network helps show where A. De Iorio may publish in the future.

Co-authorship network of co-authors of A. De Iorio

This figure shows the co-authorship network connecting the top 25 collaborators of A. De Iorio. A scholar is included among the top collaborators of A. De Iorio based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with A. De Iorio. A. De Iorio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

Cacciapaglia, Giacomo, A. Cagnotta, Roberta Calabrese, et al.. (2023). Radiative flavor template at the LHC: g−2 and the W mass. Physical review. D. 107(5). 2 indexed citations

Cagnotta, A., et al.. (2022). Machine Learning Applications for Jet Tagging in the CMS Experiment. Applied Sciences. 12(20). 10574–10574. 4 indexed citations

Calabrese, Roberta, A. De Iorio, Damiano F. G. Fiorillo, et al.. (2021). Top-flavor scheme in the context of W′ searches at LHC. Physical review. D. 104(5). 2 indexed citations

Bozza, Fabio, Luigi Teodosio, Vincenzo De Bellis, Stefano Fontanesi, & A. De Iorio. (2018). A Refined 0D Turbulence Model to Predict Tumble and Turbulence in SI Engines. SAE International Journal of Engines. 12(1). 15–30. 25 indexed citations

Bozza, Fabio, Luigi Teodosio, Vincenzo De Bellis, Stefano Fontanesi, & A. De Iorio. (2018). Refinement of a 0D Turbulence Model to Predict Tumble and Turbulent Intensity in SI Engines. Part II: Model Concept, Validation and Discussion. SAE technical papers on CD-ROM/SAE technical paper series. 1. 28 indexed citations

Cameretti, Maria Cristina, et al.. (2017). 3D CFD Analyses of Intake Duct Geometry Impact on Tumble Motion and Turbulence Production in SI Engines. SAE technical papers on CD-ROM/SAE technical paper series. 1. 3 indexed citations

Senatore, Adolfo, et al.. (2014). A Simulated Analysis of the Lubrication Circuit of an In-Line Twin Automotive Engine. SAE technical papers on CD-ROM/SAE technical paper series. 12 indexed citations

Bonavolontà, C., G. Peluso, M. Valentino, A. De Iorio, & Francesco Penta. (2009). Detection of Magnetomechanical Effect in Structural Steel Using SQUIDs and Flux-gate Sensors. Journal of Superconductivity and Novel Magnetism. 22(8). 833–839. 6 indexed citations

Caruso, Serafino, et al.. (2009). A new mechanical variable valve actuation system for motorcycle engines. SAE technical papers on CD-ROM/SAE technical paper series. 1. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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