S. Piva

43 papers receiving 380 citations

Peers

S. Piva
Comparison fields: 5 of 53
  • Renewable Energy, Sustainability and the Environment 83
  • Mechanical Engineering 188
  • Computational Mechanics 101
  • Computer Vision and Pattern Recognition 60
  • Biomedical Engineering 101
Replace Ru Yang with:
Ru Yang China
Wanli Liu China
Yang He China
Vedang Chauhan United States
Chuande Zhou China
Guoqiang Wang China
Giampietro Fabbri Italy
Jean Mercklé France
Younghwan Joo South Korea
S. Piva relative to Ru Yang China Ru Yang's profile →
Citations per field
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Citations per year

Countries citing papers authored by S. Piva

Since Specialization
Citations

This map shows the geographic impact of S. Piva'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 S. Piva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Piva more than expected).

Fields of papers citing papers by S. Piva

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Piva. 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 S. Piva. The network helps show where S. Piva may publish in the future.

Co-authors

The 15 scholars most cited alongside S. Piva, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with S. Piva Line = papers co-authored together S. Piva links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200854
2 201144
3 200230
4 200327
5 200423
6 200720
7 199215
8 200713
9 200413
10 199512
11 200611
12 199511
13 200710
14 20159
15 19968
16 20048
17 20078
18 20058
19 20147
20 20177

About S. Piva

S. Piva is a scholar working on Mechanical Engineering, Computational Mechanics, Renewable Energy, Sustainability and the Environment, Computer Vision and Pattern Recognition and Biomedical Engineering, having authored 45 papers that have together received 408 indexed citations. Recurring topics across this work include Heat Transfer and Optimization (10 papers), Nanofluid Flow and Heat Transfer (7 papers), Fluid Dynamics and Turbulent Flows (7 papers), Solar Thermal and Photovoltaic Systems (7 papers), Heat Transfer Mechanisms (7 papers), Phase Change Materials Research (5 papers), Advanced Thermoelectric Materials and Devices (5 papers) and Advanced Battery Technologies Research (4 papers). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (83 citations), Mechanical Engineering (188 citations), Computational Mechanics (101 citations), Computer Vision and Pattern Recognition (60 citations) and Biomedical Engineering (101 citations). S. Piva has collaborated with scholars based in Italy, United Kingdom and Albania. Frequent co-authors include Carlo S. Regazzoni, Gian Luca Morini, Michele Pinelli, Luca Marchesotti, Patrizio Fausti, Malcolm Sabin, G. Pagliarini, Michael W. Collins, Giovanni Sebastiano Barozzi and Marco Fossa. Their work appears in journals such as Experimental Thermal and Fluid Science, International Journal of Heat and Mass Transfer, International Journal of Heat and Technology, Applied Thermal Engineering and IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans.

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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