Sergio D. Felicelli

3.0k total citations
84 papers, 2.4k citations indexed

About

Sergio D. Felicelli is a scholar working on Mechanical Engineering, Materials Chemistry and Aerospace Engineering. According to data from OpenAlex, Sergio D. Felicelli has authored 84 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Mechanical Engineering, 43 papers in Materials Chemistry and 36 papers in Aerospace Engineering. Recurrent topics in Sergio D. Felicelli's work include Solidification and crystal growth phenomena (40 papers), Aluminum Alloy Microstructure Properties (35 papers) and Metallurgical Processes and Thermodynamics (21 papers). Sergio D. Felicelli is often cited by papers focused on Solidification and crystal growth phenomena (40 papers), Aluminum Alloy Microstructure Properties (35 papers) and Metallurgical Processes and Thermodynamics (21 papers). Sergio D. Felicelli collaborates with scholars based in United States, Argentina and Australia. Sergio D. Felicelli's co-authors include D. R. Poirier, Hebi Yin, J Heinrich, Mohsen Eshraghi, Liang Wang, L. Wang, Mohsen Asle Zaeem, Bohumir Jelinek, Tian Tang and M.F. Horstemeyer and has published in prestigious journals such as Acta Materialia, International Journal of Heat and Mass Transfer and Materials Science and Engineering A.

In The Last Decade

Sergio D. Felicelli

81 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sergio D. Felicelli United States 31 1.7k 1.1k 834 543 431 84 2.4k
Matthew John M. Krane United States 23 1.1k 0.7× 851 0.7× 831 1.0× 195 0.4× 62 0.1× 76 1.5k
C. R. Swaminathan United States 12 1.0k 0.6× 391 0.3× 317 0.4× 464 0.9× 65 0.2× 13 1.6k
Chunming Wang China 37 3.7k 2.2× 666 0.6× 780 0.9× 500 0.9× 225 0.5× 149 4.0k
A. D. Brent United States 5 1.6k 1.0× 433 0.4× 274 0.3× 442 0.8× 90 0.2× 7 1.9k
Alex Plotkowski United States 29 2.7k 1.6× 670 0.6× 853 1.0× 84 0.2× 1.2k 2.9× 97 2.9k
Gaoyang Mi China 32 2.9k 1.8× 546 0.5× 722 0.9× 422 0.8× 226 0.5× 141 3.2k
Fenggui Lu China 38 4.4k 2.7× 828 0.7× 1.1k 1.3× 422 0.8× 315 0.7× 194 4.7k
Ebrahim Asadi United States 23 707 0.4× 642 0.6× 254 0.3× 72 0.1× 221 0.5× 54 1.5k
Zhixun Wen China 31 2.8k 1.7× 955 0.8× 1.1k 1.3× 402 0.7× 41 0.1× 215 3.6k
Milton Sérgio Fernandes de Lima Brazil 22 1.4k 0.8× 682 0.6× 328 0.4× 286 0.5× 161 0.4× 125 1.9k

Countries citing papers authored by Sergio D. Felicelli

Since Specialization
Citations

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

Fields of papers citing papers by Sergio D. Felicelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sergio D. Felicelli. 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 Sergio D. Felicelli. The network helps show where Sergio D. Felicelli may publish in the future.

Co-authorship network of co-authors of Sergio D. Felicelli

This figure shows the co-authorship network connecting the top 25 collaborators of Sergio D. Felicelli. A scholar is included among the top collaborators of Sergio D. Felicelli 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 Sergio D. Felicelli. Sergio D. Felicelli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Tang, Tian, et al.. (2024). SIMULATION OF THERMAL SIGNATURE OF TIRES AND TRACKS. SAE technical papers on CD-ROM/SAE technical paper series. 1.
2.
3.
Tang, Tian & Sergio D. Felicelli. (2015). Computational evaluation of effective stress relaxation behavior of polymer composites. International Journal of Engineering Science. 90. 76–85. 32 indexed citations
4.
Tang, Tian & Sergio D. Felicelli. (2014). Micromechanics modeling of electro-viscoelastic–plastic response of metal core piezoelectric fiber polymer composites. International Journal of Non-Linear Mechanics. 69. 37–44. 4 indexed citations
5.
Tang, Tian, et al.. (2013). Numerical evaluation of the temperature field of steady-state rolling tires. Applied Mathematical Modelling. 38(5-6). 1622–1637. 37 indexed citations
6.
Zaeem, Mohsen Asle, Hebi Yin, & Sergio D. Felicelli. (2012). Modeling dendritic solidification of Al–3%Cu using cellular automaton and phase-field methods. Applied Mathematical Modelling. 37(5). 3495–3503. 66 indexed citations
7.
Eshraghi, Mohsen & Sergio D. Felicelli. (2012). An implicit lattice Boltzmann model for heat conduction with phase change. International Journal of Heat and Mass Transfer. 55(9-10). 2420–2428. 103 indexed citations
8.
Felicelli, Sergio D., et al.. (2012). Quality Aspects of A356 Castings with Multiple Gates. International Journal of Metalcasting. 6(2). 67–82. 1 indexed citations
9.
Jelinek, Bohumir, Sergio D. Felicelli, Paul F. Mlakar, & John F. Peters. (2010). Temperature and viscosity effects on the velocity profile of a nanochannel electro-osmotic flow. Bulletin of the American Physical Society. 63. 1 indexed citations
10.
Wang, Liang, Hongjoo Rhee, Sergio D. Felicelli, Adrian S. Sabau, & John T. Berry. (2009). INTERDEPENDENCE BETWEEN COOLING RATE, MICROSTRUCTURE AND POROSITY IN MG ALLOY AE42. European Journal of Ageing. 16(3). 273–282. 1 indexed citations
11.
Wang, L., et al.. (2009). Pore Formation in Laser-Assisted Powder Deposition Process. Journal of Manufacturing Science and Engineering. 131(5). 30 indexed citations
12.
Felicelli, Sergio D., et al.. (2009). Element‐free Galerkin method for thermosolutal convection and macrosegregation. International Journal for Numerical Methods in Fluids. 64(7). 733–760. 2 indexed citations
13.
14.
Yin, Hebi & Sergio D. Felicelli. (2009). A cellular automaton model for dendrite growth in magnesium alloy AZ91. Modelling and Simulation in Materials Science and Engineering. 17(7). 75011–75011. 38 indexed citations
15.
Heinrich, J, et al.. (2008). Projection method for flows with large local density gradients: Application to dendritic solidification. International Journal for Numerical Methods in Fluids. 57(9). 1211–1226. 9 indexed citations
16.
Wang, L., et al.. (2007). Optimization of the LENS® process for steady molten pool size. Materials Science and Engineering A. 474(1-2). 148–156. 113 indexed citations
17.
Poirier, D. R., et al.. (2002). Continuum model for predicting microporosity in steel castings. Modelling and Simulation in Materials Science and Engineering. 10(5). 551–568. 15 indexed citations
18.
Felicelli, Sergio D., D. R. Poirier, A. F. Giamei, & J Heinrich. (1997). Simulating convection and macrosegregation in superalloys. JOM. 49(3). 21–25. 19 indexed citations
19.
Felicelli, Sergio D., J Heinrich, & D. R. Poirier. (1993). NUMERICAL MODEL FOR DENDRITIC SOLIDIFICATION OF BINARY ALLOYS. Numerical Heat Transfer Part B Fundamentals. 23(4). 461–481. 50 indexed citations
20.
Heinrich, J, Sergio D. Felicelli, & D. R. Poirier. (1991). Vertical solidification of dendritic binary alloys. Computer Methods in Applied Mechanics and Engineering. 89(1-3). 435–461. 15 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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