Juan A. Sillero

1.1k total citations · 1 hit paper
14 papers, 862 citations indexed

About

Juan A. Sillero is a scholar working on Computational Mechanics, Environmental Engineering and Aerospace Engineering. According to data from OpenAlex, Juan A. Sillero has authored 14 papers receiving a total of 862 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computational Mechanics, 9 papers in Environmental Engineering and 4 papers in Aerospace Engineering. Recurrent topics in Juan A. Sillero's work include Fluid Dynamics and Turbulent Flows (12 papers), Wind and Air Flow Studies (9 papers) and Computational Fluid Dynamics and Aerodynamics (4 papers). Juan A. Sillero is often cited by papers focused on Fluid Dynamics and Turbulent Flows (12 papers), Wind and Air Flow Studies (9 papers) and Computational Fluid Dynamics and Aerodynamics (4 papers). Juan A. Sillero collaborates with scholars based in Spain, Australia and Saudi Arabia. Juan A. Sillero's co-authors include Javier Jiménez, Robert Moser, Julio Soria, Vassili Kitsios, Callum Atkinson, Ayse G. Gungor, Juan Fernández de la Mora, Daniel Ortega, Atsushi Sekimoto and Nicholas Malaya and has published in prestigious journals such as Journal of Fluid Mechanics, Journal of Physics D Applied Physics and Physics of Fluids.

In The Last Decade

Juan A. Sillero

14 papers receiving 841 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

One-point statistics for turbulent wall-bounded flows at Reynolds numbers up to δ+ ≈ 2000

2013 325 citations Juan A. Sillero, Javier Jiménez et al. Physics of Fluids profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Juan A. Sillero Spain	8	722	406	210	194	160	14	862
K. Todd Lowe United States	14	772 1.1×	263 0.6×	582 2.8×	101 0.5×	70 0.4×	186	951
Siwei Dong China	14	539 0.7×	113 0.3×	178 0.8×	115 0.6×	59 0.4×	51	619
D. K. Bisset Australia	13	611 0.8×	320 0.8×	229 1.1×	76 0.4×	85 0.5×	31	786
Dirk Geyer Germany	19	1.2k 1.6×	101 0.2×	178 0.8×	13 0.1×	50 0.3×	59	1.2k
Fujihiro Hamba Japan	18	516 0.7×	241 0.6×	128 0.6×	61 0.3×	73 0.5×	52	762
O. R. H. Buxton United Kingdom	17	717 1.0×	259 0.6×	320 1.5×	78 0.4×	107 0.7×	58	806
Matthew J. Dunn Australia	23	1.4k 2.0×	177 0.4×	193 0.9×	19 0.1×	282 1.8×	54	2.0k
Nora Okong’o United States	17	907 1.3×	77 0.2×	190 0.9×	18 0.1×	46 0.3×	35	967
Markus Klöker Germany	28	1.5k 2.1×	204 0.5×	870 4.1×	230 1.2×	52 0.3×	84	1.9k

Countries citing papers authored by Juan A. Sillero

Since Specialization

Citations

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

Fields of papers citing papers by Juan A. Sillero

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Juan A. Sillero. 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 Juan A. Sillero. The network helps show where Juan A. Sillero may publish in the future.

Co-authorship network of co-authors of Juan A. Sillero

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

All Works

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14 of 14 papers shown

Kitsios, Vassili, Atsushi Sekimoto, Callum Atkinson, et al.. (2017). Direct numerical simulation of a self-similar adverse pressure gradient turbulent boundary layer at the verge of separation. Journal of Fluid Mechanics. 829. 392–419. 72 indexed citations

Kitsios, Vassili, Callum Atkinson, Juan A. Sillero, et al.. (2016). Direct numerical simulation of a self-similar adverse pressure gradient turbulent boundary layer. International Journal of Heat and Fluid Flow. 61. 129–136. 47 indexed citations

Kitsios, Vassili, Juan A. Sillero, Jorgen S. Frederiksen, & Julio Soria. (2016). Scale and Reynolds number dependence of stochastic subgrid energy transfer in turbulent channel flow. Computers & Fluids. 151. 132–143. 3 indexed citations

Poroseva, Svetlana, et al.. (2015). Validation of a closing procedure for fourth-order RANS turbulence models with DNS data in an incompressible zero-pressure-gradient turbulent boundary layer. International Journal of Heat and Fluid Flow. 56. 71–79. 5 indexed citations

Kitsios, Vassili, Callum Atkinson, Juan A. Sillero, et al.. (2015). DIRECT NUMERICAL SIMULATION OF A SELF-SIMILAR ADVERSE PRESSURE GRADIENT TURBULENT BOUNDARY LAYER. Istanbul Technical University Academic Open Archive (Istanbul Technical University). 429–434. 3 indexed citations

Kitsios, Vassili, Juan A. Sillero, Jorgen S. Frederiksen, & Julio Soria. (2015). Proposed stochastic parameterisations of subgrid turbulence in large eddy simulations of turbulent channel flow. Journal of Turbulence. 16(8). 729–741. 5 indexed citations

Sillero, Juan A., Javier Jiménez, & Robert Moser. (2014). Two-point statistics for turbulent boundary layers and channels at Reynolds numbers up to δ+ ≈ 2000. Physics of Fluids. 26(10). 187 indexed citations

Kitsios, Vassili, Juan A. Sillero, Julio Soria, & Jorgen S. Frederiksen. (2014). Stochastic self-energy subgrid model for the large eddy simulation of turbulent channel flows. Journal of Physics Conference Series. 506. 12001–12001. 2 indexed citations

Sillero, Juan A., Javier Jiménez, & Robert Moser. (2013). One-point statistics for turbulent wall-bounded flows at Reynolds numbers up to δ+ ≈ 2000. Physics of Fluids. 25(10). 325 indexed citations breakdown →

10.

Sillero, Juan A., et al.. (2012). Two-point correlations for zero-pressure-gradient turbulent boundary layers and channels at $Re_\tau \approx 1000-2000$. Bulletin of the American Physical Society. 1 indexed citations

11.

Sillero, Juan A., et al.. (2012). A code for direct numerical simulation of turbulent boundary layers at high Reynolds numbers in BG/P supercomputers. Computers & Fluids. 80. 37–43. 55 indexed citations

12.

Sillero, Juan A., Javier Jiménez, Robert Moser, & Nicholas Malaya. (2011). Direct simulation of a zero-pressure-gradient turbulent boundary layer up toRe_θ= 6650. Journal of Physics Conference Series. 318(2). 22023–22023. 14 indexed citations

13.

Sillero, Juan A., et al.. (2010). An experimental study of thoriated tungsten cathodes operating at different current intensities in an atmospheric-pressure plasma torch. Journal of Physics D Applied Physics. 43(18). 185204–185204. 41 indexed citations

14.

Sillero, Juan A., et al.. (2010). IMS–MS studies based on coupling a differential mobility analyzer (DMA) to commercial API–MS systems. International Journal of Mass Spectrometry. 298(1-3). 30–40. 102 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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