Fernando Tejero

515 total citations
55 papers, 398 citations indexed

About

Fernando Tejero is a scholar working on Aerospace Engineering, Computational Mechanics and Global and Planetary Change. According to data from OpenAlex, Fernando Tejero has authored 55 papers receiving a total of 398 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Aerospace Engineering, 30 papers in Computational Mechanics and 19 papers in Global and Planetary Change. Recurrent topics in Fernando Tejero's work include Advanced Aircraft Design and Technologies (19 papers), Computational Fluid Dynamics and Aerodynamics (19 papers) and Turbomachinery Performance and Optimization (16 papers). Fernando Tejero is often cited by papers focused on Advanced Aircraft Design and Technologies (19 papers), Computational Fluid Dynamics and Aerodynamics (19 papers) and Turbomachinery Performance and Optimization (16 papers). Fernando Tejero collaborates with scholars based in United Kingdom, Venezuela and Poland. Fernando Tejero's co-authors include David G. MacManus, Christopher Sheaf, Matthew Robinson, Robert Christie, Piotr Doerffer, Ioannis Goulos, Servio Urdaneta-Morales, Jorge L. Martínez, Paweł Flaszyński and Héctor J. Finol and has published in prestigious journals such as SHILAP Revista de lepidopterología, AIAA Journal and Journal of Molecular and Cellular Cardiology.

In The Last Decade

Fernando Tejero

51 papers receiving 390 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fernando Tejero United Kingdom 11 244 229 140 50 43 55 398
Luis R. Miranda United States 8 233 1.0× 178 0.8× 124 0.9× 6 0.1× 16 0.4× 16 418
Hao Kang United States 11 126 0.5× 89 0.4× 30 0.2× 21 0.4× 9 0.2× 60 390
Stefan P. Domino United States 12 85 0.3× 167 0.7× 37 0.3× 14 0.3× 39 0.9× 30 298
Christopher Sheaf United Kingdom 15 413 1.7× 386 1.7× 257 1.8× 67 1.3× 53 1.2× 53 557
Ney Rafael Sêcco Brazil 8 188 0.8× 218 1.0× 151 1.1× 45 0.9× 52 1.2× 15 353
Kevin Bowcutt Australia 13 326 1.3× 306 1.3× 41 0.3× 21 0.4× 39 0.9× 29 465
Sinan Eyi Türkiye 11 179 0.7× 253 1.1× 30 0.2× 51 1.0× 45 1.0× 51 346
Alejandra Uranga United States 11 481 2.0× 432 1.9× 423 3.0× 17 0.3× 24 0.6× 24 738
Klaus Becker Germany 7 219 0.9× 276 1.2× 36 0.3× 25 0.5× 39 0.9× 19 373
Eric B. Ting United States 13 381 1.6× 227 1.0× 85 0.6× 9 0.2× 18 0.4× 43 439

Countries citing papers authored by Fernando Tejero

Since Specialization
Citations

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

Fields of papers citing papers by Fernando Tejero

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fernando Tejero

This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Tejero. A scholar is included among the top collaborators of Fernando Tejero 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 Fernando Tejero. Fernando Tejero 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.
Tejero, Fernando, et al.. (2025). Aerodynamics of High-Bypass-Ratio Aeroengine Nacelles: Numerical and Experimental Investigation. Journal of Aircraft. 62(4). 1004–1017.
2.
Tejero, Fernando, et al.. (2024). Artificial neural network for preliminary design and optimisation of civil aero-engine nacelles. The Aeronautical Journal. 128(1328). 2261–2280. 1 indexed citations
3.
MacManus, David G., et al.. (2024). Numerical and experimental investigations of diffusion-induced boundary layer separation on aero-engine nacelles. International Journal of Heat and Fluid Flow. 109. 109530–109530. 2 indexed citations
4.
MacManus, David G., et al.. (2024). Nacelle optimisation through multi-fidelity neural networks. International Journal of Numerical Methods for Heat & Fluid Flow. 34(9). 3615–3634. 1 indexed citations
5.
MacManus, David G., et al.. (2024). Design optimisation of separate-jet exhausts with CFD in-the-loop and dimensionality reduction techniques. CERES (Cranfield University). 1 indexed citations
6.
MacManus, David G., et al.. (2024). Installed nacelle aerodynamics at cruise and windmilling conditions. Aircraft Engineering and Aerospace Technology. 96(6). 757–768.
7.
Tejero, Fernando, et al.. (2024). Point-enhanced convolutional neural network: A novel deep learning method for transonic wall-bounded flows. Aerospace Science and Technology. 155. 109689–109689. 4 indexed citations
8.
Tejero, Fernando, David G. MacManus, Ioannis Goulos, & Christopher Sheaf. (2023). Propulsion integration study of civil aero-engine nacelles. The Aeronautical Journal. 128(1320). 325–339. 4 indexed citations
9.
Tejero, Fernando, et al.. (2023). Deep-Learning for Flow-Field Prediction of 3D Non-Axisymmetric Aero-Engine Nacelles. CERES (Cranfield University). 2 indexed citations
10.
Goulos, Ioannis, et al.. (2021). Civil turbofan propulsion aerodynamics: Thrust-drag accounting and impact of engine installation position. Aerospace Science and Technology. 111. 106533–106533. 32 indexed citations
11.
Tejero, Fernando, David G. MacManus, & Christopher Sheaf. (2020). Impact of Droop and Scarf on the Aerodynamic Performance of Compact Aero-Engine Nacelles. AIAA Scitech 2020 Forum. 9 indexed citations
12.
Doerffer, Piotr, et al.. (2016). Passive control of rotorcraft high-speed impulsive noise. Journal of Physics Conference Series. 760. 12031–12031. 8 indexed citations
13.
Tejero, Fernando, et al.. (2015). SHOCK WAVE INDUCED FLOW SEPARATION CONTROL BY AIR-JET AND ROD VORTEX GENERATORS. SHILAP Revista de lepidopterología. 9 indexed citations
14.
Martínez, Jorge L., et al.. (2015). AERODYNAMIC ANALYSIS OF WIND TURBINE ROTOR BLADES. SHILAP Revista de lepidopterología. 11 indexed citations
15.
Tejero, Fernando, et al.. (2015). Application of a Passive Flow Control Device on Helicopter Rotor Blades. Journal of the American Helicopter Society. 61(1). 1–13. 7 indexed citations
16.
Tejero, Fernando, et al.. (2009). Trypanosoma evansi: Analysis of the Structural Changes in Hepatic cells During Murine Experimental Infections. 18(1). 28–32. 1 indexed citations
17.
Tejero, Fernando, et al.. (2008). Trypanosoma evansi: A quantitative approach to the understanding of the morphometry-hematology relationship throughout experimental murine infections. Obihiro University of Agriculture and Veterinary Medicine Institutional Repository. 18(1). 34–47. 6 indexed citations
18.
Tejero, Fernando, et al.. (1998). Carnitine Promotes Heat Shock Protein Synthesis in Adriamycin-induced Cardiomyopathy in a Neonatal Rat Experimental Model. Journal of Molecular and Cellular Cardiology. 30(11). 2319–2325. 18 indexed citations
19.
Tejero, Fernando, Héctor J. Finol, & Servio Urdaneta-Morales. (1994). Ultrastructural Morphology of Trypanosoma rangeli. Archiv für Protistenkunde. 144(1). 91–96. 1 indexed citations
20.
Tejero, Fernando, et al.. (1984). Trypanosoma evansi: in-vitro serum-dependent phagocytosis.. PubMed. 37 Spec No. 263–9. 1 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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