David G. MacManus

2.4k total citations
147 papers, 1.8k citations indexed

About

David G. MacManus is a scholar working on Computational Mechanics, Aerospace Engineering and Global and Planetary Change. According to data from OpenAlex, David G. MacManus has authored 147 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 113 papers in Computational Mechanics, 110 papers in Aerospace Engineering and 40 papers in Global and Planetary Change. Recurrent topics in David G. MacManus's work include Computational Fluid Dynamics and Aerodynamics (78 papers), Fluid Dynamics and Turbulent Flows (54 papers) and Turbomachinery Performance and Optimization (51 papers). David G. MacManus is often cited by papers focused on Computational Fluid Dynamics and Aerodynamics (78 papers), Fluid Dynamics and Turbulent Flows (54 papers) and Turbomachinery Performance and Optimization (51 papers). David G. MacManus collaborates with scholars based in United Kingdom, France and United States. David G. MacManus's co-authors include Christopher Sheaf, Pavlos K. Zachos, Fernando Tejero, John P. Murphy, Robert Christie, Ioannis Goulos, Matthew Robinson, Geoffrey Tanguy, John Eaton and Trevor Birch and has published in prestigious journals such as Journal of Fluid Mechanics, AIAA Journal and Review of Scientific Instruments.

In The Last Decade

David G. MacManus

135 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David G. MacManus United Kingdom 27 1.3k 1.3k 482 199 136 147 1.8k
James Reuther United States 20 674 0.5× 1.0k 0.8× 282 0.6× 67 0.3× 229 1.7× 48 1.6k
Zhoujie Lyu United States 13 564 0.4× 642 0.5× 392 0.8× 49 0.2× 188 1.4× 16 1.0k
Siva Nadarajah Canada 22 716 0.5× 1.5k 1.1× 71 0.1× 99 0.5× 158 1.2× 96 1.7k
Eusebio Valero Spain 25 530 0.4× 1.2k 0.9× 76 0.2× 177 0.9× 124 0.9× 111 1.7k
Karthikeyan Duraisamy United States 18 618 0.5× 1.1k 0.8× 84 0.2× 105 0.5× 80 0.6× 65 1.4k
Kwanjung Yee South Korea 19 740 0.5× 354 0.3× 122 0.3× 138 0.7× 86 0.6× 148 1.0k
Richard A. Wahls United States 19 1.3k 0.9× 1.9k 1.5× 247 0.5× 29 0.1× 81 0.6× 58 2.3k
Jamshid A. Samareh United States 17 520 0.4× 653 0.5× 107 0.2× 134 0.7× 197 1.4× 60 1.2k
Guillermo Paniagua United States 27 2.5k 1.8× 1.7k 1.3× 97 0.2× 830 4.2× 65 0.5× 269 3.0k
Mehdi Ghoreyshi United States 19 1.0k 0.8× 1.1k 0.8× 160 0.3× 38 0.2× 111 0.8× 133 1.4k

Countries citing papers authored by David G. MacManus

Since Specialization
Citations

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

Fields of papers citing papers by David G. MacManus

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David G. MacManus. 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 David G. MacManus. The network helps show where David G. MacManus may publish in the future.

Co-authorship network of co-authors of David G. MacManus

This figure shows the co-authorship network connecting the top 25 collaborators of David G. MacManus. A scholar is included among the top collaborators of David G. MacManus 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 David G. MacManus. David G. MacManus 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.
Zachos, Pavlos K., et al.. (2025). Experimental investigation of unsteady fan-intake interactions using time-resolved stereoscopic particle image velocimetry. Experimental Thermal and Fluid Science. 166. 111482–111482.
2.
3.
Tejero, Fernando, et al.. (2025). Aerodynamics of High-Bypass-Ratio Aeroengine Nacelles: Numerical and Experimental Investigation. Journal of Aircraft. 62(4). 1004–1017.
4.
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
5.
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
6.
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
7.
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
8.
Zachos, Pavlos K., et al.. (2024). Unsteady Swirl Distortion in a Short Intake Under Crosswind Conditions. CERES (Cranfield University). 1 indexed citations
9.
MacManus, David G., et al.. (2024). Installed nacelle aerodynamics at cruise and windmilling conditions. Aircraft Engineering and Aerospace Technology. 96(6). 757–768.
10.
Zachos, Pavlos K., et al.. (2024). Unsteady Swirl Distortion in a Short Intake Under Crosswind Conditions. AIAA Journal. 63(5). 1867–1884. 1 indexed citations
11.
Zachos, Pavlos K., et al.. (2024). Aerodynamic Instabilities in High-Speed Air Intakes and Their Role in Propulsion System Integration. Aerospace. 11(1). 75–75. 4 indexed citations
12.
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
13.
Doll, Ulrich, Nicholas J. Lawson, Sergey M. Melnikov, et al.. (2024). Advancements on the use of Filtered Rayleigh Scattering (FRS) with Machine learning methods for flow distortion in Aero-Engine intakes. Experimental Thermal and Fluid Science. 160. 111325–111325. 1 indexed citations
14.
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
15.
Zachos, Pavlos K., et al.. (2023). High-resolution turbofan intake flow characterization by automated stereoscopic-PIV in an industrial wind tunnel environment. Measurement Science and Technology. 35(2). 25210–25210. 7 indexed citations
16.
Doll, Ulrich, Pavlos K. Zachos, I. Röhle, et al.. (2022). Non-intrusive flow diagnostics for unsteady inlet flow distortion measurements in novel aircraft architectures. Progress in Aerospace Sciences. 130. 100810–100810. 25 indexed citations
17.
MacManus, David G., et al.. (2020). Dynamic distortion simulations for curved aeronautical intakes. CERES (Cranfield University). 4 indexed citations
18.
Nikolaidis, Theoklis, et al.. (2019). A numerical model for predicting the aerodynamic characteristics of propelling nozzles. CERES (Cranfield University).
19.
Robinson, Matthew, et al.. (2017). Short and slim nacelle design for ultra-high BPR engines. 55th AIAA Aerospace Sciences Meeting. 16 indexed citations
20.
Lawson, Nicholas J., et al.. (2009). Schlieren visualization of high-speed flows using a continuous LED light source. Journal of Visualization. 12(4). 289–290. 5 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by James Reuther Breakdown of academic impact, for papers by Zhoujie Lyu Breakdown of academic impact, for papers by Siva Nadarajah Breakdown of academic impact, for papers by Eusebio Valero Breakdown of academic impact, for papers by Karthikeyan Duraisamy Breakdown of academic impact, for papers by Kwanjung Yee Breakdown of academic impact, for papers by Richard A. Wahls Breakdown of academic impact, for papers by Jamshid A. Samareh Breakdown of academic impact, for papers by Guillermo Paniagua Breakdown of academic impact, for papers by Mehdi Ghoreyshi
Rankless by CCL
2026