Afaque Shams

2.0k total citations
92 papers, 1.5k citations indexed

About

Afaque Shams is a scholar working on Computational Mechanics, Aerospace Engineering and Mechanical Engineering. According to data from OpenAlex, Afaque Shams has authored 92 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Computational Mechanics, 44 papers in Aerospace Engineering and 22 papers in Mechanical Engineering. Recurrent topics in Afaque Shams's work include Fluid Dynamics and Turbulent Flows (31 papers), Nuclear Engineering Thermal-Hydraulics (31 papers) and Nuclear reactor physics and engineering (30 papers). Afaque Shams is often cited by papers focused on Fluid Dynamics and Turbulent Flows (31 papers), Nuclear Engineering Thermal-Hydraulics (31 papers) and Nuclear reactor physics and engineering (30 papers). Afaque Shams collaborates with scholars based in Netherlands, Saudi Arabia and United States. Afaque Shams's co-authors include F. Roelofs, E.M.J. Komen, D. De Santis, Emilio Baglietto, Andrea De Santis, Barry Koren, Sylvain Lardeau, Saša Kenjereš, Bernard J. Geurts and D. Lakehal and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Power Sources and Journal of Computational Physics.

In The Last Decade

Afaque Shams

86 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Afaque Shams Netherlands 23 1.1k 677 320 225 163 92 1.5k
Emilio Baglietto United States 24 1.1k 1.0× 780 1.2× 688 2.1× 198 0.9× 115 0.7× 96 1.8k
M. Souli France 14 769 0.7× 274 0.4× 152 0.5× 295 1.3× 52 0.3× 40 1.3k
Imran Afgan United Kingdom 24 745 0.7× 632 0.9× 414 1.3× 66 0.3× 359 2.2× 87 1.6k
Ming‐Chia Lai United States 24 1.3k 1.2× 420 0.6× 155 0.5× 397 1.8× 73 0.4× 85 2.5k
Sofiane Benhamadouche France 19 1.3k 1.2× 798 1.2× 305 1.0× 39 0.2× 552 3.4× 62 1.6k
С. В. Алексеенко Russia 23 1.4k 1.3× 270 0.4× 511 1.6× 143 0.6× 79 0.5× 71 1.6k
Nobuyuki FUJISAWA Japan 19 704 0.7× 788 1.2× 225 0.7× 98 0.4× 325 2.0× 99 1.3k
M.R.H. Nobari Iran 22 937 0.9× 173 0.3× 369 1.2× 80 0.4× 129 0.8× 62 1.3k
Rui Zhou China 23 526 0.5× 335 0.5× 324 1.0× 72 0.3× 361 2.2× 74 1.2k

Countries citing papers authored by Afaque Shams

Since Specialization
Citations

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

Fields of papers citing papers by Afaque Shams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Afaque Shams

This figure shows the co-authorship network connecting the top 25 collaborators of Afaque Shams. A scholar is included among the top collaborators of Afaque Shams 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 Afaque Shams. Afaque Shams 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.
Shams, Afaque, et al.. (2025). Research reactors: Global perspectives and insights into Saudi Arabia’s advancements and future prospects. Annals of Nuclear Energy. 224. 111740–111740.
2.
Pucciarelli, Andrea, et al.. (2025). An overview of the prediction methods for the heat transfer of supercritical fluids. Progress in Nuclear Energy. 181. 105654–105654. 3 indexed citations
3.
Khan, Safyan Akram, Abdul Ghafoor Abid, Afaque Shams, et al.. (2025). Solar-driven photocatalytic and photoelectrocatalytic hydrogen evolution reaction: Advances, challenges, and future directions. International Journal of Hydrogen Energy. 149. 150109–150109. 1 indexed citations
4.
Siddiqui, Osman K., et al.. (2025). Critical assessment of turbulence prediction in square duct flow. Nuclear Engineering and Technology. 58(2). 103937–103937. 1 indexed citations
5.
Yusuf, Muhammad, et al.. (2025). Green technology for radioactive waste treatment: Bio membrane perspective. Chemosphere. 385. 144587–144587.
6.
Soomro, Afzal Ahmed, et al.. (2025). Machine learning applications in nuclear power plant piping inspection: A review of methods, data, and future trends. Annals of Nuclear Energy. 225. 111760–111760. 1 indexed citations
7.
Siddiqui, Osman K., et al.. (2025). Flow accelerated corrosion in nuclear power plants: a detailed review on mechanisms, mitigation, and management. Engineering Failure Analysis. 183. 110203–110203.
8.
Manzoor, Sumaira, Muhammad Mansha, Shahid Ali, et al.. (2025). Facile synthesis of porous multiple hydroxyl and amine polymer@NiO composite for stable and efficient electrochemical water splitting. Journal of Power Sources. 657. 238229–238229. 1 indexed citations
9.
Ali, Amjad, et al.. (2024). Generating optimal reloading patterns for CNPGS Unit-3 core using multi-objective elitist teaching-learning-based optimizer. Progress in Nuclear Energy. 178. 105507–105507. 1 indexed citations
10.
Shams, Afaque. (2024). Turbulence Modelling for Advanced Nuclear Reactor Fuel Assemblies: Current Status and Future Perspectives. Arabian Journal for Science and Engineering. 50(5). 3337–3359.
11.
Alharbi, Talal, et al.. (2024). The Feasibility of Small Modular Reactors (SMRs) in the Energy Mix of Saudi Arabia. Nuclear Engineering and Design. 418. 112896–112896. 8 indexed citations
12.
Toor, Ihsan‐ul‐Haq, et al.. (2024). Application of Machine Learning and Deep Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants: A Review. Arabian Journal for Science and Engineering. 50(5). 3017–3045. 6 indexed citations
13.
Siddiqui, Osman K., et al.. (2024). A Detailed Review of Numerical Modeling of Flow Accelerated Corrosion: Challenges and Opportunities for the Future. Materials and Corrosion. 76(3). 368–385. 3 indexed citations
14.
Ali, Amjad, et al.. (2023). Saudi Arabia's nuclear energy ambition and its compliance with IAEA guidelines for newcomers: An overview. Nuclear Engineering and Design. 411. 112448–112448. 8 indexed citations
15.
Pucciarelli, Andrea, Afaque Shams, & Nicola Forgione. (2023). Numerical prediction of turbulent flow and heat transfer in buoyancy-affected liquid metal flows. Annals of Nuclear Energy. 186. 109773–109773. 2 indexed citations
16.
Shams, Afaque, et al.. (2019). Study of Formation Bonding in the Wells of the Varg Field Based on Ultrasonic and Sonic Wireline Log Data. SPE/IADC International Drilling Conference and Exhibition. 14 indexed citations
17.
Shams, Afaque, et al.. (2019). High-Performance Computing for Nuclear Reactor Design and Safety Applications. Nuclear Technology. 206(2). 283–295. 13 indexed citations
18.
Roelofs, F., Afaque Shams, Vincent Moreau, et al.. (2017). The SESAME project. State of the art liquid metal thermal hydraulics and beyond. 62. 531–537. 2 indexed citations
19.
Santis, D. De & Afaque Shams. (2017). Numerical modeling of flow induced vibration of nuclear fuel rods. Nuclear Engineering and Design. 320. 44–56. 52 indexed citations
20.
Shams, Afaque, F. Roelofs, Katrien Van Tichelen, et al.. (2015). CFD Benchmark for a heavy liquid metal fuel assembly. Ghent University Academic Bibliography (Ghent University). 7 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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