Sayeed Rushd

533 total citations
38 papers, 396 citations indexed

About

Sayeed Rushd is a scholar working on Ocean Engineering, Mechanical Engineering and Biomedical Engineering. According to data from OpenAlex, Sayeed Rushd has authored 38 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Ocean Engineering, 12 papers in Mechanical Engineering and 11 papers in Biomedical Engineering. Recurrent topics in Sayeed Rushd's work include Fluid Dynamics and Mixing (11 papers), Petroleum Processing and Analysis (7 papers) and Drilling and Well Engineering (7 papers). Sayeed Rushd is often cited by papers focused on Fluid Dynamics and Mixing (11 papers), Petroleum Processing and Analysis (7 papers) and Drilling and Well Engineering (7 papers). Sayeed Rushd collaborates with scholars based in Saudi Arabia, Canada and Qatar. Sayeed Rushd's co-authors include Noor E. Hafsa, Mohammad Azizur Rahman, Mohammed Al‐Yaari, Muhammad Muhitur Rahman, Vassilios C. Kelessidis, Sohrab Zendehboudi, Anwarul Hasan, Md Arifuzzaman, R. Sean Sanders and Syed Raza ur Rehman and has published in prestigious journals such as Sustainability, Powder Technology and International Journal of Multiphase Flow.

In The Last Decade

Sayeed Rushd

38 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
Sayeed Rushd Saudi Arabia 13 134 118 78 62 60 38 396
Iman Jafari Iran 14 124 0.9× 125 1.1× 77 1.0× 66 1.1× 122 2.0× 27 505
Lu Jiang China 11 131 1.0× 67 0.6× 48 0.6× 35 0.6× 23 0.4× 53 384
Mohsen Talebkeikhah Iran 12 320 2.4× 250 2.1× 169 2.2× 24 0.4× 32 0.5× 17 573
Fernando Betancourt Chile 11 70 0.5× 47 0.4× 37 0.5× 108 1.7× 76 1.3× 28 356
M.A. Latifi France 15 123 0.9× 65 0.6× 150 1.9× 158 2.5× 206 3.4× 62 846
Haojie Fan China 14 186 1.4× 73 0.6× 208 2.7× 183 3.0× 16 0.3× 31 489
Shouxi Wang China 9 66 0.5× 70 0.6× 33 0.4× 22 0.4× 29 0.5× 29 333
Girma T. Chala Malaysia 14 137 1.0× 291 2.5× 95 1.2× 58 0.9× 23 0.4× 56 748
Bonchan Koo South Korea 11 88 0.7× 26 0.2× 34 0.4× 44 0.7× 42 0.7× 28 349
В. П. Мешалкин Russia 11 171 1.3× 44 0.4× 87 1.1× 15 0.2× 30 0.5× 101 427

Countries citing papers authored by Sayeed Rushd

Since Specialization
Citations

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

Fields of papers citing papers by Sayeed Rushd

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sayeed Rushd

This figure shows the co-authorship network connecting the top 25 collaborators of Sayeed Rushd. A scholar is included among the top collaborators of Sayeed Rushd 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 Sayeed Rushd. Sayeed Rushd 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.
Rushd, Sayeed, et al.. (2023). Applications of drag reducers for the pipeline transportation of heavy crude oils: A critical review and future research directions. The Canadian Journal of Chemical Engineering. 102(1). 438–458. 3 indexed citations
2.
Hafsa, Noor E., et al.. (2023). Comparative Performance of Machine-Learning and Deep-Learning Algorithms in Predicting Gas–Liquid Flow Regimes. Processes. 11(1). 177–177. 17 indexed citations
3.
Hafsa, Noor E., et al.. (2023). Accurate prediction of pressure losses using machine learning for the pipeline transportation of emulsions. Heliyon. 10(1). e23591–e23591. 7 indexed citations
4.
Rushd, Sayeed, et al.. (2023). Modeling the Mechanical Properties of a Polymer-Based Mixed-Matrix Membrane Using Deep LearningNeural Networks. ChemEngineering. 7(5). 80–80. 3 indexed citations
5.
Rahman, Mohammad Azizur, et al.. (2022). 3D numerical and experimental modelling of multiphase flow through an annular geometry applied for cuttings transport. International Journal of Multiphase Flow. 151. 104044–104044. 13 indexed citations
6.
Rushd, Sayeed, Uneb Gazder, Hisham Jahangir Qureshi, & Md Arifuzzaman. (2022). Advanced Machine Learning Applications to Viscous Oil-Water Multi-Phase Flow. Applied Sciences. 12(10). 4871–4871. 12 indexed citations
7.
Rahman, Muhammad Muhitur, Saidur Rahman Chowdhury, Shaikh Abdur Razzak, et al.. (2022). Greenhouse Gas Emissions in the Industrial Processes and Product Use Sector of Saudi Arabia—An Emerging Challenge. Sustainability. 14(12). 7388–7388. 13 indexed citations
8.
Rushd, Sayeed & Mohamed A. Ismail. (2022). Pipeline Engineering - Design, Failure, and Management. IntechOpen eBooks. 6 indexed citations
9.
Rushd, Sayeed, Md Moklesur Rahman, Md Arifuzzaman, & Md Aktaruzzaman. (2021). A decision support system for predicting settling velocity of spherical and non-spherical particles in Newtonian fluids. Particulate Science And Technology. 40(5). 609–619. 3 indexed citations
10.
Rahman, Muhammad Muhitur, et al.. (2021). Greenhouse Gas Emissions from Solid Waste Management in Saudi Arabia—Analysis of Growth Dynamics and Mitigation Opportunities. Applied Sciences. 11(4). 1737–1737. 23 indexed citations
11.
Rushd, Sayeed, et al.. (2021). Predicting pressure losses in the water-assisted flow of unconventional crude with machine learning. Petroleum Science and Technology. 39(21-22). 926–943. 2 indexed citations
12.
Hafsa, Noor E., Mohammed Al‐Yaari, & Sayeed Rushd. (2020). Prediction of arsenic removal in aqueous solutions with non‐neural network algorithms. The Canadian Journal of Chemical Engineering. 99(S1). 12 indexed citations
13.
Hafsa, Noor E., Sayeed Rushd, Mohammed Al‐Yaari, & Muhammad Muhitur Rahman. (2020). A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms. Water. 12(12). 3490–3490. 45 indexed citations
14.
Dubdub, Ibrahim, et al.. (2020). Application of ANN to the water-lubricated flow of non-conventional crude. Chemical Engineering Communications. 209(1). 47–61. 7 indexed citations
15.
Rahman, Mohammad Azizur, et al.. (2019). CFD Analysis of Pressure Losses and Deposition Velocities in Horizontal Annuli. International Journal of Chemical Engineering. 2019. 1–17. 14 indexed citations
16.
Rushd, Sayeed & Mohammad Azizur Rahman. (2019). A study on friction loss and holdup ratio in the water lubricated pipeline transportation of heavy oil. 9(2). 200–206. 1 indexed citations
17.
Rushd, Sayeed, Ibrahim Hassan, Vassilios C. Kelessidis, et al.. (2018). Terminal settling velocity of a single sphere in drilling fluid. Particulate Science And Technology. 37(8). 943–952. 22 indexed citations
18.
Rushd, Sayeed & R. Sean Sanders. (2017). A parametric study of the hydrodynamic roughness produced by a wall coating layer of heavy oil. Petroleum Science. 14(1). 155–166. 6 indexed citations
19.
Rushd, Sayeed, Ashraful Islam, & R. Sean Sanders. (2017). CFD Methodology to Determine the Hydrodynamic Roughness of a Surface with Application to Viscous Oil Coatings. Journal of Hydraulic Engineering. 144(2). 8 indexed citations
20.
Rushd, Sayeed & R. Sean Sanders. (2014). Application of a capacitance sensor for monitoring water lubricated pipeline flows. The Canadian Journal of Chemical Engineering. 92(9). 1643–1650. 2 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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2026