Mahdi Abkar

2.5k total citations · 1 hit paper
62 papers, 1.7k citations indexed

About

Mahdi Abkar is a scholar working on Computational Mechanics, Aerospace Engineering and Environmental Engineering. According to data from OpenAlex, Mahdi Abkar has authored 62 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Computational Mechanics, 34 papers in Aerospace Engineering and 33 papers in Environmental Engineering. Recurrent topics in Mahdi Abkar's work include Wind and Air Flow Studies (33 papers), Fluid Dynamics and Turbulent Flows (27 papers) and Wind Energy Research and Development (26 papers). Mahdi Abkar is often cited by papers focused on Wind and Air Flow Studies (33 papers), Fluid Dynamics and Turbulent Flows (27 papers) and Wind Energy Research and Development (26 papers). Mahdi Abkar collaborates with scholars based in Denmark, United States and Switzerland. Mahdi Abkar's co-authors include Fernando Porté‐Agel, Xiang I. A. Yang, Parviz Moin, Pourya Forooghi, John O. Dabiri, Majid Bastankhah, H. Jane Bae, Alexandros Iosifidis, Jens Nørkær Sørensen and Mario A. Rotea and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Fluid Mechanics and Energy.

In The Last Decade

Mahdi Abkar

59 papers receiving 1.7k 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.

Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study

2015 308 citations Mahdi Abkar, Fernando Porté‐Agel Physics of Fluids profile →

Peers

Mahdi Abkar

AE

EE

CM

AS

SNP

Giacomo Valerio Iungo United States

Hee-Chang Lim South Korea

Raúl Bayoán Cal United States

Stefan Ivanell Sweden

Chuangxin He China

Haili Liao China

Catherine Gorlé United States

Niels Troldborg Denmark

Antonio Segalini Sweden

Giacomo Valerio Iungo United States

Mahdi Abkar

1.1k ×0.9

AE

1.0k ×1.0

EE

788 ×0.8

CM

162 ×1.0

AS

51 ×0.4

SNP

Citations per year, relative to Mahdi Abkar Mahdi Abkar (= 1×) peers Giacomo Valerio Iungo

Countries citing papers authored by Mahdi Abkar

Since Specialization

Citations

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

Fields of papers citing papers by Mahdi Abkar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mahdi Abkar

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

All Works

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20 of 20 papers shown

1.

Yang, Xiang I. A., et al.. (2025). A generalisable data-augmented turbulence model with progressive and interpretable corrections for incompressible wall-bounded flows. International Journal of Heat and Fluid Flow. 116. 109970–109970. 1 indexed citations

2.

Abkar, Mahdi, et al.. (2024). An analysis of drag reduction using spanwise forcing on rough walls. International Journal of Heat and Fluid Flow. 106. 109306–109306. 2 indexed citations

3.

Sørensen, Jens Nørkær, et al.. (2024). Extension of the law of the wall exploiting weak similarity of velocity fluctuations in turbulent channels. Physics of Fluids. 36(1). 2 indexed citations

4.

Abkar, Mahdi, et al.. (2024). Flow investigation of two-stand ultrasonic flow meters in a wide dynamic range by numerical and experimental methods. Flow Measurement and Instrumentation. 96. 102543–102543. 1 indexed citations

5.

Iosifidis, Alexandros, et al.. (2023). Physics-Guided Machine Learning for Wind-Farm Power Prediction: Toward Interpretability and Generalizability. SHILAP Revista de lepidopterología. 2(1). 13 indexed citations

6.

Yang, Xiang I. A., et al.. (2023). Log-law recovery through reinforcement-learning wall model for large eddy simulation. Physics of Fluids. 35(5). 13 indexed citations

7.

Yang, Xiang I. A., et al.. (2023). Validating the design optimisation of ultrasonic flow meters using computational fluid dynamics and surrogate modelling. International Journal of Heat and Fluid Flow. 100. 109112–109112. 15 indexed citations

8.

Abkar, Mahdi, et al.. (2023). Reinforcement learning for wind-farm flow control: Current state and future actions. Theoretical and Applied Mechanics Letters. 13(6). 100475–100475. 20 indexed citations

9.

Abkar, Mahdi, et al.. (2023). An extended k−ɛ model for wake-flow simulation of wind farms. Renewable Energy. 222. 119904–119904. 4 indexed citations

10.

Forooghi, Pourya, et al.. (2022). Secondary flows in statistically unstable turbulent boundary layers with spanwise heterogeneous roughness. Theoretical and Applied Mechanics Letters. 12(2). 100317–100317. 7 indexed citations

11.

Forooghi, Pourya, et al.. (2022). Frozen propagation of Reynolds force vector from high-fidelity data into Reynolds-averaged simulations of secondary flows. Physics of Fluids. 34(11). 19 indexed citations

12.

Abkar, Mahdi, et al.. (2022). Turbulent flow in small-diameter ultrasonic flow meters: A numerical and experimental study. Flow Measurement and Instrumentation. 87. 102227–102227. 6 indexed citations

13.

Forooghi, Pourya, Xiang I. A. Yang, & Mahdi Abkar. (2020). Roughness-induced secondary flows in stably stratified turbulent boundary layers. Physics of Fluids. 32(10). 27 indexed citations

14.

Yang, Xiang I. A., Sergio Pirozzoli, & Mahdi Abkar. (2020). Scaling of velocity fluctuations in statistically unstable boundary-layer flows. Journal of Fluid Mechanics. 886. 23 indexed citations

15.

Abkar, Mahdi, et al.. (2019). Reynolds Stress Perturbation for Epistemic Uncertainty Quantification of RANS Models Implemented in OpenFOAM. Fluids. 4(2). 113–113. 9 indexed citations

16.

Bastankhah, Majid & Mahdi Abkar. (2019). Multirotor wind turbine wakes. Physics of Fluids. 31(8). 51 indexed citations

17.

Abkar, Mahdi. (2018). Theoretical Modeling of Vertical-Axis Wind Turbine Wakes. Energies. 12(1). 10–10. 37 indexed citations

18.

Yang, Xiang I. A. & Mahdi Abkar. (2018). A hierarchical random additive model for passive scalars in wall-bounded flows at high Reynolds numbers. Journal of Fluid Mechanics. 842. 354–380. 34 indexed citations

19.

Abkar, Mahdi. (2018). Impact of Subgrid-Scale Modeling in Actuator-Line Based Large-Eddy Simulation of Vertical-Axis Wind Turbine Wakes. Atmosphere. 9(7). 257–257. 22 indexed citations

20.

Abkar, Mahdi, Jens Nørkær Sørensen, & Fernando Porté‐Agel. (2018). An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes. Energies. 11(7). 1838–1838. 62 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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